Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

Autoři: Wei Cheng aff001;  Sohini Ramachandran aff001;  Lorin Crawford aff002
Působiště autorů: Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America aff001;  Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America aff002;  Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America aff003;  Center for Statistical Sciences, Brown University, Providence, Rhode Island, United States of America aff004
Vyšlo v časopise: Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits. PLoS Genet 16(6): e32767. doi:10.1371/journal.pgen.1008855
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008855


Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.

Klíčová slova:

Genome-wide association studies – Genomics statistics – Heredity – Molecular genetics – Quantitative traits – Simulation and modeling – Magma – Complex traits


1. Visscher PM, Hill WG, Wray NR. Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet. 2008;9(4):255–266. doi: 10.1038/nrg2322 18319743

2. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. Available from: 19812666

3. Visscher PM, Brown MA, McCarthy MI, Yang J. Five Years of GWAS Discovery. Am J Hum Genet. 2012;90(1):7–24. Available from: 22243964

4. 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

5. Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM. Common disease is more complex than implied by the core gene omnigenic model. Cell. 2018;173(7):1573–1580. Available from: 29906445

6. 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;42(7):565–569. doi: 10.1038/ng.608 20562875

7. Liu JZ, Mcrae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. 2010;87(1):139–145. doi: 10.1016/j.ajhg.2010.06.009 20598278

8. Carbonetto P, Stephens M. Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn’s disease. PLoS Genet. 2013;9(10):e1003770–. Available from: 24098138

9. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X. Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. 2013;92(6):841–853. Available from: 23684009

10. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput Biol. 2015;11(4):e1004219–. Available from: 25885710

11. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLOS Comput Biol. 2016;12(1):e1004714–. Available from: 26808494

12. Nakka P, Raphael BJ, Ramachandran S. Gene and network analysis of common variants reveals novel associations in multiple complex diseases. Genetics. 2016;204(2):783–798. Available from: 27489002

13. Wang M, Huang J, Liu Y, Ma L, Potash JB, Han S. COMBAT: a combined association test for genes using summary statistics. Genetics. 2017;207(3):883–891. doi: 10.1534/genetics.117.300257 28878002

14. Zhu X, Stephens M. Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes. Nat Comm. 2018;9(1):4361. doi: 10.1038/s41467-018-06805-x

15. Zhou X, Carbonetto P, Stephens M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 2013;9(2):e1003264. doi: 10.1371/journal.pgen.1003264 23408905

16. Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. 2014;46(2):100–106. doi: 10.1038/ng.2876 24473328

17. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, of the Psychiatric Genomics Consortium SWG, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–295. Available from: 25642630

18. 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. Nat Genet. 2018;50(9):1318–1326. doi: 10.1038/s41588-018-0193-x 30104760

19. 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. Available from: 26909100

20. Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, et al. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010;86(6):929–942. doi: 10.1016/j.ajhg.2010.05.002 20560208

21. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–209. Available from: 30305743

22. Stephens M. False discovery rates: a new deal. Biostatistics. 2017;18(2):275–294. Available from: 27756721

23. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58(1):267–288.

24. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67(2):301–320. doi: 10.1111/j.1467-9868.2005.00503.x

25. Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67. doi: 10.1080/00401706.1970.10488634

26. Imhof JP. Computing the distribution of quadratic forms in normal variables. Biometrika. 1961;48(3/4):419–426. Available from:

27. Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005;33(Database issue):D501–4. doi: 10.1093/nar/gki025 15608248

28. Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91(2):224–237. Available from: 22863193

29. Zhu X, Stephens M. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann Appl Stat. 2017;11(3):1561–1592. Available from: 29399241

30. Barbieri MM, Berger JO. Optimal predictive model selection. Ann Statist. 2004;32(3):870–897. Available from:

31. Zaitlen N, Kraft P, Patterson N, Pasaniuc B, Bhatia G, Pollack S, et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 2013;9(5):e1003520–. Available from: 23737753

32. 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. Nat Genet. 2014;46(11):1173–1186. doi: 10.1038/ng.3097 25282103

33. Heckerman D, Gurdasani D, Kadie C, Pomilla C, Carstensen T, Martin H, et al. Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proc Natl Acad Sci U S A. 2016;113(27):7377–7382. Available from: 27382152

34. Shi H, Kichaev G, Pasaniuc B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am J Hum Genet. 2016;99(1):139–153. Available from: 27346688

35. Xia C, Amador C, Huffman J, Trochet H, Campbell A, Porteous D, et al. Pedigree- and SNP-associated genetics and recent environment are the major contributors to anthropometric and cardiometabolic trait variation. PLoS Genet. 2016;12(2):e1005804–. Available from: 26836320

36. Ge T, Chen CY, Neale BM, Sabuncu MR, Smoller JW. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet. 2017;13(4):e1006711–. Available from: 28388634

37. Speed D, Cai N, The UCLEB Consortium, Johnson MR, Nejentsev S, Balding DJ. Reevaluation of SNP heritability in complex human traits. Nat Genet. 2017;49:986–992. Available from: 28530675

38. Marouli E, Graff M, Medina-Gomez C, Lo KS, Wood AR, Kjaer TR, et al. Rare and low-frequency coding variants alter human adult height. Nature. 2017;542(7640):186–190. doi: 10.1038/nature21039 28146470

39. Wainschtein P, Jain DP, Yengo L, Zheng Z, TOPMed Anthropometry Working Group, Trans-Omics for Precision Medicine Consortium, et al. Recovery of trait heritability from whole genome sequence data. bioRxiv. 2019;p. 588020. Available from:

40. Goldstein DB. Common genetic variation and human traits. N Engl J Med. 2009;360(17):1696–1698. doi: 10.1056/NEJMp0806284 19369660

41. Lello L, Avery SG, Tellier L, Vazquez AI, de los Campos G, Hsu SDH. Accurate Genomic Prediction of Human Height. Genetics. 2018;210(2):477–497. Available from: 30150289

42. Vattikuti S, Guo J, Chow CC. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 2012;8(3):e1002637. doi: 10.1371/journal.pgen.1002637 22479213

43. 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. Nat Genet. 2015;47(10):1114. doi: 10.1038/ng.3390 26323059

44. Robinson MR, English G, Moser G, Lloyd-Jones LR, Triplett MA, Zhu Z, et al. Genotype–covariate interaction effects and the heritability of adult body mass index. Nat Genet. 2017;49(8):1174. doi: 10.1038/ng.3912 28692066

45. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature. 2018;555:210–215. Available from: 29489753

46. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013;14(1):128. Available from:

47. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–330. Available from: 25693563

48. Eicher JD, Chami N, Kacprowski T, Nomura A, Chen MH, Yanek LR, et al. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals. Am J Hum Genet. 2016;99(1):40–55. doi: 10.1016/j.ajhg.2016.05.005 27346686

49. Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat Genet. 2016;48(11):1303–1312. Available from: 27668658

50. Finberg KE, Heeney MM, Campagna DR, Aydinok Y, Pearson HA, Hartman KR, et al. Mutations in TMPRSS6 cause iron-refractory iron deficiency anemia (IRIDA). Nat Genet. 2008;40(5):569–571. Available from: 18408718

51. Andrews NC. Genes determining blood cell traits. Nat Genet. 2009;41:1161–1162. Available from: 19862006

52. Benyamin B, Ferreira MAR, Willemsen G, Gordon S, Middelberg RPS, McEvoy BP, et al. Common variants in TMPRSS6 are associated with iron status and erythrocyte volume. Nat Genet. 2009;41(11):1173–1175. doi: 10.1038/ng.456 19820699

53. Chambers JC, Zhang W, Li Y, Sehmi J, Wass MN, Zabaneh D, et al. Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels. Nat Genet. 2009;41(11):1170–1172. doi: 10.1038/ng.462 19820698

54. Soranzo N, Spector TD, Mangino M, Kühnel B, Rendon A, Teumer A, et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet. 2009;41(11):1182–1190. Available from: 19820697

55. Ganesh SK, Zakai NA, van Rooij FJA, Soranzo N, Smith AV, Nalls MA, et al. Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. Nat Genet. 2009;41(11):1191–1198. doi: 10.1038/ng.466 19862010

