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From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases

Cano-Gamez, Eddie, Trynka, Gosia·2020·Frontiers in Genetics
DOI 10.3389/fgene.2020.00424PMC PMC7237642
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  1. Abraham G. Havulinna A. S. Bhalala O. G. Byars S. G. De Livera A. M. Yetukuri L. (2016). Genomic prediction of coronary heart disease. Eur. Heart J. 37 3267– 3278. 27655226 PMC5146693
  2. Amariuta T. Ishigaki K. Sugishita H. Ohta T. Matsuda K. Murakami Y. (2020). In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores. bioRxiv [Preprint]
  3. Banovich N. E. Lan X. McVicker G. van de Geijn B. Degner J. F. Blischak J. D. (2014). Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 10: e1004663. 10.1371/journal.pgen.1004663 25233095 PMC4169251 · DOI
  4. Barbeira A. N. Dickinson S. P. Bonazzola R. Zheng J. Wheeler H. E. Torres J. M. (2018). Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9: 1825. PMC5940825 29739930
  5. Beatrix Bartok G. S. F. (2010). Fibroblast-like synoviocytes: key effector cells in rheumatoid arthritis. Immunol. Rev. 233: 233. 10.1111/j.0105-2896.2009.00859.x 20193003 PMC2913689 · DOI
  6. Bikard D. Jiang W. Samai P. Hochschild A. Zhang F. Marraffini L. A. (2013). Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. Nucleic Acids Res. 41 7429– 7437. 10.1093/nar/gkt520 23761437 PMC3753641 · DOI
  7. Bossini-Castillo L. Glinos D. A. Kunowska N. Golda G. Lamikanra A. Spitzer M. (2019). Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets. bioRxiv [Preprint] PMC9010307 35591976
  8. Boyle A. P. Davis S. Shulha H. P. Meltzer P. Margulies E. H. Weng Z. (2008). High-resolution mapping and characterization of open chromatin across the genome. Cell 132 311– 322. 10.1016/j.cell.2007.12.014 18243105 PMC2669738 · DOI · Paper page
  9. Boyle E. A. Li Y. I. Pritchard J. K. (2017). An expanded view of complex traits: from polygenic to omnigenic. Cell 169 1177– 1186. 10.1016/j.cell.2017.05.038 28622505 PMC5536862 · DOI · Paper page
  10. Buenrostro J. D. Giresi P. G. Zaba L. C. Chang H. Y. Greenleaf W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10 1213– 1218. 10.1038/nmeth.2688 24097267 PMC3959825 · DOI · Paper page
  11. Buenrostro J. D. Wu B. Litzenburger U. M. Ruff D. Gonzales M. L. Snyder M. P. (2015). Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523 486– 490. 10.1038/nature14590 26083756 PMC4685948 · DOI · Paper page
  12. Bugatti S. Vitolo B. Caporali R. Montecucco C. Manzo A. (2014). B Cells in rheumatoid arthritis: from pathogenic players to disease biomarkers. Biomed Res. Int. 2014: 681678. 10.1155/2014/681678 24877127 PMC4022166 · DOI · Paper page
  13. Bycroft C. Freeman C. Petkova D. Band G. Elliott L. T. Sharp K. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature 562 203– 209. 10.1038/s41586-018-0579-z 30305743 PMC6786975 · DOI · Paper page
  14. Calderon D. Nguyen M. L. T. Mezger A. Kathiria A. Müller F. Nguyen V. (2019). Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51 1494– 1505. 10.1038/s41588-019-0505-9 31570894 PMC6858557 · DOI
  15. Chatterjee N. Shi J. García-Closas M. (2016). Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17 392– 406. 10.1038/nrg.2016.27 27140283 PMC6021129 · DOI
  16. Chen L. Ge B. Casale F. P. Vasquez L. Kwan T. Garrido-Martín D. (2016). Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells. Cell 167 1398– 1414.e24. 27863251 PMC5119954
