Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults
Autoři:
Binglan Li aff001; Yogasudha Veturi aff002; Anurag Verma aff002; Yuki Bradford aff002; Eric S. Daar aff003; Roy M. Gulick aff004; Sharon A. Riddler aff005; Gregory K. Robbins aff006; Jeffrey L. Lennox aff007; David W. Haas aff008; Marylyn D. Ritchie aff002
Působiště autorů:
Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
aff001; Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
aff002; Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
aff003; Weill Cornell Medicine, New York City, New York, United States of America
aff004; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
aff005; Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
aff006; Emory University School of Medicine, Atlanta, Georgia, United States of America
aff007; Departments of Medicine, Pharmacology, Pathology, Microbiology & Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
aff008; Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, United States of America
aff009; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
aff010
Vyšlo v časopise:
Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults. PLoS Genet 17(4): e1009464. doi:10.1371/journal.pgen.1009464
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009464
Souhrn
As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.
Klíčová slova:
Blood counts – Gene expression – Genetic loci – Genome-wide association studies – Heredity – Research errors – Total cell counting – Viral load
Zdroje
1. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics. ElsevierCompany; 2017 Jul 6;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856
2. Lappalainen T. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Research. Cold Spring Harbor Lab; 2015 Oct;25(10):1427–31. doi: 10.1101/gr.190983.115 26430152
3. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Research. 2017 Jan 4;45(D1):D896–D901. doi: 10.1093/nar/gkw1133 27899670
4. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. American Association for the Advancement of Science; 2012 Sep 7;337(6099):1190–5. doi: 10.1126/science.1222794 22955828
5. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. Nature Publishing Group; 2015 Aug 10;47(9):1091–8. doi: 10.1038/ng.3367 26258848
6. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. Nature Publishing Group; 2016 Mar;48(3):245–52. doi: 10.1038/ng.3506 26854917
7. Thériault S, Gaudreault N, Lamontagne M, Rosa M, Boulanger M-C, Messika-Zeitoun D, et al. A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis. Nature Communications. Nature Publishing Group; 2018 Mar 7;9(1):988. doi: 10.1038/s41467-018-03260-6 29511167
8. Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J, et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet. Nature Publishing Group; 2018 Jun 18;50(7):968–78. doi: 10.1038/s41588-018-0132-x 29915430
9. Mancuso N, Shi H, Goddard P, Kichaev G, Gusev A, Pasaniuc B. Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits. American journal of human genetics. Elsevier; 2017 Mar 2;100(3):473–87. doi: 10.1016/j.ajhg.2017.01.031 28238358
10. Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature. Nature Publishing Group; 2017 Oct 12;550(7675):204–13. doi: 10.1038/nature24277 29022597
11. Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. 2018.
12. Li B, Veturi Y, Bradford Y, Verma SS, Verma A, Lucas AM, et al. Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies. Pac Symp Biocomput. 2019;24:296–307. 30864331
13. Flutre T, Wen X, Pritchard J, Stephens M. A statistical framework for joint eQTL analysis in multiple tissues. Gibson G, editor. PLoS Genet. Public Library of Science; 2013 May;9(5):e1003486. doi: 10.1371/journal.pgen.1003486 23671422
14. Sul JH, Han B, Ye C, Choi T, Eskin E. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. Schork NJ, editor. PLoS Genet. Public Library of Science; 2013 Jun;9(6):e1003491. doi: 10.1371/journal.pgen.1003491 23785294
15. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. Nature Publishing Group; 2019 Mar;51(3):568–76. doi: 10.1038/s41588-019-0345-7 30804563
16. Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. Plagnol V, editor. PLoS Genet. 2019 Jan;15(1):e1007889. doi: 10.1371/journal.pgen.1007889 30668570
17. Liu X, Finucane HK, Gusev A, Bhatia G, Gazal S, O’Connor L, et al. Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues. American journal of human genetics. 2017 Apr 6;100(4):605–16. doi: 10.1016/j.ajhg.2017.03.002 28343628
18. Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. Nature Publishing Group; 2019 Apr;51(4):592–9. doi: 10.1038/s41588-019-0385-z 30926968
19. Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. American Society for Biochemistry and Molecular Biology; 2014 Feb;13(2):397–406.
