Combining effects from rare and common genetic variants in an exome-wide association study of sequence data

Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants

Exploring Genome-Wide – Dietary Heme Iron Intake Interactions and the Risk of Type 2 Diabetes

Aims/hypothesis: Genome-wide association studies have identified over 50 new genetic loci for type 2 diabetes (T2D). Several studies conclude that higher dietary heme iron intake increases the risk of T2D. Therefore we assessed whether the relation between genetic loci and T2D is modified by dietary heme iron intake. Methods: We used Affymetrix Genome-Wide Human 6.0 array data [681,770 single nucleotide polymorphisms (SNPs)] and dietary information collected in the Health Professionals Follow-up Study (n = 725 cases; n = 1,273 controls) and the Nurses’ Health Study (n = 1,081 cases; n = 1,692 controls). We assessed whether genome-wide SNPs or iron metabolism SNPs interacted with dietary heme iron intake in relation to T2D, testing for associations in each cohort separately and then meta-analyzing to pool the results. Finally, we created 1,000 synthetic pathways matched to an iron metabolism pathway on number of genes, and number of SNPs in each gene. We compared the iron metabolic pathway SNPs with these synthetic SNP assemblies in their relation to T2D to assess if the pathway as a whole interacts with dietary heme iron intake. Results: Using a genomic approach, we found no significant gene–environment interactions with dietary heme iron intake in relation to T2D at a Bonferroni corrected genome-wide significance level of $7.33 ×10^{-8}$ (top SNP in pooled analysis: intergenic rs10980508; $p = 1.03 × 10^{-6}$). Furthermore, no SNP in the iron metabolic pathway significantly interacted with dietary heme iron intake at a Bonferroni corrected significance level of $2.10 × 10^{-4}$ (top SNP in pooled analysis: rs1805313; $p = 1.14 × 10^{-3}$). Finally, neither the main genetic effects (pooled empirical p by SNP = 0.41), nor gene – dietary heme–iron interactions (pooled empirical p-value for the interactions = 0.72) were significant for the iron metabolic pathway as a whole. Conclusions: We found no significant interactions between dietary heme iron intake and common SNPs in relation to T2D

Statin Use in Relation to Intraocular Pressure, Glaucoma, and Ocular Coherence Tomography Parameters in the UK Biobank

PURPOSE. The purpose of this study was to evaluate the relationship between statin use and glaucoma-related traits. METHODS. In a cross-sectional study, we included 118,153 UK Biobank participants with data on statin use and corneal-compensated IOP. In addition, we included 192,283 participants (8982 cases) with data on glaucoma status. After excluding participants with neurodegenerative diseases, 41,638 participants with macular retinal nerve fiber layer thickness (mRNFL) and 41,547 participants with macular ganglion cell inner plexiform layer thickness (mGCIPL) were available for analysis. We examined associations of statin use with IOP, mRNFL, mGCIPL, and glaucoma status utilizing multivariable-adjusted regression models. We assessed whether a glaucoma polygenic risk score (PRS) modified associations. We performed Mendelian randomization (MR) experiments to investigate associations with various glaucoma-related outcomes. RESULTS. Statin users had higher unadjusted mean IOP ± SD than nonusers, but in a multivariable-adjusted model, IOP did not differ by statin use (difference = 0.05 mm Hg, 95% confidence interval [CI] = −0.02 to 0.13, P = 0.17). Similarly, statin use was not associated with prevalent glaucoma (odds ratio [OR] = 1.05, 95% CI = 0.98 to 1.13). Statin use was weakly associated with thinner mRNFL (difference = −0.15 microns, 95% CI = −0.28 to −0.01, P = 0.03) but not with mGCIPL thickness (difference = −0.12 microns, 95% CI = −0.29 to 0.05, P = 0.17). No association was modified by the glaucoma PRS (Pinteraction ≥ 0.16). MR experiments showed no evidence for a causal association between the cholesterol-altering effect of statins and several glaucoma traits (inverse weighted variance P ≥ 0.14). CONCLUSIONS. We found no evidence of a protective association between statin use and glaucoma or related traits after adjusting for key confounders

Intraocular pressure, glaucoma and dietary caffeine consumption: a gene-diet interaction study from the UK Biobank

