Generalized estimating equations for genome-wide association studies using longitudinal phenotype data

A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

Sparse group variable selection for gene-environment interactions in the longitudinal study

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G  × $\times $  E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G  × $\times $  E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits

Correlated phenotypes often share common genetic determinants. Thus, a multi‐trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

In utero and peripubertal metals exposure in relation to reproductive hormones and sexual maturation and progression among boys in Mexico City

BACKGROUND

Endocrine disrupting chemicals (EDCs) such as metals have been reported to alter circulating reproductive hormone concentrations and pubertal development in animals. However, the relationship has rarely been investigated among humans, with the exception of heavy metals, such as Pb and Cd. Our aim was to investigate measures of in utero and peripubertal metal exposure in relation to reproductive hormone concentrations and sexual maturation and progression among boys from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) cohorts.

METHODS

Our analysis included 118 pregnant women and their male children from the ELEMENT study. Essential and non-essential metals were measured in urine collected from the mothers during the third trimester of pregnancy and their male children at 8-14 years. Reproductive hormone concentrations [serum testosterone, estradiol, dehydroepiandrosterone sulfate (DHEA-S), inhibin B, and sex hormone-binding globulin (SHBG)] were measured in blood samples from the children at 8-14 years. We also assessed Tanner stages for sexual maturation (genital, pubic hair development, and testicular volume), at two time points (8-14, 10-18 years). We used linear regression to independently examine urinary metal concentrations in relation to each peripubertal reproductive hormones adjusting for child age and BMI. Generalized estimation equations (GEEs) were used to evaluate the association of in utero and peripubertal metal exposures with sexual maturation and progression during follow-up based on Tanner staging and testicular volume.

RESULTS

In utero and prepubertal concentrations of some urinary metals were associated with increased concentrations of peripubertal reproductive hormones, especially non-essential metal(loid)s As and Cd (in utero), and Ba (peripubertal) as well as essential metal Mo (in utero) in association with testosterone. More advanced pubic hair developmental stage and higher testicular volume at the early teen visit was observed for boys with higher non-essential metal concentrations, including in utero Al and peripubertal Ba, and essential metal Zn concentration (peripubertal). These metals were also associated with slower pubertal progression between the two visits.

CONCLUSION

These findings suggest that male reproductive development may be associated with both essential and non-essential metal exposure during in utero and peripubertal windows.

Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5 × 10-8) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

Evolutionary history of disease-susceptibility loci identified in longitudinal exome-wide association studies

Background

Our longitudinal exome‐wide association studies previously detected various genetic determinants of complex disorders using ~26,000 single‐nucleotide polymorphisms (SNPs) that passed quality control and longitudinal medical examination data (mean follow‐up period, 5 years) in 4884–6022 Japanese subjects. We found that allele frequencies of several identified SNPs were remarkably different among four ethnic groups. Elucidating the evolutionary history of disease‐susceptibility loci may help us uncover the pathogenesis of the related complex disorders.

Methods

In the present study, we conducted evolutionary analyses such as extended haplotype homozygosity, focusing on genomic regions containing disease‐susceptibility loci and based on genotyping data of our previous studies and datasets from the 1000 Genomes Project.

Results

Our evolutionary analyses suggest that derived alleles of rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs closely located at 12q24.1 associated with type 2 diabetes mellitus, obesity, dyslipidemia, and three complex disorders (hypertension, hyperuricemia, and dyslipidemia), respectively, rapidly expanded after the human dispersion from Africa (Out‐of‐Africa). Allele frequencies of GGA3 and six SNPs at 12q24.1 appeared to have remarkably changed in East Asians, whereas the derived alleles of rs34902660 of SLC17A3 and rs7656604 at 4q13.3 might have spread across Japanese and non‐Africans, respectively, although we cannot completely exclude the possibility that allele frequencies of disease‐associated loci may be affected by demographic events.

Conclusion

Our findings indicate that derived allele frequencies of nine disease‐associated SNPs (rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs at 12q24.1) identified in the longitudinal exome‐wide association studies largely increased in non‐Africans after Out‐of‐Africa.

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

BACKGROUNDS

Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants.

RESULTS

Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method.

CONCLUSION

In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .

Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry

We evaluated interactions of SNP-by-ACE-I/ARB and SNP-by-TD on serum potassium (K+) among users of antihypertensive treatments (anti-HTN). Our study included seven European-ancestry (EA) (N = 4835) and four African-ancestry (AA) cohorts (N = 2016). We performed race-stratified, fixed-effect, inverse-variance-weighted meta-analyses of 2.5 million SNP-by-drug interaction estimates; race-combined meta-analysis; and trans-ethnic fine-mapping. Among EAs, we identified 11 significant SNPs (P < 5 × 10-8) for SNP-ACE-I/ARB interactions on serum K+ that were located between NR2F1-AS1 and ARRDC3-AS1 on chromosome 5 (top SNP rs6878413 P = 1.7 × 10-8; ratio of serum K+ in ACE-I/ARB exposed compared to unexposed is 1.0476, 1.0280, 1.0088 for the TT, AT, and AA genotypes, respectively). Trans-ethnic fine mapping identified the same group of SNPs on chromosome 5 as genome-wide significant for the ACE-I/ARB analysis. In conclusion, SNP-by-ACE-I /ARB interaction analyses uncovered loci that, if replicated, could have future implications for the prevention of arrhythmias due to anti-HTN treatment-related hyperkalemia. Before these loci can be identified as clinically relevant, future validation studies of equal or greater size in comparison to our discovery effort are needed.

Six novel susceptibility loci for coronary artery disease and cerebral infarction identified by longitudinal exome-wide association studies in a Japanese population

Coronary artery disease (CAD) and cerebral infarction (CI) remain major causes of morbidity and mortality in humans. Recent genome-wide association studies have identified various genetic variants associated with these diseases. However, these studies were commonly conducted in a cross-sectional manner. Therefore, the present research performed longitudinal exome-wide association studies for CAD and CI using data on ~244,000 genotyped variants and the clinical data of 6,026 Japanese individuals who had attended annual health checkups for several years (mean followed-up period, 5±3 years). Following quality controls, the significance [false discovery rate (FDR) of <0.05] of association of the diseases with 24,651 single nucleotide polymorphisms (SNPs) in 5,989 individuals for three inheritance models was tested using the generalized estimating equation model. SNPs that reached statistical significance were further screened against a threshold of approxdf (a scale of small effective sample size) of >30. The longitudinal exome-wide association studies revealed that three SNPs [rs4606855 of ADGRE3 (P=2.5×10^-6; FDR=0.031; approxdf=71), rs3746414 of ZFP64 (P=5.9×10^-6; FDR=0.048; approxdf=93) and rs7132908 of FAIM2 (P<2.0×10^-16; FDR<4.9×10^-12; approxdf=65)] were significantly associated with the prevalence of CAD. A different set of three SNPs [rs6580741 of FAM186A (P<2.0×10^-16; FDR<4.9×10^-12; approxdf=48), rs1324015 of LINC00400 (P<2.0×10^-16; FDR<4.9×10^-12; approxdf=49) and rs884205 of TNFRSF11A (P<2.0×10^-16; FDR<4.9×10^-12; approxdf=32)] was significantly associated with CI. The comparison of disease incidence with these SNPs demonstrated that all the minor alleles were associated with decreased susceptibility to CAD or CI. In conclusion, six novel SNPs were identified as susceptibility loci for CAD (rs4606855 of ADGRE3, rs3746414 of ZFP64, and rs7132908 of FAIM2) or CI (rs6580741 of FAM186A, rs1324015 of LINC00400, and rs884205 of TNFRSF11A).

Identification of nine novel loci related to hematological traits in a Japanese population

Recent genome-wide association studies have identified various genetic variants associated with hematological traits. Although it is possible that quantitative data of hematological traits are varied among the years examined, conventional genome-wide association studies have been conducted in a cross-sectional manner that measures traits at a single point in time. To address this issue, we have traced blood profiles in 4,884 Japanese individuals who underwent annual health check-ups for several years. In the present study, longitudinal exome-wide association studies were conducted to identify genetic variants related to 13 hematological phenotypes. The generalized estimating equation model showed that a total of 67 single nucleotide polymorphisms (SNPs) were significantly [false discovery rate (FDR) of <0.01] associated with hematological phenotypes. Of the 67 SNPs, nine SNPs were identified as novel hematological markers: rs4686683 of SENP2 for red blood cell count (FDR = 0.008, P = 5.5 × 10⁻⁶); rs3917688 of SELP for mean corpuscular volume (FDR = 0.005, P = 2.4 × 10⁻⁶); rs3133745 of C8orf37-AS1 for white blood cell count (FDR = 0.003, P = 1.3 × 10⁻⁶); rs13121954 at 4q31.2 for basophil count (FDR = 0.007, P = 3.1 × 10⁻⁵); rs7584099 at 2q22.3 (FDR = 2.6 × 10⁻⁵, P = 8.8 × 10⁻⁸), rs1579219 of HCG17 (FDR = 0.003, P = 2.0 × 10⁻⁵), and rs10757049 of DENND4C (FDR = 0.008, P = 5.6 × 10⁻⁵) for eosinophil count; rs12338 of CTSB for neutrophil count (FDR = 0.007, P = 2.9 × 10⁻⁵); and rs395967 of OSMR-AS1 for monocyte count (FDR = 0.008, P = 3.2 × 10⁻⁵).

