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Article Contents

Genetic diversity in humans, examples: interactions of genetic diversity and social factors, an effect of genetic diversity on economic development, perspective, acknowledgments, literature cited.

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Genetic Diversity and Societally Important Disparities

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Noah A Rosenberg, Jonathan T L Kang, Genetic Diversity and Societally Important Disparities, Genetics , Volume 201, Issue 1, 1 September 2015, Pages 1–12, https://doi.org/10.1534/genetics.115.176750

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The magnitude of genetic diversity within human populations varies in a way that reflects the sequence of migrations by which people spread throughout the world. Beyond its use in human evolutionary genetics, worldwide variation in genetic diversity sometimes can interact with social processes to produce differences among populations in their relationship to modern societal problems. We review the consequences of genetic diversity differences in the settings of familial identification in forensic genetic testing, match probabilities in bone marrow transplantation, and representation in genome-wide association studies of disease. In each of these three cases, the contribution of genetic diversity to social differences follows from population-genetic principles. For a fourth setting that is not similarly grounded, we reanalyze with expanded genetic data a report that genetic diversity differences influence global patterns of human economic development, finding no support for the claim. The four examples describe a limit to the importance of genetic diversity for explaining societal differences while illustrating a distinction that certain biologically based scenarios do require consideration of genetic diversity for solving problems to which populations have been differentially predisposed by the unique history of human migrations.

THE publication of an article suggesting that geographic patterns in economic development across countries worldwide have been driven by genetic diversity ( Ashraf and Galor 2013 ) has generated considerable controversy ( Callaway 2012 ; Chin 2012 ; Gelman 2013 ; Feldman 2014 ). Connecting data on measures of genetic diversity in different human populations to proxy measures of economic success, Ashraf and Galor (2013) argued that an optimal level of genetic diversity exists for enhancing the economic development of nations and that the optimum lies in an intermediate range characteristic of populations of Europe and Asia. In response to prepublication reports of the upcoming paper, a large interdisciplinary group of scholars vehemently criticized the methods and conclusions, objecting to the line of inquiry on genetic determination of economic outcomes on the grounds of its potential for misuse ( d’Alpoim Guedes et al. 2013 ).

This controversial attempt to apply a population-genetic variable in an analysis of a societal outcome provides an occasion to examine the ways in which population differences in genetic diversity might contribute to consequential societal differences across populations. Several such examples have been reported. After reviewing the origins of differences across human populations in levels of genetic diversity, we describe three documented cases in which the variation in genetic diversity across populations interacts with social processes to produce population differences in important outcomes. We then return to the economic development study, investigating a genetic data set that expands beyond the data examined by Ashraf and Galor (2013) . Our analysis finds that even when the same methods used by Ashraf and Galor are applied to this larger data set, no support for their claims of a major role of genetic diversity in economic development is evident. We discuss the characteristics that distinguish between this case in which no role for genetic diversity is observed and the three examples in which genetic diversity is seen to be important.

Measuring genetic diversity

We first clarify that the concept of genetic diversity of interest in the examples we consider is the diversity of genetic types observed among members of a population—and not the diversity in a collection of populations that is contributed by differences across the constituent groups. This concept of the genetic diversity of a population is computed from data on that population alone, and it is unaffected by the composition of the larger data set of populations used for its calculation.

Expected heterozygosity as a measurement of diversity. Each axis in the unit square represents an allele frequency distribution, with each area representing the probability that an individual has a particular ordered pair of alleles. The shaded regions represent heterozygous combinations. The two loci shown represent different expected heterozygosity levels (equation 1). (A) A smaller heterozygosity (0.540). (B) A larger heterozygosity (0.725).

Expected heterozygosity as a measurement of diversity. Each axis in the unit square represents an allele frequency distribution, with each area representing the probability that an individual has a particular ordered pair of alleles. The shaded regions represent heterozygous combinations. The two loci shown represent different expected heterozygosity levels ( equation 1 ). (A) A smaller heterozygosity (0.540). (B) A larger heterozygosity (0.725).

Origins of human genetic diversity

Surveys of genetic diversity in indigenous human populations worldwide have documented considerable variation in the level of heterozygosity present within a population ( Bowcock et al. 1994 ; Rosenberg et al. 2002 ; Prugnolle et al. 2005 ; Ramachandran et al. 2005 ). These differences in heterozygosity follow a geographic pattern, with a systematic linear decline occurring as a function of increasing distance from East Africa, measured over land-based routes. The highest heterozygosities appear in populations from Africa, followed by populations from the Middle East, Europe, and Central and South Asia. Populations of East Asia have still lower heterozygosities, and Pacific Islander and Native American populations, at the greatest geographic distance from Africa over migration paths traversed in human evolution, are the least heterozygous. The linear decrease in heterozygosity with increasing distance from Africa is a strong and replicable relationship, achieving correlation coefficients near −0.9 in a variety of studies of different genetic markers and sets of populations ( Prugnolle et al. 2005 ; Ramachandran et al. 2005 ; Conrad et al. 2006 ; Jakobsson et al. 2008 ; Li et al. 2008 ; Pemberton et al. 2013 ).

Population-genetic models have explained the pattern of variation in human genetic diversity, with a decrease in heterozygosity at a greater distance from Africa, in relation to the relatively recent history of human migrations starting from an African origin. Under these models, during a geographic expansion, new regions are occupied not by expansion in the range of an existing population in its entirety but instead by a recursive procedure of new settlement formation by subgroups that separate from their parental colonies ( Ramachandran et al. 2005 ; Liu et al. 2006 ; DeGiorgio et al. 2009 , 2011 ; Deshpande et al. 2009 ). Each founding group carries only a subset of the total genetic diversity of its parental population, leading to a loss of genetic diversity in the new group ( Figure 2 ). Newly established populations then generate their own subgroups that again separate to found new populations, and the founding process is repeated anew. In this model of serial founder effects, each founding event produces a loss of genetic diversity, so the populations at the greatest distance from the starting point possess the lowest heterozygosity.

The serial founder model in human evolution. (A) A schematic of the model. Each color represents a distinct allele. Migration events outward from Africa tend to carry with them only a subset of the genetic diversity from the source population, and some alleles are lost during migration events. (B) An example of the model at a particular genetic locus, TGA012. Each set of vertical bars depicts the allele frequencies in a population, with different colors representing distinct alleles. Within continental regions, populations are plotted from left to right in decreasing order of expected heterozygosity at the locus [equation (3)]. This figure illustrates the loss of alleles across geographic regions; Native Americans all possess the same allele. The allele frequencies are taken from Rosenberg et al. (2005).

The serial founder model in human evolution. (A) A schematic of the model. Each color represents a distinct allele. Migration events outward from Africa tend to carry with them only a subset of the genetic diversity from the source population, and some alleles are lost during migration events. (B) An example of the model at a particular genetic locus, TGA012 . Each set of vertical bars depicts the allele frequencies in a population, with different colors representing distinct alleles. Within continental regions, populations are plotted from left to right in decreasing order of expected heterozygosity at the locus [ equation (3) ]. This figure illustrates the loss of alleles across geographic regions; Native Americans all possess the same allele. The allele frequencies are taken from Rosenberg et al. (2005) .

Although other processes, such as admixture between populations and changes in population size, also affect genetic diversity patterns ( DeGiorgio et al. 2009 ; Pickrell and Reich 2014 ), the general utility of serial founder models in human evolution as a first approximation for explaining the global pattern of genetic diversity is supported both by the strength of the correlation between heterozygosity and distance from Africa and by an observation that within large geographic regions, source regions more accessible to colonizing populations along likely migration routes also have greater heterozygosity than more distant regions—for example, southern Europe compared to northern Europe ( Lao et al. 2008 ), coastal Melanesia compared to inland Melanesia ( Friedlaender et al. 2008 ), and northwest South America compared to the Amazon region ( Wang et al. 2007 ). Further, serial founder models explain patterns in statistics of genetic differentiation and allelic correlation along the genome that other, substantially different models cannot explain ( DeGiorgio et al. 2009 , 2011 ).

We can therefore observe that population-genetic models of the spread of human populations explain the variation across human populations in levels of genetic diversity and that this variation is informative about the particular history of human migrations. We now turn to examining the effects of these genetic diversity differences on a variety of societally important scenarios.

Familial identification in forensic genetic testing

A comparatively new form of forensic DNA testing uses crime-scene samples to identify unknown suspects through genetic relatedness profiling ( Bieber et al. 2006 ; Butler 2011 ; Gershaw et al. 2011 ). When no perfect DNA match of a crime-scene sample to an entrant in a database of potential suspects is found, investigators can test for a partial match to assess whether the crime-scene sample might be from a genetic relative of an entrant in the database. A positive test leads investigators to consider as potential suspects genetic relatives of the person with the partial match.

The identification of relatives through partial matches raises new statistical and population-genetic issues largely absent in the standard setting of forensic profiling via exact matches. In the basic forensic scenario, a crime-scene sample is tested at a number of DNA markers that is small, but large enough that a false-positive match of a genetically unrelated noncontributor to the crime-scene sample at all the loci is extremely unlikely. Forensic marker systems are designed so that the false-positive probability is acceptably low for use as evidence in court irrespective of the actual alleles found in the genetic profile.

In familial identification, the false-positive rate is substantially higher because familial identification generally must rely on databases and marker sets designed for the simpler exact-match problem. In the absence of genotyping error, across a DNA marker set, the sibling of the actual contributor of a DNA sample, for instance, will match the crime-scene sample for a substantial fraction of the alleles. A sibling is expected to share both alleles identically by descent at a quarter of all loci—inheriting the same pair of alleles from shared parents—and one allele identically by descent at half the loci. Thus, the fraction of alleles shared with the sibling is one-half or more: on average, half the alleles are shared identically by descent, and an extra contribution arises from the chance that alleles not shared identically by descent have the same state nonetheless. The sibling, however, is not expected to match all alleles with the crime-scene sample; on average, at a quarter of loci, siblings share neither allele identically by descent, and these generally will not have the same allelic type. A partial match of the DNA profile is therefore expected for the sibling, with different loci having no, one, or two matching alleles. Thus, for a fixed marker set, because a true genetic relative of the contributor has only a partial match with the crime-scene sample, the chance of a false-positive match—the probability that a nonrelative also achieves the less stringent partial-match threshold—greatly exceeds the probability that the same nonrelative is a false exact match.

The underlying genetic diversity in a population affects the probability that a nonrelative of the DNA contributor produces a false-positive partial match close enough to appear to be a relative of the contributor of the crime-scene sample. Consider a hypothetical low-diversity population in which all members are homozygous at some locus for the same allele, implying a complete absence of genetic diversity. Suppose also that the contributor to the crime-scene sample has this same homozygous genotype. The locus contains no identifying information, and every individual in the population has an exact match at the locus—both genetic relatives and nonrelatives of the contributor.

Now consider a hypothetical high-diversity population with many rare alleles, so that individuals tend to have more distinctive genotypes at the locus. In this population, the contributor’s homozygous genotype would be rare rather than common, and most of the individuals who possess a partial or exact match to the contributor at the locus could only do so if they had obtained the same alleles through shared familial lines of descent. The locus is highly informative for individual identification in this population, and it is primarily the genetic relatives who have a partial match.

These two extreme scenarios convey the idea that different levels of genetic diversity confer varying degrees of individual identifiability. The more genetically homogeneous a population is, the less identifying information an ostensible genetic match provides, and the conclusion that a partial match indicates a direct familial genetic relationship is more likely to represent a false-positive result ( Figure 3 ).

Familial identification in forensic testing. A contributor to a crime scene DNA sample has genotype AA at a locus. A sibling of the contributor is likely to share more alleles with the contributor than are unrelated individuals; the probability of an exact match at a locus, as shown, exceeds 25% for a sibling. This figure illustrates that in a low-diversity population, the chance of a false-positive match of an unrelated individual to a crime-scene contributor at a locus is greater than in a high-diversity population. In the low-diversity population, two nonrelatives have exact matches, and one has a partial match, whereas in the high-diversity population, the nonrelatives do not have exact or partial matches.

Familial identification in forensic testing. A contributor to a crime scene DNA sample has genotype AA at a locus. A sibling of the contributor is likely to share more alleles with the contributor than are unrelated individuals; the probability of an exact match at a locus, as shown, exceeds 25% for a sibling. This figure illustrates that in a low-diversity population, the chance of a false-positive match of an unrelated individual to a crime-scene contributor at a locus is greater than in a high-diversity population. In the low-diversity population, two nonrelatives have exact matches, and one has a partial match, whereas in the high-diversity population, the nonrelatives do not have exact or partial matches.

The connection between genetic diversity and the distinguishability of genetic relatives and unrelated individuals was demonstrated by Rohlfs et al. (2012 , 2013 ), who studied the effect of differences in genetic diversity on the extent to which relatives and nonrelatives can be distinguished in a familial identification context, employing the 13 Combined DNA Index System ( codis ) forensic identification loci widely used as the standard marker panel in forensic testing. Relying on allele-frequency distributions at the codis loci for each of five population samples that represent different levels of genetic diversity—in decreasing order, largely following the serial founder model ( Figure 2 ), African American, Latino, European American, Vietnamese, and Navajo—the authors simulated unrelated pairs of individuals and sibling pairs, generating sibling pairs by transmitting alleles through pedigrees with shared unrelated parents. Next, they computed a likelihood ratio to quantify whether a partial genetic match between two individuals is more likely under the hypothesis of a familial relationship—a sibling relationship in this case—or under the hypothesis of a chance partial match of unrelated individuals. For each population, Rohlfs et al. (2012) measured a “distinguishability statistic” based on the likelihood-ratio distributions for the simulated unrelated pairs and sibling pairs, arguing that significant overlap between the two distributions indicates reduced potential to distinguish between siblings and unrelated pairs—and more chance partial matches of nonrelatives—as more pairs are assigned likelihood ratios compatible with either category.

