The present study characterized associations between 2544 SNPs from the PharmGKB and the GWAS Catalog and 774 context-derived phenotypes among 1181 HIV-infected participants from ACTG protocol A5202. Several associations replicated previous reports. We readily replicated the known association between CYP2B6 variants and plasma EFV concentrations 4,55,56. The lowest P-value was with rs3745274, which was associated with numerous context-derived EFV phenotypes. This genetic association appeared to persist irrespective of context (i.e. sex, race/ethnicity, randomized antiretroviral regimen, component antiretroviral drug, baseline age, BMI, CD4+ T-cell count, and plasma HIV-1 RNA). In contrast, the association between EFV concentration and rs10871777 (an SNP previously associated with obesity 57) is very likely spurious, as this was only observed among individuals with baseline BMI in the lowest 10th percentile.
In this analysis, we used context as a strategy to derive multiple subphenotypes from each primary phenotype. Our rationale is that genetic associations for a given SNP–trait association may differ depending on context; thus, we expect that context-dependent associations may be more readily identified and understood using this approach. One advantage of this approach is that it allows for a very granular exploration of SNP–genotype associations that may be influenced by context. We found such context dependence in the association between rs651821 and triglyceride phenotypes among patients randomized to ATV/RTV-containing ART regimens, but not among patients randomized to EFV-containing ART regimens. The random assignment of A5202 participants to receive either ATV/RTV-containing or EFV-containing ART decreases the likelihood that our finding was because of confounding as unrecognized confounders should be equally distributed across arms. This is an advantage of using clinical trials datasets for genetic association analyses. Another advantage of granular exploration of SNP–genotype associations is the ability to discern associations that are almost certainly spurious by comparing strengths of association between closely related phenotypes. For example, among individuals with baseline CD4 count of more than 302, the association between rs2302821 and change in triglycerides at week 96 (P=7.8×10−7) is almost certainly spurious as there was no such association at week 48 (P=0.45).
Multiple hypothesis testing is inherent in approaches that examine multiple phenotypes such as PheWAS, but Bonferoni correction is not appropriate because the assumption that tests are independent is violated. To address this issue, we performed 1000 permutations of the analysis to create an empirical null distribution. This showed that in the present analysis, the majority of models with P-value less than 1.5×10−4 were unlikely to be by chance alone.
This analysis leveraged extensive a priori knowledge of genes and phenotypes from PharmGKB and the GWAS Catalog. This increases the likelihood of validity, biological plausibility, and supportive publications. As was apparent in both our previous PheWAS 22 and the present study, disease-specific knowledge is useful in interpreting genetic associations and to prioritize associations for further replication and study. Because every phenome is unique, analyses that consider large numbers of phenotypes may benefit more so than GWAS from disease-specific knowledge and understanding, including relationships among phenotypes.
This study had limitations. A larger sample size may have shown additional associations and we have not yet sought to replicate associations in other datasets. We considered a limited number of phenotypes and contexts. We considered ART as intent to treat. We only used available SNPs that were available or imputed from genome-wide genotyping, without additional genotyping. We also focused analyses on individual SNPs, whereas multiple SNPs considered in combination may more strongly associate with some phenotypes. Data from prospective, randomized clinical trials offer distinct advantages (e.g. randomization tends to evenly distribute covariates across study arms), but there are limitations. Although data of ACTG clinical trials are rigorously collected and validated, electronic medical records datasets will likely contain a wider range of variables. In addition, eligibility criteria for clinical trials may exclude some individuals who would otherwise be included in electronic medical records datasets.
In summary, this pilot study supports a multiphenotype analysis strategy to explore clinical trials datasets for genetic associations and to ultimately identify genetic associations with the potential to optimize ART safety and efficacy. This approach complements more established GWAS by performing simultaneous calculations for identifying genotype–phenotype associations across numerous phenotypes. Work is ongoing to further evaluate and optimize multiphenotype analyses for clinical trials datasets. On the basis of results from this pilot study, we plan to extend the PheWAS approach both to a much more extensive set of traits and to multiple other clinical trials datasets. This will include replication of associations identified here.
