Inferring the Probability of the Derived vs. the Ancestral Allelic State at a Polymorphic Site

Abstract

It is known that the allele ancestral to the variation at a polymorphic site cannot be assigned with certainty, and that the most frequently used method to assign the ancestral state—maximum parsimony—is prone to misinference. Estimates of counts of sites that have a certain number of copies of the derived allele in a sample (the unfolded site frequency spectrum, uSFS) made by parsimony are therefore also biased. We previously developed a maximum likelihood method to estimate the uSFS for a focal species using information from two outgroups while assuming simple models of nucleotide substitution. Here, we extend this approach to allow multiple outgroups (implemented for three outgroups), potentially any phylogenetic tree topology, and more complex models of nucleotide substitution. We find, however, that two outgroups and the Kimura two-parameter model are adequate for uSFS inference in most cases. We show that using parsimony to infer the ancestral state at a specific site seriously breaks down in two situations. The first is where the outgroups provide no information about the ancestral state of variation in the focal species. In this case, nucleotide variation will be underestimated if such sites are excluded. The second is where the minor allele in the focal species agrees with the allelic state of the outgroups. In this situation, parsimony tends to overestimate the probability of the major allele being derived, because it fails to account for the fact that sites with a high frequency of the derived allele tend to be rare. We present a method that corrects this deficiency and is capable of providing nearly unbiased estimates of ancestral state probabilities on a site-by-site basis and the uSFS.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 30

1. 1.

   1000 Genomes Project Consortium, 2010 A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073 [corrigenda: Nature 473: 544 (2011)]. DOI: 10.1038/nature09534

   Not in our library10.1038/nature09534

2. 2.

   1000 Genomes Project Consortium, 2015 A global reference for human genetic variation. Nature 526: 68–74.

   Not in our library10.1038/nature15393

3. 3.

   Arndt P. F. Burge C. B. Hwa T., 2003 DNA sequence evolution with neighbor-dependent mutation. J. Comput. Biol. 10: 313–322.

   Not in our library10.1089/10665270360688039

4. 4.

   Baudry E. Depaulis F., 2003 Effect of misoriented sites on neutrality tests with outgroup. Genetics 165: 1619–1622.

   Not in our library10.1093/genetics/165.3.1619

5. 5.
   Assessing the Evolutionary Impact of Amino Acid Mutations in the Human Genome · 2008

   No notice
6. 6.

   Collins T. M. Wimberger P. H. Naylor G. J. P., 1994 Compositional bias, character state bias and character state reconstruction using parsimony. Syst. Biol. 43: 482–496.

   Not in our library10.1093/sysbio/43.4.482

7. 7.

   Dreszer T. R. Wall G. D. Haussler D. Pollard K. S., 2007 Biased clustered substitutions in the human genome: the footprints of male-driven biased gene conversion. Genome Res. 17: 1420–1430.

   Not in our library10.1101/gr.6395807

8. 8.

   Eyre-Walker A., 1998 Problems with parsimony in sequences of biased base composition. J. Mol. Evol. 47: 686–690.

   Not in our library10.1007/pl00006427

9. 9.

   Fay J. C. Wu C.-I., 2000 Hitchhiking under positive Darwinian selection. Genetics 155: 1405–1413.

   Not in our library10.1093/genetics/155.3.1405

10. 10.

    Felsenstein J., 1981 Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17: 368–376.

    Not in our library10.1007/bf01734359

11. 11.

    Halligan D. L. Kousathanas A. Ness R. W. Harr B. Eöry L., 2013 Contributions of protein-coding and regulatory change to adaptive molecular evolution in murid rodents. PLoS Genet. 9: e1003995

    Not in our library10.1371/journal.pgen.1003995

12. 12.
    Rapid evolution of the human mutation spectrum · 2017

    No notice
13. 13.

    Hernandez R. D. Williamson S. H. Bustamante C. D., 2007 Context dependence, ancestral misidentification, and spurious signatures of natural selection. Mol. Biol. Evol. 24: 1792–1800.

    Not in our library10.1093/molbev/msm108

14. 14.

    Jackson B. C. Campos J. L. Haddrill P. R. Charlesworth B. Zeng K., 2017 Variation in the intensity of selection on codon bias over time causes contrasting patterns of base composition evolution in Drosophila. Genome Biol. Evol. 9: 102–123.

    Not in our library10.1093/gbe/evw291

15. 15.

    Keightley P. D. Eöry L. Halligan D. L. Kirkpatrick M., 2011 Inference of mutation parameters and selective constraint in mammalian coding sequences by approximate Bayesian computation. Genetics 187: 1153–1161.

    Not in our library10.1534/genetics.110.124073

16. 16.

    Keightley P. D. Campos J. L. Booker T. R. Charlesworth B., 2016 Inferring the frequency spectrum of derived variants to quantify adaptive molecular evolution in protein-coding genes of Drosophila melanogaster. Genetics 203: 975–984.

    Not in our library10.1534/genetics.116.188102

17. 17.

    Keinan A. Mullikin J. C. Patterson N. Reich D., 2007 Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans. Nat. Genet. 39: 1251–1255.

    Not in our library10.1038/ng2116

18. 18.

    Langley C. H. Stevens K. Cardeno C. Lee Y. C. G. Schrider D. R., 2012 Genomic variation in natural populations of Drosophila melanogaster. Genetics 192: 533–598.

    Not in our library10.1534/genetics.112.142018

19. 19.

    Lohse R. J. H. Barton N. H., 2011 A general method for calculating likelihoods under the coalescent process. Genetics 189: 977–987.

    Not in our library10.1534/genetics.111.129569

20. 20.

    Matsumoto T. Akashi H. Yang Z., 2015 Evaluation of ancestral sequence reconstruction methods to infer nonstationary patterns of nucleotide substitution. Genetics 200: 873–890.

    Not in our library10.1534/genetics.115.177386

21. 21.

    Mondal M. Casals F. Xu T. Dall’Olio G. M. Pybus M., 2016 Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation. Nat. Genet. 48: 1066–1070.

    Not in our library10.1038/ng.3621

22. 22.

    Nachman M. W. Crowell S. L., 2000 Estimate of the mutation rate per nucleotide in humans. Genetics 156: 297–304.

    Not in our library10.1093/genetics/156.1.297

23. 23.

    Sabeti P. C. Varilly P. Fry B. Lohmueller J. Hostetter E., 2007 Genome-wide detection and characterization of positive selection in human populations. Nature 449: 913–918.

    Not in our library10.1038/nature06250

24. 24.

    Schmidt J. M. Battlay P. Gledhill-Smith R. S. Good R. T. Lumb C., 2017 Insights into DDT resistance from the Drosophila melanogaster genetic reference panel. Genetics 207: 1181–1193.

    Not in our library10.1534/genetics.117.300310

25. 25.

    Schneider A. Charlesworth B. Eyre-Walker A. Keightley P. D., 2011 A method for inferring the rate of occurrence and fitness effects of advantageous mutations. Genetics 189: 1427–1437.

    Not in our library10.1534/genetics.111.131730

26. 26.

    Tataru P. Mollion M. Glémin S. Bataillon T., 2017 Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data. Genetics 207: 1103–1119.

    Not in our library10.1534/genetics.117.300323

27. 27.
    A Map of Recent Positive Selection in the Human Genome · 2006

    Corrected
28. 28.

    Yang M. A. Slatkin M., 2016 Using ancient samples in projection analysis. G3 (Bethesda) 6: 99–105.

    Not in our library10.1534/g3.115.023788

29. 29.

    Yang Z., 2007 PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24: 1586–1591.

    Not in our library10.1093/molbev/msm088

30. 30.

    Zeng K. Fu Y.-X. Shi S. Wu C.-I., 2006 Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics 174: 1431–1439.

    Not in our library10.1534/genetics.106.061432

Cited by papers we hold 1

• The patterns of deleterious mutations during the domestication of soybean · 2021
  Not checked

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.