Objective: We examined the association of habitual caffeine intake with intraocular pressure (IOP) and glaucoma and whether these associations were modified by genetic predisposition to higher IOP. We also assessed whether genetic predisposition to higher coffee consumption was related to IOP. Design: A cross-sectional study in the UK Biobank. Participants: We included 121,374 participants (baseline ages 39-73 years) with data on coffee and tea intake (collected 2006-2010) and corneal-compensated IOP measurements in 2009. In a subset of 77,906 participants with up to five web-based 24-hour-recall food frequency questionnaires (2009-2012) we evaluated total caffeine intake. We also assessed the same relations with any glaucoma (9,286 cases and 189,763 controls). Method: We evaluated multivariable-adjusted associations with IOP using linear regression, and with glaucoma using logistic regression. For both outcomes, we examined gene-diet interactions, using a polygenic risk score (PRS), which combined the effects of 111 genetic variants associated with IOP. We also performed two-sample Mendelian Randomization (MR) using 8 genetic variants associated with coffee intake, to assess potential causal effects of coffee consumption on IOP. Main Outcome and Measures: IOP; glaucoma. Results: Mean IOP was 16.0 mmHg (Standard Deviation=3.8). MR analysis did not support a causal effect of coffee drinking on IOP (P>0.1). Greater caffeine intake was weakly associated with lower IOP: the highest (≥232mg/day) vs. lowest (480mg/day versus <80 mg/day was associated with a 0.35 mmHg higher IOP (Pinteraction=0.01). The relation between caffeine intake and glaucoma was null (P≥0.1). However, this relation was also significantly modified by IOP PRS: compared to those in the lowest IOP PRS quartile consuming no caffeine, those in the highest IOP PRS quartile consuming ≥321mg/day had a 3.90-fold higher glaucoma prevalence (Pinteraction=0.0003). Conclusions: Habitual caffeine consumption was weakly associated with lower IOP and the association between caffeine consumption and glaucoma was null. However, among participants with the strongest genetic predisposition to elevated IOP, greater caffeine consumption was associated with higher IOP and higher glaucoma prevalence

Screening for interaction effects in gene expression data

Expression quantitative trait (eQTL) studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs) and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach

The association between serum lipids and intraocular pressure in two large UK cohorts

PURPOSE: Serum lipids are modifiable, routinely collected blood tests associated with cardiovascular health. We examined the association of commonly collected serum lipid measures (total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL-C) and triglycerides (TG)) with intraocular pressure (IOP). DESIGN: Cross-sectional study in the UK Biobank and EPIC-Norfolk cohorts. PARTICIPANTS: We included 94 323 participants of UK Biobank (mean age 57 years) and 6 230 participants of EPIC-Norfolk (mean age 68 years) with data on TC, HDL-C, LDL-C, TG collected between 2006-2009. METHODS: Multivariable linear regression adjusting for demographic, lifestyle, anthropometric, medical and ophthalmic covariables was used to examine the associations of serum lipids with IOPcc. MAIN OUTCOME MEASURES: IOPcc. RESULTS: Higher levels of TC, HDL-C and LDL-C were independently associated with higher IOPcc in both cohorts after adjustment for key demographic, medical and lifestyle factors. For each standard deviation increase in TC, HDL-C, and LDL-C, IOPcc (mmHg) was higher by 0.09 (95% CI: 0.06-0.11; P<0.001), 0.11 (95% CI 0.08-0.13; P<0.001), 0.07 (95% CI: 0.05-0.09, P<0.001), respectively in the UK Biobank cohort. In the EPIC-Norfolk cohort, each additional standard deviation in TC, HDL-C, and LDL-C was associated with a higher IOPcc (mmHg) by 0.19 (95% CI 0.07-0.31, P=0.001), 0.14 (95% CI 0.03-0.25, P=0.016), and 0.17 (95% CI 0.06-0.29, P=0.003). An inverse association between TGs and IOP in the UK Biobank (-0.05, 95% CI -0.08 to -0.03, P<0.001) was not replicated in the EPIC cohort (P=0.30). CONCLUSION: Our findings suggest that serum TC, HDL-C and LDL-C are positively associated with IOP in two UK cohorts and TGs may be negatively associated. Future research is required to assess whether these associations are causal in nature

The Association of Physical Activity with Glaucoma and Related Traits in the UK Biobank