  17. Chun S. Casparino A. Patsopoulos N. A. Croteau-Chonka D. C. Raby B. A. De Jager P. L. (2017). Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49 600– 605. 10.1038/ng.3795 28218759 PMC5374036 · DOI · Paper page
  18. Cope A. P. Schulze-Koops H. Aringer M. (2007). The central role of T cells in rheumatoid arthritis. Clin. Exp. Rheumatol. 25 S4– S11. 17977483
  19. Creyghton M. P. Cheng A. W. Welstead G. G. Kooistra T. Carey B. W. Steine E. J. (2010). Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. U.S.A. 107 21931– 21936. 10.1073/pnas.1016071107 21106759 PMC3003124 · DOI · Paper page
  20. Darmanis S. Sloan S. A. Zhang Y. Enge M. Caneda C. Shuer L. M. (2015). A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. U.S.A. 112 7285– 7290. 10.1073/pnas.1507125112 26060301 PMC4466750 · DOI · Paper page
  21. Degner J. F. Pai A. A. Pique-Regi R. Veyrieras J.-B. Gaffney D. J. Pickrell J. K. (2012). DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482 390– 394. 10.1038/nature10808 22307276 PMC3501342 · DOI
  22. Dendrou C. A. Cortes A. Shipman L. Evans H. G. Attfield K. E. Jostins L. (2016). Resolving TYK2 locus genotype-to-phenotype differences in autoimmunity. Sci. Transl. Med. 8: 363ra149. 10.1126/scitranslmed.aag1974 27807284 PMC5737835 · DOI · Paper page
  23. DeWitt M. A. Corn J. E. Carroll D. (2017). Genome editing via delivery of Cas9 ribonucleoprotein. Methods 12 9– 15. 10.1016/j.ymeth.2017.04.003 28410976 PMC6698184 · DOI
  24. Diogo D. Bastarache L. Liao K. P. Graham R. R. Fulton R. S. Greenberg J. D. (2015). TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits. PLoS ONE 10: e0122271. 10.1371/journal.pone.0122271 25849893 PMC4388675 · DOI
  25. Dubois P. C. A. Trynka G. Franke L. Hunt K. A. Romanos J. Curtotti A. (2010). Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet. 42 295– 302. 20190752 PMC2847618
  26. Ernst J. Kellis M. (2012). ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9 215– 216. 10.1038/nmeth.1906 22373907 PMC3577932 · DOI
  27. Evans D. M. Davey Smith G. (2015). Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu. Rev. Genomics Hum. Genet. 16 327– 350. 10.1146/annurev-genom-090314-050016 25939054 · DOI
  28. Fairfax B. P. Humburg P. Makino S. Naranbhai V. Wong D. Lau E. (2014). Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. 10.1126/science.1246949 24604202 PMC4064786 · DOI · Paper page
  29. Farh K. K.-H. Marson A. Zhu J. Kleinewietfeld M. Housley W. J. Beik S. (2015). Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518 337– 343. 10.1038/nature13835 25363779 PMC4336207 · DOI · Paper page
  30. Finucane H. K. Bulik-Sullivan B. Gusev A. Trynka G. Reshef Y. Loh P.-R. (2015). Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47 1228– 1235. 26414678 PMC4626285
  31. Finucane H. K. Reshef Y. A. Anttila V. Slowikowski K. Gusev A. Byrnes A. (2018). Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50 621– 629. 10.1038/s41588-018-0081-4 29632380 PMC5896795 · DOI · Paper page
  32. Fortune M. D. Guo H. Burren O. Schofield E. Walker N. M. Ban M. (2015). Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47 839– 846. 10.1038/ng.3330 26053495 PMC4754941 · DOI
  33. Franceschini N. Giambartolomei C. de Vries P. S. Finan C. Bis J. C. Huntley R. P. (2018). GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat. Commun. 9: 5141. PMC6277418 30510157