The authors are grateful to the many patients with HIV infection who volunteered for A5202 and A5128. In addition, they acknowledge the contributions of study teams and site staff for these protocols. The authors thank Paul J. McLaren, PhD (Public Health Agency of Canada, Winnipeg, Canada), for previous involvement and collaborations that used these genome-wide genotype data.
The project described was supported by Award Number U01AI068636 from the National Institute of Allergy and Infectious Diseases (NIAID) and supported by the National Institute of Mental Health (NIMH) and the National Institute of Dental and Craniofacial Research (NIDCR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. This work was supported by the AIDS Clinical Trials Group funded by the National Institute of Allergy and Infectious Diseases (AI068636, AI038858, AI068634, AI038855). Grant support included AI077505, AI069439, TR000445, AI054999, AI110527 (D.W.H.), BRS-ACURE-06-00140-T001 (G.D.M.), and AI108355 (C.V.).
Clinical Research Sites that participated in ACTG protocol A5202, and collected DNA under protocol A5128, were supported by the following grants from NIAID: AI069477, AI027675, AI073961, AI069474, AI069432, AI069513, AI069423, AI050410, AI069452, AI069450, AI054907, AI069428, AI069439, AI069467, AI045008, AI069495, AI069415, AI069556, AI069484, AI069424, AI069532, AI069419, AI069471, AI025859, AI069418, AI050409, AI069423, AI069501, AI069502, AI069511, AI069434, AI069465, AI069494, AI069472, AI069470, AI046376, AI072626, AI027661, AI034853, AI069447, AI032782, AI027658, AI27666, AI058740, and AI046370, and by the following grants from the National Center for Research Resources (NCRR): RR00051, RR00046, RR025747, RR025777, RR024160, RR024996, RR024156, RR024160, and RR024160. Study drugs were provided by Bristol-Myers Squibb Co., Gilead Sciences, and GlaxoSmithKline, Inc.
Eric S. Daar has been principal investigator on research grants to Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center from Gilead, Merck, and ViiV, and a consultant/advisor to Bristol Myers Squibb, Gilead, Janssen, Merck, Teva, and ViiV. For the remaining authors there are no conflicts of interest.
1. Mallal S, Phillips E, Carosi G, Molina JM, Workman C, Tomazic J, et al. HLA-B*5701 screening for hypersensitivity to abacavir. N Engl J Med 2008; 358:568–579.
2. Rotger M, Taffe P, Bleiber G, Gunthard HF, Furrer H, Vernazza P, et al. Gilbert syndrome and the development of antiretroviral therapy
-associated hyperbilirubinemia. J Infect Dis 2005; 192:1381–1386.
3. Chen S St, Jean P, Borland J, Song I, Yeo AJ, Piscitelli S, et al. Evaluation of the effect of UGT1A1
polymorphisms on dolutegravir pharmacokinetics. Pharmacogenomics
4. Holzinger ER, Grady B, Ritchie MD, Ribaudo HJ, Acosta EP, Morse GD, et al. Genome-wide association study of plasma efavirenz pharmacokinetics in AIDS Clinical Trials
Group protocols implicates several CYP2B6
variants. Pharmacogenet Genom 2012; 22:858–867.
5. Kakuda T, Nijs S, van Hoecke G. Pharmacokinetics of etravirine according to CYP2C9 and CYP2C19 metabolizer status: a meta-analysis of phase I trials. Presented at 20th conference on retroviruses and opportunistic infections, Atlanta, GA. February; 2013.
6. Lubomirov R, di Iulio J, Fayet A, Colombo S, Martinez R, Marzolini C, et al. ADME pharmacogenetics: investigation of the pharmacokinetics of the antiretroviral agent lopinavir coformulated with ritonavir. Pharmacogenet Genomics 2010; 20:217–230.
7. Yuan J, Guo S, Hall D, Cammett AM, Jayadev S, Distel M, et al. Toxicogenomics of nevirapine-associated cutaneous and hepatic adverse events among populations of African, Asian, and European descent. AIDS 2011; 25:1271–1280.
