Human genetic admixture

Abstract

Recent empirical studies of admixed populations have emphasized inclusion of populations that have historically been excluded or underrepresented in genetic studies, producing important insights into human genetic and phenotypic variation. Many migration events have occurred through colonization or forced displacement, and ancient admixture often predates historical records. [...]genetic studies provide an opportunity to understand population history and the forces generating variation. The statistical method, SpaceMix, from Bradburd and colleagues (2016) can provide an intuitive visual summary of patterns of population structure and admixture by inferring a “geogenetic” map representing the geographic positions of the populations where distances describe rates of gene flow [35]. Considering population structure, sex-biased admixture, and effective population size changes, Font-Porterias and colleagues (2019) clarified the dynamic demographic history of European Roma groups, including showing that they share a common South Asian origin but have complex contributions from West Eurasian groups [51].

Introduction

One of the major insights from the modern genomic era is the ubiquity of migration and admixture throughout human history [1–7]. Admixed populations are formed as moderate- to large-scale movements of individuals allow the exchange of genes from 2 or more previously isolated populations, creating populations with ancestors from multiple sources (Fig 1). These processes shape modern human genetic and phenotypic variation and may lead to differences in disease risk between populations [8–10]. Indeed, admixture is one of the fastest evolutionary processes to dramatically change the composition of a population. Despite their ubiquity and importance, admixed populations remain understudied in population and medical genetics [11–13], especially from a theoretical perspective. Recent empirical studies of admixed populations have emphasized inclusion of populations that have historically been excluded or underrepresented in genetic studies, producing important insights into human genetic and phenotypic variation.

[Figure omitted. See PDF.]

Fig 1.

(Top) Large-scale movements of individuals allow haplotypes from previously isolated populations to come together in a combined gene pool. Generations of recombination between these haplotypes lead to an admixed population with genetic ancestry that varies between individuals and along haplotypes. The distribution of this variation is governed by the demographic and selection history of the admixed population and its sources. (Bottom) To leverage these patterns of admixed ancestry to better understand human history and phenotypic variation, we highlight key areas of recent progress and possible future directions in the study of human genetic admixture.

https://doi.org/10.1371/journal.pgen.1009374.g001

Human population movement frequently lacks historical records. Many migration events have occurred through colonization or forced displacement, and ancient admixture often predates historical records. Therefore, genetic studies provide an opportunity to understand population history and the forces generating variation. Recent empirical work has shown that studying the genetics of a wider set of human populations can yield historical insights as well as medically relevant information about health and phenotypes. The mosaic ancestry patterns of admixed populations can also be used to elucidate the mechanisms and timescales of evolution in humans more generally. For example, ancestry patterns in admixed populations have been used to infer recombination rates [14–15] and to identify epistatically interacting [16–17] or adaptive [18–26] alleles. In this context, we use ancestry to describe “genetic ancestry”—the population origins of material within a genome. This is related to, but distinct, from genetic similarity and from genealogical ancestry, with the relationship further discussed by Mathieson and Scally [27].

Here, we highlight recent progress and discuss future directions for the study of admixed human populations (Fig 1). The term “admixture” encompasses multiple models of migration and population interactions; we focus on scenarios of human admixture with moderate to large contributions from at least 2 source populations, emphasizing the role that admixture has played throughout human evolution. Within this theme, we have curated a collection of PLOS Genetics publications. This compilation is a limited selection of work that exemplifies recent key advances and stimulates discussion about priorities for the future.

Population history and demography

Broader genetic sampling of populations worldwide is increasingly being combined with advances in theory and computational methods to elucidate human history.

Genetic ancestry often varies along a chromosome within an individual and between individuals within an admixed population (Fig 1). Summaries of ancestry—such as the mean proportion of ancestry across individuals in a population, the proportion of ancestry at different genetic loci in a population, and the length of ancestry tracts within individuals—are shaped by the demographic history of a population. For example, drift may increase the variance in ancestry proportion within a population and across loci in small populations [28]. Similarly, recombination breaks up ancestry tracts over generations; therefore, more recent admixture events are expected to generally have longer tracts of a single ancestry within individuals.