  34. Franke A. McGovern D. P. B. Barrett J. C. Wang K. Radford-Smith G. L. Ahmad T. (2010). Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet. 42 1118– 1125. 21102463 PMC3299551
  35. Frommer M. McDonald L. E. Millar D. S. Collis C. M. Watt F. Grigg G. W. (1992). A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc. Natl. Acad. Sci. U.S.A. 89 1827– 1831. 10.1073/pnas.89.5.1827 1542678 PMC48546 · DOI · Paper page
  36. Gamazon E. R. Wheeler H. E. Shah K. P. Mozaffari S. V. Aquino-Michaels K. Carroll R. J. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47 1091– 1098. 10.1038/ng.3367 26258848 PMC4552594 · DOI
  37. Garcia-Closas M. Rothman N. Figueroa J. D. Prokunina-Olsson L. Han S. S. Baris D. (2013). Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer. Cancer Res. 73 2211– 2220. 23536561 PMC3688270
  38. Gaziano J. M. Concato J. Brophy M. Fiore L. Pyarajan S. Breeling J. (2016). Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70 214– 223. 10.1016/j.jclinepi.2015.09.016 26441289 · DOI · Paper page
  39. Giambartolomei C. Zhenli Liu J. Zhang W. Hauberg M. Shi H. Boocock J. (2018). A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34 2538– 2545. 10.1093/bioinformatics/bty147 29579179 PMC6061859 · DOI
  40. Global Lipids Genetics Consortium Willer C. J. Schmidt E. M. Sengupta S. Peloso G. M. Gustafsson S. (2013). Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45. PMC3838666 24097068
  41. GTEx Consortium (2013). The genotype-tissue expression (GTEx) project. Nat. Genet. 45 580– 585. 23715323 PMC4010069
  42. Gusev A. Ko A. Shi H. Bhatia G. Chung W. Penninx B. W. J. H. (2016). Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48 245– 252. 26854917 PMC4767558
  43. Gusev A. Mancuso N. Won H. Kousi M. Finucane H. K. Reshef Y. (2018). Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50 538– 548. 10.1038/s41588-018-0092-1 29632383 PMC5942893 · DOI · Paper page
  44. Hakonarson H. Qu H.-Q. Bradfield J. P. Marchand L. Kim C. E. Glessner J. T. (2008). A novel susceptibility locus for type 1 diabetes on Chr12q13 identified by a genome-wide association study. Diabetes Metab. Res. Rev. 57 1143– 1146. 10.2337/db07-1305 18198356 · DOI
  45. Hannon E. Spiers H. Viana J. Pidsley R. Burrage J. Murphy T. M. (2016). Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat. Neurosci. 19 48– 54. 10.1038/nn.4182 26619357 PMC4714325 · DOI
  46. Hari Dass S. A. McCracken K. Pokhvisneva I. Chen L. M. Garg E. Nguyen T. T. T. (2019). A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions. EBioMedicine 42 188– 202. 10.1016/j.ebiom.2019.03.051 30922963 PMC6491717 · DOI
  47. He X. Fuller C. K. Song Y. Meng Q. Zhang B. Yang X. (2013). Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92 667– 680. 10.1016/j.ajhg.2013.03.022 23643380 PMC3644637 · DOI · Paper page
  48. Henriksson J. Chen X. Gomes T. Ullah U. Meyer K. B. Miragaia R. (2019). Genome-wide CRISPR Screens in T helper cells reveal pervasive crosstalk between activation and differentiation. Cell 176 882– 896.e18. 10.1016/j.cell.2018.11.044 30639098 PMC6370901 · DOI
  49. Hindorff L. A. Gillanders E. M. Manolio T. A. (2011). Genetic architecture of cancer and other complex diseases: lessons learned and future directions. Carcinogenesis 32 945– 954. 10.1093/carcin/bgr056 21459759 PMC3140138 · DOI
