Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model
Autoři:
Dominic Holland aff001; Oleksandr Frei aff003; Rahul Desikan aff004; Chun-Chieh Fan aff001; Alexey A. Shadrin aff003; Olav B. Smeland aff003; V. S. Sundar aff001; Paul Thompson aff008; Ole A. Andreassen aff003; Anders M. Dale aff001
Působiště autorů:
Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
aff001; Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
aff002; NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
aff003; Department of Radiology, University of California, San Francisco, San Francisco, California, United States of America
aff004; Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
aff005; Department of Cognitive Sciences, University of California at San Diego, La Jolla, California, United States of America
aff006; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
aff007; Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
aff008; Department of Psychiatry, University of California, San Diego, La Jolla, California, United States of America
aff009
Vyšlo v časopise:
Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008612
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008612
Souhrn
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10−5 to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
Klíčová slova:
Alzheimer's disease – Amyotrophic lateral sclerosis – Fourier analysis – Genome-wide association studies – Heredity – Heterozygosity – Molecular genetics – Schizophrenia
Zdroje
1. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. The American Journal of Human Genetics. 2012;90(1):7–24. doi: 10.1016/j.ajhg.2011.11.029 22243964
2. Stahl EA, Wegmann D, Trynka G, Gutierrez-Achury J, Do R, Voight BF, et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nature genetics. 2012;44(5):483–489. doi: 10.1038/ng.2232 22446960
3. Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AA, Lee SH, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature genetics. 2015;. doi: 10.1038/ng.3390
4. So HC, Li M, Sham PC. Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study. Genetic epidemiology. 2011;35(6):447–456. doi: 10.1002/gepi.20593 21618601
5. Speed D, Hemani G, Johnson MR, Balding DJ. Improved heritability estimation from genome-wide SNPs. The American Journal of Human Genetics. 2012;91(6):1011–1021. doi: 10.1016/j.ajhg.2012.10.010 23217325
6. Lee SH, Wray NR, Goddard ME, Visscher PM. Estimating missing heritability for disease from genome-wide association studies. The American Journal of Human Genetics. 2011;88(3):294–305. doi: 10.1016/j.ajhg.2011.02.002 21376301
7. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics. 2011;88(1):76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468
8. Kumar SK, Feldman MW, Rehkopf DH, Tuljapurkar S. Limitations of GCTA as a solution to the missing heritability problem. Proceedings of the National Academy of Sciences. 2016;113(1):E61–E70. doi: 10.1073/pnas.1520109113
9. Palla L, Dudbridge F. A Fast Method that Uses Polygenic Scores to Estimate the Variance Explained by Genome-wide Marker Panels and the Proportion of Variants Affecting a Trait. The American Journal of Human Genetics. 2015;97(2):250–259. doi: 10.1016/j.ajhg.2015.06.005 26189816
10. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nature Reviews Genetics. 2010;11(7):459–463. doi: 10.1038/nrg2813 20548291
11. Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ, et al. Genomic inflation factors under polygenic inheritance. European Journal of Human Genetics. 2011;19(7):807–812. doi: 10.1038/ejhg.2011.39 21407268
12. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics. 2015;47(3):291–295. doi: 10.1038/ng.3211 25642630
13. Kang HM, Sul JH, Zaitlen NA, Kong Sy, Freimer NB, Sabatti C, et al. Variance component model to account for sample structure in genome-wide association studies. Nature genetics. 2010;42(4):348–354. doi: 10.1038/ng.548 20208533
14. Zhang Y, Qi G, Park JH, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nature genetics. 2018;50(9):1318. doi: 10.1038/s41588-018-0193-x 30104760
15. Zeng J, Vlaming R, Wu Y, Robinson MR, Lloyd-Jones LR, Yengo L, et al. Signatures of negative selection in the genetic architecture of human complex traits. Nature genetics. 2018;50(5):746. doi: 10.1038/s41588-018-0101-4 29662166
16. Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nature Reviews Genetics. 2016;. doi: 10.1038/nrg.2016.142 27840428
17. Witte JS, Visscher PM, Wray NR. The contribution of genetic variants to disease depends on the ruler. Nature Reviews Genetics. 2014;15(11):765–776. doi: 10.1038/nrg3786 25223781
18. Holland D, Wang Y, Thompson WK, Schork A, Chen CH, Lo MT, et al. Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics. Front Genet. 2016;7:15. doi: 10.3389/fgene.2016.00015 26909100
19. Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999 Dec;55(4):997–1004. doi: 10.1111/j.0006-341x.1999.00997.x 11315092
20. Holland D, Fan CC, Frei O, Shadrin AA, Smeland OB, Sundar VS, et al. Estimating degree of polygenicity, causal effect size variance, and confounding bias in GWAS summary statistics. bioRxiv. 2017; Available from: https://www.biorxiv.org/content/early/2017/05/24/133132.