Computing the distinguishability statistic in each of the five populations, Rohlfs et al. (2012) found a strong relationship across populations between population-level heterozygosity and the distinguishability measure (squared correlation of 0.95), confirming that a higher false-positive rate occurs in low-diversity populations. The authors then took their result one step further. In performing the likelihood-ratio computation, a distribution of allele frequencies must be specified for the population to which the crime-scene sample belongs. In practical conditions, this population might be unknown, so the allele frequencies used in the likelihood-ratio computation might be misspecified. The authors examined the distinguishability statistic in the context of misspecified allele frequencies, for each choice of population using each of the four other populations to misspecify the allele frequencies. In simulations with the misspecified allele frequencies, they found that distinguishability was lower than in the case in which allele frequencies were properly specified, particularly when individuals from a less genetically diverse population were erroneously assumed to belong to a more genetically diverse population.

The analysis of Rohlfs et al. (2012) and subsequent work extending beyond sibling relationships ( Rohlfs et al. 2013 ) illustrate that in familial identification based on a fixed marker system shared across all groups, populations with lower genetic diversity are likely to have higher false-positive match rates: the genetic diversity of the population has a direct impact on the familial identification setting. The results further suggest that similar issues are relevant to other forensic problems involving partial matches, such as when the crime-scene sample represents a DNA mixture from many individuals rather than a single person, potentially with degraded DNA, missing genotypes, or genotyping errors ( Balding 2013 ; Steele and Balding 2014 ). In this context, which relies on partial matches to determine whether a test individual might be included in the mixture, the false-positive probability that a noncontributor is erroneously regarded as a contributor is likely to depend on genetic diversity in a parallel manner to the familial identification setting. As in familial identification, a chance partial match of a mixed crime-scene sample with a random individual has greater probability in a low-diversity population, so the probability of a false-positive partial match is likely to exceed the corresponding probability in a more diverse population.

The demonstration of variability across populations in false-positive match rates is purely population-genetic, using population-genetic theory to evaluate the influence of genetic relatedness and population allele frequencies on the probability of DNA matches, but the result exists in a context in which substantial differences exist across groups in the probability that an individual appears in forensic databases. In the United States, representation of an individual in a database is related to the past experience of the individual with criminal investigation, a factor that varies across populations. The probability that an individual has a close genetic relative in a forensic database—and therefore has a DNA profile accessible to investigators through familial identification—then also varies by population. The potential inequalities that could arise from this variation have been much discussed ( Greely et al. 2006 ; Garrison et al. 2013 ). The analysis of Rohlfs et al. (2012) , however, indicates that outcomes of familial identification analyses depend not only on inequality across groups in representation in criminal investigations, which affects the chance that a genetic relative of the DNA contributor is in the database, but also on the difference across groups in genetic diversity, which affects the distinguishability of DNA profiles from genetic relatives and unrelated individuals. Genetic diversity and its interaction with variation across populations in representation in the justice system are therefore both essential to determining and improving the utility and fairness of a familial identification test.

Bone marrow transplantation matching

Genetic diversity has a quite different impact in another area that also relies on match probabilities: transplantation matching. In medical transplantation, an immunologic match between a recipient and donor reduces the risk that the recipient immune system will recognize the donor cells as foreign and therefore produce an undesirable immune response. The problem is particularly salient in bone marrow transplantation, which involves a transplant of donor cells from the immune system itself—cells that can recognize the recipient as foreign.

In bone marrow transplantation, the degree of matching is assessed using multilocus genotypes at a set of protein variants encoded by the genes of the human leukocyte antigen (HLA) system on chromosome 6. The HLA system contains six highly polymorphic loci whose alleles determine the core of an individual HLA multilocus genotype and that are generally matched for bone marrow transplantation. The number of alleles at highly polymorphic HLA loci can run into the thousands, and as of 2014, the database of HLA alleles ( Robinson et al. 2013 ) records more than ∼12,000 distinct alleles across the six major genes. An already large number of potential types at each locus increases to tens or hundreds of millions as multilocus types are considered to ensure a lower chance for rejection.

Owing to the fact that HLA alleles are codominantly expressed, the aim in transplantation matching is to match as many alleles as possible between donor and recipient. Close genetic relatives of a potential recipient have the greatest match probability because a recipient and a relative share a substantial fraction of their genomes identically by descent. Given the high genetic diversity that exists in the HLA system, however, the probability is low that two unrelated individuals would match perfectly at important loci.

Unlike the setting of forensic familial identification, in which low genetic diversity generates problematic false-positive matches, the challenge of transplantation matching is exacerbated by high diversity in the population to which the recipient belongs: a population with high levels of HLA genetic diversity will possess a large number of HLA multilocus combinations. Each unique combination then appears at a lower frequency, and the probability that any given pair of individuals has an exact HLA match is reduced. Conversely, a population that is more homogeneous at the HLA loci has fewer unique combinations, and the chance of a match for a random pair of individuals is increased.

In the United States, the National Marrow Donor Program (NMDP) registry contains HLA profiles of millions of potential donors, each of whom can be queried if a matching recipient is in need of a donation. In parallel with the analysis of forensic profiles by Rohlfs et al. (2012) , analyses of NMDP profiles identify an effect of population differences in genetic diversity on match probabilities for HLA ( Cao et al. 2001 ). They also illustrate the interaction between genetic diversity and social phenomena in influencing the probability that a match exists for potential recipients from each of a series of populations.

An investigation by Bergstrom et al. (2009) highlights the key issues. Using NMDP three-locus genotype frequencies reported by Mori et al. (1997) , Bergstrom et al. (2009) computed the theoretical match probabilities for pairs of HLA profiles drawn from each of five populations. High-diversity African Americans, with their high fraction of African ancestry at the source of the serial founder expansion ( Figure 2 ), had the lowest theoretical match probability, a value substantially lower than in the other groups. In increasing order, the match probabilities were greater in the Hispanic and Asian-American populations and greatest in white and Native American groups. Although the groups studied by Bergstrom et al. (2009) and Rohlfs et al. (2012) do not align exactly (nor do they align with the indigenous groups in the global characterization of genetic diversity in Figure 2 ), the pattern of decreasing transplantation match probabilities largely reverses the sequence describing increasing numbers of false-positive matches in familial identification. Similar general patterns are observed when considering theoretical match probabilities between recipient HLA profiles chosen from one population and donor profiles chosen from another.

As in the analysis of Rohlfs et al. (2012) , the theoretical computation of HLA match probabilities from transplantation database frequencies is a calculation under a model constructed from population-genetic principles showing that from population-genetic considerations alone, higher-diversity populations are expected to have lower transplantation match probabilities. Also similar to the analysis of Rohlfs et al. (2012) , for the practical setting, population-genetic match probabilities appear in a context of population differences in the frequencies with which individuals are represented in transplantation databases. Bergstrom et al. (2009 , 2012 ) comment on a number of factors that vary across populations: the overall size of the population, the rate at which members of a population choose to contribute profiles to the database, and the rate at which potential donors participate in a transplantation when queried. Incorporating these factors, including the nontrivial role played by the difficulty of characterizing HLA variation in minority populations with smaller sizes, the chance that no donor match is found is greatest for African Americans, followed by the Asian-American, Hispanic, Native American, and white groups. As in the forensic case, the population genetics of genetic diversity, together with societal factors that vary across populations, contributes to the quantity of ultimate interest. Both genetic diversity and its interaction with factors that affect participation in transplantation are important in increasing the probability that any given recipient can find a successful match.

Genome-wide association studies

A third area of influence for differences in genetic diversity is in representation in the development and application of research resources in human genomics. Genome-wide association studies (GWA studies) are genomic investigations of the statistical correlations between genetic variation among members of a population and an observed phenotype ( Hirschhorn and Daly 2005 ; McCarthy et al. 2008 ; Stranger et al. 2011 ). In humans, these studies, which seek to uncover genetic factors that underlie a phenotype, typically compare the genotypes of two groups of individuals—cases, who have a disease, and control individuals, who do not. An allele found more frequently in cases than in controls is said to be associated with the disease. In recent years, GWA studies have proliferated rapidly, producing thousands of successes in the identification of disease-associated loci ( Hardy and Singleton 2009 ; Hindorff et al. 2009 ).

GWA studies rely on linkage disequilibrium (LD), the association between allelic states at different loci along a chromosome: association with a disease occurs not only for a risk mutation but also for other alleles located proximate on the genome to the susceptibility allele ( Figure 4 ). Thus, nearby alleles in the ancestor in whom a disease mutation originally occurred are transmitted to diseased descendants with greater probability than are distant alleles. At the same time, recombination breaks down the correlation—the LD—between the disease allele and distant alleles. As a result, among diseased descendants, alleles that remain associated with the disease allele—and, consequently, with the disease phenotype—are likely to lie close on the genome to the disease mutation. The premise that LD enables variants to “tag” their neighbors ( Carlson et al. 2004 ; International HapMap Consortium 2005 ) and, hence, to facilitate the discovery of disease variants through the separate associations of a disease variant with both the disease phenotype and a tag-SNP proxy, made it possible to begin performing GWA studies without requiring full genome sequences in every sampled individual.

The principle of linkage disequilibrium that underlies genome-wide association studies. This figure depicts a series of individuals with a disease, tracing the genealogy of the section of the chromosome on which a disease-causing allele is located. A disease allele (orange) occurs on an ancestral chromosome containing several marker alleles (yellow, brown, red, and purple). Recombination events (arrows) break down correlations between the disease mutation and marker alleles, so the closer a marker allele is to the mutation, the more likely it is to be found in present-day disease cases.

The principle of linkage disequilibrium that underlies genome-wide association studies. This figure depicts a series of individuals with a disease, tracing the genealogy of the section of the chromosome on which a disease-causing allele is located. A disease allele (orange) occurs on an ancestral chromosome containing several marker alleles (yellow, brown, red, and purple). Recombination events (arrows) break down correlations between the disease mutation and marker alleles, so the closer a marker allele is to the mutation, the more likely it is to be found in present-day disease cases.

LD varies across human populations, however, with populations that possess a greater diversity of haplotypes having a lower probability that a genotype at one site on the genome will be informative about other nearby sites. In parallel with the decrease in genetic diversity with increasing distance from Africa, a decrease in haplotype diversity and a concomitant increase in LD exist with increasing distance from Africa ( Conrad et al. 2006 ; Jakobsson et al. 2008 ; Li et al. 2008 ). This pattern has had the consequence that sets of tag SNPs used in early GWA studies are less successful in tagging genetic variants in low-LD African populations at the source of the serial founder human expansion ( Figure 2 ), generating reduced potential for uncovering disease associations in these groups ( Conrad et al. 2006 ; Debakker et al. 2006 ). The problem has persisted as tag-SNP approaches have been replaced with genotype imputation studies, which rely on LD to impute unmeasured genotypes that can be tested for disease association in a similar manner to genotypes that have actually been measured: low-LD African populations generate lower imputation accuracy ( Huang et al. 2009 , 2011 ).

The heterogeneity in genetic diversity, as reflected in the low LD for African populations and its consequences in generating relatively low tag-SNP “portability” and genotype imputation accuracy, has rendered African genomes comparatively less well suited to GWA studies based on LD. Partly as a result of this phenomenon, GWA studies have been implemented unevenly across human populations, generating concerns that the benefits of human genetics research will not accrue equally in different groups ( Need and Goldstein 2009 ; Rosenberg et al. 2010 ; Bustamante et al. 2011 ). Most GWA studies have focused on populations of European ancestry, and other populations have been underrepresented, quite dramatically in some cases.

Differences in genetic diversity that have influenced GWA studies have interacted with sociological factors in the scientific community that have also prioritized the use of European samples ( Rosenberg et al. 2010 ; Teo et al. 2010 ). Because GWA studies are expensive, early studies focused on a small number of populations for which shared sets of genomic resources—standardized marker panels, shared controls, and shared databases of densely genotyped samples with deep characterization of genetic variation—could be generated. Well-developed networks of investigators in countries of Europe and North America with the resources to conduct GWA studies generally had easiest access to patient populations of European descent, further contributing to an emphasis on these populations in early studies. Though the incorporation of non-European populations has increased, initial inequalities across populations in GWA representation have persisted because subsequent investigations continue to build on patient populations, funded projects, and researcher networks from earlier studies ( Burchard 2014 ).

This interaction of a form of genetic diversity and societal variables in the structure of the scientific research enterprise has led to a situation in which one estimate recorded 96% of GWA subjects as having European ancestry ( Bustamante et al. 2011 ). Though this disparity has a basis partly in variation in access to populations generated by the structure of scientific collaboration networks and the distribution of research funding, it has been exacerbated by considerations of genetic diversity; indeed, a feedback loop exists between differences in societal variables and genetic diversity phenomena because initial differences among populations in practical and technical feasibility have contributed to overemphasis on European populations in developing technical capabilities, making further European overrepresentation enticing to researchers and funding panels. In parallel with the variation across populations observed in the familial identification and bone marrow transplantation scenarios, a consequential practical difference across populations in representation in genomic studies arises from the interaction of genetic diversity with social factors.

We have described three examples that each involve an interaction of differences in genetic diversity with population differences in society to produce a difference in an important phenomenon—false-positive matches in forensic genetics, match probabilities in transplantation, and research efforts in GWA studies. Each of these settings involves a problem that is fundamentally biological—DNA-based identification, transplantation, and genetics of disease. In each setting, principles from population-genetic theory in which aspects of genetic diversity feature prominently underlie the contribution of genetic diversity: theories of forensic and transplantation matching explicitly produce an inverse relationship between match probabilities and genetic diversity, and GWA statistics rely on models of the decay of genetic diversity and production of LD during human migrations. When genetic diversity appears as a variable in a context in which no similar theory exists, in which theoretical constructs are drawn from outside population genetics, is genetic diversity similarly important? How far do implications of genetic diversity extend for societal phenomena, in scenarios that a priori have no evident connection to biology? We have reexamined the study of Ashraf and Galor (2013) to evaluate their hypothesis that genetic diversity is a key determinant of economic development.

Economic development

Ashraf and Galor (2013) advanced the claim that genetic diversity levels have had a persistent long-term effect on comparative economic development. They argued that genetic diversity at the high and low extremes—characteristic of African and Native American populations, respectively—has been detrimental for development, whereas the intermediate genetic diversity of European and Asian populations has, however, facilitated development. In other words, economic development has a “hump-shaped” negative quadratic relationship with genetic diversity.

To argue for their hypothesis, Ashraf and Galor (2013) relied on short-tandem-repeat genetic markers genotyped in 53 worldwide populations from the Human Genome Diversity Panel ( Ramachandran et al. 2005 ; Rosenberg et al. 2005 ), using expected heterozygosities previously reported for each population according to equation (4) . They adopted the distances of Ramachandran et al. (2005) of each population from East Africa, taking into account geographic waypoints to approximate migratory paths of human populations outward from Africa.

In their economic analysis, the 53 populations were grouped into 21 present-day countries based on geographic coordinates. For each of these countries, an “observed diversity” was computed as the mean of the expected heterozygosities of populations sampled within the country. Several ordinary-least-squares regressions then were performed using as the dependent variable the natural logarithm of population density in 1500 CE, treated as a proxy for economic development, and as the independent variables the observed diversity, its square, and control variables relating to geography and to the local timing of the Neolithic transition. The regressions produced a generally significant quadratic relationship between the dependent variable and observed diversity, even after conditioning on various control variables ( Table 1 and Supporting Information , Table S1 . The authors used these results to claim that economic development has a statistically significant “hump-shaped” dependence on observed diversity.

P -values for multiple regressions of “log population density in 1500 CE” on “observed diversity” and “observed diversity squared”

The nongenetic covariates are “log Neolithic transition timing,” “log percentage of arable land,” “log absolute latitude,” and “log land suitability for agriculture.” Each variable was computed and employed as in Ashraf and Galor (2013) using their regression models and the values they reported for nongenetic variables. Regression models 1, 4, and 5 are the three models of Ashraf and Galor (2013) that use genetic data. The analysis of 53 populations in 21 countries recomputes the same analysis as in Table 1 of Ashraf and Galor (2013) using scripts they provided. Significance at the 10, 5, and 1% levels is represented by *, **, and ***, respectively. Full regression tables appear in Table S1 , Table S2 , and Table S3 .

Next, Ashraf and Galor (2013) extended their analysis to a worldwide sample of 145 countries. For most of the countries, however, information on expected heterozygosity was unavailable. In place of actual data on expected heterozygosity for most of these countries, the authors used as the observed diversity the predicted expected heterozygosity from the linear regression of expected heterozygosity in 53 populations with migratory distance from East Africa. They justified this choice on the grounds that expected heterozygosity has a strong relationship with distance from East Africa, enabling heterozygosity predictions for unsampled populations and, because the 21-country analysis produced a significant economic effect for diversity, suggesting the plausibility of using an estimated value of this quantity in place of actual genetic data. It was then possible in the absence of genetic data on additional countries to enable incorporation of economic variables on those countries.

To calculate predicted diversity, for each of the 145 countries, the migratory distance from East Africa of its capital city was substituted into the regression of expected heterozygosity on migratory distance. Using predicted diversity in regressions of the economic development variable similar to those performed with observed diversity, a broadly significant quadratic relationship between economic development and predicted diversity was observed, even after controlling for other variables. On the basis of this analysis, Ashraf and Galor (2013) claimed that a “hump-shaped” effect of genetic diversity on economic development from the 53-population data set was a general worldwide phenomenon.

The reanalysis

We sought to examine the argument of Ashraf and Galor (2013) on its own terms using their assumptions, methods, economic variables, and regression models—all contested elsewhere ( d’Alpoim Guedes et al. 2013 ; Gelman 2013 ; Feldman 2014 )—and changing the analysis only by expanding the genetic data. In particular, we revisited their analysis with a recently assembled data set that largely subsumes the earlier 53-population data set. These data consisted of 237 populations studied by Pemberton et al. (2013) , excluding from a larger set of 267 the populations with unknown or ambiguous geographic assignments, populations with sample size ≤5, and populations from Micronesia and Samoa, for which Ashraf and Galor (2013) did not provide values of the economic variables. The 237 populations represent 39 countries. From Pemberton et al. (2013) , we used the expected heterozygosities reported using 645 loci in the full 5795-individual data set; the calculation is analogous to the earlier expected heterozygosities computed with 783 loci and 1048 individuals ( Ramachandran et al. 2005 ).

Repeating the regressions of the economic development variable on “observed diversity”—with the only difference from Ashraf and Galor (2013) being use of the data on 237 populations in 39 countries instead of 53 populations in 21 countries—we observe that the quadratic relationship is no longer close to statistically significant ( Table 1 ). The magnitude of the effects for observed diversity and its square are much reduced, and none of the regressions involving either variable generates significance ( Table S2 ). This result suggests that the “hump-shaped” effect of observed diversity was limited by the particular set of countries and populations covered by the earlier available data: with an expansion of the number of countries, the observed diversity variable fails to produce an effect.

We further investigated this claim in two ways. First, we subsampled only the 136 populations studied by Pemberton et al. (2013) from countries with geographic coordinates that placed them in the earlier set of 21 countries, repeating the same regressions, again computing observed diversity for a country by averaging values for its constituent populations. This analysis represents an expansion of the data used by Ashraf and Galor (2013) , but examining only the same 21 countries they considered. For some models, as in the analysis of Ashraf and Galor (2013) , the 21-country analysis continues to produce a significant effect for observed diversity and its square when more populations are considered ( Table 1 and Table S3 ).

Next, to assess whether the significant result for observed diversity from the 21-country subset was anomalous among possible choices of countries, we considered alternative 21-country subsamples of the Pemberton et al. (2013) data set, repeating the regressions for each subsample. We randomly selected 1000 subsamples of 21 countries among the 39 countries available, maintaining the continental distribution in the original 21-country data set (eight in Africa, four in Europe, three in the Americas, and six in Asia and Oceania). Note that because the Pemberton et al. (2013) data set does not cover any countries in Europe beyond the earlier genetic data, all 1000 subsets use the same four European countries (France, Italy, Russia, and the United Kingdom). If a regression represents a true effect of observed diversity on the dependent variable irrespective of the subsample of countries, then we would expect a low P -value in most of the 1000 replicates. If, however, the 21-country subsample of Ashraf and Galor (2013) is an anomalous false-positive result, then we expect relatively few replicates to produce small P -values, with a uniform distribution of P -values across replicates occurring under the null hypothesis that observed diversity has no effect.

For each regression, P -values for the observed diversity and observed diversity squared variables in the 1000 subsamples appear in Figure 5 . For both variables, across regressions, only at most ∼27% of subsamples produce a significant effect at the 5% level. For the most complete regression, regression 5, accounting for nongenetic covariates and continent fixed effects, the P -value distributions are nearly uniform, with 4–5% of replicates producing P < 0.05. Thus, had a different set of 21 countries been used by Ashraf and Galor (2013) , effects for observed diversity and its square are unlikely to have been seen.

The distribution across 1000 replicate subsamples of regression P-values for the influence of observed genetic diversity and its square on a proxy for economic development. Each panel represents a regression model (regressions 1, 4, or 5, as in Table 1 and Table S1, Table S2, and Table S3) and a variable whose significance is tested (observed diversity or its square). Each replicate subsample considers 21 countries. The red bar indicates the fraction of subsamples for which the P-value is smaller than 0.05.

The distribution across 1000 replicate subsamples of regression P -values for the influence of observed genetic diversity and its square on a proxy for economic development. Each panel represents a regression model (regressions 1, 4, or 5, as in Table 1 and Table S1 , Table S2 , and Table S3 ) and a variable whose significance is tested (observed diversity or its square). Each replicate subsample considers 21 countries. The red bar indicates the fraction of subsamples for which the P -value is smaller than 0.05.

This analysis thus finds that the 21-country set of Ashraf and Galor (2013) is unlike both the enlarged 39-country data and most other 21-country data sets in producing significant effects for genetic diversity. Recalling that Ashraf and Galor (2013) used significance in the 21-country analysis as a basis for replacing actual values of genetic diversity with “predicted diversity,” our reanalysis both contests the result of the first component of the genetic diversity analysis of Ashraf and Galor (2013) and undermines the rationale for the second component. We can conclude that even if the suitability of the methods and data of Ashraf and Galor (2013) to studying the effect of genetic diversity on economic development is left unquestioned, the “hump-shaped” effect for genetic diversity does not persist with an expanded genetic data set.

The variability of genetic diversity across different human populations, a vestige of the history of human migrations, is consequential for population differences in a variety of settings of societal interest. These differences across populations, each of which might be viewed through a purely social-scientific lens, involve a population-genetic contribution of the properties of genetic diversity.

In the familial identification, transplantation matching, and GWA representation examples that we have examined, addressing inequalities across populations in the phenomena of ultimate interest requires a particular effort to overcome not only the sociological determinants of inequality across populations but also the intrinsic inequalities that arise from differences in genetic diversity. Thus, Bergstrom et al. (2009) study the relative value of efforts to enhance representation of high-diversity African Americans not only for the goal of achieving equality of representation but also because each African American added can have a greater chance than other individuals of providing the only database match for a potential transplantation recipient. For GWA studies, not only is reduction of inequality in participation a desirable goal achievable via mechanisms such as funding priorities emphasizing underrepresented groups, improvement in the ultimate outcome—genetic understanding of disease for all populations—can be achieved by generating new genomic resources for additional populations ( International HapMap 3 Consortium 2010 ), developing statistics for enhancing GWA designs and analyses in underrepresented and high-diversity populations ( Teo et al. 2010 ; Huang et al. 2011 ), and employing studies that capitalize on unique features of genetically admixed groups ( Winkler et al. 2010 ; Seldin et al. 2011 ). In forensic familial identification, improvements toward the goal of equally minimal false-positive matches in forensic casework can be achieved by dissemination in the legal system of knowledge about false-positive matches and the role of genetic diversity, transparency in applications of familial identification methods, and development of new marker sets with lower error rates ( Rohlfs et al. 2012 , 2013 ; Garrison et al. 2013 ).

Each of these settings can be viewed from an economic perspective: cost differences across populations can arise from the differential pursuit by law enforcement agencies of false-positive forensic identifications, the variable rates of success or failure to find transplantation matches, and the potential inequalities in the success of treatments arising from genomic medicine. Indeed, Bergstrom et al. (2009 , 2012 ) adopted an explicitly economic perspective in analyzing improvements in transplantation matching, estimating a cost and benefit for each additional registrant added to the NMDP database.

Nevertheless, despite this view that economic consequences can be traced to variation in genetic diversity, we have found no support for the claim of Ashraf and Galor (2013) that genetic diversity has been important in contributing to differences across human populations in levels of economic development. Our reanalysis has focused exclusively on the genetic data in their study, not repeating objections raised elsewhere about their demographic and economic data, statistics, and interpretations, or about the suitability of their data and genetic variables to addressing the question at hand ( d’Alpoim Guedes et al. 2013 ; Gelman 2013 ; Feldman 2014 ). Whereas genetic diversity affects differences among human populations in other scenarios, reproducing the work of Ashraf and Galor (2013) on its own terms using expanded genetic data challenges the claim for a role of genetic diversity in economic development.

What distinguishes the forensic, transplantation, and GWA scenarios in which genetic diversity has a demonstrable impact from the economic development problem? The former scenarios are each tightly connected to biological phenomena. For these cases, computations from population genetics prominently feature genetic diversity; in fact, it can be argued that population genetics suggests that proper analysis of population differences in these scenarios is incomplete without consideration of genetic diversity. In the case of economic development, however, genetic diversity is merely another variable alongside nongenetic variables in a multiple regression; although it is plausible that genetic diversity could affect the regression in the same way that nongenetic variables plausibly contribute to economic development, principles from population genetics produce no theory of the economic development of nations and thus do not contribute to this plausibility. The work of Ashraf and Galor (2013) is one of the first among recent studies seeking to identify an effect of a variable from population genetics on global economic outcomes. Given the novelty of population-genetic variables in attempts to address long-standing economic questions, such studies are likely to proliferate and deepen in methodologic sophistication. As genetic diversity and its interaction with social phenomena are considered in new contexts across different areas of inquiry, however, it will be important to take note of the distinction between fundamentally nonbiological uses of population-genetic variables and cases in which their utility is grounded in biology.

We thank E. Bendavid, W. Bodmer, M. Edge, K. Hunley, P. Norman, R. Rohlfs, M. Turelli, and an anonymous reviewer for comments on a draft of the manuscript; J. Cohen, M. Feldman, D. Laitin, and A. Saperstein for discussions; and M. Dey for research assistance.

Communicating editor: M. Turelli

Supporting information is available online at www.genetics.org/lookup/suppl/doi:10.1534/genetics.115.176750/-/DC1

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Ecological consequences of genetic diversity

Affiliation.

  • 1 Evolution and Ecology/Bodega Marine Laboratory, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA. [email protected]
  • PMID: 18400018
  • DOI: 10.1111/j.1461-0248.2008.01179.x

Understanding the ecological consequences of biodiversity is a fundamental challenge. Research on a key component of biodiversity, genetic diversity, has traditionally focused on its importance in evolutionary processes, but classical studies in evolutionary biology, agronomy and conservation biology indicate that genetic diversity might also have important ecological effects. Our review of the literature reveals significant effects of genetic diversity on ecological processes such as primary productivity, population recovery from disturbance, interspecific competition, community structure, and fluxes of energy and nutrients. Thus, genetic diversity can have important ecological consequences at the population, community and ecosystem levels, and in some cases the effects are comparable in magnitude to the effects of species diversity. However, it is not clear how widely these results apply in nature, as studies to date have been biased towards manipulations of plant clonal diversity, and little is known about the relative importance of genetic diversity vs. other factors that influence ecological processes of interest. Future studies should focus not only on documenting the presence of genetic diversity effects but also on identifying underlying mechanisms and predicting when such effects are likely to occur in nature.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Biological Evolution*
  • Ecology / methods*
  • Genetic Variation*
  • Models, Biological*
  • Population Dynamics
  • Species Specificity

Estimation of genetic diversity and its exploitation in plant breeding

  • Published: 24 November 2021
  • Volume 88 , pages 413–435, ( 2022 )

Cite this article

  • Hausila Prasad Singh   ORCID: orcid.org/0000-0002-8691-4764 1 ,
  • Om Prakash Raigar 2 &
  • Rakesh Kumar Chahota 3  

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Estimation of genetic diversity is a prerequisite to select genetically diverse parents. Availability and collection of genetically diverse parents contribute significantly towards the selection and utilization of promising parents in plant breeding to develop a commercial variety or hybrid. Germplasm is an important source for various qualitative and quantitative traits that may be used to introgress through combination breeding for the improvement of the existing cultivars or development of new cultivars and hybrids by using marker assisted selection. Genetic diversity refers to the variations among the alleles of a gene and it may be examined at nucleotide level in the DNA sequence. Various classical and DNA tools are available to access genetic diversity at morphological and molecular levels and can be expressed in the form of dendrogram, percentage polymorphic loci and genetic distance. Estimation of genetic diversity using molecular techniques is more reliable as it is based on highly polymorphic molecular markers which remain unaffected by the influence of environment. Genetically diverse genotypes are used as valuable source by the plant breeders for the development of new or improved crop varieties with desirable traits to cope up the biotic and abiotic stresses such as drought tolerant, salt tolerant, insect pest and disease resistance etc. This article reviews various traditional to molecular methods used in estimation of genetic diversity and their exploitations in plant breeding programme.