  50. Hormozdiari F. Kostem E. Kang E. Y. Pasaniuc B. Eskin E. (2014). Identifying causal variants at loci with multiple signals of association. Genetics 198 497– 508. 10.1534/genetics.114.167908 25104515 PMC4196608 · DOI · Paper page
  51. Hormozdiari F. Zhu A. Kichaev G.-T. Ju C. J. Segre A. V. Joo J. W. J. (2017). Widespread allelic heterogeneity in complex traits | Elsevier enhanced reader. Am. J. Hum. Genet. 100 789– 802. 10.1016/j.ajhg.2017.04.005 28475861 PMC5420356 · DOI
  52. Hu X. Kim H. Stahl E. Plenge R. Daly M. Raychaudhuri S. (2011). Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89 496– 506. 10.1016/j.ajhg.2011.09.002 21963258 PMC3188838 · DOI
  53. Hu Y. Lu Q. Powles R. Yao X. Yang C. Fang F. (2017). Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput. Biol. 13: e1005589. 10.1371/journal.pcbi.1005589 28594818 PMC5481142 · DOI
  54. Ihry R. J. Worringer K. A. Salick M. R. Frias E. Ho D. Theriault K. (2018). p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells. Nat. Med. 24 939– 946. 29892062
  55. Insull W. (2009). The pathology of atherosclerosis: plaque development and plaque responses to medical treatment. Am. J. Med. 122 S3– S14. 19110086
  56. International Genetics of Ankylosing Spondylitis Consortium, Cortes A. Hadler J. Pointon J. P. Robinson P. C. Karaderi T. (2013). Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat. Genet. 45 730– 738. 10.1038/ng.2667 23749187 PMC3757343 · DOI
  57. Iotchkova V. Ritchie G. R. S. Geihs M. Morganella S. Min J. L. Walter K. (2019). GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51 343– 353. 10.1038/s41588-018-0322-6 30692680 PMC6908448 · DOI
  58. Jostins L. Ripke S. Weersma R. K. Duerr R. H. McGovern D. P. Hui K. Y. (2012). Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491 119– 124. 23128233 PMC3491803
  59. Khera A. V. Chaffin M. Wade K. H. Zahid S. Brancale J. Xia R. (2019). Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177 587– 596.e9. 10.1016/j.cell.2019.03.028 31002795 PMC6661115 · DOI · Paper page
  60. Khera A. V. Emdin C. A. Drake I. Natarajan P. Bick A. G. Cook N. R. (2016). Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375 2349– 2358. 27959714 PMC5338864
  61. Kimmerling R. J. Lee Szeto G. Li J. W. Genshaft A. S. Kazer S. W. Payer K. R. (2016). A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun. 7: 10220. PMC4729820 26732280
  62. Kircher M. Xiong C. Martin B. Schubach M. Inoue F. Bell R. J. A. (2019). Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10: 3583. PMC6687891 31395865
  63. Kreins A. Y. Ciancanelli M. J. Okada S. Kong X.-F. Ramírez-Alejo N. Kilic S. S. (2015). Human TYK2 deficiency: mycobacterial and viral infections without hyper-IgE syndrome. J. Exp. Med. 212 1641– 1662. 26304966 PMC4577846
  64. Kumasaka N. Knights A. J. Gaffney D. J. (2016). Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48 206– 213. 10.1038/ng.3467 26656845 PMC5098600 · DOI
  65. Kunkel S. D. Elmore C. J. Bongers K. S. Ebert S. M. Fox D. K. Dyle M. C. (2012). Ursolic acid increases skeletal muscle and brown fat and decreases diet-induced obesity, glucose intolerance and fatty liver disease. PLoS ONE 7: e39332. 10.1371/journal.pone.0039332 22745735 PMC3379974 · DOI
  66. Lähnemann D. Köster J. Szczurek E. McCarthy D. J. Hicks S. C. Robinson M. D. (2020). Eleven grand challenges in single-cell data science. Genome Biol. 21: 31. PMC7007675 32033589
