Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits

American Journal of Human Genetics

Volumn 100, Issue 3, 2017, Pages 473-487

  Mancuso, Nicholas ^a   Shi, Huwenbo ^b   Goddard, Pagé ^b   Kichaev, Gleb ^b   Gusev, Alexander ^c,d   Pasaniuc, Bogdan ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^d BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

complex disease; complex trait; expression quantitative trait loci (eQTLs); genetic correlation; genetic covariance; genome wide association study (GWAS); susceptibility gene; transcriptome wide association study (TWAS)

Indexed keywords

AFF3 PROTEIN; CCDC65 PROTEIN; CDK5RAP3 PROTEIN; CERCAM PROTEIN; CHOLESTEROL; CISD1 PROTEIN; COG6 PROTEIN; DHRS11 PROTEIN; FATTY ACID TRANSPORTER 4; GGNBP2 PROTEIN; GLUCOSE; HEMOGLOBIN; HEMOGLOBIN A1C; HIGH DENSITY LIPOPROTEIN; INO80E PROTEIN; INSULIN; LOW DENSITY LIPOPROTEIN; NUCKS1 PROTEIN; PEPTIDES AND PROTEINS; PMS2P5 PROTEIN; RAB7L1 PROTEIN; RNF123 PROTEIN; SLC26A9 PROTEIN; STAG3L2 PROTEIN; TMEM180 PROTEIN; TRIACYLGLYCEROL; TUBA1C PROTEIN; TYROSINE 3 MONOOXYGENASE; UNCLASSIFIED DRUG; URM1 PROTEIN; WNT3 PROTEIN; TRANSCRIPTOME;

AGE; ARTICLE; BODY HEIGHT; BODY MASS; BONE DENSITY; CHOLESTEROL BLOOD LEVEL; COLLEGE; CROHN DISEASE; EDUCATIONAL STATUS; ERYTHROCYTE COUNT; FEMORAL NECK; FOREARM; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION; GENETIC CORRELATION; GENETIC SUSCEPTIBILITY; GENETIC TRAIT; GENETIC VARIABILITY; GENOME-WIDE ASSOCIATION STUDY; HEMATOCRIT; HEREDITY; HOMEOSTASIS MODEL ASSESSMENT; HUMAN; HUMAN TISSUE; INFLAMMATORY BOWEL DISEASE; LUMBAR SPINE; MAJOR CLINICAL STUDY; MEAN CORPUSCULAR HEMOGLOBIN; MEAN CORPUSCULAR VOLUME; MENARCHE; NON INSULIN DEPENDENT DIABETES MELLITUS; OVERLAPPING GENE; PHENOTYPE; PRIORITY JOURNAL; RHEUMATOID ARTHRITIS; SCHIZOPHRENIA; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; THROMBOCYTE COUNT; TRANSCRIPTOMICS; ULCERATIVE COLITIS; BLOOD; GENE LINKAGE DISEQUILIBRIUM; GENETIC PREDISPOSITION; QUANTITATIVE TRAIT LOCUS; SEQUENCE ANALYSIS; THEORETICAL MODEL;

BODY MASS INDEX; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LINKAGE DISEQUILIBRIUM; LIPOPROTEINS, LDL; MODELS, THEORETICAL; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; SEQUENCE ANALYSIS, RNA; TRANSCRIPTOME; TRIGLYCERIDES;

EID: 85013638666     PISSN: 00029297     EISSN: 15376605     Source Type: Journal    
DOI: 10.1016/j.ajhg.2017.01.031     Document Type: Article

References (63)