PURPOSE: To examine the association of physical activity (PA) with glaucoma and related traits, to assess whether genetic predisposition to glaucoma modified these associations, and to probe causal relationships using Mendelian randomization (MR). DESIGN: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia. PARTICIPANTS: UK Biobank participants with data on self-reported or accelerometer-derived PA and intraocular pressure (IOP; n = 94 206 and n = 27 777, respectively), macular inner retinal OCT measurements (n = 36 274 and n = 9991, respectively), and glaucoma status (n = 86 803 and n = 23 556, respectively). METHODS: We evaluated multivariable-adjusted associations of self-reported (International Physical Activity Questionnaire) and accelerometer-derived PA with IOP and macular inner retinal OCT parameters using linear regression and with glaucoma status using logistic regression. For all outcomes, we examined gene-PA interactions using a polygenic risk score (PRS) that combined the effects of 2673 genetic variants associated with glaucoma. MAIN OUTCOME MEASURES: Intraocular pressure, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and glaucoma status. RESULTS: In multivariable-adjusted regression models, we found no association of PA level or time spent in PA with glaucoma status. Higher overall levels and greater time spent in higher levels of both self-reported and accelerometer-derived PA were associated positively with thicker mGCIPL (P < 0.001 for trend for each). Compared with the lowest quartile of PA, participants in the highest quartiles of accelerometer-derived moderate- and vigorous-intensity PA showed a thicker mGCIPL by +0.57 μm (P < 0.001) and +0.42 μm (P = 0.005). No association was found with mRNFL thickness. High overall level of self-reported PA was associated with a modestly higher IOP of +0.08 mmHg (P = 0.01), but this was not replicated in the accelerometry data. No associations were modified by a glaucoma PRS, and MR analyses did not support a causal relationship between PA and any glaucoma-related outcome. CONCLUSIONS: Higher overall PA level and greater time spent in moderate and vigorous PA were not associated with glaucoma status but were associated with thicker mGCIPL. Associations with IOP were modest and inconsistent. Despite the well-documented acute reduction in IOP after PA, we found no evidence that high levels of habitual PA are associated with glaucoma status or IOP in the general population. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references

The association of alcohol consumption with glaucoma and related traits: findings from the UK Biobank

PURPOSE: To examine the associations of alcohol consumption with glaucoma and related traits; to assess whether a genetic predisposition to glaucoma modified these associations; and to perform Mendelian randomization (MR) experiments to probe causal effects. DESIGN: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia. PARTICIPANTS: UK Biobank participants with data on intraocular pressure (IOP) (n=109 097), OCT derived macular inner retinal layer thickness measures (n=46 236) and glaucoma status (n=173 407). METHODS: Participants were categorized according to self-reported drinking behaviors. Quantitative estimates of alcohol intake were derived from touchscreen questionnaires and food composition tables. We performed a two-step analysis, first comparing categories of alcohol consumption (never, infrequent, regular, and former drinkers), before assessing for a dose-response effect in regular drinkers only. Multivariable linear, logistic and restricted cubic spline (RCS) regression, adjusted for key sociodemographic, medical, anthropometric and lifestyle factors, were used to examine associations. We assessed whether any association was modified by a multi-trait glaucoma polygenic risk score. The inverse-variance weighted method was used for the main MR analyses. MAIN OUTCOME MEASURES: IOP, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and prevalent glaucoma. RESULTS: Compared to infrequent drinkers, regular drinkers had higher IOP (+0.17mmHg; P<0.001) and thinner mGCIPL (-0.17μm; P=0.049); while former drinkers had a higher prevalence of glaucoma (OR 1.53; P=0.002). In regular drinkers, alcohol intake was adversely associated with all outcomes in a dose-dependent manner (all P<0.001). RCS regression analyses suggested non-linear associations, with apparent threshold effects at approximately 50g (∼6 UK or 4 US alcoholic units)/week, for mRNFL and mGCIPL thickness. Significantly stronger alcohol-IOP associations were observed in participants at higher genetic susceptibility to glaucoma (Pinteraction<0.001). MR analyses provided evidence for a causal association with mGCIPL thickness. CONCLUSIONS: Alcohol intake was consistently and adversely associated with glaucoma and related traits, and at levels below current UK (<112g/week) and US (women: <98g/week; men: <196g/week) guidelines. While we cannot infer causality definitively, these results will be of interest to people with, or at risk of, glaucoma and their advising physicians

Genetic correlations between diabetes and glaucoma: an analysis of continuous and dichotomous phenotypes

Purpose: A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits.Design: Cross-sectional study.Methods: We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure (IOP), central corneal thickness (CCT), corneal hysteresis (CH), corneal resistance factor (CRF), cup-disc ratio (CDR), and primary open-angle glaucoma (POAG)). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT and selected diabetes-related traits based on individual level phenotype data in two Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines (SOLAR).Results: Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a non-significant negative correlation between T2D and POAG (rg=-0.14; p=0.16). Using SOLAR, the genetic correlations between measured IOP, CCT, FBS, fasting insulin and hemoglobin A1c, were null. In contrast, genetic correlations between IOP and POAG (rg ≥0.45; p≤3.0E-04) and between CDR and POAG were high (rg =0.57; p=2.8E-10). However, genetic correlations between corneal properties (CCT, CRF and CH) and POAG were low (rg range: -0.18 - 0.11) and non-significant (p≥0.07).Conclusion: These analyses suggest there is limited genetic correlation between diabetes- and glaucoma-related traits

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels