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Deleterious Alleles in the Human Genome Are on Average Younger Than Neutral Alleles of the Same Frequency


Large-scale population sequencing studies provide a complete picture of human genetic variation within the studied populations. A key challenge is to identify, among the myriad alleles, those variants that have an effect on molecular function, phenotypes, and reproductive fitness. Most non-neutral variation consists of deleterious alleles segregating at low population frequency due to incessant mutation. To date, studies characterizing selection against deleterious alleles have been based on allele frequency (testing for a relative excess of rare alleles) or ratio of polymorphism to divergence (testing for a relative increase in the number of polymorphic alleles). Here, starting from Maruyama's theoretical prediction (Maruyama T (1974), Am J Hum Genet USA 6:669–673) that a (slightly) deleterious allele is, on average, younger than a neutral allele segregating at the same frequency, we devised an approach to characterize selection based on allelic age. Unlike existing methods, it compares sets of neutral and deleterious sequence variants at the same allele frequency. When applied to human sequence data from the Genome of the Netherlands Project, our approach distinguishes low-frequency coding non-synonymous variants from synonymous and non-coding variants at the same allele frequency and discriminates between sets of variants independently predicted to be benign or damaging for protein structure and function. The results confirm the abundance of slightly deleterious coding variation in humans.


Vyšlo v časopise: Deleterious Alleles in the Human Genome Are on Average Younger Than Neutral Alleles of the Same Frequency. PLoS Genet 9(2): e32767. doi:10.1371/journal.pgen.1003301
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003301

Souhrn

Large-scale population sequencing studies provide a complete picture of human genetic variation within the studied populations. A key challenge is to identify, among the myriad alleles, those variants that have an effect on molecular function, phenotypes, and reproductive fitness. Most non-neutral variation consists of deleterious alleles segregating at low population frequency due to incessant mutation. To date, studies characterizing selection against deleterious alleles have been based on allele frequency (testing for a relative excess of rare alleles) or ratio of polymorphism to divergence (testing for a relative increase in the number of polymorphic alleles). Here, starting from Maruyama's theoretical prediction (Maruyama T (1974), Am J Hum Genet USA 6:669–673) that a (slightly) deleterious allele is, on average, younger than a neutral allele segregating at the same frequency, we devised an approach to characterize selection based on allelic age. Unlike existing methods, it compares sets of neutral and deleterious sequence variants at the same allele frequency. When applied to human sequence data from the Genome of the Netherlands Project, our approach distinguishes low-frequency coding non-synonymous variants from synonymous and non-coding variants at the same allele frequency and discriminates between sets of variants independently predicted to be benign or damaging for protein structure and function. The results confirm the abundance of slightly deleterious coding variation in humans.


Zdroje

1. FayJC, WyckoffGJ, WuCI (2001) Positive and negative selection on the human genome. Genetics 158: 1227–1234.

2. SunyaevS, RamenskyV, KochI, LatheW, KondrashovAS, et al. (2001) Prediction of deleterious human alleles. Human Molecular Genetics 10: 591–597.

3. WilliamsonSH, HernandezR, Fledel-AlonA, ZhuL, NielsenR, et al. (2005) Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc Natl Acad Sci USA 102: 7882–7887.

4. Eyre-WalkerA, WoolfitM, PhelpsT (2006) The Distribution of Fitness Effects of New Deleterious Amino Acid Mutations in Humans. Genetics 173: 891–900.

5. KryukovGV, PennacchioLA, SunyaevSR (2007) Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. The American Journal of Human Genetics 80: 727–739.

6. BoykoAR, WilliamsonSH, IndapAR, DegenhardtJD, HernandezRD, et al. (2008) Assessing the Evolutionary Impact of Amino Acid Mutations in the Human Genome. PLoS Genet 4: e1000083 doi:10.1371/journal.pgen.1000083.

7. KryukovGV, ShpuntA, StamatoyannopoulosJA, SunyaevSR (2009) Power of deep, all-exon resequencing for discovery of human trait genes. Proceedings of the National Academy of Sciences of the United States of America 106: 3871–3876.

8. MaruyamaT (1974) The age of a rare mutant gene in a large population. American journal of human genetics 26: 669.

9. SabetiPC, ReichDE, HigginsJM, LevineHZP, RichterDJ, et al. (2002) Detecting recent positive selection in the human genome from haplotype structure. Nature 419: 832–837.

10. VoightBF, KudaravalliS, WenX, PritchardJK (2006) A map of recent positive selection in the human genome. PLoS Biol 4: e72 doi:10.1371/journal.pbio.0040072.

11. SabetiPC, VarillyP, FryB, LohmuellerJ, HostetterE, et al. (2007) Genome-wide detection and characterization of positive selection in human populations. Nature 449: 913–918.

12. SlatkinM, RannalaB (1997) Estimating the age of alleles by use of intraallelic variability. The American Journal of Human Genetics 60: 447–458.

13. RannalaB, ReeveJP (2001) High-resolution multipoint linkage-disequilibrium mapping in the context of a human genome sequence. The American Journal of Human Genetics 69: 159–178.

14. GeninE, Tullio-PeletA, BegeotF, LyonnetS, AbelL (2004) Estimating the age of rare disease mutations: the example of Triple-A syndrome. Journal of Medical Genetics 41: 445–449.

15. AdzhubeiIA, SchmidtS, PeshkinL, RamenskyVE, BorkP, et al. (2010) A method and server for predicting damaging missense mutations. Nature Methods 7: 248–249.

16. MarthGT, YuF, IndapAR, GarimellaK, GravelS, et al. (2011) The functional spectrum of lowfrequency coding variation. Genome Biology 12: R84.

17. CohenJC, BoerwinkleE, MosleyTH, HobbsHH (2006) Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. New England Journal of Medicine 354: 1264–1272.

18. NejentsevS, WalkerN, RichesD, EgholmM, ToddJA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387–389.

19. StitzielN, KiezunA, SunyaevS (2011) Computational and statistical approaches to analyzing variants identified by exome sequencing. Genome Biology 12: 227.

20. SlatkinM (2001) Simulating genealogies of selected alleles in a population of variable size. Genetics Research 78: 49–57.

21. MaruyamaT, KimuraM (1975) Moments for sum of an arbitrary function of gene frequency along a stochastic path of gene frequency change. Proceedings of the National Academy of Sciences of the United States of America 72: 1602–4.

22. HernandezRD (2008) A flexible forward simulator for populations subject to selection and demography. Bioinformatics 24: 2786–2787.

23. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 14: 1754–1760.

24. The 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.

25. McKennaA, HannaM, BanksE, SivachenkoA, CibulskisK, et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20: 1297–1303.

26. DePristoMA, BanksE, PoplinR, GarimellaKV, MaguireJR, et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics 43: 491–498.

27. BrowningBL, BrowningSR (2009) A unified approach to genotype imputation and haplotype phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics 84: 210–223.

28. WangN, AkeyJM, ZhangK, ChakrabortyR, JinL (2002) Distribution of Recombination Crossovers and the Origin of Haplotype Blocks: The Interplay of Population History, Recombination, and Mutation. The American Journal of Human Genetics 71: 1227–1234.

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Genetika Reprodukčná medicína

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PLOS Genetics


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