1. 1 Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., Parkinson, H., The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42 (2014), D1001–D1006.
2. 2 Claussnitzer, M., Dankel, S.N., Kim, K.-H., Quon, G., Meuleman, W., Haugen, C., Glunk, V., Sousa, I.S., Beaudry, J.L., Puviindran, V., et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N. Engl. J. Med. 373 (2015), 895–907.
3. 3 Sawcer, S., Hellenthal, G., Pirinen, M., Spencer, C.C., Patsopoulos, N.A., Moutsianas, L., Dilthey, A., Su, Z., Freeman, C., Hunt, S.E., et al. International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium 2. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476 (2011), 214–219.
4. 4 Nicolae, D.L., Gamazon, E., Zhang, W., Duan, S., Dolan, M.E., Cox, N.J., Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet., 6, 2010, e1000888.
5. 5 Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A.S., Zink, F., Zhu, J., Carlson, S., Helgason, A., Walters, G.B., Gunnarsdottir, S., et al. Genetics of gene expression and its effect on disease. Nature 452 (2008), 423–428.
6. 6 Nica, A.C., Montgomery, S.B., Dimas, A.S., Stranger, B.E., Beazley, C., Barroso, I., Dermitzakis, E.T., Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet., 6, 2010, e1000895.
7. 7 Albert, F.W., Kruglyak, L., The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16 (2015), 197–212.
8. 8 Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al., GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45 (2013), 580–585.
9. 9 Lappalainen, T., Sammeth, M., Friedländer, M.R., ‘t Hoen, P.A., Monlong, J., Rivas, M.A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P.G., et al., Geuvadis Consortium. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501 (2013), 506–511.
10. 10 Gamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquino-Michaels, K., Carroll, R.J., Eyler, A.E., Denny, J.C., Nicolae, D.L., Cox, N.J., Im, H.K., GTEx Consortium. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47 (2015), 1091–1098.
11. 11 Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W., Jansen, R., de Geus, E.J., Boomsma, D.I., Wright, F.A., et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48 (2016), 245–252.
12. 12 Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M.R., Powell, J.E., Montgomery, G.W., Goddard, M.E., Wray, N.R., Visscher, P.M., Yang, J., Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48 (2016), 481–487 advance online publication.
13. 13 Bulik-Sullivan, B., Finucane, H.K., Anttila, V., Gusev, A., Day, F.R., Loh, P.-R., Duncan, L., Perry, J.R., Patterson, N., Robinson, E.B., et al. ReproGen Consortium Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47 (2015), 1236–1241.
14. 14 Davey Smith, G., Hemani, G., Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23:R1 (2014), R89–R98.
15. 15 Zheng, H.F., Forgetta, V., Hsu, Y.H., Estrada, K., Rosello-Diez, A., Leo, P.J., Dahia, C.L., Park-Min, K.H., Tobias, J.H., Kooperberg, C., et al. AOGC Consortium, UK10K Consortium. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526 (2015), 112–117.
16. 16 Morris, A.P., Voight, B.F., Teslovich, T.M., Ferreira, T., Segrè, A.V., Steinthorsdottir, V., Strawbridge, R.J., Khan, H., Grallert, H., Mahajan, A., et al. Wellcome Trust Case Control Consortium Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators Genetic Investigation of ANthropometric Traits (GIANT) Consortium Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium South Asian Type 2 Diabetes (SAT2D) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44 (2012), 981–990.
17. 17 Liu, J.Z., van Sommeren, S., Huang, H., Ng, S.C., Alberts, R., Takahashi, A., Ripke, S., Lee, J.C., Jostins, L., Shah, T., et al. International Multiple Sclerosis Genetics Consortium, International IBD Genetics Consortium. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47 (2015), 979–986.
18. 18 Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511 (2014), 421–427.
19. 19 Okada, Y., Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., Suzuki, A., Yoshida, S., et al. RACI consortium, GARNET consortium. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506 (2014), 376–381.
20. 20 Perry, J.R.B., Day, F., Elks, C.E., Sulem, P., Thompson, D.J., Ferreira, T., He, C., Chasman, D.I., Esko, T., Thorleifsson, G., et al. Australian Ovarian Cancer Study GENICA Network kConFab LifeLines Cohort Study InterAct Consortium, Early Growth Genetics (EGG) Consortium. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514 (2014), 92–97.
