A Hidden Markov Model Approach for Simultaneously Estimating Local Ancestry and Admixture Time Using Next Generation Sequence Data in Samples of Arbitrary Ploidy

Abstract

Admixture—the mixing of genomes from divergent populations—is increasingly appreciated as a central process in evolution. To characterize and quantify patterns of admixture across the genome, a number of methods have been developed for local ancestry inference. However, existing approaches have a number of shortcomings. First, all local ancestry inference methods require some prior assumption about the expected ancestry tract lengths. Second, existing methods generally require genotypes, which is not feasible to obtain for many next-generation sequencing projects. Third, many methods assume samples are diploid, however a wide variety of sequencing applications will fail to meet this assumption. To address these issues, we introduce a novel hidden Markov model for estimating local ancestry that models the read pileup data, rather than genotypes, is generalized to arbitrary ploidy, and can estimate the time since admixture during local ancestry inference. We demonstrate that our method can simultaneously estimate the time since admixture and local ancestry with good accuracy, and that it performs well on samples of high ploidy— i.e. 100 or more chromosomes. As this method is very general, we expect it will be useful for local ancestry inference in a wider variety of populations than what previously has been possible. We then applied our method to pooled sequencing data derived from populations of Drosophila melanogaster on an ancestry cline on the east coast of North America. We find that regions of local recombination rates are negatively correlated with the proportion of African ancestry, suggesting that selection against foreign ancestry is the least efficient in low recombination regions. Finally we show that clinal outlier loci are enriched for genes associated with gene regulatory functions, consistent with a role of regulatory evolution in ecological adaptation of admixed D. melanogaster populations. Our results illustrate the potential of local ancestry inference for elucidating fundamental evolutionary processes. Author Summary When divergent populations hybridize, their offspring obtain portions of their genomes from each parent population. Although the average ancestry proportion in each descendant is equal to the proportion of ancestors from each of the ancestral populations, the contribution of each ancestry type is variable across the genome. Estimating local ancestry within admixed individuals is a fundamental goal for evolutionary genetics, and here we develop a method for doing this that circumvents many of the problems associated with existing methods. Briefly, our method can use short read data, rather than genotypes and can be applied to samples with any number of chromosomes. Furthermore, our method simultaneously estimates local ancestry and the number of generations since admixture—the time that the two ancestral populations first encountered each other. Finally, in applying our method to data from an admixture zone between ancestral populations of Drosophila melanogaster , we find many lines of evidence consistent with natural selection operating to against the introduction of foreign ancestry into populations of one predominant ancestry type. Because of the generality of this method, we expect that it will be useful for a wide variety of existing and ongoing research projects.

References 97

1. 1.

   Kronforst MR, Young LG, Blume LM, Gilbert LE. Multilocus analyses of admixture and introgression among hybridizing Heliconius butterflies. Evolution. 2006;60: 1254–16.

   Not in our library

2. 2.
   Population Genomics of Sub-Saharan Drosophila melanogaster: African Diversity and Non-African Admixture · 2012

   No notice
3. 3.
   The Genomic Signature of Crop-Wild Introgression in Maize · 2013

   Corrected
4. 4.
   Speciation and Introgression between Mimulus nasutus and Mimulus guttatus · 2014

   No notice
5. 5.

   Rieseberg LH, Raymond O, Rosenthal DM, Lai Z, Livingstone K, Nakazato T, et al Major ecological transitions in wild sunflowers facilitated by hybridization. Science. 2003;301: 1211–1216.

   Not in our library10.1126/science.1086949

6. 6.

   Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, et al A draft sequence of the neandertal genome. Science. 2010;328: 710–722.

   Not in our library10.1126/science.1188021

7. 7.

   Sankararaman S, Mallick S, Dannemann M, Prüfer K, Kelso J, Pääbo S, et al The genomic landscape of Neanderthal ancestry in present-day humans. Nature. 2014;507: 354–357.

   Not in our library10.1038/nature12961

8. 8.

   Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38: 904–909.

   Not in our library10.1038/ng1847

9. 9.

   Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet. 2003;33: 177–182.

   Not in our library10.1038/ng1071

10. 10.

    Freedman ML, Reich D, Penney KL, McDonald GJ, Mignault AA, Patterson N, et al Assessing the impact of population stratification on genetic association studies. Nat Genet. 2004;36: 388–393.

    Not in our library10.1038/ng1333

11. 11.

    Caracristi G, Schlotterer C. Genetic differentiation between American and European Drosophila melanogaster populations could be attributed to admixture of African alleles. Mol Biol Evol. 2003;20: 792–799.

    Not in our library10.1093/molbev/msg091

12. 12.

    Kolbe JJ, Glor RE, Schettino LR, Lara AC, Larson A. Genetic variation increases during biological invasion by a Cuban lizard. Nature. 2004;431: 177–181.

    Not in our library10.1038/nature02807

13. 13.

    Consortium THG, Consortium G. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature. 2012;487: 94–98.

    Not in our library10.1038/nature11041

14. 14.

    Racimo F, Sankararaman S, Nielsen R, Huerta-Sanchez E. Evidence for archaic adaptive introgression in humans. Nat Genet. 2015;16: 359–371.

    Not in our library10.1038/nrg3936

15. 15.
    The Strength of Selection against Neanderthal Introgression · 2016

    No notice
16. 16.