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Singh, H.P., Raigar, O.P. & Chahota, R.K. Estimation of genetic diversity and its exploitation in plant breeding. Bot. Rev. 88 , 413–435 (2022). https://doi.org/10.1007/s12229-021-09274-y

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Researchers optimize genetic tests for diverse populations to tackle health disparities

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Improved genetic tests more accurately assess disease risk regardless of genetic ancestry.

To prevent an emerging genomic technology from contributing to health disparities, a scientific team funded by the National Institutes of Health has devised new ways to improve a genetic testing method called a polygenic risk score . Since polygenic risk scores have not been effective for all populations, the researchers recalibrated these genetic tests using ancestrally diverse genomic data. As reported in Nature Medicine , the optimized tests provide a more accurate assessment of disease risk across diverse populations.

Genetic tests look at the small differences between individuals’ genomes, known as genomic variants , and polygenic risk scores are tools for assessing many genomic variants across the genome to determine a person’s risk for disease. As the use of polygenic risk scores grows, one major concern is that the genomic datasets used to calculate the scores often heavily overrepresent people of European ancestry.

“Recently, more and more studies incorporate multi-ancestry genomic data into the development of polygenic risk scores,” said Niall Lennon, Ph.D., a scientist at the Broad Institute in Cambridge, Massachusetts and first author of the publication. “However, there are still gaps in genetic ancestral representation in many scores that have been developed to date.”

These “gaps” or missing data can cause false results, where a person could be at high risk for a disease but not receive a high-risk score because their genomic variants are not represented. Although health disparities often stem from systemic discrimination, not genetics, these false results are a way that inequitable genetic tools can exacerbate existing health disparities.

Recently, more and more studies incorporate multi-ancestry genomic data into the development of polygenic risk scores. However, there are still gaps in genetic ancestral representation in many scores that have been developed to date.

In the new study, the researchers improved existing polygenic risk scores using health records and ancestrally diverse genomic data from the All of Us  Research Program, an NIH-funded initiative to collect health data from over a million people from diverse backgrounds.

The All of Us dataset represented about three times as many individuals of non-European ancestry compared to other major datasets previously used for calculating polygenic risk scores. It also included eight times as many individuals with ancestry spanning two or more global populations. Strong representation of these individuals is key as they are more likely than other groups to receive misleading results from polygenic risk scores.

The researchers selected polygenic risk scores for 10 common health conditions, including breast cancer, prostate cancer, chronic kidney disease, coronary heart disease, asthma and diabetes. Polygenic risk scores are particularly useful for assessing risk for conditions that result from a combination of several genetic factors, as is the case for the 10 conditions selected. Many of these health conditions are also associated with health disparities.

The researchers assembled ancestrally diverse cohorts from the All of Us data, including individuals with and without each disease. The genomic variants represented in these cohorts allowed the researchers to recalibrate the polygenic risk scores for individuals of non-European ancestry.

With the optimized scores, the researchers analyzed disease risk for an ancestrally diverse group of 2,500 individuals. About 1 in 5 participants were found to be at high risk for at least one of the 10 diseases.

Most importantly, these participants ranged widely in their ancestral backgrounds, showing that the recalibrated polygenic risk scores are not skewed towards people of European ancestry and are effective for all populations.

“Our model strongly increases the likelihood that a person in the high-risk end of the distribution should receive a high-risk result regardless of their genetic ancestry,” said Dr. Lennon. “The diversity of the All of Us dataset was critical for our ability to do this.”

However, these optimized scores cannot address health disparities alone. “Polygenic risk score results are only useful to patients who can take action to prevent disease or catch it early, and people with less access to healthcare will also struggle to get the recommended follow-up activities, such as more frequent screenings,” said Dr. Lennon.

Still, this work is an important step towards routine use of polygenic risk scores in the clinic to benefit all people. The 2,500 participants in this study represent just an initial look at the improved polygenic risk scores. NIH’s Electronic Medical Health Records and Genomics (eMERGE) Network will continue this research by enrolling a total of 25,000 participants from ancestrally diverse populations in the study’s next phase.

About NHGRI and NIH

About the National Human Genome Research Institute (NHGRI):  At NHGRI, we are focused on advances in genomics research. Building on our leadership role in the initial sequencing of the human genome, we collaborate with the world's scientific and medical communities to enhance genomic technologies that accelerate breakthroughs and improve lives. By empowering and expanding the field of genomics, we can benefit all of humankind. For more information about NHGRI and its programs, visit  www.genome.gov . About the National Institutes of Health (NIH):  NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit  www.nih.gov .

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  • Review Article
  • Published: 16 October 2020

The importance of genomic variation for biodiversity, ecosystems and people

  • Madlen Stange   ORCID: orcid.org/0000-0002-4559-2535 1 ,
  • Rowan D. H. Barrett   ORCID: orcid.org/0000-0003-3044-2531 1 &
  • Andrew P. Hendry 1  

Nature Reviews Genetics volume  22 ,  pages 89–105 ( 2021 ) Cite this article

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62 Citations

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  • Conservation genomics
  • Ecological genetics
  • Evolutionary genetics
  • Genetic variation

The 2019 United Nations Global assessment report on biodiversity and ecosystem services estimated that approximately 1 million species are at risk of extinction. This primarily human-driven loss of biodiversity has unprecedented negative consequences for ecosystems and people. Classic and emerging approaches in genetics and genomics have the potential to dramatically improve these outcomes. In particular, the study of interactions among genetic loci within and between species will play a critical role in understanding the adaptive potential of species and communities, and hence their direct and indirect effects on biodiversity, ecosystems and people. We explore these population and community genomic contexts in the hope of finding solutions for maintaining and improving ecosystem services and nature’s contributions to people.

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The authors thank S. Rudman, V. Glynn, S. van Moorsel, the anonymous reviewers, I. Porth and R. Waples for comments on an earlier version of the manuscript.

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Variation in alleles of genes within and among populations of the same species.

Interspecific and intraspecific genetic variation.

(Also known as rapid evolution). Natural selection that drives adaptive evolution in populations on timescales of less than a few hundred years.

Transfer of genetic variation from one population to another usually via migrating individuals.

Change in the DNA sequence.

Stochastic process altering the genetic variation in a population, usually reducing genetic diversity.

The study of genetic variation and evolutionary history within species using single-gene markers (population genetics) and multigene markers up to full genomes for consideration of structural and epigenetic variation (population genomics).

A community is the sum of populations formed by different species within a particular geographical area. Community genetics and genomics studies the effects of interactions among genomic variation between interacting species. Such interactions are mediated through phenotypes that are determined by heritable genetic variation and environmental influences.

Phenotypes that include effects of genes on the environment, such as an organism’s behaviour or life history, or ecosystem.

A species with a disproportionate ecological effect in an ecosystem. Removal of that species would lead to a drastic change in the ecosystem.

The ability to evolve (that is, to produce genetic diversity on which selection can act).

Proportion of sites on the chromosome at which two randomly chosen copies differ in DNA sequence.

The independent genetic effect of an allele on the phenotype of an individual organism resulting in deviation from the population mean phenotype. Additive genetic variance contributes to the evolvability of a population.

A genetic interaction between the two alleles at a locus, such that the phenotype of heterozygotes deviates from the average of the two homozygotes.

Non-additive gene–gene interaction. A given allele might function well in one genetic background but poorly in another genetic background. We also refer to interspecific epistasis, in which alleles in different species interact (for example, gene–gene interactions between a native host and a parasite perform differently from an invasive host and the parasite genotype).

In contrast to chemical control agents, biocontrol agents are natural predators or parasites of a pest.

Alternative chromatin states at a given locus, defined with respect to individuals in the population for a given time point and tissue type.

A population is the sum of all individuals of the same species within a defined geographical area. Its dynamics are described as changes in the demographics of a given population (for example, age, composition or size) driven by biological and environmental factors.

Phenotypically (in a trait or ecological niche) similar but geographically or temporally co-occurring species diverge in the trait to minimize interspecific competition.

As part of a plant’s defence mechanism, lethal biochemical compounds are released into the soil to suppress neighbouring organisms.

A term describing the symbiotic interaction between a fungus and a plant’s rhizosphere.

The totality of microorganisms, their genetic information and the milieu in which they interact to perform a specific function.

Genetically engineered, synthetic genetic elements designed to increase in frequency over time in a population to propagate a certain gene variant.

A subdiscipline of machine learning, with the difference that no training data set is needed. The artificial neural network recognizes patterns from coarse to fine scale in multiple steps, so-called hidden layers, which compute increasingly more complex features by taking the results of preceding operations as input.

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Stange, M., Barrett, R.D.H. & Hendry, A.P. The importance of genomic variation for biodiversity, ecosystems and people. Nat Rev Genet 22 , 89–105 (2021). https://doi.org/10.1038/s41576-020-00288-7

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Researchers optimize genetic tests for diverse populations to tackle health disparities

Improved genetic tests more accurately assess disease risk regardless of genetic ancestry.

To prevent an emerging genomic technology from contributing to health disparities, a scientific team funded by the National Institutes of Health has devised new ways to improve a genetic testing method called a polygenic risk score . Since polygenic risk scores have not been effective for all populations, the researchers recalibrated these genetic tests using ancestrally diverse genomic data. As reported in Nature Medicine , the optimized tests provide a more accurate assessment of disease risk across diverse populations.

Genetic tests look at the small differences between individuals’ genomes, known as genomic variants , and polygenic risk scores are tools for assessing many genomic variants across the genome to determine a person’s risk for disease. As the use of polygenic risk scores grows, one major concern is that the genomic datasets used to calculate the scores often heavily overrepresent people of European ancestry.

“Recently, more and more studies incorporate multi-ancestry genomic data into the development of polygenic risk scores,” said Niall Lennon, Ph.D., a scientist at the Broad Institute in Cambridge, Massachusetts and first author of the publication. “However, there are still gaps in genetic ancestral representation in many scores that have been developed to date.”

These “gaps” or missing data can cause false results, where a person could be at high risk for a disease but not receive a high-risk score because their genomic variants are not represented. Although health disparities often stem from systemic discrimination, not genetics, these false results are a way that inequitable genetic tools can exacerbate existing health disparities.

In the new study, the researchers improved existing polygenic risk scores using health records and ancestrally diverse genomic data from the All of Us Research Program, an NIH-funded initiative to collect health data from over a million people from diverse backgrounds.

The All of Us dataset represented about three times as many individuals of non-European ancestry compared to other major datasets previously used for calculating polygenic risk scores. It also included eight times as many individuals with ancestry spanning two or more global populations. Strong representation of these individuals is key as they are more likely than other groups to receive misleading results from polygenic risk scores.

The researchers selected polygenic risk scores for 10 common health conditions, including breast cancer, prostate cancer, chronic kidney disease, coronary heart disease, asthma and diabetes. Polygenic risk scores are particularly useful for assessing risk for conditions that result from a combination of several genetic factors, as is the case for the 10 conditions selected. Many of these health conditions are also associated with health disparities.

The researchers assembled ancestrally diverse cohorts from the All of Us data, including individuals with and without each disease. The genomic variants represented in these cohorts allowed the researchers to recalibrate the polygenic risk scores for individuals of non-European ancestry.

With the optimized scores, the researchers analyzed disease risk for an ancestrally diverse group of 2,500 individuals. About 1 in 5 participants were found to be at high risk for at least one of the 10 diseases.

Most importantly, these participants ranged widely in their ancestral backgrounds, showing that the recalibrated polygenic risk scores are not skewed towards people of European ancestry and are effective for all populations.

“Our model strongly increases the likelihood that a person in the high-risk end of the distribution should receive a high-risk result regardless of their genetic ancestry,” said Dr. Lennon. “The diversity of the All of Us dataset was critical for our ability to do this.”

However, these optimized scores cannot address health disparities alone. “Polygenic risk score results are only useful to patients who can take action to prevent disease or catch it early, and people with less access to healthcare will also struggle to get the recommended follow-up activities, such as more frequent screenings,” said Dr. Lennon.

Still, this work is an important step towards routine use of polygenic risk scores in the clinic to benefit all people. The 2,500 participants in this study represent just an initial look at the improved polygenic risk scores. NIH’s Electronic Medical Health Records and Genomics (eMERGE) Network will continue this research by enrolling a total of 25,000 participants from ancestrally diverse populations in the study’s next phase.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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Genetic diversity and disease: the past, present and future of an old idea

Why do infectious diseases erupt in some host populations and not others? This question has spawned independent fields of research in evolution, ecology, public health, agriculture, and conservation. In the search for environmental and genetic factors that predict variation in parasitism, one hypothesis stands out for its generality and longevity: genetically homogeneous host populations are more likely to experience severe parasitism than genetically diverse populations. In this perspective piece, I draw on overlapping ideas from evolutionary biology, agriculture, and conservation to capture the far-reaching implications of the link between genetic diversity and disease. I first summarize the development of this hypothesis and the results of experimental tests. Given the convincing support for the protective effect of genetic diversity, I then address the following questions: 1) Where has this idea been put to use, in a basic and applied sense, and how can we better use genetic diversity to limit disease spread?; 2) What new hypotheses does the established disease-diversity relationship compel us to test? I conclude that monitoring, preserving, and augmenting genetic diversity is one of our most promising evolutionarily-informed strategies for buffering wild, domesticated, and human populations against future outbreaks.

