A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma.

Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

The power of genetic diversity in genome-wide association studies of lipids.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.

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.

Novel EDGE encoding method enhances ability to identify genetic interactions.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Pharmacogenetics of tenofovir clearance among Southern Africans living with HIV.

BACKGROUND: Tenofovir is a component of preferred combination antiretroviral therapy (ART) regimens in Africa. Few pharmacogenetic studies have been conducted on tenofovir exposure in Africa, where genetic diversity is greatest. OBJECTIVE: We characterized the pharmacogenetics of plasma tenofovir clearance in Southern Africans receiving tenofovir disoproxil fumarate (TDF) or tenofovir alafenamide (TAF). METHODS: Adults randomized to TAF or TDF in dolutegravir-containing arms of the ADVANCE trial (NCT03122262) were studied. Linear regression models stratified by study arm examined associations with unexplained variability in tenofovir clearance. We investigated genetic associations with polymorphisms selected a priori followed by genome-wide associations. RESULTS: A total of 268 participants (138 and 130 in the TAF and TDF arm, respectively) were evaluable for associations. Among polymorphisms previously associated with any drug-related phenotype, IFNL4 rs12979860 was associated with more rapid tenofovir clearance in both arms (TAF: P = 0.003; TDF: P = 0.003). Genome-wide, the lowest P values for tenofovir clearance in TAF and TDF arms were LINC01684 rs9305223 (P = 3.0 × 10-8) and intergenic rs142693425 (P = 1.4 × 10-8), respectively. CONCLUSION: Among Southern Africans randomized to TAF or TDF in ADVANCE, unexplained variability in tenofovir clearance was associated with a polymorphism in IFNL4, an immune-response gene. It is unclear how this gene would affect tenofovir disposition.

Pharmacogenetics of tenofovir renal toxicity in HIV-positive Southern Africans.

OBJECTIVE: Renal toxicity is more common with tenofovir disoproxil fumarate (TDF) than with tenofovir alafenamide fumarate (TAF). We investigated whether polymorphisms in genes relevant to tenofovir disposition affect renal toxicity among HIV-positive Southern Africans. METHODS: Genetic sub-study of adults randomized to initiate TAF or TDF together with dolutegravir and emtricitabine was conducted. Outcomes were changes from week 4 to 48 in the estimated glomerular filtration rate (eGFR) and from baseline to week 48 in urine retinol-binding protein and urine ß2-microglobulin adjusted for urinary creatinine (uRBP/Cr and uB2M/Cr). Primary analyses prioritized 14 polymorphisms previously reported to be associated with tenofovir disposition or renal outcomes, and all polymorphisms in 14 selected genes. We also explored genome-wide associations. RESULTS: 336 participants were enrolled. Among 14 polymorphisms of primary interest, the lowest P values for change in eGFR, uRBP/Cr, and uB2M/Cr were ABCC4 rs899494 ( P  = 0.022), ABCC10 rs2125739 ( P  = 0.07), and ABCC4 rs1059751 ( P  = 0.0088); and in genes of interest, the lowest P values were ABCC4 rs4148481 ( P  = 0.0013), rs691857 ( P  = 0.00039), and PKD2 rs72659631 ( P  = 0.0011). However, none of these polymorphisms withstood correction for multiple testing. Genome-wide, the lowest P values were COL27A1 rs1687402 ( P  = 3.4 × 10 -9 ), CDH4 rs66494466 ( P  = 5.6 × 10 -8 ), and ITGA4 rs3770126 ( P  = 6.1 × 10 -7 ). CONCLUSION: Two ABCC4 polymorphisms, rs899494 and rs1059751, were nominally associated with change in eGFR and uB2M/Cr, respectively, albeit in the opposite direction of previous reports. COL27A1 polymorphism was genome-wide significantly associated with change in eGFR.

Pharmacogenetics of Dolutegravir Plasma Exposure Among Southern Africans With Human Immunodeficiency Virus.

BACKGROUND: Dolutegravir is a component of preferred antiretroviral therapy (ART) regimens. We characterized the pharmacogenetics of dolutegravir exposure after ART initiation in the ADVANCE trial in South Africa. METHODS: Genome-wide genotyping followed by imputation was performed. We developed a population pharmacokinetic model for dolutegravir using nonlinear mixed-effects modeling. Linear regression models examined associations with unexplained variability in dolutegravir area under the concentration-time curve (AUCVAR). RESULTS: Genetic associations were evaluable in 284 individuals. Of 9 polymorphisms previously associated with dolutegravir pharmacokinetics, the lowest P value with AUCVAR was UGT1A1 rs887829 (P = 1.8 × 10-4), which was also associated with log10 bilirubin (P = 8.6 × 10-13). After adjusting for rs887829, AUCVAR was independently associated with rs28899168 in the UGT1A locus (P = .02), as were bilirubin concentrations (P = 7.7 × 10-8). In the population pharmacokinetic model, rs887829 T/T and C/T were associated with 25.9% and 10.8% decreases in dolutegravir clearance, respectively, compared with C/C. The lowest P value for AUCVAR genome-wide was CAMKMT rs343942 (P = 2.4 × 10-7). CONCLUSIONS: In South Africa, rs887829 and rs28899168 in the UGT1A locus were independently associated with dolutegravir AUCVAR. The novel rs28899168 association warrants replication. This study enhances understanding of dolutegravir pharmacogenetics in Africa.

Pharmacogenetics of Between-Individual Variability in Plasma Clearance of Bedaquiline and Clofazimine in South Africa.

BACKGROUND: Plasma bedaquiline clearance is reportedly more rapid with African ancestry. Our objective was to determine whether genetic polymorphisms explained between-individual variability in plasma clearance of bedaquiline, its M2 metabolite, and clofazimine in a cohort of patients treated for drug-resistant tuberculosis in South Africa. METHODS: Plasma clearance was estimated with nonlinear mixed-effects modeling. Associations between pharmacogenetic polymorphisms, genome-wide polymorphisms, and variability in clearance were examined using linear regression models. RESULTS: Of 195 cohort participants, 140 were evaluable for genetic associations. Among 21 polymorphisms selected based on prior genome-wide significant associations with any drug, rs776746 (CYP3A5∗3) was associated with slower clearance of bedaquiline (P = .0017) but not M2 (P = .25). CYP3A5∗3 heterozygosity and homozygosity were associated with 15% and 30% slower bedaquiline clearance, respectively. The lowest P value for clofazimine clearance was with VKORC1 rs9923231 (P = .13). In genome-wide analyses, the lowest P values for clearance of bedaquiline and clofazimine were with RFX4 rs76345012 (P = 6.4 × 10-7) and CNTN5 rs75285763 (P = 2.9 × 10-8), respectively. CONCLUSIONS: Among South Africans treated for drug-resistant tuberculosis, CYP3A5∗3 was associated with slower bedaquiline clearance. Different CYP3A5∗3 frequencies among populations may help explain the more rapid bedaquiline clearance reported in Africans. Associations with RFX4 and CNTN5 are likely by chance alone.

Efavirenz Pharmacogenetics and Weight Gain Following Switch to Integrase Inhibitor-Containing Regimens.

BACKGROUND: Unwanted weight gain affects some people living with human immunodeficiency virus (HIV) who are prescribed integrase strand transfer inhibitors (INSTIs). Mechanisms and risk factors are incompletely understood. METHODS: We utilized 2 cohorts to study pharmacogenetics of weight gain following switch from efavirenz- to INSTI-based regimens. In an observational cohort, we studied weight gain at 48 weeks following switch from efavirenz- to INSTI-based regimens among patients who had been virologically suppressed for at least 2 years at a clinic in the United States. Associations were characterized with CYP2B6 and UGT1A1 genotypes that affect efavirenz and INSTI metabolism, respectively. In a clinical trials cohort, we studied weight gain at 48 weeks among treatment-naive participants who were randomized to receive efavirenz-containing regimens in AIDS Clinical Trials Group studies A5095, A5142, and A5202 and did not receive INSTIs. RESULTS: In the observational cohort (n = 61), CYP2B6 slow metabolizers had greater weight gain after switch (P = .01). This was seen following switch to elvitegravir or raltegravir, but not dolutegravir. UGT1A1 genotype was not associated with weight gain. In the clinical trials cohort (n = 462), CYP2B6 slow metabolizers had lesser weight gain at week 48 among participants receiving efavirenz with tenofovir disoproxil fumarate (P = .001), but not those receiving efavirenz with abacavir (P = .65). Findings were consistent when stratified by race/ethnicity and by sex. CONCLUSIONS: Among patients who switched from efavirenz- to INSTI-based therapy, CYP2B6 genotype was associated with weight gain, possibly reflecting withdrawal of the inhibitory effect of higher efavirenz concentrations on weight gain. The difference by concomitant nucleoside analogue is unexplained.