56. Li J, Glessner JT, Zhang H, Hou C, Wei Z, Bradfield JP, et al. GWAS of blood cell traits identifies novel associated loci and epistatic interactions in Caucasian and African-American children. Hum Mol Genet. 2013;22(7):1457–1464. Available from: 23263863

57. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167(5):1415–1429. Available from: 27863252

58. Qayyum R, Snively BM, Ziv E, Nalls MA, Liu Y, Tang W, et al. A meta-analysis and genome-wide association study of platelet count and mean platelet volume in african americans. PLoS Genet. 2012;8(3):e1002491. doi: 10.1371/journal.pgen.1002491 22423221

59. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–W97. Available from: 27141961

60. Lentaigne C, Freson K, Laffan MA, Turro E, Ouwehand WH, Consortium BB, et al. Inherited platelet disorders: toward DNA-based diagnosis. Blood. 2016;127(23):2814–2823. Available from: 27095789

61. Mousas A, Ntritsos G, Chen MH, Song C, Huffman JE, Tzoulaki I, et al. Rare coding variants pinpoint genes that control human hematological traits. PLoS Genet. 2017;13(8):e1006925–. Available from: 28787443

62. Gibson WT, Hood RL, Zhan SH, Bulman DE, Fejes AP, Moore R, et al. Mutations in EZH2 cause Weaver syndrome. Am J Hum Genet. 2012;90(1):110–118. Available from: 22177091

63. Minczuk M, He J, Duch AM, Ettema TJ, Chlebowski A, Dzionek K, et al. TEFM (c17orf42) is necessary for transcription of human mtDNA. Nucleic Acids Res. 2011;39(10):4284–4299. Available from: 21278163

64. Carel JC, Lahlou N, Roger M, Chaussain JL. Precocious puberty and statural growth. Hum Reprod. 2004;10(2):135–147. Available from:

65. Gong J, Schumacher F, Lim U, Hindorff LA, Haessler J, Buyske S, et al. Fine Mapping and Identification of BMI Loci in African Americans. Am J Hum Genet. 2013;93(4):661–671. doi: 10.1016/j.ajhg.2013.08.012 24094743

66. 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):197–206. Available from: 25673413

67. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, et al. High-throughput discovery of novel developmental phenotypes. Nature. 2016;537:508–514. Available from: 27626380

68. Baranski TJ, Kraja AT, Fink JL, Feitosa M, Lenzini PA, Borecki IB, et al. A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses. PLoS Genet. 2018;14(4):e1007222–. Available from: 29608557

69. Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, et al. GeneCards Version 3: the human gene integrator. Database. 2010;2010. Available from: 20689021

70. Vuillaume ML, Naudion S, Banneau G, Diene G, Cartault A, Cailley D, et al. New candidate loci identified by array-CGH in a cohort of 100 children presenting with syndromic obesity. Am J Med Genet. 2014;164(8):1965–1975. Available from:

71. Wheeler E, Leong A, Liu CT, Hivert MF, Strawbridge RJ, Podmore C, et al. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med. 2017;14(9):e1002383. Available from: 28898252

72. Linder S, Nelson D, Weiss M, Aepfelbacher M. Wiskott-Aldrich syndrome protein regulates podosomes in primary human macrophages. Proc Natl Acad Sci U S A. 1999;96(17):9648–9653. Available from: 10449748

73. Steele BM, Harper MT, Macaulay IC, Morrell CN, Perez-Tamayo A, Foy M, et al. Canonical Wnt signaling negatively regulates platelet function. Proc Natl Acad Sci U S A. 2009;106(47):19836–19841. doi: 10.1073/pnas.0906268106 19901330

74. Macaulay IC, Thon JN, Tijssen MR, Steele BM, MacDonald BT, Meade G, et al. Canonical Wnt signaling in megakaryocytes regulates proplatelet formation. Blood. 2013;121(1):188–196. Available from: 23160460