Classic statistical methods in population genetics typically rely on allele frequencies, patterns of linkage disequilibrium (LD), and interpopulation sequence differences. However, the admixture process may distort patterns of LD, break up runs of homozygosity, and combine allele frequency distributions from distinct parental populations [28–29]. Yet, the patterns of genetic ancestry within an admixed population also provide another set of information about the history of a population. Theoretical work has sought to understand the relationship between demography and these summaries of genetic variation in admixed populations. Computational methods have built on this theory to infer population histories generating observed patterns of genetic ancestry [30–33]. Recent progress has focused on dynamic and complex population histories that may more accurately represent realistic population histories.

A number of major theoretical and methodological advances have recently been published in PLOS Genetics that improve our understanding of the demographic processes shaping genetic variation in admixed populations and human evolutionary history. Ragsdale and colleagues (2019) generalize 1- and 2-locus genetic summary statistics in a computationally efficient algorithm able to explore complex demographic models [34]. Other recent approaches focus on inferring population structure without known source populations or admixture history. The statistical method, SpaceMix, from Bradburd and colleagues (2016) can provide an intuitive visual summary of patterns of population structure and admixture by inferring a “geogenetic” map representing the geographic positions of the populations where distances describe rates of gene flow [35]. Methodological advances in applying LD and allele frequency summary statistics to admixed populations can clarify demographic histories even when reference genomes from contributing populations are unknown or poor quality. For example, the statistical method of Durvasula and Sankararaman (2019) combines several population genetic summary statistics to infer archaic ancestry along the genomes of modern individuals without relying on archaic reference genomes [36]. Identifying such segments of ancestry facilitates the use of methods that leverage local ancestry information. Advances in methods to study the demographic histories of admixed populations often leverage population- and genome-wide patterns of ancestry segments. For example, the IBD method of Browning and Browning (2015) was recently extended to capture ancestry-specific effective population size in admixed populations [37–38]. Browning and colleagues (2018) demonstrated a strategy for using identity-by-descent sharing in conjunction with local ancestry to detect fluctuations in historical population sizes [38]. Other methods leverage ancestral recombination graphs (ARGs), which can be used to trace coalescence and recombination events for each genomic position [39–41]. A recent application of ARGs by Hubisz and colleagues (2020) probabilistically samples ARGs to report probabilities of introgression along the genome [42].

Intricate sociocultural practices such as marriage customs, colonization events, and phenotypic preferences direct how parental populations interact to form admixed human populations. Whereas computational methods are increasingly considering these intricacies, empirical studies of admixed populations have already made substantial progress on fine-scale demography providing insight into how sociocultural practices shape the admixture process and within-region variation.

Using these new methods, empirical studies have brought to the forefront within-continent variation, particularly for historically excluded and under-sampled populations. Analyses of genomic ancestry within the context of geography have revealed between and within continent population structure [43]. For example, Moreno-Estrada and colleagues (2013) and Ruiz-Linares and colleagues (2014) show that the geographic distribution of admixture proportions in Latin America reflects complex demographic history and extensive admixture between Indigenous American, European, and African groups over Latin America’s history of peopling, colonization events, and forced displacement [44–45]. Recent studies of populations in the United States have also highlighted the complexities of admixture histories in the Americas [46–48]. Baharian and colleagues (2016) find that relatedness and ancestry patterns in African Americans show that the distribution of genetic diversity varies by region, reflecting differences in admixture history both before the Civil War and during the Great Migration in the 1900s [46]. Verdu and colleagues (2014) analyze genetic data from Indigenous communities in North America, finding different timing and sources of admixture compared to admixed populations of Central and South America, consistent with differences in historically documented migrations [47]. In Iran, Mehrjoo and colleagues (2019) found substantial population structure and variable levels of consanguinity across ethnic groups, highlighting the need to consider how population structure relates to cultural groups [49]. As cultural groups and customs within these groups shift over time, cultural transitions can further complicate the admixture process. For example, Martiniano and colleagues (2017) analyzed ancient genomes from Portugal to find changes in population structure, migration rates, and phenotypic predictions associated with Neolithic to Bronze Age cultural shifts [50].