21. Thompson WK, Wang Y, Schork A, Zuber V, Andreassen OA, Dale AM, et al. An empirical Bayes method for estimating the distribution of effects in genome-wide association studies. PLoS Genetics. 2015;[in press].
22. Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM, et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature genetics. 2011;43(6):519–525. doi: 10.1038/ng.823 21552263
23. Gelman A, Stern HS, Carlin JB, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. Chapman and Hall/CRC; 2013.
24. Laird NM, Lange C. The fundamentals of modern statistical genetics. Springer Science & Business Media; 2010.
25. Wu C, DeWan A, Hoh J, Wang Z. A comparison of association methods correcting for population stratification in case–control studies. Annals of human genetics. 2011;75(3):418–427. doi: 10.1111/j.1469-1809.2010.00639.x 21281271
26. Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E. Identifying causal variants at loci with multiple signals of association. Genetics. 2014;198(2):497–508. doi: 10.1534/genetics.114.167908 25104515
27. Holland D. GWAS-Causal-Effects-Model; 2019. https://github.com/dominicholland/GWAS-Causal-Effects-Model.
28. Consortium GP, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393
29. Consortium GP, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. doi: 10.1038/nature11632
30. Sveinbjornsson G, Albrechtsen A, Zink F, Gudjonsson SA, Oddson A, Másson G, et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nature genetics. 2016;. doi: 10.1038/ng.3507
31. Li N, Stephens M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics. 2003;165(4):2213–2233. 14704198
32. Spencer CC, Su Z, Donnelly P, Marchini J. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet. 2009;5(5):e1000477. doi: 10.1371/journal.pgen.1000477 19492015
33. Su Z, Marchini J, Donnelly P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics. 2011;27(16):2304–2305. doi: 10.1093/bioinformatics/btr341 21653516
34. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics. 2018;50(5):668. doi: 10.1038/s41588-018-0090-3 29700475
35. Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. Nature genetics. 2019;p. 1. doi: 10.1038/s41588-019-0397-8
36. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014 Jul;511(7510):421–427. doi: 10.1038/nature13595
37. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nature genetics. 2015;47(10):1121. doi: 10.1038/ng.3396 26343387
38. de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nature genetics. 2017;49(2):256. doi: 10.1038/ng.3760 28067908
39. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature genetics. 2013;45(12):1452–1458. doi: 10.1038/ng.2802 24162737
40. Jansen I, Savage J, Watanabe K, Bryois J, Williams D, Steinberg S, et al. Genetic meta-analysis identifies 10 novel loci and functional pathways for Alzheimer’s disease risk. bioRxiv. 2018;p. 258533.