8. Pendergrass SA, Brown-Gentry K, Dudek S, Frase A, Torstenson ES, Goodloe R, et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet 2013; 9:e1003087.
9. Pendergrass SA, Brown-Gentry K, Dudek SM, Torstenson ES, Ambite JL, Avery CL, et al. The use of phenome-wide association studies
(PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol 2011; 35:410–422.
10. Pendergrass SA, Verma A, Okula A, Hall MA, Crawford DC, Ritchie MD. Phenome-wide association studies
: embracing complexity for discovery. Hum Hered 2015; 79:111–123.
11. Pendergrass SA, Ritchie MD. Phenome-wide association studies
: leveraging comprehensive phenotypic and genotypic data for discovery. Curr Genet Med Rep 2015; 3:92–100.
12. Bush WS, Oetjens MT, Crawford DC. Unravelling the human genome-phenome relationship using phenome-wide association studies
. Nat Rev Genet 2016; 17:129–145.
13. Tyler AL, Crawford DC, Pendergrass SA. The detection and characterization of pleiotropy: discovery, progress, and promise. Brief Bioinform 2016; 17:13–22.
14. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 2013; 14:483–495.
15. Kwara A, Lartey M, Sagoe KW, Court MH. Paradoxically elevated efavirenz concentrations in HIV/tuberculosis-coinfected patients with CYP2B6
516TT genotype on rifampin-containing antituberculous therapy. AIDS 2011; 25:388–390.
16. McIlleron HM, Schomaker M, Ren Y, Sinxadi P, Nuttall JJ, Gous H, et al. Effects of rifampin-based antituberculosis therapy on plasma efavirenz concentrations in children vary by CYP2B6
genotype. AIDS 2013; 27:1933–1940.
17. Luetkemeyer AF, Rosenkranz SL, Lu D, Grinsztejn B, Sanchez J, Ssemmanda M, et al. Combined effect of CYP2B6
genotype on plasma efavirenz exposure during rifampin-based antituberculosis therapy in the STRIDE study. Clin Infect Dis 2015; 60:1860–1863.
18. Ribaudo HJ, Liu H, Schwab M, Schaeffeler E, Eichelbaum M, Motsinger-Reif AA, et al. Effect of CYP2B6
, and CYP3A5
polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials
Group study. J Infect Dis 2010; 202:717–722.
19. Leger P, Chirwa S, Turner M, Richardson DM, Baker P, Leonard M, et al. Pharmacogenetics of efavirenz discontinuation for reported central nervous system symptoms appears to differ by race. Pharmacogenet Genomics 2016; 26:473–480.
20. Vardhanabhuti S, Ribaudo HJ, Landovitz RJ, Ofotokun I, Lennox JL, Currier JS, et al. Screening for UGT1A1
genotype in study A5257 would have markedly reduced premature discontinuation of atazanavir for hyperbilirubinemia. Open Forum Infect Dis 2015; 2:ofv085.
21. Martin AM, Nolan D, James I, Cameron P, Keller J, Moore C, et al. Predisposition to nevirapine hypersensitivity associated with HLA-DRB1*0101 and abrogated by low CD4 T-cell counts. AIDS 2005; 19:97–99.
22. Moore CB, Verma A, Pendergrass S, Setia S, Johnson DH, Daar ES, et al. Phenome-wide associations study (PheWAS) relating pre-treatment laboratory parameters with human genetic variants in AIDS clinical trials
group protocols. Open Forum Infect Dis 2014; 2:ofu113.
23. NHGRI-EBI. GWAS Catalog – The NHGRI-EBI Catalog of published genome-wide association studies. Available at: https://www.ebi.ac.uk/gwas/
. [Accessed 1 September 2016].
24. 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 2009; 361:2230–2240.
25. 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
. Ann Intern Med 2011; 154:445–456.