Introduction

The hypothesis that genetic diversity limits parasitism is arguably one of the most broadly influential ideas in the study of host-parasite interactions. If parasites have some degree of genetic specificity for infection, then we expect them to transmit more readily between closely-related hosts than distantly-related hosts. From this assumption, we arrive at the hypothesis that genetically diverse host populations face a lower risk from infectious disease than do genetically homogeneous populations. This idea has garnered significant empirical support, spurred influential evolutionary hypotheses, and instigated genetic diversification as a critical tool in the sustainable management of infectious diseases in crop and wildlife populations.

Here, I draw on insights from evolutionary biology, agriculture, and conservation to provide a broad perspective on the link between genetic diversity and disease. The varied application of the diversity-disease connection necessitates a few clarifications up front: I use the term diversity to refer to intraspecific genetic diversity of hosts within population. Genetic diversity may be functional or neutral and can be quantified in many ways, like allelic or genotypic richness (see Table 1 in Hughes et al. 2008a ). For the most part, I will not directly consider the effects of interspecific diversity ( Halliday and Rohr 2019 ; Halliday et al. 2020b ) or genetic heterozygosity of individuals ( Spurgin and Richardson 2010 ). I use the term “parasite” broadly to encompass organisms historically separated as microparasites (e.g. viruses, bacteria) and macroparasites (e.g. mites, trematodes) ( Lafferty and Kuris 2002 ). Empirical studies quantify population-level parasitism in many ways, including estimates of the fraction of hosts infected (e.g. prevalence) and the average size of a host’s infection (e.g. load) ( Gibson and Nguyen 2021 ). For my purposes, I use the term “parasitism” to broadly refer to this suite of approaches, with the recognition that metric may matter in interpretation of specific experiments. I adopt this general terminology in order to provide an inclusive treatment of the varied fields that have interrogated the relationship between parasitism and the genetic diversity of host populations.

I begin with a brief overview of the hypothesis that genetic diversity limits parasitism – how has this idea developed, and how does it work? I conclude that experiments and quantitative syntheses now provide substantial evidence that genetically diverse host populations experience less parasitism on average than genetically homogeneous populations. I then consider the implications of this protective effect of genetic diversity - what opportunities and challenges does it present, and what questions and hypotheses does it inspire? I highlight the clear significance of the disease-reducing benefit of genetic diversity today, as we face down the threat of emerging infectious diseases. The link between diversity and disease provides powerful motivation and practical guidance for rectifying the ongoing loss of genetic diversity in crop and wildlife species.

Foundations of the hypothesis

In this section, I introduce some historical events relevant to development of the hypothesis that genetic diversity limits parasitism. I then summarize the findings of theoretical and empirical tests of this hypothesis.

The idea has its origins in agriculture. In one of the early recorded observations of disease in crop mixtures, the pathologist Giovanni Tozzetti (1767) puzzled over an epidemic of stem rust in 1766 in Italy: “It is not so easy to render a reason, why Wheat growing seeded with Rye, or with Vetch, was not damaged by the rust, while a Field of Wheat alone, standing between one of rye, and one of Vetch, yielded scarcely any seed, and that the most miserable.”

In the second half of the 1800s, repeated failure of the potato crop in Europe due to the oomycte Phytopthora infestans fueled ongoing concern about the clonal propagation of crop varieties ( Gray 1875 ). Hunting for a solution to the potato problem, the Irish merchant James Torbitt found inspiration in Charles Darwin’s writings on variation and natural selection. He exchanged 141 letters with Darwin seeking intellectual and financial support for his scheme to propagate potatoes by seed (products of sexual reproduction) rather than by tubers (products of clonal reproduction) ( DeArce 2008 ). To test his idea, Torbitt oversaw a large-scale field experiment in which farmers planted his variable potato seeds near infected clonal varieties. Farmers reported relatively low rates of P. infestans in the variable set, with several reporting no infection at all ( Torbitt 1867 ). In spite of these promising results, Torbitt’s scheme never took off. Its greatest strength – the variation among individuals – was a commercial weakness: buyers preferred the consistent phenotype of Scotch Champion, a clonal variety bred for P. infestans resistance that came to occupy 80–90% of Ireland’s late-maturing potato acreage in the late 1800s ( DeArce 2008 ). Torbitt’s idea, however, persisted.

Subsequent catastrophic crop failures gave further weight to a connection between homogeneity and infection. In 1882, Harry Marshall Ward warned against the dangers of dense, homogeneous plantings following the emergence of coffee rust on Sri Lanka (then Ceylon), which destroyed the island’s coffee industry ( Ward 1882 ; Ainsworth 1994 ). The banana industry developed around the Gros Michel cultivar only to face fusarium wilt in the early 1900’s, a drawn-out battle with devastating economic, environmental and social ramifications. In 1962, the industry was restructured around monocultures of Cavendish, a cultivar that was originally resistant but is now succumbing to another Fusarium lineage ( Marquardt 2001 ; Ploetz 2015 ; Kema et al. 2020 ). Modern breeding practices fostered further epidemics by facilitating wide dissemination of varieties derived from a single parent lineage: 80% of acreage planted to a single lineage explained the severity of the 1940s epidemic of crown rust on oats and the 1970 Southern leaf blight on corn in the US ( Browning 1972 ). These epidemics are not merely historical anecdotes. The latest global threat to wheat comes from the Ug99 stem rust group. Several major rust resistance genes are ineffective against Ug99, and the overrepresentation of these genes in commercial stock resulted in >85% of wheat varieties grown globally being at risk of Ug99 infection in the 2000’s ( CIMMYT 2005 ; Singh et al. 2011 ). These repeated disease-induced collapses of crop monocultures have led to widespread adoption of the idea that genetic homogeneity promotes disease risk, and of its corollary, that genetic diversity limits disease risk.

Why would genetic diversity limit parasitism? We first assume host genotypes vary in their susceptibility to a given parasite genotype. Parasites achieve high fitness when they encounter a susceptible host genotype, while encountering a resistant host genotype curtails transmission. Genetic diversity in host populations could then limit the success of parasites in at least three ways: 1) Increasing diversity reduces the frequency of any given host genotype, thus reducing the rate at which a parasite encounters a susceptible host genotype. 2) For a finite population size, more host genotypes means fewer individuals of the susceptible genotype(s), suppressing density-dependent transmission. 3) Both of the above mechanisms may contribute to limiting adaptation of parasites ( King and Lively 2012 ). The agricultural literature cites additional mechanisms that may apply in specific contexts, including resistant hosts as physical barriers to parasite dispersal (“fly-paper effect” - Trenbath 1975 ). These mechanisms are not mutually exclusive.

Theory has evaluated these verbal arguments through epidemiological, evolutionary, and agricultural lenses. Leonard (1969) ’s model provided a valuable guide for agricultural researchers. Building off ideas put forth by Borlaug (1953 ; 1958 ) and Jensen (1952) , Leonard (1969) modeled the spread of stem rust in a simple mixture of two oat varieties, one susceptible and one entirely resistant. In his model, infection of the susceptible genotype declines logarithmically as its frequency in the mixture declines. All else equal, this result supports the planting of resistant monocultures rather than mixtures. Leonard, however, inferred from his findings that mixtures may have special value against diverse parasite populations. This model inspired subsequent theory (e.g. Kiyosawa and Shiyomi 1972 ; Kiyosawa 1976 ; Jeger et al. 1981 ) and garnered empirical support (e.g. Burdon and Chilvers 1977 ; Luthra and Rao 1979 ). With livestock populations in mind, Springbett et al. (2003) allowed for multiple genotypes with susceptibility varying quantitatively in a classic Susceptible-Infected-Recovered (SIR) model. Genetic variation had no effect on the average probability of an epidemic occurring: in other words, it did not change the probability that the expected number of secondary infections produced by an initial infection (R 0 ) was equal to or greater than one (see also Nath et al. 2008 ). It did, however, lower the proportion of individuals infected during an epidemic for a given R 0 . A key feature of the models above is that host genotypes vary in susceptibility to a single parasite genotype.

Later theory allowed for multiple parasite genotypes. These models predict that genetic diversity strongly limits disease spread when parasite genotypes vary in their host specificity. Several models follow the matching-alleles assumption, a classic representation of genetic specificity in which each host genotype is susceptible to a different “matching” parasite genotype and resistant to all others. In finite populations, increasing host diversity decreases the host density required for a parasite to spread ( Lively 2010a ) and the size of an epidemic ( Ashby and King 2015 ). Diversity limits disease spread even in very large populations, because it reduces the frequency of a parasite’s matching host genotype ( R − 0 = B / G where B is the realized fecundity of the parasite and G is the number of host genotypes in the population) ( Lively 2010a ). Lively (2016) added reciprocal adaptation to these models, showing that when the host population becomes dominated by a single host genotype, adaptation of the parasite population to the common host escalates R 0 . Mikaberidze et al. (2015) varied the degree of genetic specificity, demonstrating that the prevalence of infection in mixtures relative to monocultures declines with increasing specificity and number of host genotypes in the mixture (see also Gumpert and Geiger 1995 ; Ohtsuki and Sasaki 2006 ).

Does genetic diversity in fact limit parasitism? Until relatively recently in the history of this idea, the nature of the evidence was observational. The first direct experimental tests began in the 1950’s with mixtures of different varieties of the same crop (varietal mixtures) ( Rothman and Frey 1953 ; Leonard 1969 ). Crop experiments have typically compared field plots planted with monocultures and mixtures of a few varieties with known disease resistance phenotypes (e.g. resistant or susceptible). Many specifically addressed fungal diseases of wheat and barley. Their results clearly support a reduction in parasitism with diversity: of the 55 studies included in a meta-analysis by Gibson and Nguyen (2021) , 48 reported a mean reduction in parasitism in mixtures relative to the means of their component monocultures. The estimated effect of diversity is staggering: mixtures reduced estimates of parasitism by ~50% on average relative to monocultures (see also Huang et al. 2012 ). In the most famous example of the protective effect of genetic diversity, mixtures of japonica and indica rice varieties had 75–95% less rice blast than the means of their component monocultures ( Zhu et al. 2000 ). This massive effect may reflect parasite specificity: japonica and indica rice differ in the rice blast lineages to which they are susceptible ( Liao et al. 2016 ).

Enthusiasm for the diversity-disease connection spilled over to evolutionary biology ( Haldane 1949 ) and conservation ( Elton 1958 ; O’Brien and Evermann 1988 ), and experiments on non-crop systems began in the late 1980’s ( Jarosz and Levy 1988 ; Alexander 1991 ; Schmid 1994 ). These studies have tested the effect of host diversity in a wide range of host species, from plants to invertebrates to bacteria. Experimental designs vary substantially: some mix clonal or family lineages, while others manipulate mate number to increase offspring diversity. In contrast to crop studies, phenotypes of host genotypes are often unknown, so mixtures are constructed for genetic diversity alone, blind to the consequences for phenotypic diversity in disease resistance (i.e. functional diversity). It is thus all the more striking that genetic diversity still has a negative effect on parasitism in these non-crop experiments: two independent meta-analyses reported moderate reductions in parasitism (~20%) with genetic diversity ( Ekroth et al. 2019 ; Gibson and Nguyen 2021 ). Altermatt and Ebert (2008) provided one of the clearest demonstrations of this effect: they established Daphnia magna populations with high diversity (10 genotypes) and low diversity (1 genotype). Host genotypes were randomly selected from different natural rock pools, so traits did not differ systematically between hosts in the two diversity treatments. Moreover, each host genotype was represented in only one population, so each replicate host population represented an independent test of its diversity level. They then tracked the spread of a microsporidian parasite for three years. The parasite spread more rapidly and maintained higher prevalence in low diversity populations. These studies established that the benefits of genetic diversity are not limited to the unique genetics and environments of crops. Together, this large body of experimental work leaves little doubt that, on average, genetic diversity limits parasitism in host populations.

Applying the hypothesis

Given the weight of evidence that genetic diversity limits parasitism and the long-standing acceptance of this hypothesis as “conventional wisdom” ( King and Lively 2012 ), how are we making use of this idea in a basic and applied sense? Evolutionary biologists, agricultural researchers, and conservationists have all found inspiration in this idea, spurring the development of independent fields built on a shared foundation. In this section, I provide an overview of the opportunities and challenges presented by the diversity-disease connection in these three areas of research.

In agriculture

The link between diversity and disease initially emerged in response to the devastation wreaked by epidemics in crop fields. Therefore, the agricultural community has decades of practical experience with the benefits and challenges of genetic diversification as a tool in disease management. The interest in diversity stems from its potential to both promote crop yield within a growing season and protect valuable resistance genes from parasite counter-adaptation (“durable resistance” - Mundt 2014 ; Brown 2015 ). Diversity also enhances resilience in the face of many other biotic and abiotic variables, like insect pests and drought ( Hajjar et al. 2008 ; Grettenberger and Tooker 2015 ; Yang et al. 2019 ). As a result, varietal mixtures have larger, more stable yields on average, with greater gains when disease pressure is high ( Kiær et al. 2009 ; Borg et al. 2018 ; Reiss and Drinkwater 2018 ).

Given these positive results, how, and to what extent, are diversification strategies adopted in agriculture? There are many comprehensive reviews of the strategies used to increase genetic diversity for disease protection in crops ( Browning and Frey 1969 ; Wolfe 1985 ; Smithson and Lenne 1996 ; Mundt 2002 ; Finckh and Wolfe 2006 ; Newton et al. 2009 ). I provide a brief overview of the key strategies and problems. Though these concepts can be extended to other domesticated organisms (e.g. microbes - Rahn 1922 ; livestock - Bruford et al. 2015 ; fish - Ren et al. 2018 ; forestry - Ingvarsson and Dahlberg 2019 ), I focus on crop plants given the depth and breadth of research in this area.