75. Stocks T, Angquist L, Hager J, Charon C, Holst C, Martinez JA, et al. TFAP2B-dietary protein and glycemic index interactions and weight maintenance after weight loss in the DiOGenes trial. Hum Hered. 2013;75(2-4):213–219. doi: 10.1159/000353591

76. Xiang J, Yang S, Xin N, Gaertig MA, Reeves RH, Li S, et al. DYRK1A regulates Hap1–Dcaf7/WDR68 binding with implication for delayed growth in down syndrome. Proc Natl Acad Sci U S A. 2017;114(7):E1224–E1233. Available from: 28137862

77. Smith CM, Finger JH, Hayamizu TF, McCright IJ, Eppig JT, Kadin JA, et al. The mouse gene expression database (GXD): 2007 update. Nucleic Acids Res. 2006;35:D618–D623. Available from: 17130151

78. Bult CJ, Krupke DM, Begley DA, Richardson JE, Neuhauser SB, Sundberg JP, et al. Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res. 2014;43(D1):D818–D824. Available from: 25332399

79. Smith CL, Blake JA, Kadin JA, Richardson JE, Bult CJ, Group MGD. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 2017;46(D1):D836–D842. Available from:

80. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82–93. doi: 10.1016/j.ajhg.2011.05.029 21737059

81. Lee S, Abecasis GR, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95(1):5–23. Available from: 24995866

82. Zuk O, Schaffner SF, Samocha K, Do R, Hechter E, Kathiresan S, et al. Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci U S A. 2014;111(4):E455–E464. Available from: 24443550

83. Gazal S, Loh PR, Finucane HK, Ganna A, Schoech A, Sunyaev S, et al. Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet. 2018;50(11):1600–1607. Available from: 30297966

84. Wojcik G, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, et al. The PAGE Study: how genetic diversity improves our understanding of the architecture of complex traits. bioRxiv. 2018;p. 188094. Available from:

85. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–591. Available from: 30926966

86. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–213. Available from: 29022597

87. Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Comm. 2018;9(1):918. Available from:

88. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Comm. 2018;9(1):2941. Available from:

89. Smemo S, Tena JJ, Kim KH, Gamazon ER, Sakabe NJ, Gomez-Marin C, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 2014;507(7492):371–375. doi: 10.1038/nature13138 24646999

90. Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W, Haugen C, et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N Engl J Med. 2015;373(10):895–907. Available from: 26287746

91. Lloyd-Jones LR, Zeng J, Sidorenko J, Yengo L, Moser G, Kemper KE, et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Comm. 2019;10(1):5086. Available from:

92. Zeng P, Zhou X. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat Comm. 2017;8:456. Available from:

93. Lee SH, Wray NR, Goddard ME, Visscher PM. Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet. 2011;88(3):294–305. Available from: 21376301

94. Golan D, Lander ES, Rosset S. Measuring missing heritability: inferring the contribution of common variants. Proc Natl Acad Sci U S A. 2014;111(49):E5272–E5281. Available from: 25422463

95. Weissbrod O, Lippert C, Geiger D, Heckerman D. Accurate liability estimation improves power in ascertained case-control studies. Nat Meth. 2015;12:332–334. Available from:

96. 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. Available from: 25104515

97. Hormozdiari F, van de Bunt M, Segrè AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99(6):1245–1260. Available from: 27866706

98. Wold S, Ruhe A, Wold H, Dunn W III. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Comput. 1984;5(3):735–743. doi: 10.1137/0905052

99. Carvalho CM, Polson NG, Scott JG. The horseshoe estimator for sparse signals. Biometrika. 2010;97(2):465–480. doi: 10.1093/biomet/asq017

100. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B Stat Methodol. 1977;39(1):1–22.