26. Baker JV, Peng G, Rapkin J, Abrams DI, Silverberg MJ, MacArthur RD, et al. CD4+ count and risk of non-AIDS diseases following initial treatment for HIV infection. AIDS 2008; 22:841–848.
27. van Lelyveld SF, Gras L, Kesselring A, Zhang S, De Wolf F, Wensing AM, et al. Long-term complications in patients with poor immunological recovery despite virological successful HAART in Dutch ATHENA cohort. AIDS 2012; 26:465–474.
28. Marin B, Thiebaut R, Bucher HC, Rondeau V, Costagliola D, Dorrucci M, et al. Non-AIDS-defining deaths and immunodeficiency in the era of combination antiretroviral therapy
. AIDS 2009; 23:1743–1753.
29. Baker JV, Peng G, Rapkin J, Krason D, Reilly C, Cavert WP, et al. Poor initial CD4+ recovery with antiretroviral therapy
prolongs immune depletion and increases risk for AIDS and non-AIDS diseases. JAIDS 2008; 48:541–546.
30. Tenorio AR, Zheng Y, Bosch RJ, Krishnan S, Rodriguez B, Hunt PW, et al. Soluble markers of inflammation and coagulation but not T-cell activation predict non-AIDS-defining morbid events during suppressive antiretroviral treatment. J Infect Dis 2014; 210:1248–1259.
31. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Prevention of HIV-1
infection with early antiretroviral therapy
. N Engl J Med 2011; 365:493–505.
32. Cholesterol Treatment Trialists C, Mihaylova B, Emberson J, Blackwell L, Keech A, Simes J, et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012; 380:581–590.
33. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 2013; 45:1345–1352.
34. Gutierrez F, Navarro A, Padilla S, Anton R, Masia M, Borras J, et al. Prediction of neuropsychiatric adverse events associated with long-term efavirenz therapy, using plasma drug level monitoring. Clin Infect Dis 2005; 41:1648–1653.
35. Gounden V, van Niekerk C, Snyman T, George JA. Presence of the CYP2B6
516G> T polymorphism, increased plasma efavirenz concentrations and early neuropsychiatric side effects in South African HIV-infected patients. AIDS Res Ther 2010; 7:32.
36. Clifford DB, Evans S, Yang Y, Acosta EP, Goodkin K, Tashima K, et al. Impact of efavirenz on neuropsychological performance and symptoms in HIV-infected individuals. Ann Intern Med 2005; 143:714–721.
37. Rotger M, Colombo S, Furrer H, Bleiber G, Buclin T, Lee BL, et al. Influence of CYP2B6
polymorphism on plasma and intracellular concentrations and toxicity of efavirenz and nevirapine in HIV-infected patients. Pharmacogenet Genom 2005; 15:1–5.
38. Marzolini C, Telenti A, Decosterd LA, Greub G, Biollaz J, Buclin T. Efavirenz plasma levels can predict treatment failure and central nervous system side effects in HIV-1
-infected patients. AIDS 2001; 15:71–75.
39. Gallego L, Barreiro P, del Rio R, Gonzalez de Requena D, Rodriguez-Albarino A, Gonzalez-Lahoz J, et al. Analyzing sleep abnormalities in HIV-infected patients treated with Efavirenz. Clin Infect Dis 2004; 38:430–432.
40. Gandhi M, Ameli N, Bacchetti P, Anastos K, Gange SJ, Minkoff H, et al. Atazanavir concentration in hair is the strongest predictor of outcomes on antiretroviral therapy
. Clin Infect Dis 2011; 52:1267–1275.
41. Johnson DH, Venuto C, Ritchie MD, Morse GD, Daar ES, McLaren PJ, et al. Genomewide association study of atazanavir pharmacokinetics and hyperbilirubinemia in AIDS Clinical Trials
Group protocol A5202. Pharmacogenet Genom 2014; 24:195–203.
42. Pereyra F, Jia X, McLaren PJ, Telenti A, de Bakker PI, Walker BD, et al. The major genetic determinants of HIV-1
control affect HLA class I peptide presentation. Science 2010; 330:1551–1557.
43. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81:559–575.
