#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Quantifying Missing Heritability at Known GWAS Loci


Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.


Vyšlo v časopise: Quantifying Missing Heritability at Known GWAS Loci. PLoS Genet 9(12): e32767. doi:10.1371/journal.pgen.1003993
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003993

Souhrn

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.


Zdroje

1. MaherB (2008) Personal genomes: The case of the missing heritability. Nature 456: 18–21.

2. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

3. EichlerEE, FlintJ, GibsonG, KongA, LealSM, et al. (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11: 446–50.

4. Lango AllenH, EstradaK, LettreG, BerndtSI, WeedonMN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838.

5. SannaS, LiB, MulasA, SidoreC, KangHM, et al. (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 7: e1002198.

6. DicksonSP, WangK, KrantzI, HakonarsonH, GoldsteinDB (2010) Rare variants create synthetic genome-wide associations. PLoS Biol 8: e1000294.

7. VisscherPM, BrownMA, McCarthyMI, YangJ (2012) Five years of gwas discovery. American journal of human genetics 90: 7–24.

8. SpencerC, HechterE, VukcevicD, DonnellyP (2011) Quantifying the underestimation of relative risks from genome-wide association studies. PLoS Genet 7: e1001337.

9. PlengeRM, CotsapasC, DaviesL, PriceAL, de BakkerPIW, et al. (2007) Two independent alleles at 6q23 associated with risk of rheumatoid arthritis. Nat Genet 39: 1477–1482.

10. StahlEA, RaychaudhuriS, RemmersEF, XieG, EyreS, et al. (2010) Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet 42: 508–514.

11. TeslovichTM, MusunuruK, SmithAV, EdmondsonAC, StylianouIM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.

12. WTCCC (2011) Genetic risk and a primary role for cell-mediated immune mechanisms in multiplesclerosis. Nature 476: 214–219.

13. WTCCC (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.

14. FrankeA, McGovernDPB, BarrettJC, WangK, Radford-SmithGL, et al. (2010) Genome-wide meta-analysis increases to 71 the number of confirmed crohn's disease susceptibility loci. Nat Genet 42: 1118–1125.

15. YangJ, FerreiraT, MorrisAP, MedlandSE, MaddenPAF, et al. (2012) Conditional and joint multiple-snp analysis of gwas summary statistics identifies additional variants inuencing complex traits. Nat Genet 44: 369–375.

16. EhretGB, LamparterD, HoggartCJ, WhittakerJC, BeckmannJS, et al. (2012) A multi-snp locus-association method reveals a substantial fraction of the missing heritability. American journal of human genetics 91: 863–871.

17. KeX (2012) Presence of multiple independent effects in risk loci of common complex human diseases. Am J Hum Genet 91: 185–192.

18. GalarneauG, PalmerCD, SankaranVG, OrkinSH, HirschhornJN, et al. (2010) Fine-mapping at three loci known to affect fetal hemoglobin levels explains additional genetic variation. Nat Genet 42: 1049–51.

19. RivasMA, BeaudoinM, GardetA, StevensC, SharmaY, et al. (2011) Deep resequencing of gwas loci identifies independent rare variants associated with inammatory bowel disease. Nat Genet 43: 1066–1073.

20. MallerJB, McVeanG, ByrnesJ, VukcevicD, PalinK, et al. (2012) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44: 1294–1301.

21. TrynkaG, HuntKA, BockettNA, RomanosJ, MistryV, et al. (2011) Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet 43: 1193–201.

22. CotsapasC, VoightBF, RossinE, LageK, NealeBM, et al. (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 7: e1002254.

23. LeeS, YangJ, GoddardM, VisscherP, WrayN (2012) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28: 2540–2542.

24. VattikutiS, GuoJ, ChowCC (2012) Heritability and genetic correlations explained by common snps for metabolic syndrome traits. PLoS Genet 8: e1002637.

25. SheaJ, AgarwalaV, PhilippakisAA, MaguireJ, BanksE, et al. (2011) Comparing strategies to fine-map the association of common snps at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nat Genet 43: 801–5.

26. ZaitlenNA, KraftP, PattersonN, PasaniucB, BhatiaG, et al. (2013) Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. (in press). PLoS Genet 9: e1003520.

27. ZaitlenN, KraftP (2012) Heritability in the genome-wide association era. Hum Genet 131: 1655–1664.

28. YangJ, LeeT, KimJ, ChoMC, HanBG, et al. (2013) Ubiquitous polygenicity of human complex traits: Genome-wide analysis of 49 traits in koreans. PLoS Genet 9: e1003355.

29. LeeSH, WrayNR, GoddardME, VisscherPM (2011) Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88: 294–305.

30. SpeedD, HemaniG, JohnsonM, BaldingD (2012) Improved heritability estimation from genome-wide snps. Am J Hum Genet 91: 1011–1021.

31. ConsortiumTGP (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.

32. VisscherPM, HillWG, WrayNR (2008) Heritability in the genomics era [mdash] concepts and misconceptions. Nat Rev Genet 9: 255–266.