41. Van Rheenen W, Shatunov A, Dekker AM, McLaughlin RL, Diekstra FP, Pulit SL, et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nature genetics. 2016;48(9):1043. doi: 10.1038/ng.3622 27455348
42. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533(7604):539–542. doi: 10.1038/nature17671 27225129
43. Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JR, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature genetics. 2017;49(7):1107. doi: 10.1038/ng.3869 28530673
44. Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, De Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature genetics. 2018;50(7):912. doi: 10.1038/s41588-018-0152-6 29942086
45. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197. doi: 10.1038/nature14177 25673413
46. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature genetics. 2014;46(11):1173–1186. doi: 10.1038/ng.3097 25282103
47. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivières S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;. doi: 10.1038/nature14101 25607358
48. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nature genetics. 2013;45(11):1274. doi: 10.1038/ng.2797 24097068
49. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in 700,000 individuals of European ancestry. bioRxiv. 2018;p. 274654.
50. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019;8:e39702. doi: 10.7554/eLife.39702 30895926
51. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife. 2019;8:e39725. doi: 10.7554/eLife.39725 30895923
52. Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genetic epidemiology. 2008;32(4):381–385. doi: 10.1002/gepi.20303 18348202
53. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews genetics. 2008;9(5):356–369. doi: 10.1038/nrg2344 18398418
54. Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413. doi: 10.1093/biomet/26.4.404
55. Falconer DS. The inheritance of liability to certain diseases, estimated from the incidence among relatives. Annals of human genetics. 1965;29(1):51–76. doi: 10.1111/j.1469-1809.1965.tb00500.x
56. Dempster ER, Lerner IM. Heritability of threshold characters. Genetics. 1950;35(2):212. 17247344
57. NIMH. Prevalence of Major Depressive Episode Among Adults; 2016. (accessed December 27, 2018). Available from: https://www.nimh.nih.gov/health/statistics/major-depression.shtml.
58. Merikangas KR, Jin R, He JP, Kessler RC, Lee S, Sampson NA, et al. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Archives of general psychiatry. 2011;68(3):241–251. doi: 10.1001/archgenpsychiatry.2011.12 21383262
59. Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ, Consortium U, et al. Reevaluation of SNP heritability in complex human traits. Nature genetics. 2017;49(7):986. doi: 10.1038/ng.3865 28530675
60. Sanchis-Gomar F, Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Annals of translational medicine. 2016;4(13). doi: 10.21037/atm.2016.06.33
61. Burisch J, Jess T, Martinato M, Lakatos PL, ECCO-EpiCom. The burden of inflammatory bowel disease in Europe. Journal of Crohn’s and Colitis. 2013;7(4):322–337. doi: 10.1016/j.crohns.2013.01.010 23395397
62. Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology. 2007;29(1-2):125–132. doi: 10.1159/000109998 17975326
63. Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia. 2018;14(3):367–429.
64. Mehta P, Kaye W, Raymond Jea. Prevalence of Amyotrophic Lateral Sclerosis 2014 United States. MMWR Morb Mortal Wkly Rep. 2018;67:216–218. doi: 10.15585/mmwr.mm6707a3 29470458
65. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature genetics. 2019;p. 1.
66. Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–752. doi: 10.1038/nature08185 19571811
67. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet. 2013;382(9904):1575–1586. doi: 10.1016/S0140-6736(13)61611-6
68. Kinney DK, Teixeira P, Hsu D, Napoleon SC, Crowley DJ, Miller A, et al. Relation of schizophrenia prevalence to latitude, climate, fish consumption, infant mortality, and skin color: a role for prenatal vitamin d deficiency and infections? Schizophrenia bulletin. 2009;p. sbp023.
69. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010 Jul;42(7):565–569. doi: 10.1038/ng.608 20562875
70. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature genetics. 2018;50(8):1112. doi: 10.1038/s41588-018-0147-3 30038396
71. Balendra R, Isaacs AM. C9orf72-mediated ALS and FTD: multiple pathways to disease. Nature Reviews Neurology. 2018;p. 1.