21. 21 Rietveld, C.A., Medland, S.E., Derringer, J., Yang, J., Esko, T., Martin, N.W., Westra, H.-J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., et al., LifeLines Cohort Study. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340 (2013), 1467–1471.
22. 22 Willer, C.J., Schmidt, E.M., Sengupta, S., Peloso, G.M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich, M.L., Mora, S., et al., Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45 (2013), 1274–1283.
23. 1(C) levels via glycemic and nonglycemic pathways. Diabetes 59 (2010), 3229–3239.
24. 24 Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, A.U., Wheeler, E., Glazer, N.L., Bouatia-Naji, N., Gloyn, A.L., et al. DIAGRAM Consortium GIANT Consortium Global BPgen Consortium Anders Hamsten on behalf of Procardis Consortium, MAGIC investigators. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42 (2010), 105–116.
25. 25 Gieger, C., Radhakrishnan, A., Cvejic, A., Tang, W., Porcu, E., Pistis, G., Serbanovic-Canic, J., Elling, U., Goodall, A.H., Labrune, Y., et al. New gene functions in megakaryopoiesis and platelet formation. Nature 480 (2011), 201–208.
26. 26 van der Harst, P., Zhang, W., Mateo Leach, I., Rendon, A., Verweij, N., Sehmi, J., Paul, D.S., Elling, U., Allayee, H., Li, X., et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492 (2012), 369–375.
27. 27 Locke, A.E., Kahali, B., Berndt, S.I., Justice, A.E., Pers, T.H., Day, F.R., Powell, C., Vedantam, S., Buchkovich, M.L., Yang, J., et al. LifeLines Cohort Study ADIPOGen Consortium AGEN-BMI Working Group CARDIOGRAMplusC4D Consortium CKDGen Consortium GLGC ICBP MAGIC Investigators MuTHER Consortium MIGen Consortium PAGE Consortium ReproGen Consortium GENIE Consortium, International Endogene Consortium. Genetic studies of body mass index yield new insights for obesity biology. Nature 518 (2015), 197–206.
28. 28 Wood, A.R., Esko, T., Yang, J., Vedantam, S., Pers, T.H., Gustafsson, S., Chu, A.Y., Estrada, K., Luan, J., Kutalik, Z., et al. Electronic Medical Records and Genomics (eMEMERGEGE) Consortium MIGen Consortium PAGEGE Consortium, LifeLines Cohort Study. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46 (2014), 1173–1186.
29. 29 Fromer, M., Roussos, P., Sieberts, S.K., Johnson, J.S., Kavanagh, D.H., Perumal, T.M., Ruderfer, D.M., Oh, E.C., Topol, A., Shah, H.R., et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19 (2016), 1442–1453.
30. 30 Raitakari, O.T., Juonala, M., Rönnemaa, T., Keltikangas-Järvinen, L., Räsänen, L., Pietikäinen, M., Hutri-Kähönen, N., Taittonen, L., Jokinen, E., Marniemi, J., et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int. J. Epidemiol. 37 (2008), 1220–1226.
31. 31 Stancáková, A., Civelek, M., Saleem, N.K., Soininen, P., Kangas, A.J., Cederberg, H., Paananen, J., Pihlajamäki, J., Bonnycastle, L.L., Morken, M.A., et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes 61 (2012), 1895–1902.
32. 32 Stancáková, A., Javorský, M., Kuulasmaa, T., Haffner, S.M., Kuusisto, J., Laakso, M., Changes in insulin sensitivity and insulin release in relation to glycemia and glucose tolerance in 6,414 Finnish men. Diabetes 58 (2009), 1212–1221.
33. 33 Nuotio, J., Oikonen, M., Magnussen, C.G., Jokinen, E., Laitinen, T., Hutri-Kähönen, N., Kähönen, M., Lehtimäki, T., Taittonen, L., Tossavainen, P., et al. Cardiovascular risk factors in 2011 and secular trends since 2007: the Cardiovascular Risk in Young Finns Study. Scand. J. Public Health 42 (2014), 563–571.
34. 34 Wright, F.A., Sullivan, P.F., Brooks, A.I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46 (2014), 430–437.
35. 35 Okbay, A., Beauchamp, J.P., Fontana, M.A., Lee, J.J., Pers, T.H., Rietveld, C.A., Turley, P., Chen, G.-B., Emilsson, V., Meddens, S.F.W., et al., LifeLines Cohort Study. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533 (2016), 539–542.
36. 36 Yang, J., Lee, S.H., Goddard, M.E., Visscher, P.M., GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88 (2011), 76–82.
37. 37 de Los Campos, G., Vazquez, A.I., Fernando, R., Klimentidis, Y.C., Sorensen, D., Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet., 9, 2013, e1003608.
38. 38 Bulik-Sullivan, B.K., Loh, P.-R., Finucane, H.K., Ripke, S., Yang, J., Patterson, N., Daly, M.J., Price, A.L., Neale, B.M., Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47 (2015), 291–295.
39. 39 Finucane, H.K., Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., Xu, H., Zang, C., Farh, K., et al. ReproGen Consortium Schizophrenia Working Group of the Psychiatric Genomics Consortium, RACI Consortium. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47 (2015), 1228–1235.