  67. Lappalainen T. Sammeth M. Friedländer M. R. t’Hoen P. A. C. Monlong J. Rivas M. A. (2013). Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501 506– 511. 24037378 PMC3918453
  68. Li Y. Oosting M. Smeekens S. P. Jaeger M. Aguirre-Gamboa R. Le K. T. T. (2016). A functional genomics approach to understand variation in cytokine production in humans. Cell 167 1099– 1110.e14. 10.1016/j.cell.2016.10.017 27814507 · DOI · Paper page
  69. Liu B. Gloudemans M. J. Rao A. S. Ingelsson E. Montgomery S. B. (2019). Abundant associations with gene expression complicate GWAS follow-up. Nat. Genet. 51 768– 769. 10.1038/s41588-019-0404-0 31043754 PMC6904208 · DOI
  70. Liu X. Li Y. I. Pritchard J. K. (2019). Trans effects on gene expression can drive omnigenic inheritance. Cell 177 1022– 1034.e6. 10.1016/j.cell.2019.04.014 31051098 PMC6553491 · DOI · Paper page
  71. Liu B. Pjanic M. Wang T. Nguyen T. Gloudemans M. Rao A. (2018). Genetic regulatory mechanisms of smooth muscle cells map to coronary artery disease risk loci. Am. J. Hum. Genet. 103 377– 388. 10.1016/j.ajhg.2018.08.001 30146127 PMC6128252 · DOI · Paper page
  72. Lloyd-Jones L. R. Holloway A. McRae A. Yang J. Small K. Zhao J. (2017). The genetic architecture of gene expression in peripheral blood. Am. J. Hum. Genet. 100 228– 237. 28065468 PMC5294670
  73. Maas P. Barrdahl M. Joshi A. D. Auer P. L. Gaudet M. M. Milne R. L. (2016). Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the united states. JAMA Oncol. 2 1295– 1302. 27228256 PMC5719876
  74. MacArthur J. Bowler E. Cerezo M. Gil L. Hall P. Hastings E. (2017). The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45 D896– D901. 27899670 PMC5210590
  75. Macosko E. Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. (2015). Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 1202– 1214. 10.1016/j.cell.2015.05.002 26000488 PMC4481139 · DOI · Paper page
  76. Manrai A. K. Funke B. H. Rehm H. L. Olesen M. S. Maron B. A. Szolovits P. (2016). Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375 655– 665. 10.1056/nejmsa1507092 27532831 PMC5292722 · DOI · Paper page
  77. Marigorta U. M. Navarro A. (2013). High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet. 9: e1003566. 10.1371/journal.pgen.1003566 23785302 PMC3681663 · DOI
  78. Márquez-Luna C. Gazal S. Loh P.-R. Furlotte N. Auton A. Me Research Team (2018). Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. bioRxiv [Preprint] PMC8523709 34663819
  79. Mavaddat N. Pharoah P. D. P. Michailidou K. Tyrer J. Brook M. N. Bolla M. K. (2015). Prediction of breast cancer risk based on profiling with common genetic variants. J. Natl. Cancer Inst. 107: djv036. 10.1093/jnci/djv036 25855707 PMC4754625 · DOI
  80. McCullum E. O. Williams B. A. R. Zhang J. Chaput J. C. (2010). “ Random mutagenesis by Error-Prone PCR,” in In Vitro Mutagenesis Protocols, 3rd Edn, ed. Braman J. (Totowa, NJ: Humana Press), 103– 109. 10.1007/978-1-60761-652-8_7 20676978 · DOI
  81. Melé M. Ferreira P. G. Reverter F. DeLuca D. S. Monlong J. Sammeth M. (2015). Human genomics. The human transcriptome across tissues and individuals. Science 348 660– 665. 25954002 PMC4547472
  82. Melzer D. Perry J. R. B. Hernandez D. Corsi A.-M. Stevens K. Rafferty I. (2008). A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 4: e1000072. 10.1371/journal.pgen.1000072 18464913 PMC2362067 · DOI · Paper page
  83. Monlong J. Calvo M. Ferreira P. G. Guigó R. (2014). Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat. Commun. 5: 4698. PMC4143934 25140736