Identification of six novel susceptibility loci for dyslipidemia using longitudinal exome-wide association studies in a Japanese population

Recent genome-wide association studies have identified various dyslipidemia-related genetic variants. However, most studies were conducted in a cross-sectional manner. We thus performed longitudinal exome-wide association studies of dyslipidemia in a Japanese population. We used ~244,000 genetic variants and clinical data of 6022 Japanese individuals who had undergone annual health checkups for several years. After quality control, the association of dyslipidemia-related phenotypes with 24,691 single nucleotide polymorphisms (SNPs) was tested using the generalized estimating equation model. In total, 82 SNPs were significantly (P < 2.03 × 10^-6) associated with dyslipidemia phenotypes. Of these SNPs, four (rs74416240 of TCHP, rs925368 of GIT2, rs7969300 of ATXN2, and rs12231744 of NAA25) and two (rs34902660 of SLC17A3 and rs1042127 of CDSN) were identified as novel genetic determinants of hypo-HDL- and hyper-LDL-cholesterolemia, respectively. A replication study using the cross-sectional data of 8310 Japanese individuals showed the association of the six identified SNPs with dyslipidemia-related traits.

Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, trans-ethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N=78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P<5 × 10-8), we found suggestive evidence (P<5 × 10-6) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions.

Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

Identification of three genetic variants as novel susceptibility loci for body mass index in a Japanese population

Recent genome-wide association studies have identified various obesity or metabolic syndrome (MetS) susceptibility loci. However, most studies were conducted in a cross-sectional manner. To address this gap, we performed a longitudinal exome-wide association study to identify susceptibility loci for obesity and MetS in a Japanese population. We traced clinical data of 6,022 Japanese subjects who had annual health check-ups for several years (mean follow-up period, 5 yr) and genotyped ~244,000 genetic variants. The association of single nucleotide polymorphisms (SNPs) with body mass index (BMI) or the prevalence of obesity and MetS was examined in a generalized estimating equation model. Our longitudinal exome-wide association studies detected 21 BMI- and five MetS-associated SNPs (false discovery rate, FDR <0.01). Among these SNPs, 16 have not been previously implicated as determinants of BMI or MetS. Cross-sectional data for obesity- and MetS-related phenotypes in 7,285 Japanese subjects were examined in a replication study. Among the 16 SNPs, three ( rs9491140 , rs145848316 , and rs7863248 ) were related to BMI in the replication cohort ( P < 0.05). In conclusion, three SNPs [ rs9491140 of NKAIN2 (FDR = 0.003, P = 1.9 × 10-5), rs145848316 of KMT2C (FDR = 0.007, P = 4.5 × 10-5), and rs7863248 of AGTPBP1 (FDR = 0.006, P = 4.2 × 10-5)] were newly identified as susceptibility loci for BMI.

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10-8), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis.

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F2 population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F2 population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q 2) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F2 chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2nd to 10th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F2 chickens when compared with conventional methods.

Longitudinal exome-wide association study to identify genetic susceptibility loci for hypertension in a Japanese population

Genome-wide association studies have identified various genetic variants associated with complex disorders. However, these studies have commonly been conducted in a cross-sectional manner. Therefore, we performed a longitudinal exome-wide association study (EWAS) in a Japanese cohort. We aimed to identify genetic variants that confer susceptibility to hypertension using ~244 000 single-nucleotide variants (SNVs) and physiological data from 6026 Japanese individuals who underwent annual health check-ups for several years. After quality control, the association of hypertension with SNVs was tested using a generalized estimating equation model. Finally, our longitudinal EWAS detected seven hypertension-related SNVs that passed strict criteria. Among these variants, six SNVs were densely located at 12q24.1, and an East Asian-specific motif (haplotype) 'CAAAA' comprising five derived alleles was identified. Statistical analyses showed that the prevalence of hypertension in individuals with the East Asian-specific haplotype was significantly lower than that in individuals with the common haplotype 'TGGGT'. Furthermore, individuals with the East Asian haplotype may be less susceptible to the adverse effects of smoking on hypertension. The longitudinal EWAS for the recessive model showed that a novel SNV, rs11917356 of COL6A5, was significantly associated with systolic blood pressure, and the derived allele at the SNV may have spread throughout East Asia in recent evolutionary time.

Identification of CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population

Chronic kidney disease and hyperuricemia are serious global health problems. Recent genome-wide association studies have identified various genetic variants related to these disorders. However, most studies have been conducted in a cross-sectional manner. To identify novel susceptibility loci for chronic kidney disease or hyperuricemia, we performed longitudinal exome-wide association studies (EWASs), using ~ 244,000 genetic variants and clinical data of Japanese individuals who had undergone annual health checkups for several years. After establishing quality controls, the association of renal function-related traits in 5648 subjects (excluding patients with dialysis and population outliers) with 24,579 single nucleotide variants (SNVs) for three genetic models (P < 3.39 × 10^− 7) was tested using generalized estimating equation models. The longitudinal EWASs revealed novel relations of five SNVs to renal function-related traits. Cross-sectional data for renal function-related traits in 7699 Japanese subjects were examined in a replication study. Among the five SNVs, rs55975541 in CDC42BPG was significantly (P < 4.90 × 10^− 4) related to the serum concentration of uric acid in the replication cohort. We also examined the SNVs detected in our longitudinal EWASs with the information on P values in GKDGEN meta-analysis data. Four SNVs in SLC15A2 were significantly associated with the estimated glomerular filtration rate in European ancestry populations, although these SNVs were related to the serum concentration of uric acid with borderline significance in our longitudinal EWASs. Our findings indicate that CDC42BPG may be a novel susceptibility locus for hyperuricemia.

Parental and offspring contribution of genetic markers of adult blood pressure in early life: The FAMILY study

Previous genome wide association studies (GWAS) identified associations of multiple common variants with diastolic and systolic blood pressure traits in adults. However, the contribution of these loci to variations of blood pressure in early life is unclear. We assessed the child and parental contributions of 33 GWAS single-nucleotide polymorphisms (SNPs) for blood pressure in 1,525 participants (515 children, 406 mothers and 237 fathers) of the Family Atherosclerosis Monitoring In early life (FAMILY) study followed-up for 5 years. Two genotype scores for systolic (29 SNPs) and diastolic (24 SNPs) blood pressure were built. Linear mixed-effect regressions showed significant association between rs1378942 in CSK and systolic blood pressure (β = 0.98±0.46, P = 3.4×10⁻²). The child genotype scores for diastolic and systolic blood pressure were not associated in children. Nominally significant parental genetic effects were found between the SNPs rs11191548 (CYP17A1) (paternal, β = 2.78±1.49, P = 6.1×10⁻² for SBP and β = 3.60±1.24, P = 3.7×10⁻³ for DBP), rs17367504 (MTHFR) (paternal, β = 2.42±0.93, P = 9.3×10⁻³ for SBP and β = 1.89±0.80, P = 1.8×10⁻² for DBP and maternal, β = -1.32±0.60, P = 2.9×10⁻² and β = -1.97±0.77, P = 1.0×10⁻², for SBP and DBP respectively) and child blood pressure. Our study supports the view that adult GWAS loci have a limited impact on blood pressure during the five first years of life. The parental genetic effects observed on blood pressure in children may suggest epigenetic mechanisms in the transmission of the risk of hypertension. Further replication is needed to confirm our results.

A score test for genetic class-level association with nonlinear biomarker trajectories

Emerging data suggest that the genetic regulation of the biological response to inflammatory stress may be fundamentally different to the genetic underpinning of the homeostatic control (resting state) of the same biological measures. In this paper, we interrogate this hypothesis using a single-SNP score test and a novel class-level testing strategy to characterize protein-coding gene and regulatory element-level associations with longitudinal biomarker trajectories in response to stimulus. Using the proposed class-level association score statistic for longitudinal data, which accounts for correlations induced by linkage disequilibrium, the genetic underpinnings of evoked dynamic changes in repeatedly measured biomarkers are investigated. The proposed method is applied to data on two biomarkers arising from the Genetics of Evoked Responses to Niacin and Endotoxemia study, a National Institutes of Health-sponsored investigation of the genomics of inflammatory and metabolic responses during low-grade endotoxemia. Our results suggest that the genetic basis of evoked inflammatory response is different than the genetic contributors to resting state, and several potentially novel loci are identified. A simulation study demonstrates appropriate control of type-1 error rates, relative computational efficiency, and power. Copyright © 2017 John Wiley & Sons, Ltd.

Genome-wide Association Study of Susceptibility to Particulate Matter-Associated QT Prolongation

BACKGROUND

Ambient particulate matter (PM) air pollution exposure has been associated with increases in QT interval duration (QT). However, innate susceptibility to PM-associated QT prolongation has not been characterized.

OBJECTIVE

To characterize genetic susceptibility to PM-associated QT prolongation in a multi-racial/ethnic, genome-wide association study (GWAS).

METHODS

Using repeated electrocardiograms (1986–2004), longitudinal data on PM<10 μm in diameter (PM10), and generalized estimating equations methods adapted for low-prevalence exposure, we estimated approximately 2.5×106 SNP×PM10 interactions among nine Women’s Health Initiative clinical trials and Atherosclerosis Risk in Communities Study subpopulations (n=22,158), then combined subpopulation-specific results in a fixed-effects, inverse variance-weighted meta-analysis.

RESULTS

A common variant (rs1619661; coded allele: T) significantly modified the QT-PM10 association (p=2.11×10−8). At PM10 concentrations >90th percentile, QT increased 7 ms across the CC and TT genotypes: 397 (95% confidence interval: 396, 399) to 404 (403, 404) ms. However, QT changed minimally across rs1619661 genotypes at lower PM10 concentrations. The rs1619661 variant is on chromosome 10, 132 kilobase (kb) downstream from CXCL12, which encodes a chemokine, stromal cell-derived factor 1, that is expressed in cardiomyocytes and decreases calcium influx across the L-type Ca2+ channel.

CONCLUSIONS

The findings suggest that biologically plausible genetic factors may alter susceptibility to PM10-associated QT prolongation in populations protected by the U.S. Environmental Protection Agency’s National Ambient Air Quality Standards. Independent replication and functional characterization are necessary to validate our findings. https://doi.org/10.1289/EHP347

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium

BACKGROUND

Increased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals.

METHODS AND RESULTS

We conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in BRE (β=56.3, pinteraction=3.9e-9) and rs9830388 in UBE2E2 (β=25.2, pinteraction=1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3 significantly modified the association between TCAs and QT intervals (β=9.3, pinteraction=2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (pinteraction>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries.

CONCLUSIONS

Among Europeans, TCA interactions with variants in BRE and UBE2E2 were identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

Identifying genetic loci affecting antidepressant drug response in depression using drug-gene interaction models

Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified. Within the present review, we will discuss the methodological challenges and limitations of the studies that have been conducted on this topic to date (e.g., 'treated-only design', statistical power) and we will discuss how specifically drug-gene interaction models can be used to be better able to identify genetic variants associated with antidepressant drug response in depression.

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Increasing the power of genome wide association studies in natural populations using repeated measures - evaluation and implementation

Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long‐term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single‐nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful.

Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long‐term study of collared flycatchers in Sweden.

The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response.

The method is available in the r package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long‐term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.

Genome-wide gene-environment interactions on quantitative traits using family data

Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.

The Rotterdam Study: 2016 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Phenome-Wide Association Studies: Embracing Complexity for Discovery

The inherent complexity of biological systems can be leveraged for a greater understanding of the impact of genetic architecture on outcomes, traits, and pharmacological response. The genome-wide association study (GWAS) approach has well-developed methods and relatively straight-forward methodologies; however, the bigger picture of the impact of genetic architecture on phenotypic outcome still remains to be elucidated even with an ever-growing number of GWAS performed. Greater consideration of the complexity of biological processes, using more data from the phenome, exposome, and diverse -omic resources, including considering the interplay of pleiotropy and genetic interactions, may provide additional leverage for making the most of the incredible wealth of information available for study. Here, we describe how incorporating greater complexity into analyses through the use of additional phenotypic data and widespread deployment of phenome-wide association studies may provide new insights into genetic factors influencing diseases, traits, and pharmacological response.