40. 40 Auton, A., Brooks, L.D., Durbin, R.M., Garrison, E.P., Kang, H.M., Korbel, J.O., Marchini, J.L., McCarthy, S., McVean, G.A., Abecasis, G.R., 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526 (2015), 68–74.
41. 41 Shi, H., Kichaev, G., Pasaniuc, B., Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data. Am. J. Hum. Genet. 99 (2016), 139–153.
42. 42 Shi, H., Mancuso, N., Spendlove, S., Pasaniuc, B., Local genetic correlation gives insights into the shared genetic architecture of complex traits. bioRxiv, 2016, 10.1101/092668.
43. 43 Yang, J., Ferreira, T., Morris, A.P., Medland, S.E., Madden, P.A.F., Heath, A.C., Martin, N.G., Montgomery, G.W., Weedon, M.N., Loos, R.J., et al. Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44 (2012), 369–375 S1–S3.
44. 44 Welch, B.L., The generalisation of student's problems when several different population variances are involved. Biometrika 34 (1947), 28–35.
45. 45 Pickrell, J.K., Berisa, T., Liu, J.Z., Ségurel, L., Tung, J.Y., Hinds, D.A., Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48 (2016), 709–717.
46. 46 Do, R., Willer, C.J., Schmidt, E.M., Sengupta, S., Gao, C., Peloso, G.M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat. Genet. 45 (2013), 1345–1352.
47. 47 Mi, H., Muruganujan, A., Casagrande, J.T., Thomas, P.D., Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8 (2013), 1551–1566.
48. 48 Won, H., de la Torre-Ubieta, L., Stein, J.L., Parikshak, N.N., Huang, J., Opland, C.K., Gandal, M.J., Sutton, G.J., Hormozdiari, F., Lu, D., et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538 (2016), 523–527.
49. 49 Kennedy, J.M., Fodil, N., Torre, S., Bongfen, S.E., Olivier, J.-F., Leung, V., Langlais, D., Meunier, C., Berghout, J., Langat, P., et al. CCDC88B is a novel regulator of maturation and effector functions of T cells during pathological inflammation. J. Exp. Med. 211 (2014), 2519–2535.
50. 50 Pavlides, J.M.W., Zhu, Z., Gratten, J., McRae, A.F., Wray, N.R., Yang, J., Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med., 8, 2016, 84.
51. 51 Hosokawa, Y., Maeda, Y., Takahashi, E.-i., Suzuki, M., Seto, M., Human aiolos, an ikaros-related zinc finger DNA binding protein: cDNA cloning, tissue expression pattern, and chromosomal mapping. Genomics 61 (1999), 326–329.
52. 52 Quintana, F.J., Jin, H., Burns, E.J., Nadeau, M., Yeste, A., Kumar, D., Rangachari, M., Zhu, C., Xiao, S., Seavitt, J., et al. Aiolos promotes TH17 differentiation by directly silencing Il2 expression. Nat. Immunol. 13 (2012), 770–777.
53. 53 Farh, K.K.-H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W.J., Beik, S., Shoresh, N., Whitton, H., Ryan, R.J.H., Shishkin, A.A., et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518 (2015), 337–343.
54. 54 Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A.A.E., Lee, S.H., Robinson, M.R., Perry, J.R.B., Nolte, I.M., van Vliet-Ostaptchouk, J.V., et al., LifeLines Cohort Study. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47 (2015), 1114–1120.
55. 55 Gutierrez-Arcelus, M., Ongen, H., Lappalainen, T., Montgomery, S.B., Buil, A., Yurovsky, A., Bryois, J., Padioleau, I., Romano, L., Planchon, A., et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet., 11, 2015, e1004958.
56. 56 Parsons, T.J., Power, C., Logan, S., Summerbell, C.D., Childhood predictors of adult obesity: a systematic review. Int. J. Obes. Relat. Metab. Disord. 23:Suppl 8 (1999), S1–S107.
57. 57 Fall, T., Hägg, S., Mägi, R., Ploner, A., Fischer, K., Horikoshi, M., Sarin, A.-P., Thorleifsson, G., Ladenvall, C., Kals, M., et al., European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLoS Med., 10, 2013, e1001474.
58. 58 Gusev, A., Mancuso, N., Finucane, H.K., Reshef, Y., Song, L., Safi, A., Oh, E., McCaroll, S., Neale, B., Ophoff, R., et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. bioRxiv, 2016, 10.1101/067355.
59. 59 Pickrell, J., Fulfilling the promise of Mendelian randomization. bioRxiv, 2015, 10.1101/018150.
60. 60 Smith, G.D., Ebrahim, S., ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?. Int. J. Epidemiol. 32 (2003), 1–22.
61. 61 Wang, J., Gamazon, E.R., Pierce, B.L., Stranger, B.E., Im, H.K., Gibbons, R.D., Cox, N.J., Nicolae, D.L., Chen, L.S., Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. Am. J. Hum. Genet. 98 (2016), 697–708.
62. 62 Pickrell, J.K., Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94 (2014), 559–573.
63. 63 Kichaev, G., Yang, W.-Y., Lindstrom, S., Hormozdiari, F., Eskin, E., Price, A.L., Kraft, P., Pasaniuc, B., Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet., 10, 2014, e1004722.