Empirical studies such as these highlight the role social processes play in shaping genetic variation; for example, male and female demographic histories can differ. Considering population structure, sex-biased admixture, and effective population size changes, Font-Porterias and colleagues (2019) clarified the dynamic demographic history of European Roma groups, including showing that they share a common South Asian origin but have complex contributions from West Eurasian groups [51]. Another theme that surfaces in the populations highlighted above is the role of population structure and nonrandom mating in shaping the genetics of admixed populations. Empirical analyses of mating pairs have documented nonrandom pairings with respect to genetic ancestry, socioeconomic factors, and phenotypes, referred to as assortative mating [32,52–55]. Assortative mating has also been hypothesized to cause geographic structure in patterns of genetic and phenotypic variation in admixed populations [45]. Despite this complexity, methods often consider admixed populations as simple linear combinations of their parental populations. Moving forward, these empirical studies will inform the development of methods that account for social structure, as well as variation in the admixture history of a population over time and across geography. In turn, such models will improve inference of demographic history and neutral processes shaping diversity and act as better null models for selection scans and identification of phenotypically important loci.

Phenotypically important loci and regions under selection

Selective pressures, such as those from environments and pathogens, play an important role in genetic variation and disease risk. Admixture both obscures genetic signatures of selection in source populations and provides new genetic material upon which selection can rapidly act.

Despite major progress on theory and methods to study demography in admixed human populations, methodological advances to study other processes such as adaptation and phenotypic variation remains an open area with substantial room for growth. Recent admixture may obscure genetic signatures of selection in the source populations by distorting linkage, rapidly changing allele frequencies, and breaking up homozygosity [28,56]. Therefore, current methods, which were built and tested on homogeneous populations, are underpowered and difficult to interpret. Although challenging, studying selection and identifying phenotypically important loci in admixed populations can provide a unique window into evolutionary processes. For example, admixed populations may provide pathways to studying genetic variation in populations that no longer exist in unadmixed form today, such as many Indigenous populations in the Americas. Similarly, admixed populations can be leveraged to increase sample sizes for understudied populations, for example, using African Americans to study West African evolutionary history. This is a critical area of current work, as much of our knowledge of the genetic basis of phenotypically diverse human traits is based on studies consisting overwhelmingly of individuals of European ancestry [11,57,58].

A common approach to identify candidate regions under selection post-admixture looks for outliers in local ancestry [18–21,59,60]. That is, regions of the genome with a higher frequency of ancestry from one source populations than genome-wide patterns are hypothesized to be enriched for genes under selection, as selection drives the haplotype of a single ancestry to higher frequency when the allele frequency of the locus differs substantially in the source populations. This approach has identified loci under strong recent selection. For example, the Duffy-null allele at the DARC locus is protective against malaria-causing Plasmodium vivax infection, which is estimated to be one of the strongest selective pressures in recent human history. The Duffy-null allele is nearly fixed in sub-Saharan African populations and mostly absent in non-African populations, producing a classic signal in which the DARC locus is an outlier in its proportion of local African ancestry in multiple admixed populations [21–25]. However, the strength of selection at the DARC locus is not typical of human adaptation. These local ancestry outlier approaches likely miss many loci under weaker or polygenic selection or those not highly differentiated in the sources. Additionally, outlier approaches generate false positives due to drift or long-range LD and discard other genetic information along the genome. For example, in Western African rainforest hunter–gatherer populations, Jarvis and colleagues (2012) found reduced levels of switching between ancestry types in a genomic region that may contribute to adaptive phenotypes such as short stature [26]. Despite the absence of outliers in local ancestry, multiple tests for selection corroborated evidence of local adaptation involving this region. New methods are needed to capture multiple modes of adaptation on the background of different demographic histories. Methods that leverage local genomic patterns and linkage may help to characterize the timing and strength of selection. In order to detect polygenic selection and understand quantitative trait variation, methods that leverage subtle shifts in ancestry across the genomes and GWAS in diverse populations may also be important.

Despite the challenges of identifying phenotypically important loci and regions under selection in admixed populations, recent empirical studies have highlighted the importance of using admixed populations to understand how selective pressures shape genetic variation and disease risk. Discovering phenotypically important loci in admixed populations requires careful consideration of the data, including strategies to integrate genotypes, local ancestry, and information from source populations. Once phenotypically important loci are identified, admixed populations can provide insight into how these loci interact with different ancestral backgrounds and how characteristics of the source populations shape post-admixture selection.

In addition to looking for correlations between genotype and phenotype, phenotypically important loci in admixed populations can be identified by looking for correlation between local ancestry and phenotype when the phenotype differs in the source populations [61]. A combination of these approaches can be used to understand quantitative trait variation in admixed populations. Beleza and colleagues (2013) provide one salient example of how genotype-based and ancestry-based association analyses can be integrated, in this case to understand the wide range of pigmentary phenotypic variation in skin and eyes in the admixed population of Cabo Verde [62]. This kind of integration of genotype- and ancestry- based approaches may help us understand how loci interact with different ancestral backgrounds. In admixed populations, local ancestry surrounding a known genetic risk factor can give clues about the other genetic variants that modulate that risk factor. For example, ApoE is the strongest known risk gene for late-onset Alzheimer disease; Rajabli and colleagues (2018) showed that variation in risk for Alzheimer disease across populations may be at least partially explained by the ancestral background interacting with the risk allele [63]. The relationship between phenotypes and genome-wide ancestry highlights the polygenic nature of many traits. Jeong and colleagues (2018) combined genomic and phenotypic data from Tibetan women from Nepal, adapted to high altitude, to find signatures of polygenic adaptation for traits involved in fertility and offspring survival [64]. In Latin American populations, Ruiz-Linares and colleagues (2014) jointly analyzed genetic variation, self-perceived ancestry, and a variety of physical traits, emphasizing the multifaceted relationship between genetic ancestry, social discussions of ancestry often forced into a classification system, and the genetic basis of traits. [45]. Going forward, it will be important to tease apart social and environmental factors that are often correlated with genetic ancestry and the genetic basis of complex traits.

Notably, these types of studies of quantitative variation and disease risk in admixed populations rely on careful consideration of the genotype and local ancestry calls, including reference-bias and characteristics of the source populations. Identifying phenotypically important loci in admixed populations is challenging in the absence of appropriate reference panels and knowledge of the source populations. The growing availability of genetic data from diverse groups shows that the unique LD structure of admixed groups and limited availability of suitable reference panels can particularly impair variant mapping and detection for novel or rare alleles. For example, Kowalski and colleagues (2019) recently analyzed a large cohort of phased genomes from African and Hispanic/Latino individuals (NHLBI TOPMed), revealing significant phenotypic associations for rare variants that were not detected with imputation using 1000 Genomes reference data [65].

Contributing source populations also have their own distinct selective and demographic histories. Although recent admixture obscures genetic signatures of selection in the source populations, consideration of past selective pressures can help identify important loci and clarify the genetic basis of disease risk. For example, Yao and colleagues (2018) found that in African ancestral backgrounds, past selection for the Duffy-null allele may also contribute to population differences in plasma levels of chemokines involved in a variety immune processes and diseases [66]. In Indigenous southern African populations, Chimusa and colleagues (2015) found signatures of selection both preceding and following inferred admixture events, including loci associated with selection imposed by diseases such as malaria, influenza, tuberculosis, and HIV/AIDS [67]. Considering the effective population sizes of source populations can also help explain how selection operates in an admixed population. Kim and colleagues (2018) used simulations of gene flow under various demographic scenarios and found that admixture can temporarily reduce genetic load in smaller populations, leading to an increase in the frequency of introgressed ancestry, particularly when existing and new deleterious mutations are recessive [68]. Relatedly, work from Juric and colleagues (2016) suggests that negative selection against Neanderthal sequences in the background of modern humans may be partially explained by Neanderthals having a much smaller effective population size compared to modern humans [69]. These examples demonstrate that understanding selection is closely related to demographic histories, including fluctuations in population size and migration. Future work inferring selection under complex demographies will be important, as well as further consideration of other modes of selection shaping variation in admixed populations, such as polygenic adaptation, background selection, and balancing selection.

Conclusions

The set of papers highlighted here exemplifies recent advances and important areas for future work in the study of genetic admixture and its roles in human evolution. Recent theoretical and methodological advances have improved our understanding of the dynamic and complex demographic histories of modern human populations. New empirical insights will continue to emerge with the development of approaches that consider complex sociocultural variables, account for within-population heterogeneity, and avoid reference bias. With increasing genomic and phenotypic data available from populations around the world, we are only beginning to characterize genetic underpinnings of traits, the importance of their genetic background, and mechanisms of adaptation. Importantly, as we move forward, the field must emphasize the inclusion of local communities, ensure that people maintain agency over their genomic information, prioritize infrastructure for science, and improve scientific theories by collaborating across disciplinary knowledge [70–75].

