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Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls


We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.


Vyšlo v časopise: Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls. PLoS Genet 9(4): e32767. doi:10.1371/journal.pgen.1003443
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003443

Souhrn

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.


Zdroje

1. PintoD, PagnamentaAT, KleiL, AnneyR, MericoD, et al. (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466: 368–72.

2. LevyD, RonemusM, YamromB, LeeY, LeottaA, et al. (2011) Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70: 886–897.

3. SandersS, HusV, LuoR, MurthaM, Moreno-De-LucaD, et al. (2011) Multiple recurrent de novo cnvs, including duplications of the 7q11. 23 williams syndrome region, are strongly associated with autism. Neuron 70: 863–885.

4. SandersS, MurthaM, GuptaA, MurdochJ, RaubesonM, et al. (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485: 82–93.

5. NealeB, KouY, LiuL, Ma'ayanA, SamochaK, et al. (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485: 242–245.

6. O'RoakB, VivesL, GirirajanS, KarakocE, KrummN, et al. (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485: 246–250.

7. O'RoakB, DeriziotisP, LeeC, VivesL, SchwartzJ, et al. (2011) Exome sequencing in sporadicautism spectrum disorders identifies severe de novo mutations. Nat Genet 43: 585–589.

8. IossifovI, RonemusM, LevyD, WangZ, HakkerI, et al. (2012) De novo gene disruptions in children on the autistic spectrum. Neuron 74: 285–299.

9. ChahrourM, TimothyW, LimE, AtamanB, CoulterM, et al. (2012) Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism. PLoS Genet 8: e1002635 doi:10.1371/journal.pgen.1002635.

10. AnneyR, KleiL, PintoD, AlmeidaJ, BacchelliE, et al. (2012) Individual common variants exert weak effects on risk for autism spectrum disorders. Hum Mol Genet 21: 4781–4792.

11. KleiL, SandersSJ, MurthaMT, HusV, LoweJK, et al. (2012) Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism 3: 9.

12. O'RoakBJ, VivesL, FuW, EgertsonJD, StanawayIB, et al. (2012) Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338: 1619–22 doi: 10.1126/science.1227764.

13. CohenJ, KissR, PertsemlidisA, MarcelY, McPhersonR, et al. (2004) Multiple rare alleles contribute to low plasma levels of hdl cholesterol. Science 305: 869–872.

14. JiW, FooJ, O'RoakB, ZhaoH, LarsonM, et al. (2008) Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat Genet 40: 592–599.

15. JohansenC, WangJ, LanktreeM, CaoH, McIntyreA, et al. (2010) Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat Genet 42: 684–687.

16. NejentsevS, WalkerN, RichesD, EgholmM, ToddJ (2009) Rare variants of ifih1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387–389.

17. AhituvN, KavaslarN, SchackwitzW, UstaszewskaA, MartinJ, et al. (2007) Medical sequencing at the extremes of human body mass. Am J Hum Genet 80: 779–791.

18. RomeoS, YinW, KozlitinaJ, PennacchioL, BoerwinkleE, et al. (2009) Rare loss-of-function mutations in angptl family members contribute to plasma triglyceride levels in humans. J Clin Invest 119: 70–79.

19. KiezunA, GarimellaK, DoR, StitzielN, NealeB, et al. (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 44: 623–630.

20. MorgenthalerS, ThillyWG (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (cast). Mutat Res 615: 28–56.

21. LiB, LealSM (2009) Discovery of rare variants via sequencing: implications for the design of complex trait association studies. PLoS Genet 5: e1000481 doi:10.1371/journal.pgen.1000481.

22. MadsenBE, BrowningSR (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5: e1000384 doi:10.1371/journal.pgen.1000384.

23. HanF, PanW (2010) Powerful multi-marker association tests: Unifying genomic distance-based regression and logistic regression. Genet Epidemiol 680–688.

24. MorrisAP, ZegginiE (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34: 188–193.

25. NealeBM, RivasMA, VoightBF, AltshulerD, DevlinB, et al. (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7: e1001322 doi:10.1371/journal.pgen.1001322.

26. WuM, LeeS, CaiT, LiY, BoehnkeM, et al. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89: 82–93.

27. ZegginiE, ScottL, SaxenaR, VoightB, MarchiniJ, et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638–645.

28. HigginsJ, ThompsonS, DeeksJ, AltmanD (2003) Measuring inconsistency in meta-analyses. BMJ 327: 557–560.

29. PriceA, PattersonN, PlengeR, WeinblattM, ShadickN, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.

30. ChallisD, YuJ, EvaniUS, JacksonAR, PaithankarS, et al. (2012) An integrative variant analysis suite for whole exome next-generation sequencing data. BMC Bioinformatics 13: 8.

31. DepristoMA, BanksE, PoplinR, GarimellaKV, MaguireJR, et al. (2011) A framework for variation discovery and genotyping using next-generation dna sequencing data. Nat Genet 43: 491–498.

32. Laird NM, Lange C (2010) The fundamentals of modern statistical genetics. Springer.

33. LeeAB, LucaD, KleiL, DevlinB, RoederK (2010) Discovering genetic ancestry using spectral graph theory. Genet Epidemiol 34: 51–59.

34. DevlinB, RoederK (1999) Genomic control for association studies. Biometrics 55: 997–1004.

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

36. BetancurC (2011) Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res 1380: 42–77.

37. DevlinB, SchererS (2012) Genetic architecture in autism spectrum disorder. Curr Opin Genet Dev 22: 229–237.

38. LimE, RaychaudhuriS, SandersS, StevensC, SaboA, et al. (2013) Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77: 235–242.

39. TennessenJ, BighamA, O'ConnorT, FuW, KennyE, et al. (2012) Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337: 64–69.

40. NelsonM, WegmannD, EhmM, KessnerD, JeanP, et al. (2012) An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337: 100–104.

41. BuxbaumJ, DalyM, DevlinB, LehnerT, RoederK, et al. (2012) The autism sequencing consortium: Large-scale, high-throughput sequencing in autism spectrum disorders. Neuron 76: 1052–1056.

42. LinD, ZengD (2010) Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet Epidemiol 34: 60–66.

43. MathiesonI, McVeanG (2012) Differential confounding of rare and common variants in spatially structured populations. Nat Genet 44: 243–246.

44. ZhangY, GuanW, PanW (2013) Adjustment for population stratification via principal components in association analysis of rare variants. Genet Epidemiol 37: 99–109 doi: 10.1002/gepi.21691.

45. BuxbaumJ, BolshakovaN, BrownfeldJ, AnneyR, BenderP, et al. (2012) The autism simplex collection: An international, expertly phenotyped autism sample for genetic and phenotypic analyses. Mol Autism : in press

46. ChapmanJ, WhittakerJ (2008) Analysis of multiple snps in a candidate gene or region. Genet Epidemiol 32: 560–566.

47. LinX (1997) Variance component testing in generalised linear models with random effects. Biometrika 84: 309–326.

48. DaviesR (1980) The distribution of a linear combination of chi-squared random variables. Applied Statistics 29: 323–33.

49. PritchardJK (2001) Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 69: 124–137.

50. PritchardJ, CoxN (2002) The allelic architecture of human disease genes: common disease–common variant… or not? Hum Mol Genet 11: 2417–2423.

51. FraleyC, RafteryA (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97: 611–631.

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Genetika Reprodukčná medicína

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PLOS Genetics


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