    Harris K, Nielsen R. The genetic cost of neanderthal introgression. Genetics. 2016;203: 881–891.

    Not in our library10.1534/genetics.116.186890

17. 17.

    Pool JE. The mosaic ancestry of the Drosophila Genetic Reference Panel and the D. melanogaster reference genome reveals a network of epistatic fitness interactions. Mol Biol Evol. 2015;: msv194–16.

    Not in our library10.1093/molbev/msv194

18. 18.
    High-resolution mapping reveals hundreds of genetic incompatibilities in hybridizing fish species · 2014

    No notice
19. 19.

    Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155: 945–959.

    Not in our library10.1093/genetics/155.2.945

20. 20.

    Skotte L, Korneliussen TS, Albrechtsen A. Estimating individual admixture proportions from next generation sequencing data. Genetics. 2013;195: 693–702.

    Not in our library10.1534/genetics.113.154138

21. 21.

    Bergland AO, Tobler R, González J, Schmidt P, Petrov D. Secondary contact and local adaptation contribute to genome-wide patterns of clinal variation in Drosophila melanogaster. Mol Ecol. 2016;25: 1157–1174.

    Not in our library10.1111/mec.13455

22. 22.

    Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003;164: 1567–1587.

    Not in our library10.1093/genetics/164.4.1567

23. 23.

    Sankararaman S, Sridhar S, Kimmel G, Halperin E. Estimating local ancestry in admixed populations. Am J Hum Genet. 2008;82: 290–303.

    Not in our library10.1016/j.ajhg.2007.09.022

24. 24.

    Maples BK, Gravel S, Kenny EE, Bustamante CD. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am J Hum Genet. 2013;93: 278–288.

    Not in our library10.1016/j.ajhg.2013.06.020

25. 25.

    Baran Y, Pasaniuc B, Sankararaman S, Torgerson DG, Gignoux C, Eng C, et al Fast and accurate inference of local ancestry in Latino populations. Bioinformatics. 2012;28: 1359–1367.

    Not in our library10.1093/bioinformatics/bts144

26. 26.

    Pool JE, Nielsen R. Inference of historical changes in migration rate from the lengths of migrant tracts. Genetics. 2009;181: 711–719.

    Not in our library10.1534/genetics.108.098095

27. 27.

    Koopman W, Li Y, Coart E. Linked vs. unlinked markers: multilocus microsatellite haplotype‐sharing as a tool to estimate gene flow and introgression. Mol Ecol. 2007;16: 243–256.

    Not in our library10.1111/j.1365-294x.2006.03137.x

28. 28.

    Patterson N, Hattangadi N, Lane B. Methods for high-density admixture mapping of disease genes. Am J Hum Genet. 2004;74: 979–1000.

    Not in our library10.1086/420871

29. 29.

    Gravel S. Population Genetics Models of Local Ancestry. Genetics. 2012;191: 607–619.

    Not in our library10.1534/genetics.112.139808

30. 30.
    The History of African Gene Flow into Southern Europeans, Levantines, and Jews · 2011

    No notice
31. 31.

    Loh PR, Lipson M, Patterson N, Moorjani P. Inferring admixture histories of human populations using linkage disequilibrium. Genetics. 2013;193: 1233–1254.

    Not in our library10.1534/genetics.112.147330

32. 32.

    Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C, Falush D, et al A Genetic Atlas of Human Admixture History. Science. 2014;343: 747–751.

    Not in our library10.1126/science.1243518

33. 33.

    Consortium 1GP, BGI-Shenzhen, European Bioinformatics Institute, Illumina, Hospital BAW, College B, et al An integrated map of genetic variation from 1,092 human genomes. Nature. 2012.

    Not in our library10.1038/nature11632

34. 34.

    Pasaniuc B, Sankararaman S, Kimmel G, Halperin E. Inference of locus-specific ancestry in closely related populations. Bioinformatics. 2009;25: i213–i221.

    Not in our library10.1093/bioinformatics/btp197

35. 35.

    Haddrill PR, Thornton KR, Charlesworth B, Andolfatto P. Multilocus patterns of nucleotide variability and the demographic and selection history of Drosophila melanogaster populations. Genome Res. 2005;15: 790–799.

    Not in our library10.1101/gr.3541005

36. 36.

    Thornton K, Andolfatto P. Approximate Bayesian inference reveals evidence for a recent, severe bottleneck in a Netherlands population of Drosophila melanogaster. Genetics. 2006;172: 1607–1619.

    Not in our library10.1534/genetics.105.048223

37. 37.
    Inferring the Demographic History and Rate of Adaptive Substitution in Drosophila · 2006

    No notice
38. 38.

    Duchen P, Živković D, Hutter S, Stephan W. Demographic inference reveals African and European admixture in the North American Drosophila melanogaster population. Genetics. 2013;193: 291–301.

    Not in our library10.1534/genetics.112.145912

39. 39.

    Langley CH, Stevens K, Cardeno C, Lee YCG, Schrider DR, Pool JE, et al Genomic variation in natural populations of Drosophila melanogaster. Genetics. 2012;192: 533–598.

    Not in our library10.1534/genetics.112.142018

40. 40.
    Genomic Evidence of Rapid and Stable Adaptive Oscillations over Seasonal Time Scales in Drosophila · 2014

    No notice

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