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Integrated Model of and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes


De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.


Vyšlo v časopise: Integrated Model of and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes. PLoS Genet 9(8): e32767. doi:10.1371/journal.pgen.1003671
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003671

Souhrn

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.


Zdroje

1. SandersSJ, MurthaMT, GuptaAR, MurdochJD, RaubesonMJ, et al. (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485: 237–241.

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

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

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

5. VeltmanJA, BrunnerHG (2012) De novo mutations in human genetic disease. Nat Rev Genet 13: 565–575.

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

7. LiuL, SaboA, NealeBM, NagaswamyU, StevensC, et al. (2013) Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls. PLoS Genet 9: e1003443.

8. BansalV, LibigerO, TorkamaniA, SchorkNJ (2010) Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11: 773–785.

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

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

11. BenjaminiY, HochbergY (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57: 289–300.

12. VisscherPM, BrownMA, McCarthyMI, YangJ (2012) Five years of gwas discovery. Am J Hum Genet 90: 7–24.

13. 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.

14. AnneyR, KleiL, PintoD, ReganR, ConroyJ, et al. (2010) A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet 19: 4072–82.

15. DevlinB, MelhemN, RoederK (2011) Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Res 1380: 78–84.

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

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

18. 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.

19. O'RoakB, DeriziotisP, LeeC, VivesL, SchwartzJ, et al. (2012) Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature genetics 44: 471–471.

20. ChahrourM, TimothyW, LimE, AtamanB, CoulterM, et al. (2012) Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism. PLoS Genetics 8: e1002635.

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

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

23. O'RoakBJ, VivesL, FuW, EgertsonJD, StanawayIB, et al. (2012) Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338: 1619–1622.

24. LinMY, LinYM, KaoTC, ChuangHH, ChenRH (2011) PDZ-RhoGEF ubiquitination by Cullin3-KLHL20 controls neurotrophin-induced neurite outgrowth. J Cell Biol 193: 985–994.

25. SobieszczukDF, PoliakovA, XuQ, WilkinsonDG (2010) A feedback loop mediated by degradation of an inhibitor is required to initiate neuronal differentiation. Genes Dev 24: 206–218.

26. SchaeferH, RongoC (2006) KEL-8 is a substrate receptor for CUL3-dependent ubiquitin ligase that regulates synaptic glutamate receptor turnover. Mol Biol Cell 17: 1250–1260.

27. KongA, FriggeML, MassonG, BesenbacherS, SulemP, et al. (2012) Rate of de novo mutations and the importance of father's age to disease risk. Nature 488: 471–5.

28. TongY, XuY, Scearce-LevieK, PtacekLJ, FuYH (2010) COL25A1 triggers and promotes Alzheimer's disease-like pathology in vivo. Neurogenetics 11: 41–52.

29. LiD, ZhaoH, KranzlerHR, OslinD, AntonRF, et al. (2012) Association of COL25A1 with comorbid antisocial personality disorder and substance dependence. Biol Psychiatry 71: 733–740.

30. BedogniF, HodgeRD, ElsenGE, NelsonBR, DazaRA, et al. (2010) Tbr1 regulates regional and laminar identity of postmitotic neurons in developing neocortex. Proc Natl Acad Sci USA 107: 13129–13134.

31. AngusSP, NevinsJR (2012) A role for Mediator complex subunit MED13L in Rb/E2F-induced growth arrest. Oncogene 31: 4709–4717.

32. GhanemN, AndrusiakMG, SvobodaD, Al LafiSM, JulianLM, et al. (2012) The Rb/E2F pathway modulates neurogenesis through direct regulation of the Dlx1/Dlx2 bigene cluster. J Neurosci 32: 8219–8230.

33. AndrusiakMG, McClellanKA, Dugal-TessierD, JulianLM, RodriguesSP, et al. (2011) Rb/E2F regulates expression of neogenin during neuronal migration. Mol Cell Biol 31: 238–247.

34. TomppoL, EkelundJ, LichtermannD, VeijolaJ, JarvelinMR, et al. (2012) DISC1 conditioned GWAS for psychosis proneness in a large Finnish birth cohort. PLoS ONE 7: e30643.

35. ZhengS, EackerSM, HongSJ, GronostajskiRM, DawsonTM, et al. (2010) NMDA-induced neuronal survival is mediated through nuclear factor I-A in mice. J Clin Invest 120: 2446–2456.

36. ShuT, ButzKG, PlachezC, GronostajskiRM, RichardsLJ (2003) Abnormal development of forebrain midline glia and commissural projections in Nfia knock-out mice. J Neurosci 23: 203–212.

37. GordonD, HeathSC, LiuX, OttJ (2001) A transmission/disequilibrium test that allows for genotyping errors in the analysis of single-nucleotide polymorphism data. Am J Hum Genet 69: 371–380.

38. KimS, MillardSP, YuCE, LeongL, RadantA, et al. (2012) Inheritance model introduces differential bias in CNV calls between parents and offspring. Genet Epidemiol 36: 488–498.

39. LeekJT, ScharpfRB, BravoHC, SimchaD, LangmeadB, et al. (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11: 733–739.

40. TennessenJA, BighamAW, O'ConnorTD, FuW, KennyEE, et al. (2012) Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337: 64–69.

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Genetika Reprodukčná medicína

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PLOS Genetics


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