A simple and effective diversification strategy is to plant mixtures of multiple distinct crop varieties. Historically, agriculture entailed the planting of multiple landraces, which are highly diverse lineages selected for performance in local areas. These practices persist today in many regions ( Villa et al. 2007 ; Jarvis et al. 2008 ). Surveys show that small-scale farmers continue to grow more than one variety of a crop, with communities and regions collectively growing many varieties ( Jarvis et al. 2008 ; Kiwuka et al. 2012 ; Mulumba et al. 2012 ; Katwal et al. 2015 ; Tiongco and Hossain 2015 ; Ruelle et al. 2019 ). Seeds may be mixed randomly or spatially, in rows or small plots ( Mulumba et al. 2012 ). Mixtures do not appear to be assembled specifically for disease protection; many factors motivate the preservation of varietal diversity on small farms ( Perales et al. 2003 ; Jiao et al. 2012 ; Dedeurwaerdere and Hannachi 2019 ). The Yunnan rice terrace system provides a compelling example of long-term mixture use: a 2008 survey reported that residents of Yuanyang County draw from at least 47 landraces to plant complex mosaics of rice genotypes in these ancient mountain terraces. This diversity may explain the relatively high yield and very low disease impacts in this region ( Jiao et al. 2012 ; Liao et al. 2016 ; Dedeurwaerdere and Hannachi 2019 ). In contrast, industrial agriculture and commercial breeding has historically been dominated by the pursuit of uniformity ( Newton et al. 2009 ; Wolfe and Ceccarelli 2020 ). Several large-scale programs have reduced disease by introducing varietal diversity at regional scales. Famously, the former German Democratic Republic converted the majority of barley acreage to varietal mixtures during the 1980’s to control powdery mildew, and the fraction of fields with severe mildew infections declined from 50% to 10% (rev. in Finckh et al. 2000 ; Mundt 2002 ). Such programs have fostered optimism about the growth of mixtures in intensive agriculture, notably for wheat and barley, but data on the frequency of their use remains sparse ( Finckh et al. 2000 ; Mundt 2002 ; Newton 2009 ; Wolfe and Ceccarelli 2020 ).

Multilines provide a more targeted alternative to varietal mixtures ( Borlaug and Gibler 1953 ; Browning et al. 1964 ; Groenewegen 1977 ). Multilines mix genotypes that resemble one another in all but the pathogen genotypes to which they are susceptible. Transgenic methods can produce near-isogenic lines that vary only at specific resistance loci ( Brunner et al. 2012 ). Several multilines have been successfully deployed for control of specific diseases ( Smithson and Lenne 1996 ). In 2009, governmental and coffee organizations in Columbia supported farmers in replanting >50% of coffee fields with a resistant multiline, driving coffee rust incidence down from >40% to 3% by 2013 ( Avelino et al. 2015 ). The advantage of multilines is that they preserve crop uniformity while incorporating functional diversity for resistance to a focal parasite. They have proven less popular than mixtures, however ( Mundt 2002 ): their genetic base is narrow, restricting the potential benefits of diversification, and they can be challenging to breed.

Though I focus on intraspecific diversity, the protective effect of diversification can also be achieved by increasing interspecific diversity in space or time. Intercropping – the practice of planting multiple species in spatial proximity – remains a dominant practice in many parts of the world, but it is rare in intensive agriculture where the varied traits of mixed species impede mechanization (rev. in Brooker et al. 2015 ). Rotation – the practice of alternating between two or more species in time – has much more adherence in intensive agriculture: USDA survey data reported that, from 1996–2010, 82–94% of US wheat, corn and soybean acreage was grown under rotations of 1–3 years ( Wallander 2020 ). Adoption of this practice does, however, vary with location, crop species, and a crop’s current value ( Plourde et al. 2013 ; McBride et al. 2018 ). There are many differences between the effect of inter- and intraspecific diversity on disease. From a practical standpoint, however, both have the potential to limit parasitism ( Curl 1963 ; Boudreau 2013 ; Civitello et al. 2015 ).

Scientific and technological advances hold enormous promise in furthering the pursuit of genetic diversification in agriculture. Landraces and crop wild relatives serve as valuable sources of genetic variation in disease resistance ( Dinoor 1970 ; Harlan 1976 ; Dempewolf et al. 2017 ). For example, wild relatives of potatoes show extensive variation in resistance to P. infestans ( Pérez et al. 2014 ; Karki et al. 2021 ). Genetic mapping and modification tools can readily identify and mobilize these resistance loci for use in breeding new crop varieties ( Arora et al. 2019 ; Wolter et al. 2019 ). Critically, these technologies can and should be used for innovative long-term management of disease risk through dynamic diversification in space and time, rather than short-term maximization of resistance by widespread deployment of resistance loci ( McDonald 2014 ; Stam and McDonald 2018 ). Ecological and evolutionary theory provide guidance for how to implement diversification in breeding and planting programs so as to minimize the spread and adaptive potential of parasites ( Zhan et al. 2015 ; Wuest et al. 2021 ). This accumulation of scientific, technological, and conceptual knowledge argues that genetic diversification in agriculture is no longer limited by awareness of the problem or by breeding technology. Rather, progress depends upon the social, regulatory and economic factors that govern information exchange, planting practices, and access to diversity at the regional and farm level ( Finckh 2008 ; Labrada 2009 ; Lin 2011 ; Louwaars 2018 ; Wolfe and Ceccarelli 2020 ; Halewood et al. 2021 ).

In evolutionary biology

I now move on to evolutionary biology, where the relationship between diversity and disease contributed to the development of foundational evolutionary and coevolutionary theory. This idea is a key component of hypotheses on rare advantage and the evolution of reproductive modes. Haldane (1949) helped lay the groundwork for these hypotheses. Generalizing from the collapse of Gros Michel due to fusarium wilt and the consistent ability of rust fungi to adapt to infect initially resistant wheat varieties, Haldane argued that parasite populations rapidly adapt to infect common host genotypes in a population. Therefore, rare host genotypes have a fitness advantage in the presence of parasites, and genetically diverse host populations - with more rare genotypes - maintain lower levels of parasitism. He hypothesized that this parasite-mediated rare advantage explains the immense variation in parasite resistance maintained in host populations.

This line of reasoning has been applied to explain polyandry (multiple mating by females) in social Hymenoptera. Multiple mating appears paradoxical in eusociality: if kin selection explains the evolution of eusociality, why do some social insects engage in mating behaviors that reduce the genetic relatedness of nest mates? Polygyny (multiple queens) is similarly paradoxical ( Hughes et al. 2008b ). Hamilton (1987) and Sherman et al. (1988) hypothesized that behaviors that increase genetic variation are favored because they reduce the potential for parasites to severely damage a colony. Experimental tests of this hypothesis report striking reductions in parasitism when colonies are established by females mated with multiple, unrelated males (e.g. Baer and Schmid-Hempel 1999 ; Tarpy and Seeley 2006 ) (though see Schmidt et al. 2011 ).

Though the idea that polyandry reduces disease was developed for eusocial Hymenoptera ( Soper et al. 2021 ), there is some evidence for related phenomena in other taxa. Soper et al. (2014) found that female snails increased their mating rate and number of distinct mating partners when exposed to native parasites. A recent meta-analysis of a taxonomically broad dataset supports the idea that parasites inflict particular damage when members of a group are related: in the presence of parasites and herbivores, mortality rates increase with relatedness of group members, even though grouping with related individuals appears to carry a fitness benefit in the absence of enemies ( Bensch et al. 2021 ).

The idea that genetic diversity defends against parasites also features as a crucial assumption of the Red Queen Hypothesis, a major hypothesis for the maintenance of sex. This hypothesis seeks to explain why sexual reproduction is maintained when the necessary production of male offspring means that sexual females have half the per-capita birth rate of asexual females ( Maynard Smith 1971 ; Gibson et al. 2017 ). The Red Queen hypothesis argues that coevolving parasites counterbalance the cost of sex, because the potential to produce genetically diverse offspring gives sexual females an advantage over asexual females in the presence of parasites. If parasites rapidly adapt to infect common clonal lineages, then a fit asexual genotype becomes a target of coevolving parasites when it reaches high frequency (e.g. Lively and Dybdahl 2000 ). Meanwhile, parasite spread and adaptation is stymied by the diversity of rare genotypes that constitute sexual lineages ( Jaenike 1978 ; Hamilton 1980 ; Bell 1982 ).

There is significant empirical support for the Red Queen hypothesis (rev. in Lively and Morran 2014 ). In one illustrative body of work on sweet vernal grass ( Anthoxanthum odoratum ), Kelley et al. (1988) planted paired arrays of clonal progeny and sexual progeny from the same parent plant. The fitness of sexual progeny was 1.43 times that of their paired asexual siblings. Were parasites responsible for this large fitness difference? Aphid infestation reduced survival by 23% for groups of related A. odoratum relative to groups of unrelated plants ( Schmitt and Antonovics 1986 ). Moreover, groups of clonal progeny had double the prevalence of an aphid-transmitted virus relative to groups of sexual progeny, suggesting disease reduction as a mechanism for the fitness advantage of sexual progeny. Individuals also had much higher infection rates when planted with their clonal siblings than with unrelated individuals ( Kelley 1994 ). As a whole, these experiments and many others lend weight to the idea that coevolving parasites pose a greater risk to genetically depauperate populations of hosts and impose negative-frequency dependent selection that maintains genetic variation in host populations.

What relationship do we then predict to find between genetic diversity and disease in natural populations? If parasites transmit more readily between closely-related individuals, we intuitively predict a negative correlation between markers of genetic diversity and parasitism across host populations (e.g. Whiteman et al. 2006 ). However, by preferentially infecting hosts of common genotypes, parasites can impose negative frequency-dependent selection, which maintains genetic variation in coevolving host populations. By this argument, we predict a positive correlation between diversity and parasitism (e.g. King et al. 2011 ). Thus the same process – the limited ability of parasites to spread between unrelated hosts– can generate a negative or a positive correlation between host diversity and parasitism depending on whether host populations evolve in response to parasite selection ( Meagher 1999 ). Coevolutionary simulations by Lively (2021) demonstrate that even when genetic diversity and parasite prevalence have a strong causal relationship, the measured correlation between these two variables can vary from negative to zero to positive. Consistent with this theoretical work, Gibson and Nguyen (2021) found no mean correlation between genetic diversity and parasitism in observational studies of natural populations. They did, however, find strong negative correlations of diversity and parasitism for threatened and island host populations, where founder effects and reduced genetic variation may limit the potential for hosts to evolve in response to parasite selection. In summary, for natural populations, the absence of a correlation between genetic diversity and parasitism, or even a positive correlation, should not be taken as evidence against the significance of host diversity in limiting parasitism. This important lesson from coevolutionary theory bears upon interpretation of data for conservation purposes.

In conservation

Lastly, I review the significance of the diversity-disease connection as a driver of conservation research and policy. The relationship between genetic diversity and disease matters in conservation because it predicts that the loss of genetic diversity during population bottlenecks puts populations at heightened risk of disease epidemics. This idea gained traction in the 1980’s with epidemics in threatened vertebrate species ( O’Brien and Evermann 1988 ), including canine distemper in black-footed ferrets ( Williams et al. 1988 ) and feline infectious peritonitis in captive cheetahs ( O’Brien et al. 1985 ).

The problem begins with the reductions in population size that define threatened populations. These population bottlenecks are predicted to drive particularly large declines in allelic diversity of loci involved in disease resistance. If hosts with rare alleles at resistance loci have an advantage in the presence of coevolving parasites, then negative frequency-dependent selection maintains many alleles, with few to none at high frequency in a host population ( Clarke 1976 ; Tellier and Brown 2007 ). Indeed, genes linked to disease resistance typically show extremely high allelic diversity relative to the rest of the genome ( Bodmer 1972 ; Hedrick 1998 ; Rose et al. 2004 ; Norman et al. 2017 ; Koenig et al. 2019 ). Allendorf (1986) demonstrated that an allele’s probability of retention after a bottleneck declines with the size of the bottleneck, the number of other alleles at the locus, and the initial rarity of the allele. Thus individuals in declining populations are predicted to become relatively much more similar to one another at loci involved in disease resistance. Population bottlenecks can also increase the frequency of homozygous individuals, who may be more susceptible to parasites because of their limited repertoire of resistance alleles ( Oliver et al. 2009 ; Radwan et al. 2020 ). Allendorf (1986) , however, predicted that allelic richness at resistance loci declines much more dramatically than heterozygosity during population bottlenecks.

Experimental and observational data support the prediction that the genetic homogeneity of bottlenecked populations increases their disease risk. Marden et al. (2017) found a negative relationship between local population size of six tropical tree species and diversity of parasite resistance genes (relative and absolute abundance of nonsynonymous polymorphisms). Population size did not, however, correlate with diversity at other loci. Lower resistance gene diversity corresponded to reduced induction of defense genes in response to parasites and less variation among maternal families in susceptibility. For vertebrate populations, bottlenecked populations show reduced diversity at MHC loci, key immune genes involved in self-nonself recognition ( O’Brien et al. 1985 ; Mikko and Andersson 1995 ; Eimes et al. 2011 ; Sutton et al. 2015 ) (though see: Aguilar et al. 2004 ; Jarvi et al. 2004 ). Limited MHC diversity likely contributed to the rapid spread of two transmissible facial cancers in Tasmanian Devils: devils fail to recognize and resist transmissible tumors in part because host and parasite share MHC alleles that are common in devil populations ( Siddle et al. 2007 ; Cheng et al. 2012 ; Caldwell et al. 2018 ). Importantly, neutral genetic diversity can also strongly inform disease risk: for populations of the endangered Italian agile frog, genetic diversity at microsatellite loci declines with founder events and population isolation ( Garner et al. 2004 ). Pearman and Garner (2005) exposed populations subsampled from this diversity gradient to an emerging virus that had not yet spread to these populations. After exposure to a low dose of virus, low diversity populations experienced 100% mortality by the experiment’s midpoint, while high diversity populations persisted for the duration. These studies validate the concern that threatened populations are genetically predisposed to more severe outbreaks upon exposure to parasites.