  84. Musunuru K. Strong A. Frank-Kamenetsky M. Lee N. E. Ahfeldt T. Sachs K. V. (2010). From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466 714– 719. 10.1038/nature09266 20686566 PMC3062476 · DOI · Paper page
  85. Nagai A. Hirata M. Kamatani Y. Muto K. Matsuda K. Kiyohara Y. (2017). Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27 S2– S8. 28189464 PMC5350590
  86. Nica A. C. Dermitzakis E. T. (2013). Expression quantitative trait loci: present and future. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368 20120362. 10.1098/rstb.2012.0362 23650636 PMC3682727 · DOI
  87. Nica A. C. Montgomery S. B. Dimas A. S. Stranger B. E. Beazley C. Barroso I. (2010). Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6: e1000895. 10.1371/journal.pgen.1000895 20369022 PMC2848550 · DOI
  88. Nicolae D. L. Gamazon E. Zhang W. Duan S. Dolan M. E. Cox N. J. (2010). Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6: e1000888. 10.1371/journal.pgen.1000888 20369019 PMC2848547 · DOI · Paper page
  89. Okada Y. Wu D. Trynka G. Raj T. Terao C. Ikari K. (2014). Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506 376– 381. 24390342 PMC3944098
  90. Onengut-Gumuscu S. Chen W.-M. Burren O. Cooper N. J. Quinlan A. R. Mychaleckyj J. C. (2015). Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47 381– 386. 10.1038/ng.3245 25751624 PMC4380767 · DOI
  91. Ongen H. Dermitzakis E. T. (2015). Alternative splicing QTLs in european and african populations. Am. J. Hum. Genet. 97 567– 575. 10.1016/j.ajhg.2015.09.004 26430802 PMC4596912 · DOI
  92. Pasaniuc B. Zaitlen N. Shi H. Bhatia G. Gusev A. Pickrell J. (2014). Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics 30: 2906. 10.1093/bioinformatics/btu416 24990607 PMC4184260 · DOI
  93. Pasquali L. Gaulton K. J. Rodríguez-Seguí S. A. Mularoni L. Miguel-Escalada I. Akerman ı, et al. (2014). Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46 136– 143. 10.1038/ng.2870 24413736 PMC3935450 · DOI
  94. Pelikan R. C. Kelly J. A. Fu Y. Lareau C. A. Tessneer K. L. Wiley G. B. (2018). Enhancer histone-QTLs are enriched on autoimmune risk haplotypes and influence gene expression within chromatin networks. Nat. Commun. 9: 2905. PMC6060153 30046115
  95. Pickrell J. K. (2014). Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94 559– 573. 10.1016/j.ajhg.2014.03.004 24702953 PMC3980523 · DOI
  96. Pickrell J. K. Berisa T. Liu J. Z. Ségurel L. Tung J. Y. Hinds D. A. (2016). Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48 709– 717. 10.1038/ng.3570 27182965 PMC5207801 · DOI · Paper page
  97. ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489 57– 74. 10.1038/nature11247 22955616 PMC3439153 · DOI · Paper page
  98. Ripatti S. Tikkanen E. Orho-Melander M. Havulinna A. S. Silander K. Sharma A. (2010). A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376 1393– 1400. 10.1016/s0140-6736(10)61267-6 20971364 PMC2965351 · DOI · Paper page
  99. Rotem A. Ram O. Shoresh N. Sperling R. A. Goren A. Weitz D. A. (2015). Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33 1165– 1172. 10.1038/nbt.3383 26458175 PMC4636926 · DOI · Paper page
  100. Saelens W. Cannoodt R. Todorov H. Saeys Y. (2019). A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37 547– 554. 10.1038/s41587-019-0071-9 30936559 · DOI · Paper page