72. Fomin V, Richard P, Hoque M, Li C, Gu Z, Fissore-O’Leary M, et al. The C9ORF72 gene, implicated in amyotrophic lateral sclerosis and frontotemporal dementia, encodes a protein that functions in control of endothelin and glutamate signaling. Molecular and cellular biology. 2018;38(22):e00155–18. doi: 10.1128/MCB.00155-18 30150298
73. Loh PR, Bhatia G, Gusev A, Finucane HK, Bulik-Sullivan BK, Pollack SJ, et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nature genetics. 2015;. doi: 10.1038/ng.3431
74. Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. science. 2013;340(6139):1467–1471. doi: 10.1126/science.1235488 23722424
75. Cesarini D, Visscher PM. Genetics and educational attainment. npj Science of Learning. 2017;2(1):4. doi: 10.1038/s41539-017-0005-6 30631451
76. Plomin R, von Stumm S. The new genetics of intelligence. Nature Reviews Genetics. 2018;. doi: 10.1038/nrg.2017.104 29335645
77. Speed D, Balding DJ. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nature genetics. 2019;51(2):277. doi: 10.1038/s41588-018-0279-5 30510236
78. Ridge PG, Hoyt KB, Boehme K, Mukherjee S, Crane PK, Haines JL, et al. Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiology of aging. 2016;41:200–e13. doi: 10.1016/j.neurobiolaging.2016.02.024 27036079
79. Ridge PG, Mukherjee S, Crane PK, Kauwe JS, et al. Alzheimer’s disease: analyzing the missing heritability. PloS One. 2013;8(11):e79771. doi: 10.1371/journal.pone.0079771 24244562
80. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, et al. Role of genes and environments for explaining Alzheimer disease. Archives of general psychiatry. 2006;63(2):168–174. doi: 10.1001/archpsyc.63.2.168 16461860
81. Evans LM, Tahmasbi R, Vrieze SI, Abecasis GR, Das S, Gazal S, et al. Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nature genetics. 2018;50(5):737. doi: 10.1038/s41588-018-0108-x 29700474
82. Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. Concepts, estimation and interpretation of SNP-based heritability. Nature genetics. 2017;49(9):1304. doi: 10.1038/ng.3941 28854176
83. Zhou X, Carbonetto P, Stephens M. Polygenic modeling with bayesian sparse linear mixed models. PLoS genetics. 2013;9(2):e1003264. doi: 10.1371/journal.pgen.1003264 23408905
84. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33(2):272–279. doi: 10.1093/bioinformatics/btw613 27663502
85. Holland D, Desikan RS, Dale AM, McEvoy LK, Initiative ADN, et al. Rates of decline in Alzheimer disease decrease with age. PloS one. 2012;7(8):e42325. doi: 10.1371/journal.pone.0042325 22876315
86. Desikan RS, Fan CC, Wang Y, Schork AJ, Cabral HJ, Cupples LA, et al. Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS medicine. 2017;14(3):e1002258. doi: 10.1371/journal.pmed.1002258 28323831
87. Golan D, Lander ES, Rosset S. Measuring missing heritability: inferring the contribution of common variants. Proceedings of the National Academy of Sciences. 2014;111(49):E5272–E5281. doi: 10.1073/pnas.1419064111
88. Lee SH, DeCandia TR, Ripke S, Yang J, Sullivan PF, Goddard ME, et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature genetics. 2012;44(3):247–250. doi: 10.1038/ng.1108 22344220
89. Branigan AR, McCallum KJ, Freese J. Variation in the heritability of educational attainment: An international meta-analysis. Social Forces. 2013;p. 109–140. doi: 10.1093/sf/sot076
90. Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017;169(7):1177–1186. doi: 10.1016/j.cell.2017.05.038 28622505
91. Zhu X, Stephens M. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. The annals of applied statistics. 2017;11(3):1561. doi: 10.1214/17-aoas1046 29399241
92. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466(7307):707. doi: 10.1038/nature09270 20686565
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