20. Kryuchkova-Mostacci N, Robinson-Rechavi M. Tissue-Specificity of Gene Expression Diverges Slowly between Orthologs, and Rapidly between Paralogs. Ouzounis CA, editor. PLoS Comput Biol. Public Library of Science; 2016 Dec;12(12):e1005274. doi: 10.1371/journal.pcbi.1005274 28030541
21. Veturi Y, Ritchie MD. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures? Pac Symp Biocomput. 2018;23:228–39. 29218884
22. Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, GTEx Consortium, et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. Montgomery SB, editor. PLoS Genet. 2016 Nov 11;12(11):e1006423–3. doi: 10.1371/journal.pgen.1006423 27835642
23. Ongen H, Brown AA, Delaneau O, Panousis NI, Nica AC, GTEx Consortium, et al. Estimating the causal tissues for complex traits and diseases. Nat Genet. 2017 Dec;49(12):1676–83. doi: 10.1038/ng.3981 29058715
24. Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019 Apr;51(4):675–82. doi: 10.1038/s41588-019-0367-1 30926970
25. Moore CB, Verma A, Pendergrass S, Verma SS, Johnson DH, Daar ES, et al. Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols. Open Forum Infect Dis. 2015 Jan;2(1):ofu113. doi: 10.1093/ofid/ofu113 25884002
26. Verma A, Bradford Y, Verma SS, Pendergrass SA, Daar ES, Venuto C, et al. Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202. Pharmacogenetics and Genomics. 2017 Mar;27(3):101–11. doi: 10.1097/FPC.0000000000000263 28099408
27. Coltell O, Asensio EM, Sorlí JV, Barragán R, Fernández-Carrión R, Portolés O, et al. Genome-Wide Association Study (GWAS) on Bilirubin Concentrations in Subjects with Metabolic Syndrome: Sex-Specific GWAS Analysis and Gene-Diet Interactions in a Mediterranean Population. Nutrients. Multidisciplinary Digital Publishing Institute; 2019 Jan 4;11(1):90.
28. Dai X, Wu C, He Y, Gui L, Zhou L, Guo H, et al. A genome-wide association study for serum bilirubin levels and gene-environment interaction in a Chinese population. Genet Epidemiol. 2013 Apr;37(3):293–300. doi: 10.1002/gepi.21711 23371916
29. Tukey RH, Strassburg CP. Human UDP-glucuronosyltransferases: metabolism, expression, and disease. Annu Rev Pharmacol Toxicol. 2000;40:581–616. doi: 10.1146/annurev.pharmtox.40.1.581 10836148
30. Barter PJ, H Bryan Brewer J, Chapman MJ, Hennekens CH, Rader DJ, Tall AR. Cholesteryl Ester Transfer Protein. Arterioscler Thromb Vasc Biol. Lippincott Williams & Wilkins; 2003 Feb 1;23(2):160–7. doi: 10.1161/01.atv.0000054658.91146.64 12588754
31. Chambers JC, Zhang W, Sehmi J, Li X, Wass MN, Van der Harst P, et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet. 2011 Oct 16;43(11):1131–8. doi: 10.1038/ng.970 22001757
32. Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. Nature Publishing Group; 2018 Mar;50(3):390–400. doi: 10.1038/s41588-018-0047-6 29403010
33. Le Clerc S, Coulonges C, Delaneau O, van Manen D, Herbeck JT, Limou S, et al. Screening low-frequency SNPS from genome-wide association study reveals a new risk allele for progression to AIDS. J Acquir Immune Defic Syndr. 2011 Mar 1;56(3):279–84. doi: 10.1097/QAI.0b013e318204982b 21107268
34. Rhee EP, Ho JE, Chen M-H, Shen D, Cheng S, Larson MG, et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013 Jul 2;18(1):130–43. doi: 10.1016/j.cmet.2013.06.013 23823483
35. Lingwood CA, Branch DR. The role of glycosphingolipids in HIV/AIDS. Discov Med. Discov Med; 2011 Apr;11(59):303–13. 21524384
36. van Til NP, Heutinck KM, van der Rijt R, Paulusma CC, van Wijland M, Markusic DM, et al. Alteration of viral lipid composition by expression of the phospholipid floppase ABCB4 reduces HIV vector infectivity. Retrovirology. BioMed Central; 2008 Feb 1;5(1):14–9. doi: 10.1186/1742-4690-5-14 18241333
37. Wu B, Ouyang Z, Lyon CJ, Zhang W, Clift T, Bone CR, et al. Plasma Levels of Complement Factor I and C4b Peptides Are Associated with HIV Suppression. ACS Infect Dis. 2017 Dec 8;3(12):880–5. doi: 10.1021/acsinfecdis.7b00042 28862830
38. Dunn SJ, Khan IH, Chan UA, Scearce RL, Melara CL, Paul AM, et al. Identification of cell surface targets for HIV-1 therapeutics using genetic screens. Virology. 2004 Apr 10;321(2):260–73. doi: 10.1016/j.virol.2004.01.010 15051386
39. Migueles SA, Sabbaghian MS, Shupert WL, Bettinotti MP, Marincola FM, Martino L, et al. HLA B*5701 is highly associated with restriction of virus replication in a subgroup of HIV-infected long term nonprogressors. PNAS. 2000 Mar 14;97(6):2709–14. doi: 10.1073/pnas.050567397 10694578
40. Kjolby M, Andersen OM, Breiderhoff T, Fjorback AW, Pedersen KM, Madsen P, et al. Sort1, encoded by the cardiovascular risk locus 1p13.3, is a regulator of hepatic lipoprotein export. Cell Metab. 2010 Sep 8;12(3):213–23. doi: 10.1016/j.cmet.2010.08.006 20816088
41. Arvind P, Nair J, Jambunathan S, Kakkar VV, Shanker J. CELSR2-PSRC1-SORT1 gene expression and association with coronary artery disease and plasma lipid levels in an Asian Indian cohort. J Cardiol. 2014 Nov;64(5):339–46. doi: 10.1016/j.jjcc.2014.02.012 24674750
42. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature Publishing Group. Nature Publishing Group; 2010 Aug 5;466(7307):714–9. doi: 10.1038/nature09266 20686566
43. Akay C, Lindl KA, Shyam N, Nabet B, Goenaga-Vazquez Y, Ruzbarsky J, et al. Activation status of integrated stress response pathways in neurones and astrocytes of HIV-associated neurocognitive disorders (HAND) cortex. Neuropathol Appl Neurobiol. John Wiley & Sons, Ltd; 2012 Apr;38(2):175–200. doi: 10.1111/j.1365-2990.2011.01215.x 21883374
44. Grunfeld C, Pang M, Doerrler W, Shigenaga JK, Jensen P, Feingold KR. Lipids, lipoproteins, triglyceride clearance, and cytokines in human immunodeficiency virus infection and the acquired immunodeficiency syndrome. J Clin Endocrinol Metab. 1992 May;74(5):1045–52. doi: 10.1210/jcem.74.5.1373735 1373735
45. Michalak P, Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes. Genomics. 2008 Mar;91(3):243–8. doi: 10.1016/j.ygeno.2007.11.002 18082363
46. Pividori M, Rajagopal PS, Barbeira A, Liang Y, Melia O, Bastarache L, et al. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Sci Adv. 2020 Sep;6(37). doi: 10.1126/sciadv.aba2083 32917697
47. Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y, Kim-Hellmuth S, et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. bioRxiv. Cold Spring Harbor Laboratory; 2020 May 23;42(D1):814350.
48. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics. 2011 Jan 7;88(1):76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468
49. Robbins GK, De Gruttola V, Shafer RW, Smeaton LM, Snyder SW, Pettinelli C, et al. Comparison of sequential three-drug regimens as initial therapy for HIV-1 infection. N Engl J Med. Massachusetts Medical Society; 2003 Dec 11;349(24):2293–303. doi: 10.1056/NEJMoa030264 14668455
50. Gulick RM, Ribaudo HJ, Shikuma CM, Lustgarten S, Squires KE, Meyer WA, et al. Triple-nucleoside regimens versus efavirenz-containing regimens for the initial treatment of HIV-1 infection. N Engl J Med. Massachusetts Medical Society; 2004 Apr 29;350(18):1850–61. doi: 10.1056/NEJMoa031772 15115831
51. Gulick RM, Ribaudo HJ, Shikuma CM, Lalama C, Schackman BR, Meyer WA, et al. Three- vs four-drug antiretroviral regimens for the initial treatment of HIV-1 infection: a randomized controlled trial. JAMA. 2006 Aug 16;296(7):769–81. doi: 10.1001/jama.296.7.769 16905783
52. Riddler SA, Haubrich R, DiRienzo AG, Peeples L, Powderly WG, Klingman KL, et al. Class-sparing regimens for initial treatment of HIV-1 infection. N Engl J Med. Massachusetts Medical Society; 2008 May 15;358(20):2095–106. doi: 10.1056/NEJMoa074609 18480202