In contrast, the ecology of threatened populations can limit their risk of initial exposure to parasites. Threatened populations may be too small and fragmented to support parasite populations ( Carlsson-Granér and Thrall 2002 ). Altizer et al. (2007) reported fewer parasite species infecting threatened primate species relative to non-threatened primates. Gibson et al. (2010) found a lower prevalence of anther-smut disease ( Microbotryum violaceum ) on threatened vs. non-threatened Silene species, and lower richness of fungal parasite species on federally endangered vs. non-endangered plant species (see also Smith et al. 2006 ; Heard et al. 2013 ; Farrell et al. 2015 ). Thus, there is a contrast between the genetic vulnerability of threatened populations and their ecological defense. This contrast means there may be high variability in the degree to which infectious disease actually impact threatened populations: parasite exposure may be rare, but these rare exposure events – often via spillover of parasites from neighboring species ( Pedersen et al. 2007 ; Das et al. 2020 ) – can have devastating consequences in naïve, genetically homogeneous populations ( Duxbury et al. 2019 ). This potential for extreme variability argues for prioritizing infectious disease management in conservation, even when disease does not appear to pose an immediate problem.

Given these arguments, how should the genetics of threatened populations be managed to reduce disease risk? This question raises the important and interesting challenge of how best to maintain and augment genetic diversity. Gene flow can rapidly restore genetic diversity, particularly for loci with rare advantage ( Muirhead 2001 ; Fijarczyk et al. 2018 ; Phillips et al. 2018 ), but it can also generate outbreeding depression ( Frankham et al. 2011 ). A quantitative synthesis addressed this problem, finding a consistent net fitness benefit of gene flow for populations thought to be genetically depauperate and/or inbred ( Frankham 2015 ) (see also Fitzpatrick et al. 2020 ). Decisions about breeding and translocations also require a weighing of neutral vs. functional diversity. Some have argued that conservation programs should focus less on increasing neutral diversity and instead actively increase functional diversity, notably diversity at loci under balancing selection ( Teixeira and Huber 2021 ). In an earlier version of this argument, Hughes (1991) proposed that vertebrate breeding programs be specifically designed to prioritize MHC diversity. This viewpoint has been sharply criticized as overly simplistic and counter to scientific evidence ( Miller and Hedrick 1991 ; Vrijenhoek and Leberg 1991 ; DeWoody et al. 2021 ). Akin to the issues with crop multilines, prioritizing specific immune loci neglects diversity at loci unrelated to disease but nonetheless valuable for adaptation and long-term stability ( Radwan et al. 2010 ). Moreover, in wild host-parasite systems, we have not characterized the majority of loci underlying variation in the diverse strategies hosts use to fight parasites in natural settings. Thus prioritizing one to a few resistance loci will not reflect genetic diversity at the full suite of loci involved in defense. The experimental literature provides valuable insight on this problem: parasitism declines with increased genetic diversity, even when diversity is increased without consideration of its functional consequences ( Baer and Schmid-Hempel 1999 ; Altermatt and Ebert 2008 ; Kristoffersen et al. 2020 ). Currently, the most tractable option in conservation genetics is the maintenance and augmentation of neutral genetic diversity ( DeWoody et al. 2021 ), and the data argue that this approach works for reducing disease risk in threatened populations ( Gibson and Nguyen 2021 ).

Open questions on genetic diversity and disease

The rich and varied work reviewed above has built a deep conceptual foundation on the link between genetic diversity and disease. Looking towards the future, what new conceptual questions does this foundation compel us to ask? Below, I outline several outstanding problems.

When does genetic diversity fail to limit parasitism?

The mean effect of diversity on parasitism is negative, but it varies dramatically, with some experiments reporting increased parasitism with diversity ( Smithson and Lenne 1996 ; Ekroth et al. 2019 ; Gibson and Nguyen 2021 ). What factors explain this variation? This question matters both for understanding the mechanisms underlying the relationship between diversity and disease and for applying genetic diversification as a management tool.

Meta-analyses have attempted to identify factors that explain variation in the effect of genetic diversity on disease risk in experiments with non-crop hosts. Ekroth et al. (2019) examined seven factors that differed between experiments, including aspects of experimental design, host traits, and parasite traits. They found a negative effect of diversity on microparasite infection but not macroparasite infection and in field settings but not laboratory settings. Gibson and Nguyen (2021) did not replicate these findings: the effect of genetic diversity did not vary with any of 11 factors tested. Taken together, these two meta-analyses do not consistently identify factors that explain variation in the effect of genetic diversity. I believe this reflects limitations of the available data: the analyzed studies differ in too many ways to isolate a single variable with much power, and very few studies explicitly test hypotheses for variation in the effect of diversity. It would be particularly valuable to test the hypothesis that the protective effect of host diversity grows with increasing amounts of genetic variation in both the host and parasite populations (see below). Both meta-analyses found insignificant trends consistent with this idea. Controlled experiments are needed to test these hypothesized effects in isolation, building off examples like van Houte et al. (2016) and Ganz and Ebert (2010) .

For crops, variation in the performance of varietal mixtures is a key obstacle to their widespread adoption ( Smithson and Lenne 1996 ; Cowger and Mundt 2002 ; Mundt 2002 , 2014 ). The protective effect of genetic variation in crop fields is predicted to increase with the number of functionally distinct host genotypes in the mixture ( Mundt et al. 1996 ), host specificity of the parasite population ( Lively 2010a ; Mikaberidze et al. 2015 ), scale of mixture deployment ( Newton and Guy 2011 ), and parasite dispersal ability ( Cox et al. 2004 ). Evidence for each of these predictions is mixed ( Mundt 2002 ). Most syntheses on the drivers of variation have largely been qualitative, but the extent of experimental work in crop systems suggests that quantitative syntheses may have sufficient sampling to test specific hypotheses for factors that explain variation in the effect of diversity (e.g. Huang et al. 2012 ; Gibson and Nguyen 2021 ). Identification of sources of variation would enhance commercial appeal by facilitating the design of more reliable mixtures ( Lopez and Mundt 2000 ; Mikaberidze et al. 2015 ; Wuest et al. 2021 ).

Does host diversity stabilize disease risk?

Genetic variation stabilizes population dynamics ( Forsman and Wennersten 2016 ). For example, genetic variation reduces extinction rates and reduces variation in population sizes for experimental populations of flour beetles ( Agashe 2009 ). Crop yield is also less variable across years for varietal mixtures relative to monocultures ( Reiss and Drinkwater 2018 ).

Does host diversity also reduce variation in parasitism? Springbett et al. (2003) simulated epidemics in host populations with and without diversity in susceptibility and found that host diversity can reduce variation in parasitism across populations. Populations with diversity were more likely to experience small epidemics than populations without diversity, but they were less likely to have major epidemics (>10% of individuals infected). This finding supports the idea that diverse populations may not avoid infection altogether – they are likely to contain some susceptible individuals – but the presence of resistant hosts limits parasite spread. In an experimental test of this idea, Ganz and Ebert (2010) found that host diversity reduced variation in parasite prevalence across D. magna populations, though only at intermediate levels of parasite diversity (see also Tarpy 2003 ; Bensch et al. 2021 ).

Genetic diversity could also dampen fluctuations in the size of parasite populations through time. Dwyer et al. (2000) demonstrated theoretically that heterogeneity in susceptibility stabilizes epidemiological dynamics by reducing the fraction of hosts infected in a single epidemic. These models suggested that the degree of heterogeneity in susceptibility observed in gypsy moth populations could stabilize the dynamics of baculovirus epidemics in nature. Coevolutionary theory further predicts that genetic diversity reduces fluctuations in parasite population size through time by limiting rapid expansion as parasites adapt to common host genotypes ( Lively 2010b ; Gibson et al. 2018 ). These studies make important predictions that have rarely been directly tested. The answers could prove particularly valuable for agriculture and conservation, where managing variability in disease, to limit the risk of very large outbreaks, may reap more long-term benefits than managing mean levels of disease.

How much is enough?

King and Lively (2012) raised the idea of the diversity threshold, the level of host diversity at which parasite transmission is sufficiently impeded that R 0 falls below one. Their simulations show that, with high host specificity of parasites, increasing the number of host genotypes in a population can drive R 0 below one, even if the density of the host population is more than doubled to accommodate the new host genotypes. Mikaberidze et al. (2015) also identified diversity thresholds in their simulations of varietal mixtures, demonstrating that the number of host genotypes necessary to eliminate parasites increases with decreasing specificity and increasing transmission rates. As parasites become less specialized, they can infect multiple host genotypes, so disease eradication becomes less likely, even at very high diversity.

Practical application of diversity thresholds requires empirical estimates of the degree of genetic variation necessary to prevent initial invasion and subsequent spread of disease in natural populations. This represents a significant but worthwhile challenge: quantification of diversity thresholds has clear value for agriculture, conservation, and management of human disease vectors (e.g. Campbell et al. 2010 ).

Does parasite diversity increase parasitism?

Genetic diversity of parasites has received very little attention relative to host diversity. Experiments have addressed the effect of parasite diversity on features of individual infections, like virulence and transmission (e.g. Davies et al. 2002 ; de Roode et al. 2005 ). The question posed here is at the population-level: do genetically diverse parasite populations have on average higher performance at the population level (e.g. higher prevalence) than genetically homogeneous parasite populations? If parasite genotypes vary in which host genotypes they are best able to infect, then parasite diversity should increase the establishment and spread of parasites in the host population by increasing the probability that a host genotype encounters an infective parasite genotype (sampling effect). Parasite genotypes may also facilitate one another, perhaps by compromising the immune system (complementarity) ( Karvonen et al. 2012 ; Halliday et al. 2020a ). Beyond the establishment phase, genetic diversity accelerates adaptation to the host population. On the other hand, parasite diversity could reduce parasitism in a host population if parasite genotypes interfere with one another ( Lannou et al. 1995 ).

Ganz and Ebert (2010) addressed this question by exposing D. magna populations to one, two, three, or four strains of a microsporidia parasite, with total dose equal across diversity treatments. Mean parasite prevalence increased with parasite diversity, approximately doubling in going from one to four parasite genotypes. Mean prevalence increased more sharply for host monocultures than for genetically diverse host populations. This result suggests that parasite diversity increases average establishment success across a range of genetically distinct host environments, akin to the hypothesis that genetic diversity increases colonization success ( Crawford and Whitney 2010 ; Vahsen et al. 2018 ). Moreover, this study supported the idea that the protective effect of host diversity varies with the diversity of the parasite population ( van Baalen and Beekman 2006 ): diverse host populations had lower parasite prevalence than monocultures only when hosts were exposed to a diverse parasite population.

These findings argue that parasite diversity has value as a tool for increasing the mean establishment success of biological control parasites. Phage therapy uses viruses of bacteria (phages), rather than antibiotics, to fight bacterial infections in medical or agricultural settings. Phages tend to be quite specific, infecting only a subset of strains of a particular bacterial species ( de Jonge et al. 2019 ). Hence, combining multiple phages with diverse specificities can increase the probability of controlling the bacterial infection initially and limit the evolution of resistance in the bacterial population ( Chan and Abedon 2012 ). The same approach may prove useful in other biological control systems showing strong host specificity, like parasitoid wasps used in control of aphids ( Rouchet and Vorburger 2014 ) and bacterial parasites used in control of plant-parasitic nematodes ( Channer and Gowen 1992 ).

How do parasites evolve in genetically diverse host populations?

First, host diversity may slow the rate at which parasites evolve to overcome host resistance. Several recent experiments provide direct support for this hypothesis. Van Houte et al. (2016) generated bacterial genotypes that each recognized, and thus resisted, a distinct phage sequence via CRISPR-Cas immunity. Phage populations quickly evolved to overcome immunity in monocultures of single bacterial genotypes. As host diversity increased, the extinction rate of phage populations increased, indicating a failure to evolve to overcome host immunity with increased host diversity. Host diversity may impede parasite adaptation through trade-offs in performance across genotypes (as in Sant et al. 2021 ) and through reduced opportunities for selection on any one host genotype (as in Chabas et al. 2018 ). The relative contribution of these two factors remains an open question ( Bono et al. 2017 ; White et al. 2020 ).

If host diversity slows parasite adaptation, mixtures can be used as a tool to preserve valuable resistance mechanisms. In agriculture, varietal mixtures should increase the durability, or lifespan, of resistance genes ( Zhan and McDonald 2013 ; Mundt 2014 ). This approach has been successfully implemented to delay evolution of resistance against Bacillus thuringiensis toxins in insect pest populations ( Shelton et al. 2000 ; Tabashnik et al. 2008 ). Similarly, evolution to overcome vaccines may be limited by the variation in immune responses across individuals elicited by a single vaccine ( Kennedy and Read 2017 ) or by the distribution of multiple vaccines against different targets of a single parasite (mosaic vaccination: McLeod et al. 2020 ). These practical applications raise the question of deployment: what distribution of diversity best limits parasite evolution – between-individual variation (i.e. mixtures of individuals with distinct resistance genes or vaccines), or within-individual variation (“pyramiding” of resistance genes in one host genotype or a single vaccine against multiple parasite genotypes)? Though theoretical treatments of this question do not agree (REX Consortium 2016 ; Djidjou-Demasse et al. 2017 ; Rimbaud et al. 2018 ), between-individual variation has growing experimental support as a brake on parasite evolution. Moreover, from a logistical perspective, mixtures can be constructed from existing diversity and readily changed by swapping in new components (e.g. distinct host genotypes), so they may prove to be quicker and cheaper in many cases.