Citation: Korunes KL, Goldberg A (2021) Human genetic admixture. PLoS Genet 17(3): e1009374. https://doi.org/10.1371/journal.pgen.1009374

References

1. Mathias RA, Taub MA, Gignoux CR, Fu W, Musharoff S, O’Connor TD, et al. A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome. Nat Commun. 2016 Nov;7(1):12522. pmid:27725671

2. Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C, Falush D, et al. A Genetic Atlas of Human Admixture History. Science. 2014 Feb 14;343(6172):747–51. pmid:24531965

3. Busby GB, Band G, Si Le Q, Jallow M, Bougama E, Mangano VD, et al. Admixture into and within sub-Saharan Africa. Pickrell JK, editor. Elife. 2016 Jun 21;5:e15266. pmid:27324836

4. Nielsen R, Akey JM, Jakobsson M, Pritchard JK, Tishkoff S, Willerslev E. Tracing the peopling of the world through genomics. Nature. 2017 Jan;541(7637):302–10. pmid:28102248

5. Hudjashov G, Karafet TM, Lawson DJ, Downey S, Savina O, Sudoyo H, et al. Complex Patterns of Admixture across the Indonesian Archipelago. Mol Biol Evol. 2017 01;34(10):2439–52. pmid:28957506

6. Skoglund P, Mathieson I. Ancient Genomics of Modern Humans: The First Decade. Annu Rev Genomics Hum Genet. 2018;19(1):381–404. pmid:29709204

7. Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, et al. Ancient admixture in human history. Genetics. 2012 Nov;192(3):1065–93. pmid:22960212

8. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet. 2017 Apr 6;100(4):635–49. pmid:28366442

9. Simons YB, Sella G. The impact of recent population history on the deleterious mutation load in humans and close evolutionary relatives. Curr Opin Genet Dev. 2016 Dec 1;41:150–8. pmid:27744216

10. Simonti CN, Vernot B, Bastarache L, Bottinger E, Carrell DS, Chisholm RL, et al. The phenotypic legacy of admixture between modern humans and Neandertals. Science. 2016 Feb 12;351(6274):737–41. pmid:26912863

11. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016 13;538(7624):161–4. pmid:27734877

12. Popejoy AB, Ritter DI, Crooks K, Currey E, Fullerton SM, Hindorff LA, et al. The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics. Hum Mutat. 2018;39(11):1713–20. pmid:30311373

13. Landry LG, Ali N, Williams DR, Rehm HL, Bonham VL. Lack Of Diversity In Genomic Databases Is A Barrier To Translating Precision Medicine Research Into Practice. Health Aff (Millwood). 2018;37(5):780–5.

14. Wegmann D, Kessner DE, Veeramah KR, Mathias RA, Nicolae DL, Yanek LR, et al. Recombination rates in admixed individuals identified by ancestry-based inference. Nat Genet. 2011 Sep;43(9):847–53. pmid:21775992

15. O’Reilly PF, Balding DJ. Admixture provides new insights into recombination. Nat Genet. 2011 Sep;43(9):819–20. pmid:21874032

16. Wang H, Choi Y, Tayo B, Wang X, Morris N, Zhang X, et al. Genome-wide survey in African Americans demonstrates potential epistasis of fitness in the human genome. Genet Epidemiol. 2017;41(2):122–35. pmid:27917522

17. Ni X, Zhou M, Wang H, He KY, Broeckel U, Hanis C, et al. Detecting fitness epistasis in recently admixed populations with genome-wide data. BMC Genomics. 2020 Jul 11;21(1):476. pmid:32652930

18. Tang H, Choudhry S, Mei R, Morgan M, Rodriguez-Cintron W, Burchard EG, et al. Recent Genetic Selection in the Ancestral Admixture of Puerto Ricans. Am J Hum Genet. 2007 Sep;81(3):626–33. pmid:17701908

19. Jin W, Xu S, Wang H, Yu Y, Shen Y, Wu B, et al. Genome-wide detection of natural selection in African Americans pre- and post-admixture. Genome Res. 2012 Mar;22(3):519–27. pmid:22128132

20. Rishishwar L, Conley AB, Wigington CH, Wang L, Valderrama-Aguirre A, Jordan IK. Ancestry, admixture and fitness in Colombian genomes. Sci Rep. 2015 Jul 21;5:12376. pmid:26197429

21. Triska P, Soares P, Patin E, Fernandes V, Cerny V, Extensive Admixture PL. Selective Pressure Across the Sahel Belt. Genome Biol Evol. 2015 Dec 1;7(12):3484–95. pmid:26614524

22. Laso-Jadart R, Harmant C, Quach H, Zidane N, Tyler-Smith C, Mehdi Q, et al. The Genetic Legacy of the Indian Ocean Slave Trade: Recent Admixture and Post-admixture Selection in the Makranis of Pakistan. Am J Hum Genet. 2017 Dec;101(6):977–84. pmid:29129317

23. Pierron D, Heiske M, Razafindrazaka H, Pereda-loth V, Sanchez J, Alva O, et al. Strong selection during the last millennium for African ancestry in the admixed population of Madagascar. Nat Commun. 2018 Dec;9(1):932. pmid:29500350

24. Fernandes V, Brucato N, Ferreira JC, Pedro N, Cavadas B, Ricaut F-X, et al. Genome-Wide Characterization of Arabian Peninsula Populations: Shedding Light on the History of a Fundamental Bridge between Continents. Mulligan C, editor. Mol Biol Evol. 2019 Mar;36(3):575–86. pmid:30649405

25. Hamid I, Korunes K, Beleza S. Goldberg A. Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde eLife. 2021; 10:e63177.

26. Jarvis JP, Scheinfeldt LB, Soi S, Lambert C, Omberg L, Ferwerda B, et al. Patterns of Ancestry, Signatures of Natural Selection, and Genetic Association with Stature in Western African Pygmies. PLoS Genet. 2012 Apr 26;8(4):e1002641. pmid:22570615

27. Mathieson I, Scally A. What is ancestry? PLoS Genet. 2020 Mar 9;16(3):e1008624. pmid:32150538

28. Gravel S, Stephens M, Pritchard JK. Population genetics models of local ancestry. Genetics. 2012 Jun;191(2):607–19. pmid:22491189

29. Lohmueller KE, Bustamante CD, Clark AG. The Effect of Recent Admixture on Inference of Ancient Human Population History. Genetics. 2010 Jun 1;185(2):611–22. pmid:20382834

30. Ni X, Yuan K, Liu C, Feng Q, Tian L, Ma Z, et al. MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures. Eur J Hum Genet. 2019 Jan;27(1):133–9. pmid:30206356

31. Schiffels S, Durbin R. Inferring human population size and separation history from multiple genome sequences. Nat Genet. 2014 Aug;46(8):919–25. pmid:24952747

32. Zaitlen N, Huntsman S, Hu D, Spear M, Eng C, Oh SS, et al. The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium. Genetics. 2017 Jan;205(1):375–83. pmid:27879348

33. Goldberg A, Rosenberg NA. Beyond 2/3 and 1/3: The Complex Signatures of Sex-Biased Admixture on the X Chromosome. Genetics. 2015 Sep;201(1):263–79. pmid:26209245

34. Ragsdale AP, Gravel S. Models of archaic admixture and recent history from two-locus statistics. PLoS Genet. 2019 Jun 10;15(6):e1008204. pmid:31181058

35. Bradburd GS, Ralph PL, Coop GMA. Spatial Framework for Understanding Population Structure and Admixture. PLoS Genet. 2016 Jan 15;12(1):e1005703. pmid:26771578

36. Durvasula A. Sankararaman S. A statistical model for reference-free inference of archaic local ancestry. PLoS Genet. 2019 May 28;15(5):e1008175. pmid:31136573

37. Browning SR, Browning BL. Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. Am J Hum Genet. 2015 Sep 3;97(3):404–18. pmid:26299365

38. Browning SR, Browning BL, Daviglus ML, Durazo-Arvizu RA, Schneiderman N, Kaplan RC, et al. Ancestry-specific recent effective population size in the Americas. PLoS Genet. 2018 May 24;14(5):e1007385. pmid:29795556

39. Griffiths RC, Marjoram P. Ancestral inference from samples of DNA sequences with recombination. J Comput Biol. 1996;3(4):479–502. pmid:9018600

40. Rasmussen MD, Hubisz MJ, Gronau I, Siepel A. Genome-Wide Inference of Ancestral Recombination Graphs. PLoS Genet. 2014 May 15;10(5):e1004342. pmid:24831947

41. Griffiths RC, Marjoram P. An ancestral recombination graph. In: Donnelly P, Tavaré S, editors. Progress in population genetics and human evolution. New York: Springer; 1997. p. 257–70.