101. Benaglia T, Chauveau D, Hunter D, Young D. Mixtools: an R package for analyzing finite mixture models. J Stat Softw. 2009;32(6):1–29. doi: 10.18637/jss.v032.i06

102. McLachlan GJ, Lee SX, Rathnayake SI. Finite mixture models. Annual Review of Statistics and Its Application. 2019;6(1):355–378. Available from:

103. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 2016;8(1):289–317. Available from: 27818791

104. Schwarz G. Estimating the Dimension of a Model. Ann Statist. 1978;6(2):461–464. Available from:

105. Zhou X. A unified framework for variance component estimation with summary statistics in genome-wide association studies. Ann Appl Stat. 2017;11(4):2027–2051. Available from: 29515717

106. Crawford L, Zeng P, Mukherjee S, Zhou X. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genet. 2017;13(7):e1006869. Available from: 28746338

107. Chen Z, Lin T, Wang K. A powerful variant-set association test based on chi-square distribution. Genetics. 2017;207(3):903–910. doi: 10.1534/genetics.117.300287 28912342

108. Zhongxue C, Yan L, Tong L, Qingzhong L, Kai W. Gene-based genetic association test with adaptive optimal weights. Genet Epidemiol. 2017;42(1):95–103. Available from:

109. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1. doi: 10.18637/jss.v033.i01 20808728

110. Zeng Y, Breheny P. The biglasso package: a memory-and computation-efficient solver for lasso model fitting with big data in R. arXiv. 2017;p. 1701.05936.

111. Duchesne P, Lafaye De Micheaux P. Computing the distribution of quadratic forms: Further comparisons between the Liu–Tang–Zhang approximation and exact methods. Comput Stat Data Anal. 2010;54(4):858–862. Available from:

112. Acikgoz N, Karincaoglu Y, Ermis N, Yagmur J, Atas H, Kurtoglu E, et al. Increased mean platelet volume in Behcet’s disease with thrombotic tendency. Tohoku J Exp Med. 2010;221(2):119–123. doi: 10.1620/tjem.221.119 20484842

113. Canpolat F, Akpinar H, Eskioglu F. Mean platelet volume in psoriasis and psoriatic arthritis. Clin Rheumatol. 2010;29(3):325–328. doi: 10.1007/s10067-009-1323-8 20012663

114. Faeh D, Braun J, Bopp M. Body mass index vs cholesterol in cardiovascular disease risk prediction models. JAMA Intern Med. 2012;172(22):1766–1768. doi: 10.1001/2013.jamainternmed.327

115. Kurth T, Gaziano JM, Berger K, Kase CS, Rexrode KM, Cook NR, et al. Body mass index and the risk of stroke in men. JAMA Intern Med. 2002;162(22):2557–2562. doi: 10.1001/archinte.162.22.2557

116. Speakman JR, Loos RJF, O’Rahilly S, Hirschhorn JN, Allison DB. GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int J Obes (Lond). 2018;42(8):1524–1531. doi: 10.1038/s41366-018-0147-5

117. Garner C, Tatu T, Reittie J, Littlewood T, Darley J, Cervino S, et al. Genetic influences on F cells and other hematologic variables: a twin heritability study. Blood. 2000;95(1):342–346. doi: 10.1182/blood.V95.1.342.001k33_342_346 10607722

118. Van’t Erve TJ, Wagner BA, Martin SM, Knudson CM, Blendowski R, Keaton M, et al. The heritability of hemolysis in stored human red blood cells. Transfusion. 2015;55(6):1178–1185. doi: 10.1111/trf.12992

119. Guerrero JA, Rivera J, Quiroga T, Martinez-Perez A, Antón AI, Martínez C, et al. Novel loci involved in platelet function and platelet count identified by a genome-wide study performed in children. Haematologica. 2011;96(9):1335–1343. Available from: 21546496

120. Justice AE, Winkler TW, Feitosa MF, Graff M, Fisher VA, Young K, et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Comm. 2017;8:14977 EP –. Available from:

121. Loh PR, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet. 2018;50(7):906–908. Available from: 29892013

122. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–196. Available from: 25673412

123. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA. 2017;317(6):626–634. Available from: 28196256

Genetika Reprodukční medicína

Článek vyšel v časopise

PLOS Genetics

2020 Číslo 6

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…

Kurzy Doporučená témata Časopisy
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.


Nemáte účet?  Registrujte se

VIRTUÁLNÍ ČEKÁRNA ČR Jste praktický lékař nebo pediatr? Zapojte se! Jste praktik nebo pediatr? Zapojte se!