53. Sax PE, Tierney C, Collier AC, Fischl MA, Mollan K, Peeples L, et al. Abacavir-lamivudine versus tenofovir-emtricitabine for initial HIV-1 therapy. N Engl J Med. Massachusetts Medical Society; 2009 Dec 3;361(23):2230–40. doi: 10.1056/NEJMoa0906768 19952143
54. Daar ES, Tierney C, Fischl MA, Sax PE, Mollan K, Budhathoki C, et al. Atazanavir Plus Ritonavir or Efavirenz as Part of a 3-Drug Regimen for Initial Treatment of HIV-1: A Randomized Trial. Ann Intern Med. American College of Physicians; 2011 Apr 5;154(7):445–56. doi: 10.7326/0003-4819-154-7-201104050-00316 21320923
55. Lennox JL, Landovitz RJ, Ribaudo HJ, Ofotokun I, Na LH, Godfrey C, et al. A Phase III Comparative Study of the Efficacy and Tolerability of Three Non-Nucleoside Reverse Transcriptase Inhibitor-Sparing Antiretroviral Regimens for Treatment-Naïve HIV-1-Infected Volunteers: A Randomized, Controlled Trial. Ann Intern Med. NIH Public Access; 2014 Oct 7;161(7):461–71. doi: 10.7326/M14-1084 25285539
56. Turner S, Armstrong LL, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, et al. Quality control procedures for genome-wide association studies. Haines JL, Korf BR, Morton CC, Seidman CE, Seidman JG, Smith DR, editors. Curr Protoc Hum Genet. 2011 Jan;Chapter 1(1):Unit1.19–1.19.18. doi: 10.1002/0471142905.hg0119s68 21234875
57. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics. 2007 Sep;81(3):559–75. doi: 10.1086/519795 17701901
58. Howie BN, Donnelly P, Marchini J. A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies. Schork NJ, editor. PLoS Genet. Public Library of Science; 2009 Jun 19;5(6):e1000529. doi: 10.1371/journal.pgen.1000529 19543373
59. 1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, et al. A map of human genome variation from population-scale sequencing. Nature Publishing Group. 2010 Oct 28;467(7319):1061–73. doi: 10.1038/nature09534 20981092
60. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. Nature Publishing Group; 2006 Aug;38(8):904–9. doi: 10.1038/ng1847 16862161
61. Lucas AM, Palmiero NE, McGuigan J, Passero K, Zhou J, Orie D, et al. CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits. Front Genet. Frontiers; 2019 Dec 18;10:1164. doi: 10.3389/fgene.2019.01164 31824569
62. Langsted A, Nordestgaard BG. Nonfasting versus fasting lipid profile for cardiovascular risk prediction. Pathology. 2019 Feb;51(2):131–41. doi: 10.1016/j.pathol.2018.09.062 30522787
63. Nordestgaard BG. A Test in Context: Lipid Profile, Fasting Versus Nonfasting. J Am Coll Cardiol. 2017 Sep 26;70(13):1637–46. doi: 10.1016/j.jacc.2017.08.006 28935041
64. Mora S, Chang CL, Moorthy MV, Sever PS. Association of Nonfasting vs Fasting Lipid Levels With Risk of Major Coronary Events in the Anglo-Scandinavian Cardiac Outcomes Trial-Lipid Lowering Arm. JAMA Intern Med. American Medical Association; 2019 May 28;179(7):898–905. doi: 10.1001/jamainternmed.2019.0392 31135812
65. Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y, Kim-Hellmuth S, et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. bioRxiv. Cold Spring Harbor Laboratory; 2020 May 23;42(D1):814350.
66. Hall MA, Wallace J, Lucas A, Kim D, Basile AO, Verma SS, et al. PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies. Nature Communications. Nature Publishing Group; 2017 Oct 27;8(1):1167. doi: 10.1038/s41467-017-00802-2 29079728
67. Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD. Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data. Pac Symp Biocomput. 2010:315–26. 19908384
68. Wolfe D, Dudek S, Ritchie MD, Pendergrass SA. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. BioMed Central; 2013 Oct 16;6(1):18–12. doi: 10.1186/1756-0381-6-18 24131735
69. Wen X. Effective QTL Discovery Incorporating Genomic Annotations. bioRxiv. Cold Spring Harbor Laboratory; 2015 Nov 16:032003.
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