A second prediction is that host diversity may select for generalist parasites. This hypothesis emerged in agriculture, when concerns surfaced that the use of varietal mixtures to limit parasitism in the short-term would come with the long-term price of “super” parasites able to overcome multiple resistance genes ( Groth 1976 ; Marshall 1989 ; Lannou and Mundt 1996 ). Field trials demonstrated that barley mixtures favored generalist genotypes of the fungus Blumeria graminis, the causal agent of powdery mildew ( Chin and Wolfe 1984 ; Huang et al. 1994 , 1995 ). Chin and Wolfe (1984) , however, argued that the reduced size of the fungal population in mixtures limited the threat posed by these generalists. Several experimental evolution studies have expanded upon this idea in phage-bacteria systems (see also Gibson et al. 2020 ; Ekroth et al. 2021 in nematode systems). The evolution of increased host range is predicted to peak at intermediate levels of host diversity: low diversity results in weak selection for increased host range, because parasites may frequently encounter the same host genotype, while high diversity slows the response to selection by limiting the effective population size of the parasite ( Benmayor et al. 2009 ; Chabas et al. 2018 ). Consistent with this prediction, Common et al. (2020) found that generalist phages evolved readily in bacterial populations with intermediate diversity, but less so when host diversity was low and not at all when it was maximal. Sant et al. (2021) also reported the evolution of generalist phages in moderately diverse bacterial populations, where the high probability of encountering an alternate host genotype selected against specialists. Generalists had lower fitness on any individual host genotype, however, resulting in slower rates of phage adaptation in diverse bacterial populations. These experimental evolution studies differ from prior genetic diversity-disease literature in following host and parasite populations over multiple generations (see also Altermatt and Ebert 2008 ). This approach has made it feasible to address an important open question: what are the relative contributions of ecological vs. evolutionary processes to the protective effect of genetic diversity (e.g. van Houte et al. 2016 ; Common et al. 2020 )?

I have provided a perspective on the historical development, current state, and possible future of the hypothesis that genetic diversity in host populations limits parasitism. Though disease protection is among the most well-supported consequences of genetic diversity in populations, it is but one of the proposed benefits of diversification. Many studies speak to the broader role genetic diversity plays in supporting adaptation, growth, and stability for wild and managed populations (reviewed in Hughes et al. 2008a ; Forsman and Wennersten 2016 ; Reiss and Drinkwater 2018 ).

This clear significance of genetic diversity contrasts with its low prioritization in management of wild and domesticated species. The agricultural community has long warned of “genetic erosion” or the loss of genetic diversity in crop species ( Browning 1972 ; Harlan 1972 ; Commission on Genetic Resources for Food and Agriculture 2010 ; Van de Wouw et al. 2010a ; Thormann and Engels 2015 ). This loss has been attributed to early bottlenecks under domestication and dissemination ( Vavilov 1926 ; Haudry et al. 2007 ), the loss of diverse, local landraces ( Van de Wouw et al. 2010a ; Bonnin et al. 2014 ; Sthapit et al. 2020 ), and the promotion of uniformity by twentieth century plant breeding’s selection for desirable traits and dissemination of high-yielding lineages ( Jordan et al. 1998 ). Strikingly, Gatto et al. (2021) estimated that the adoption of a few commercial varieties resulted in an 88% reduction in acreage planted with diverse landraces in Asia from 1970–2014. Hopes of reversing this loss of genetic variation rest in part on the preservation of landraces and wild crop relatives in national and international germplasm centers ( Hoisington et al. 1999 ; Halewood et al. 2020 ). The long-term success of these collections requires more consistent characterization and curation of their genetic resources ( Singh et al. 2019 ) and better representation of wild crop relatives, which are in urgent need of conservation ( Khoury et al. 2020 ; Warschefsky and Rieseberg 2021 ). While historically blamed for the genetic erosion of crop species, scientific plant breeding has a critical role to play in leveraging these resources to maintain diversity in space and time by testing and disseminating old varieties and breeding new ones ( Van de Wouw et al. 2010b ; Swarup et al. 2021 ). Progress depends equally on changes to regulatory structures to prioritize access to diversity in the breeding and sharing of varieties ( Louwaars 2018 ). Motivation for these changes is in place: there is a general consensus in the community that homogeneity leaves crops vulnerable to collapse in the face of disease and environmental change.

Relative to crop species, genetic diversity in populations of wild species has received even lower prioritization. The scope of the problem is emerging. The World Wildlife Fund’s Living Planet Index reports a 68% drop in population sizes of vertebrates since 1970, which is expected to drive major losses of genetic diversity ( WWF 2020 ). Indeed, evaluating trends for 91 species (largely vertebrates) over the past ~100 years, Leigh et al. (2019) estimated a mean decline in allelic richness of 6.5%, with an even larger decline of 31% for island species. Yet the Convention on Biological Diversity and similar initiatives have been criticized for a lack of commitment to conserving genetic diversity beyond crops and livestock, and a failure to articulate specific, measurable goals for the future ( Laikre 2010 ; Willoughby et al. 2015 ; Hoban et al. 2020 ; Hoban et al. 2021b ; Thomson et al. 2021 ). Hoban et al. (2021a) argues that the necessary knowledge, tools, and infrastructure are now in place to set quantitative goals for genetic diversity of wild species. They recommend ambitious global monitoring programs that leverage advances in collection of genetic and non-genetic data, data analysis, data sharing, and conservation policies and networks. For both wild and crop species, genetic diversity is a public good for which monitoring and maintenance is feasible and urgently needed.

I have devoted a substantial amount of this perspective piece to the diversity-disease connection in agriculture and conservation, because of the idea’s historical development and its ongoing importance in resolving the major challenges facing these fields. To conclude, I’d like to emphasize that evolution is the undercurrent uniting the independent areas of research built on the hypothesis that genetic diversity limits parasitism. Indeed, the literature on the relationship of genetic diversity and disease provides a compelling example of the pervasiveness and value of evolutionary thinking across basic and applied scientific fields. Evolutionary principles have often been enlisted to justify the pursuit of homogeneity. For example, breeding of domesticated species has commonly sought to create and distribute optimal genotypes via selection for favorable traits, like parasite resistance. Yet the data on genetic diversity and disease soundly reject homogeneity. They teach the opposite lesson: for managing infectious diseases, the most powerful evolutionarily-informed approach is the relentless pursuit of diversity.

Acknowledgements

I would like to thank M. Rebolleda-Gómez and R. Shaw for their efforts in putting together the fantastic symposium “SSE at 75 Years: Continuity and change in evolutionary research” at the 2021 Evolution meeting and for their invitation to participate in it. I am also grateful to Editor-in-Chief T. Chapman for her effort in bringing together this issue and to my fellow speakers at the symposium for their enthusiasm and inspiring work. Finally, I appreciate the valuable comments from C. Amoroso, L. Bubrig, and other members of the Gibson Lab on early drafts of the manuscript. AKG was supported by funding from the National Institute of General Medical Sciences (R35 GM137975-01).

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IMAGES

  1. Genetic Diversity

    research paper on genetic diversity

  2. Mapping Human Genetic Diversity in Asia

    research paper on genetic diversity

  3. Schematic illustrating the relationship between genetic diversity and

    research paper on genetic diversity

  4. Full article: Genetic Diversity

    research paper on genetic diversity

  5. Genetic diversity contribution to errors in short

    research paper on genetic diversity

  6. Geneti diversity research papers

    research paper on genetic diversity

VIDEO

  1. BSc 1st semester||zoology paper || cytology genetic and infectious disease||

  2. Evolution in enzymology (Klenow, T7 polymerase, Taq polymerase)

  3. Phylogenetic comparative approaches to uncover the genomic basis of species’ phenotypic differences

  4. Google Research Vs. Genomic Bias

  5. Genetic Paper Discussion

  6. 2022 GCE Biology paper 2_ Genetics [The genetic mutations, diagrams and stunted growth]

COMMENTS

  1. Global genetic diversity status and trends: towards a suite of

    Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition Sean Hoban, Frederick I. Archer, Laura D. Bertola, Jason G. Bragg, Martin F. Breed, Michael W. Bruford, Melinda A. Coleman, Robert Ekblom, W. Chris Funk … See all authors First published: 12 April 2022

  2. Ambitious survey of human diversity yields millions of undiscovered

    The researchers analysed genetic information from several databases, including All of Us, for a total of more than 2.5 million people; nearly 40% of the data came from individuals not of European ...

  3. Determinants of genetic diversity

    In particular, genetic diversity contributes to the ability of a species to respond to environmental changes, with implications in terms of, for example, human health 3, 4, breeding strategies...

  4. Diversity and inclusion in genomic research: why the uneven progress?

    The importance of diversity and inclusion in genomic research has long been appreciated. Promoting genomic research in diverse populations could be described as predominantly motivated by two goals.

  5. A roadmap to increase diversity in genomic studies

    Fig. 1 The proportion of samples from individuals cumulatively reported by the GWAS Catalog 1 as of 8 July 2021. Full size image Fig. 2: Disparities in the representation of continents in genomic...

  6. Genetic Diversity and Societally Important Disparities

    THE publication of an article suggesting that geographic patterns in economic development across countries worldwide have been driven by genetic diversity ( Ashraf and Galor 2013) has generated considerable controversy ( Callaway 2012; Chin 2012; Gelman 2013; Feldman 2014 ).

  7. Global Commitments to Conserving and Monitoring Genetic Diversity Are

    First set of conditions: Necessary knowledge. The first elements needed to motivate and guide genetic diversity monitoring are knowledge regarding the importance of genetic diversity and the current rate of loss of genetic diversity (figure 1).This knowledge tells policy makers that genetic diversity is critical, and genetic diversity is declining rapidly.

  8. On the concepts and measures of diversity in the genomics era

    Realizing the importance of genetic diversity. • Reviewed on abiotic stress that are endangering the environment, as seen by climate change. Abstract Diversity serves as the foundation for breeding and evolution. In the science of genomics, the investigation of genetic variation has long been a prominent subject.

  9. (PDF) Genetic Diversity: Its Importance and Measurements.

    Genetic diversity among individuals reflects the presence of different alleles in the gene pool, and hence different genotypes within populations. Genetic diversity has a great importance...

  10. Genetic Diversity, Conservation, and Utilization of Plant Genetic

    1. Introduction Genetic diversity is the amount of genetic variability present among individuals of a variety or a population within a species.

  11. SSR markers development and their application in genetic diversity

    For genetic diversity evaluation, SSR markers have been widely applied in many plant species to evaluate genetic diversity, to construct genetic maps, and to determine species lineages. However, the insufficient number of SSR markers is a major obstacle for the related genetic studies of garlic.

  12. Special Issue : Genetic Diversity and Molecular Evolution

    The study of genetic diversity is important for conservation biologists because ecosystems possessing a high degree of genetic diversity are generally the healthiest, most stable, and most able to adapt to changing environmental conditions.

  13. Genetic diversity fuels gene discovery for tobacco and alcohol use

    Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of ...

  14. The Genetic Diversity of the Americas

    The genetic diversity of the present-day Americas is thus, in a sense, the genetic diversity of the world. However, four factors had a prominent impact on the current genetic makeup of the Western Hemisphere: the variable native population density at the arrival of Europeans, the extent of European immigration to specific geographic areas, the ...

  15. Challenging Evolution: How GMOs Can Influence Genetic Diversity

    by Heather Landry Summary: The vast diversity in gene sequences are what create the large variety of plants and animals we see today. Genetic diversity is crucial for adapting to new environments, as more variation in genes leads to more individuals of a population having favorable traits to withstand harsh conditions. Low genetic diversity, on the other hand, can be very problematic during ...

  16. Ecological consequences of genetic diversity

    Understanding the ecological consequences of biodiversity is a fundamental challenge. Research on a key component of biodiversity, genetic diversity, has traditionally focused on its importance in evolutionary processes, but classical studies in evolutionary biology, agronomy and conservation biology indicate that genetic diversity might also have important ecological effects.

  17. Importance of Genetic Diversity Assessment in Crop Plants and Its

    This paper comprehensively reviews four important areas; (i) the significance of plant genetic diversity (PGD) and PGR especially on agriculturally important crops (mostly field crops); (ii) risk associated with narrowing the genetic base of current commercial cultivars and climate change; (iii) analysis of existing PGD analytical methods in pre...

  18. (PDF) Review Article Importance of Genetic Diversity ...

    The importance of plant genetic diversity (PGD) is now being recognized as a specific area since exploding population with urbanization and decreasing cultivable lands are the critical factors ...

  19. Genetic analyses of diverse populations improves discovery for ...

    Abstract. Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of ...

  20. Estimation of genetic diversity and its exploitation in plant breeding

    Genetic diversity is variation of heritable traits present in the population of the same species (Swingland, 2001) and it is a measure of genetic distance or genetic similarity, both of which imply that there are differences or similarities at the genetic level (Weir, 1990 ).

  21. Researchers optimize genetic tests for diverse populations to tackle

    To prevent an emerging genomic technology from contributing to health disparities, a scientific team funded by the National Institutes of Health has devised new ways to improve a genetic testing method called a polygenic risk score.Since polygenic risk scores have not been effective for all populations, the researchers recalibrated these genetic tests using ancestrally diverse genomic data.

  22. The importance of genomic variation for biodiversity ...

    Andrew P. Hendry Nature Reviews Genetics 22 , 89-105 ( 2021) Cite this article 9417 Accesses 61 Citations 87 Altmetric Metrics Abstract The 2019 United Nations Global assessment report on...

  23. Population genetics: past, present, and future

    Excellent reviews of population genetics have been written (Chakraborty 2006; Charlesworth and Charlesworth 2017; Crow 1987; Crow and Kimura 1970) documenting the development of population genetics from early achievements by Mendel ( 1866 ), Hardy ( 1908 ), and Weinberg ( 1908) up to highly sophisticated theoretical developments, mostly by Ameri...

  24. Researchers optimize genetic tests for diverse populations to tackle

    Genetic tests look at the small differences between individuals' genomes, known as genomic variants, and polygenic risk scores are tools for assessing many genomic variants across the genome to determine a person's risk for disease. As the use of polygenic risk scores grows, one major concern is that the genomic datasets used to calculate ...

  25. PDF Genetic divergence studies in blackgram (Vigna mungo L.) genotypes

    The present study was performed to investigate the extent of genetic diversity and grain yield components in black gram using agro-morphological and qualitative traits. 2. Material and Methods. The genetic material used for the current study consists, Fifty-five black gram genotypes and four check varieties viz., LBG 752, LBG 787, IPU 2-43, TU ...

  26. Genetic diversity and disease: the past, present and future of an old

    In the search for environmental and genetic factors that predict variation in parasitism, one hypothesis stands out for its generality and longevity: genetically homogeneous host populations are more likely to experience severe parasitism than genetically diverse populations.