42. Hubisz MJ, Williams AL, Siepel A. Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph. PLoS Genet. 2020 Aug 6;16(8):e1008895. pmid:32760067

43. Johnson NA, Coram MA, Shriver MD, Romieu I, Barsh GS, London SJ, et al. Ancestral Components of Admixed Genomes in a Mexican Cohort. PLoS Genet. 2011 Dec 15;7(12):e1002410. pmid:22194699

44. Moreno-Estrada A, Gravel S, Zakharia F, McCauley JL, Byrnes JK, Gignoux CR, et al. Reconstructing the Population Genetic History of the Caribbean. PLoS Genet. 2013 Nov 14;9(11):e1003925. pmid:24244192

45. Ruiz-Linares A, Adhikari K, Acuña-Alonzo V, Quinto-Sanchez M, Jaramillo C, Arias W, et al. Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals. PLoS Genet. 2014 Sep 25;10(9):e1004572. pmid:25254375

46. Baharian S, Barakatt M, Gignoux CR, Shringarpure S, Errington J, Blot WJ, et al. The Great Migration and African-American Genomic Diversity. PLoS Genet. 2016 May 27;12(5):e1006059. pmid:27232753

47. Verdu P, Pemberton TJ, Laurent R, Kemp BM, Gonzalez-Oliver A, Gorodezky C, et al. Patterns of Admixture and Population Structure in Native Populations of Northwest North America. PLoS Genet. 2014 Aug 14;10(8):e1004530. pmid:25122539

48. Jordan IK, Rishishwar L, Conley AB. Native American admixture recapitulates population-specific migration and settlement of the continental United States. PLoS Genet. 2019 Sep 23;15(9):e1008225. pmid:31545791

49. Mehrjoo Z, Fattahi Z, Beheshtian M, Mohseni M, Poustchi H, Ardalani F, et al. Distinct genetic variation and heterogeneity of the Iranian population. PLoS Genet. 2019 Sep 24;15(9):e1008385. pmid:31550250

50. Martiniano R, Cassidy LM, Ó’Maoldúin R, McLaughlin R, Silva NM, Manco L, et al. The population genomics of archaeological transition in west Iberia: Investigation of ancient substructure using imputation and haplotype-based methods. PLoS Genet. 2017 Jul 27;13(7):e1006852. pmid:28749934

51. Font-Porterias N, Arauna LR, Poveda A, Bianco E, Rebato E, Prata MJ, et al. European Roma groups show complex West Eurasian admixture footprints and a common South Asian genetic origin. PLoS Genet. 2019 Sep 23;15(9):e1008417. pmid:31545809

52. Risch N, Choudhry S, Via M, Basu A, Sebro R, Eng C, et al. Ancestry-related assortative mating in Latino populations. Genome Biol. 2009 Nov 20;10:R132. pmid:19930545

53. Zou JY, Park DS, Burchard EG, Torgerson DG, Pino-Yanes M, Song YS, et al. Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns. Proc Natl Acad Sci U S A. 2015 Nov;112(44):13621–6. pmid:26483472

54. Robinson MR, Kleinman A, Graff M, Vinkhuyzen AAE, Couper D, Miller MB, et al. Genetic evidence of assortative mating in humans. Nat Hum Behav. 2017 Jan 9;1(1):1–13.

55. Sebro R, Hoffman TJ, Lange C, Rogus JJ, Risch NJ. Testing for Non-Random Mating: Evidence for Ancestry-Related Assortative Mating in the Framingham Heart Study. Genet Epidemiol. 2010 Nov;34(7):674–9. pmid:20842694

56. Lohmueller KE, Bustamante CD, Clark AG. Detecting Directional Selection in the Presence of Recent Admixture in African-Americans. Genetics. 2011 Mar 1;187(3):823–35. pmid:21196524

57. Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019 Jul 25;10(1):3328. pmid:31346163