  101. Schadt E. E. Molony C. Chudin E. Hao K. Yang X. Lum P. Y. (2008). Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6: e107. 10.1371/journal.pbio.0060107 18462017 PMC2365981 · DOI
  102. Schaid D. J. Chen W. Larson N. B. (2018). From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19 491– 504. 10.1038/s41576-018-0016-z 29844615 PMC6050137 · DOI
  103. Schmidt E. M. Zhang J. Zhou W. Chen J. Mohlke K. L. Chen Y. E. (2015). GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31 2601– 2606. 10.1093/bioinformatics/btv201 25886982 PMC4612390 · DOI
  104. Slowikowski K. Hu X. Raychaudhuri S. (2014). SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30 2496– 2497. 10.1093/bioinformatics/btu326 24813542 PMC4147889 · DOI
  105. Smith G. D. Ebrahim S. Lewis S. Hansell A. L. Palmer L. J. Burton P. R. (2005). Genetic epidemiology and public health: hope, hype, and future prospects. Lancet 366 1484– 1498. 10.1016/s0140-6736(05)67601-5 16243094 · DOI
  106. Soskic B. Cano-Gamez E. Smyth D. J. Rowan W. C. Nakic N. Esparza-Gordillo J. (2019). Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases. Nat. Genet. 51 1486– 1493. 10.1038/s41588-019-0493-9 31548716 PMC6872452 · DOI
  107. Strober B. J. Elorbany R. Rhodes K. Krishnan N. Tayeb K. Battle A. (2019). Dynamic genetic regulation of gene expression during cellular differentiation. Science 364 1287– 1290. 10.1126/science.aaw0040 31249060 PMC6623972 · DOI · Paper page
  108. Tehranchi A. Hie B. Dacre M. Kaplow I. Pettie K. Combs P. (2019). Fine-mapping cis-regulatory variants in diverse human populations. Elife 8: e39595. 10.7554/eLife.39595 30650056 PMC6335058 · DOI
  109. The PsychENCODE Consortium, Akbarian S. Liu C. Knowles J. A. Vaccarino F. M. Farnham P. J. (2015). The PsychENCODE project. Nat. Neurosci. 18 1707– 1712 26605881 PMC4675669
  110. Torkamani A. Wineinger N. E. Topol E. J. (2018). The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19 581– 590. 10.1038/s41576-018-0018-x 29789686 · DOI
  111. Trapnell C. (2015). Defining cell types and states with single-cell genomics. Genome Res. 25 1491– 1498. 10.1101/gr.190595.115 26430159 PMC4579334 · DOI · Paper page
  112. Trynka G. Raychaudhuri S. (2013). Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases. Curr. Opin. Genet. Dev. 23 635– 641. 10.1016/j.gde.2013.10.009 24287333 PMC4073234 · DOI
  113. Trynka G. Sandor C. Han B. Xu H. Stranger B. E. Liu X. S. (2013). Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45 124– 130. 10.1038/ng.2504 23263488 PMC3826950 · DOI · Paper page
  114. Trynka G. Westra H.-J. Slowikowski K. Hu X. Xu H. Stranger B. E. (2015). Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am. J. Hum. Genet. 97 139– 152. 10.1016/j.ajhg.2015.05.016 26140449 PMC4572568 · DOI
  115. Udalova I. A. Mantovani A. Feldmann M. (2016). Macrophage heterogeneity in the context of rheumatoid arthritis. Nat. Rev. Rheumatol. 12: 472. 10.1038/nrrheum.2016.91 27383913 · DOI
  116. Ulirsch J. C. Lareau C. A. Bao E. L. Ludwig L. S. Guo M. H. Benner C. (2019). Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51 683– 693. 10.1038/s41588-019-0362-6 30858613 PMC6441389 · DOI · Paper page
  117. Ulirsch J. C. Nandakumar S. K. Wang L. Giani F. C. Zhang X. Rogov P. (2016). Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell 165 1530– 1545. 10.1016/j.cell.2016.04.048 27259154 PMC4893171 · DOI · Paper page
