8 CHAPTER 8 186 46. Kerimov N, Hayhurst JD, Peikova K, et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet. 2021;53(9):1290-1299. 47. Schmiedel BJ, Singh D, Madrigal A, et al. Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression. Cell. 2018;175(6):1701-1715.e16. 48. van der Wijst MGP, Brugge H, de Vries DH, et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat Genet. 2018;50(4):493-497. 49. Wang D, Liu S, Warrell J, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY). 2018;362(6420):eaat8464. 50. Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236-1241. 51. Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genomewide association studies. Nat Genet. 2015;47(3):291-295. 52. Price AL, Weale ME, Patterson N, et al. Long-range LD can confound genome scans in admixed populations. Elsevier; 2008. p. 132139. 53. 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. 54. Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47(11):1228-1235. 55. Gazal S, Finucane HK, Furlotte NA, et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat Genet. 2017;49(10):1421-1427. 56. Hujoel MLA, Gazal S, Hormozdiari F, van de Geijn B, Price AL. Disease Heritability Enrichment of Regulatory Elements Is Concentrated in Elements with Ancient Sequence Age and Conserved Function across Species. Am J Hum Genet. 2019;104(4):611-624. 57. Trochet H, Pirinen M, Band G, et al. Bayesian meta-analysis across genome-wide association studies of diverse phenotypes. Genet Epidemiol. 2019;43(5):532-547. 58. Cichonska A, Rousu J, Marttinen P, et al. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics. 2016;32(13):1981-1989. 59. Nelson CP, Goel A, Butterworth AS, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017;49(9):1385-1391. 60. Loh P-R, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet. 2018;50(7):906-908. 61. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219. 62. Liberzon A, Subramanian A, Pinchback R, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-1740. 63. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: A knowledgebased approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102(43):1554515550. 64. Pers TH, Karjalainen JM, Chan Y, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nature Communications. 2015;6 :5890 65. Barbeira AN, Dickinson SP, Bonazzola R, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9(1):1825. 66. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383. 67. Mancuso N, Freund MK, Johnson R, et al. Probabilistic fine-mapping of transcriptomewide association studies. Nat Genet. 2019;51(4):675-682.
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