58. Bustamante CD, De La Vega FM, Burchard EG. Genomics for the world. Nature. 2011 Jul;475(7355):163–5. pmid:21753830

59. Bryc K, Velez C, Karafet T, Moreno-Estrada A, Reynolds A, Auton A, et al. Genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proc Natl Acad Sci U S A. 2010 May 11;107(Supplement 2):8954–61. pmid:20445096

60. Bhatia G, Tandon A, Patterson N, Aldrich MC, Ambrosone CB, Amos C, et al. Genome-wide scan of 29,141 African Americans finds no evidence of directional selection since admixture. Am J Hum Genet. 2014 Oct 2;95(4):437–44. pmid:25242497

61. McKeigue PM. Prospects for Admixture Mapping of Complex Traits. Am J Hum Genet. 2005 Jan 1;76(1):1–7. pmid:15540159

62. Beleza S, Johnson NA, Candille SI, Absher DM, Coram MA, Lopes J, et al. Genetic Architecture of Skin and Eye Color in an African-European Admixed Population. Spritz RA, editor. PLoS Genet. 2013 Mar;9(3):e1003372. pmid:23555287

63. Rajabli F, Feliciano BE, Celis K, Hamilton-Nelson KL, Whitehead PL, Adams LD, et al. Ancestral origin of ApoE ε4 Alzheimer disease risk in Puerto Rican and African American populations. PLoS Genet. 2018 Dec 5;14(12):e1007791. pmid:30517106

64. Jeong C, Witonsky DB, Basnyat B, Neupane M, Beall CM, Childs G, et al. Detecting past and ongoing natural selection among ethnically Tibetan women at high altitude in Nepal. PLoS Genet. 2018 Sep 6;14(9):e1007650. pmid:30188897

65. Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL, Shan Y, et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 2019 Dec 23;15(12):e1008500. pmid:31869403

66. Yao S, Hong C-C, Ruiz-Narváez EA, Evans SS, Zhu Q, Schaefer BA, et al. Genetic ancestry and population differences in levels of inflammatory cytokines in women: Role for evolutionary selection and environmental factors. PLoS Genet. 2018 Jun 7;14(6):e1007368. pmid:29879116

67. Chimusa ER, Meintjies A, Tchanga M, Mulder N, Seoighe C, Soodyall H, et al. A Genomic Portrait of Haplotype Diversity and Signatures of Selection in Indigenous Southern African Populations. PLoS Genet. 2015 Mar 26;11(3):e1005052. pmid:25811879

68. Kim BY, Huber CD, Lohmueller KE. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 2018 Oct 22;14(10):e1007741. pmid:30346959

69. Juric I, Aeschbacher S, Coop G. The Strength of Selection against Neanderthal Introgression. PLoS Genet. 2016 Nov 8;12(11):e1006340. pmid:27824859

70. Garrison NA, Hudson M, Ballantyne LL, Garba I, Martinez A, Taualii M, et al. Genomic Research Through an Indigenous Lens: Understanding the Expectations. Annu Rev Genomics Hum Genet. 2019;20(1):495–517. pmid:30892943

71. Jackson L, Kuhlman C, Jackson F, Fox PK. Including Vulnerable Populations in the Assessment of Data From Vulnerable Populations. Front Big Data [Internet]. 2019;2. [cited 2020 Oct 30]. Available from: https://www.frontiersin.org/articles/10.3389/fdata.2019.00019/full.

72. Hudson M, Garrison NA, Sterling R, Caron NR, Fox K, Yracheta J, et al. Rights, interests and expectations: Indigenous perspectives on unrestricted access to genomic data. Nat Rev Genet. 2020 Jun;21(6):377–84. pmid:32251390

73. Tsosie KS, Yracheta JM, Dickenson D. Overvaluing individual consent ignores risks to tribal participants. Nat Rev Genet. 2019 Sep;20(9):497–8. pmid:31308520

74. Claw KG, Anderson MZ, Begay RL, Tsosie KS, Fox K. Garrison NA. A framework for enhancing ethical genomic research with Indigenous communities. Nat Commun. 2018 Jul 27;9(1):2957. pmid:30054469

75. Kuhlman C, Jackson L, Chunara R. No computation without representation: Avoiding data and algorithm biases through diversity. ArXiv [Internet]. 2020 Feb 26 [cited 2020 Oct 30]. Available from: http://arxiv.org/abs/2002.11836.