33. YangJ, BenyaminB, McEvoyBP, GordonS, HendersAK, et al. (2010) Common snps explain a large proportion of the heritability for human height. Nat Genet 42: 565–9.

34. Pasaniuc B, Zaitlen N, Bhatia G, Gusev A, Patterson N, et al.. (2012) Fast and accurate 1000 genomes imputation using summary statistics or low-coverage sequencing data. Talk presented at the 62th annual meeting of the American Society of Human Genetics San Francisco, CA.

35. SolovieffN, CotsapasC, LeePH, PurcellSM, SmollerJW (2013) Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 14: 483–495.

36. SmollerJW, CraddockN, KendlerK, LeePH, NealeBM, et al. (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381: 1371–1379.

37. RamosPS, CriswellLA, MoserKL, ComeauME, WilliamsAH, et al. (2011) A comprehensive analysis of shared loci between systemic lupus erythematosus (sle) and sixteen autoimmune diseases reveals limited genetic overlap. PLoS Genet 7: e1002406.

38. EllinghausD, EllinghausE, NairRP, StuartPE, EskoT, et al. (2012) Combined analysis of genome-wide association studies for crohn disease and psoriasis identifies seven shared susceptibility loci. American journal of human genetics 90: 636–647.

39. Richard-MiceliC, CriswellL (2012) Emerging patterns of genetic overlap across autoimmune disorders. Genome Medicine 4: 1–9.

40. JostinsL, RipkeS, WeersmaRK, DuerrRH, McGovernDP, et al. (2012) Host-microbe interactions have shaped the genetic architecture of inammatory bowel disease. Nature 491: 119–124.

41. EyreS, BowesJ, DiogoD, LeeA, BartonA, et al. (2012) High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat Genet 44: 1336–1340.

42. StahlEA, WegmannD, TrynkaG, Gutierrez-AchuryJ, DoR, et al. (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44: 483–489.

43. DiogoD, KurreemanF, StahlEA, LiaoKP, GuptaN, et al. (2013) Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from gwass contribute to risk of rheumatoid arthritis. Am J Hum Genet 92: 15–27.

44. ZhouX, CarbonettoP, StephensM (2013) Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet 9: e1003264.

45. HuntKA, MistryV, BockettNA, AhmadT, BanM, et al. (2013) Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498: 232–235.

46. ChatterjeeN, WheelerB, SampsonJ, HartgeP, ChanockSJ, et al. (2013) Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet 45: 400–405.

47. ParkJH, WacholderS, GailMH, PetersU, JacobsKB, et al. (2010) Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 42: 570–575.

48. SoHC, GuiAHS, ChernySS, ShamPC (2011) Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet Epidemiol 35: 310–317.

49. DunhamI, KundajeA, AldredSF, CollinsPJ, DavisCA, et al. (2012) An integrated encyclopedia of dna elements in the human genome. Nature 489: 57–74.

50. Ionita-LazaI, LeeS, MakarovV, BuxbaumJ, LinX (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet 92: 841–853.

51. ListgartenJ, LippertC, KangEY, XiangJ, KadieCM, et al. (2013) A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics 29: 1526–1533.

52. ClaytonDG, WalkerNM, SmythDJ, PaskR, CooperJD, et al. (2005) Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet 37: 1243–1246.

53. WilliamsAL, PattersonN, GlessnerJ, HakonarsonH, ReichD (2012) Phasing of many thousands of genotyped samples. American journal of human genetics 91: 238–251.

54. HowieB, FuchsbergerC, StephensM, MarchiniJ, AbecasisGR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44: 955–959.

55. PattersonHD, ThompsonR (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58: 545–554.

56. GilmourAR, ThompsonR, CullisBR (1995) Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440–1450.

57. FischerT, GilmourA, WerfJ (2004) Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information reml. Genetics Selection Evolution 36: 363–369.

58. YangJ, LeeSH, GoddardME, VisscherPM (2011) Gcta: a tool for genome-wide complex trait analysis. Am J Hum Genet 88: 76–82.

59. FalconerDS (1965) The inheritance of liability to certain diseases, estimated from the incidence among relatives. Annals of Human Genetics 29: 51–76.

60. PattersonN, PriceAL, ReichD (2006) Population structure and eigenanalysis. PLoS Genet 2: e190.

61. ZouF, LeeS, KnowlesMR, WrightFA (2010) Quantification of population structure using correlated snps by shrinkage principal components. Human Heredity 70: 9–22.

62. YangJ, ManolioTA, PasqualeLR, BoerwinkleE, CaporasoN, et al. (2011) Genome partitioning of genetic variation for complex traits using common snps. Nat Genet 43: 519–525.

63. LucaD, RingquistS, KleiL, LeeAB, GiegerC, et al. (2008) On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants. Am J Hum Genet 82: 453–463.

64. RobinsonRF, BatiskyDL, HayesJR, NahataMC, MahanJD (2005) Significance of heritability in primary and secondary pediatric hypertension. Am J Hypertens 18: 917–921.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2013 Číslo 12
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#