  118. van der Wijst M. G. P. Brugge H. de Vries D. H. Deelen P. Swertz M. A. LifeLines Cohort (2018). Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50 493– 497. 10.1038/s41588-018-0089-9 29610479 PMC5905669 · DOI · Paper page
  119. Vilhjálmsson B. J. Yang J. Finucane H. K. Gusev A. Lindström S. Ripke S. (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97 576– 592. 26430803 PMC4596916
  120. Visscher P. M. Wray N. R. Zhang Q. Sklar P. McCarthy M. I. Brown M. A. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet. 101 5– 22. 10.1016/j.ajhg.2017.06.005 28686856 PMC5501872 · DOI · Paper page
  121. Viswanath B. Jose S. P. Squassina A. Thirthalli J. Purushottam M. Mukherjee O. (2015). Cellular models to study bipolar disorder: a systematic review. J. Affect. Disord. 184 36– 50. 26070045
  122. Wainberg M. Sinnott-Armstrong N. Mancuso N. Barbeira A. N. Knowles D. A. Golan D. (2019). Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51 592– 599. 10.1038/s41588-019-0385-z 30926968 PMC6777347 · DOI
  123. Wallace C. (2013). Statistical testing of shared genetic control for potentially related traits. Genet. Epidemiol. 37 802– 813. 10.1002/gepi.21765 24227294 PMC4158901 · DOI · Paper page
  124. Wallace C. Rotival M. Cooper J. D. Rice C. M. Yang J. H. M. McNeill M. (2012). Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum. Mol. Genet. 21 2815– 2824. 10.1093/hmg/dds098 22403184 PMC3363338 · DOI
  125. Wang H. La Russa M. Qi L. S. (2016). CRISPR/Cas9 in genome editing and beyond. Annu. Rev. Biochem. 85 227– 264. 27145843 PMC13384723
  126. Wellcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447 661– 678. 10.1038/nature05911 17554300 PMC2719288 · DOI · Paper page
  127. Wen X. Pique-Regi R. Luca F. (2017). Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLoS Genet. 13: e1006646. 10.1371/journal.pgen.1006646 28278150 PMC5363995 · DOI
  128. Westra H.-J. Peters M. J. Esko T. Yaghootkar H. Schurmann C. Kettunen J. (2013). Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45 1238– 1243. 24013639 PMC3991562
  129. Xu Y. Vuckovic D. Ritchie S. C. Akbari P. Jiang T. Grealey J. (2020). Learning polygenic scores for human blood cell traits. bioRxiv [Preprint] PMC8758502 35072137
  130. Yang J. Benyamin B. McEvoy B. P. Gordon S. Henders A. K. Nyholt D. R. (2010). Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42 565– 569. 10.1038/ng.608 20562875 PMC3232052 · DOI · Paper page
  131. Yang J. Zeng J. Goddard M. E. Wray N. R. Visscher P. M. (2017). Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49 1304– 1310. 10.1038/ng.3941 28854176 · DOI
  132. Yao C. Chen G. Song C. Keefe J. Mendelson M. Huan T. (2018). Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun. 9: 3268. PMC6093935 30111768
  133. Zhang W. Voloudakis G. Rajagopal V. M. Readhead B. Dudley J. T. Schadt E. E. (2019). Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10: 3834. PMC6707297 31444360
  134. Zhu Z. Zhang F. Hu H. Bakshi A. Robinson M. R. Powell J. E. (2016). Integration of summary data from GWAS. 48 481– 487. 10.1038/ng.3538 27019110 · DOI · Paper page

Cited by

Sorted by citations

Authors
Cano-Gamez, E., & Trynka, G.
Venue
Frontiers in Genetics
Publisher
Frontiers Media SA
Published
2020-05-13
Volume
11
ISSN
1664-8021
DOI
10.3389/fgene.2020.00424
PMC
PMC7237642
Cited by
608
References
37
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Wellcome Trust
  • Gates Cambridge Trust

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