44. Team RDC. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
45. 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 2010; 467:1061–1073.
47. Delaneau O, Zagury JF, Marchini J. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 2013; 10:5–6.
48. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5:e1000529.
49. Verma SS, de Andrade M, Tromp G, Kuivaniemi H, Pugh E, Namjou B, et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front Genet 2014; 5:370.
50. PharmGKB – The Pharmacogenomics
Knowledgebase. Available at: https://www.pharmgkb.org/
; 2016. [Acecessed 1 September 2016].
51. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 2009; 106:9362–9367.
53. Good PI. Permutation, parametric and bootstrap tests of hypotheses: a practical guide to resampling methods for testing hypotheses 2004. 3rd ed. New York, NY: Springer.
54. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 2012; 44:269–276.
55. Rotger M, Tegude H, Colombo S, Cavassini M, Furrer H, Decosterd L, et al. Predictive value of known and novel alleles of CYP2B6
for efavirenz plasma concentrations in HIV-infected individuals. Clin Pharmacol Ther 2007; 81:557–566.
56. Haas DW, Ribaudo HJ, Kim RB, Tierney C, Wilkinson GR, Gulick RM, et al. Pharmacogenetics of efavirenz and central nervous system side effects: an adult AIDS Clinical Trials
Group study. AIDS 2004; 18:2391–2400.
57. Berndt SI, Gustafsson S, Magi R, Ganna A, Wheeler E, Feitosa MF, et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 2013; 45:501–512.
58. Rasmussen-Torvik LJ, Pacheco JA, Wilke RA, Thompson WK, Ritchie MD, Kho AN, et al. High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE
. Clin Transl Sci 2012; 5:394–399.
59. Weissglas-Volkov D, Aguilar-Salinas CA, Nikkola E, Deere KA, Cruz-Bautista I, Arellano-Campos O, et al. Genomic study in Mexicans identifies a new locus for triglycerides and refines European lipid loci. J Med Genet 2013; 50:298–308.
60. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 2009; 41:666–676.
61. Tan A, Sun J, Xia N, Qin X, Hu Y, Zhang S, et al. A genome-wide association and gene–environment interaction study for serum triglycerides levels in a healthy Chinese male population. Hum Mol Genet 2012; 21:1658–1664.
62. Coram MA, Duan Q, Hoffmann TJ, Thornton T, Knowles JW, Johnson NA, et al. Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am J Hum Genet 2013; 92:904–916.
63. Kraja AT, Vaidya D, Pankow JS, Goodarzi MO, Assimes TL, Kullo IJ, et al. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes 2011; 60:1329–1339.
64. Kooner JS, Chambers JC, Aguilar-Salinas CA, Hinds DA, Hyde CL, Warnes GR, et al. Genome-wide scan identifies variation in MLXIPL
associated with plasma triglycerides. Nat Genet 2008; 40:149–151.
65. Luo JQ, He FZ, Luo ZY, Wen JG, Wang LY, Sun NL, et al. Rs495828 polymorphism of the ABO
gene is a predictor of enalapril-induced cough in Chinese patients with essential hypertension. Pharmacogenet Genomics 2014; 24:306–313.
66. Lopez-Lopez E, Gutierrez-Camino A, Pinan MA, Sanchez-Toledo J, Uriz JJ, Ballesteros J, et al. Pharmacogenetics of microRNAs and microRNAs biogenesis machinery in pediatric acute lymphoblastic leukemia. PLoS One 2014; 9:e91261.
67. Wild PS, Zeller T, Schillert A, Szymczak S, Sinning CR, Deiseroth A, et al. A genome-wide association study identifies LIPA
as a susceptibility gene for coronary artery disease. Circ Cardiovasc Genet 2011; 4:403–412.
68. Nakano M, Ikeda Y, Tokuda Y, Fuwa M, Omi N, Ueno M, et al. Common variants in CDKN2B-AS1
associated with optic-nerve vulnerability of glaucoma identified by genome-wide association studies in Japanese. PLoS One 2012; 7:e33389.