Genetics of height and risk of atrial fibrillation: A Mendelian randomization study.

BACKGROUND: Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation. METHODS AND FINDINGS: In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations. CONCLUSIONS: In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium.

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.

Genetic Susceptibility to Postdiarrheal Hemolytic-Uremic Syndrome After Shiga Toxin-Producing Escherichia coli Infection: A Centers for Disease Control and Prevention FoodNet Study.

Background: Postdiarrheal hemolytic-uremic syndrome (D+HUS) following Shiga toxin-producing Escherichia coli (STEC) infection is a serious condition lacking specific treatment. Host immune dysregulation and genetic susceptibility to complement hyperactivation are implicated in non-STEC-related HUS. However, genetic susceptibility to D+HUS remains largely uncharacterized. Methods: Patients with culture-confirmed STEC diarrhea, identified through the Centers for Disease Control and Prevention FoodNet surveillance system (2007-2012), were serotyped and classified by laboratory and/or clinical criteria as having suspected, probable, or confirmed D+HUS or as controls and underwent genotyping at 200 loci linked to nondiarrheal HUS or similar pathologies. Genetic associations with D+HUS were explored by multivariable regression, with adjustment for known risk factors. Results: Of 641 enrollees with STEC O157:H7, 80 had suspected D+HUS (41 with probable and 32 with confirmed D+HUS). Twelve genes related to cytokine signaling, complement pathways, platelet function, pathogen recognition, iron transport, and endothelial function were associated with D+HUS in multivariable-adjusted analyses (P ≤ .05). Of 12 significant single-nucleotide polymorphisms (SNPs), 5 were associated with all levels of D+HUS (intergenic SNP rs10874639, TFRC rs3804141, EDN1 rs5370, GP1BA rs121908064, and B2M rs16966334), and 7 SNPs (6 non-complement related) were associated with confirmed D+HUS (all P < .05). Conclusions: Polymorphisms in many non-complement-related genes may contribute to D+HUS susceptibility. These results require replication, but they suggest novel therapeutic targets in patients with D+HUS.

A simulation study investigating power estimates in phenome-wide association studies.

BACKGROUND: Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance. RESULTS: We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants. CONCLUSIONS: This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events.

OBJECTIVE: We characterized associations between central nervous system (CNS) adverse events and brain neurotransmitter transporter/receptor genomics among participants randomized to efavirenz-containing regimens in AIDS Clinical Trials Group studies in the USA. PARTICIPANTS AND METHODS: Four clinical trials randomly assigned treatment-naive participants to efavirenz-containing regimens. Genome-wide genotype and PrediXcan were used to infer gene expression levels in tissues including 10 brain regions. Multivariable regression models stratified by race/ethnicity were adjusted for CYP2B6/CYP2A6 genotypes that predict plasma efavirenz exposure, age, and sex. Combined analyses also adjusted for genetic ancestry. RESULTS: Analyses included 167 cases with grade 2 or greater efavirenz-consistent CNS adverse events within 48 weeks of study entry, and 653 efavirenz-tolerant controls. CYP2B6/CYP2A6 genotype level was independently associated with CNS adverse events (odds ratio: 1.07; P=0.044). Predicted expression of six genes postulated to mediate efavirenz CNS side effects (SLC6A2, SLC6A3, PGR, HTR2A, HTR2B, HTR6) were not associated with CNS adverse events after correcting for multiple testing, the lowest P value being for PGR in hippocampus (P=0.012), nor were polymorphisms in these genes or AR and HTR2C, the lowest P value being for rs12393326 in HTR2C (P=6.7×10(-4)). As a positive control, baseline plasma bilirubin concentration was associated with predicted liver UGT1A1 expression level (P=1.9×10(-27)). CONCLUSION: Efavirenz-related CNS adverse events were not associated with predicted neurotransmitter transporter/receptor gene expression levels in brain or with polymorphisms in these genes. Variable susceptibility to efavirenz-related CNS adverse events may not be explained by brain neurotransmitter transporter/receptor genomics.

Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202.

BACKGROUND: High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202. PARTICIPANTS AND METHODS: From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values. RESULTS: This analysis included 1181 patients. At P less than 1.5×10, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait. CONCLUSION: Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials' datasets for genetic associations.

Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk.

BACKGROUND: ECG QRS duration, a measure of cardiac intraventricular conduction, varies ≈2-fold in individuals without cardiac disease. Slow conduction may promote re-entrant arrhythmias. METHODS AND RESULTS: We performed a genome-wide association study to identify genomic markers of QRS duration in 5272 individuals without cardiac disease selected from electronic medical record algorithms at 5 sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium QRS genome-wide association study meta-analysis. Twenty-three single-nucleotide polymorphisms in 5 loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 single-nucleotide polymorphisms were in the chromosome 3 SCN5A and SCN10A loci, where the most significant single-nucleotide polymorphisms were rs1805126 in SCN5A with P=1.2×10(-8) (eMERGE) and P=2.5×10(-20) (CHARGE) and rs6795970 in SCN10A with P=6×10(-6) (eMERGE) and P=5×10(-27) (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. We then performed phenome-wide association studies on variants in these 5 loci in 13859 European Americans to search for diagnoses associated with these markers. Phenome-wide association study identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5272 "heart-healthy" study population. CONCLUSIONS: We conclude that DNA biobanks coupled to electronic medical records not only provide a platform for genome-wide association study but also may allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The phenome-wide association study approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.

Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

We repurposed existing genotypes in DNA biobanks across the Electronic Medical Records and Genomics network to perform a genome-wide association study for primary hypothyroidism, the most common thyroid disease. Electronic selection algorithms incorporating billing codes, laboratory values, text queries, and medication records identified 1317 cases and 5053 controls of European ancestry within five electronic medical records (EMRs); the algorithms' positive predictive values were 92.4% and 98.5% for cases and controls, respectively. Four single-nucleotide polymorphisms (SNPs) in linkage disequilibrium at 9q22 near FOXE1 were associated with hypothyroidism at genome-wide significance, the strongest being rs7850258 (odds ratio [OR] 0.74, p = 3.96 × 10(-9)). This association was replicated in a set of 263 cases and 1616 controls (OR = 0.60, p = 5.7 × 10(-6)). A phenome-wide association study (PheWAS) that was performed on this locus with 13,617 individuals and more than 200,000 patient-years of billing data identified associations with additional phenotypes: thyroiditis (OR = 0.58, p = 1.4 × 10(-5)), nodular (OR = 0.76, p = 3.1 × 10(-5)) and multinodular (OR = 0.69, p = 3.9 × 10(-5)) goiters, and thyrotoxicosis (OR = 0.76, p = 1.5 × 10(-3)), but not Graves disease (OR = 1.03, p = 0.82). Thyroid cancer, previously associated with this locus, was not significantly associated in the PheWAS (OR = 1.29, p = 0.09). The strongest association in the PheWAS was hypothyroidism (OR = 0.76, p = 2.7 × 10(-13)), which had an odds ratio that was nearly identical to that of the curated case-control population in the primary analysis, providing further validation of the PheWAS method. Our findings indicate that EMR-linked genomic data could allow discovery of genes associated with many diseases without additional genotyping cost.

Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy.

Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ∼26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ∼7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ∼6-10% of variability in multivariate models (including age, sex, and PCs) but only ∼1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.