Multi-ancestry Genome-Wide Association Study of Early Childhood Caries.

Early childhood caries (ECC) is the most common non-communicable childhood disease. It is an important health problem with known environmental and social/behavioral influences that lacks evidence for specific associated genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multi-ancestry population of U.S. preschool-age children (n=6,103) participating in a community-based epidemiologic study of early childhood oral health. Calibrated examiners used ICDAS criteria to measure ECC with the primary trait using the dmfs index with decay classified as macroscopic enamel loss (ICDAS ≥3). We estimated heritability, concordance rates, and conducted genome-wide association analyses to estimate overall genetic effects; the effects stratified by sex, household water fluoride, and dietary sugar; and leveraged the combined gene/gene-environment effects using the 2-degree-of-freedom (2df) joint test. The common genetic variants explained 24% of the phenotypic variance (heritability) of the primary ECC trait and the concordance rate was higher with a higher degree of relatedness. We identified 21 novel non-overlapping genome-wide significant loci for ECC. Two loci, namely RP11-856F16 . 2 (rs74606067) and SLC41A3 (rs71327750) showed evidence of association with dental caries in external cohorts, namely the GLIDE consortium adult cohort (n=∼487,000) and the GLIDE pediatric cohort (n=19,000), respectively. The gene-based tests identified TAAR6 as a genome-wide significant gene. Implicated genes have relevant biological functions including roles in tooth development and taste. These novel associations expand the genomics knowledge base for this common childhood disease and underscore the importance of accounting for sex and pertinent environmental exposures in genetic investigations of oral health.

Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk.

The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase ~50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.

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.

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.

Do Genetic Markers of Inflammation Modify the Relationship between Periodontitis and Nonalcoholic Fatty Liver Disease? Findings from the SHIP Study.

An association between periodontitis and nonalcoholic fatty liver disease (NAFLD) has been reported by experimental animal and epidemiologic studies. This study investigated whether circulating levels of serum C-reactive protein (CRP) and a weighted genetic CRP score representing markers of inflammatory burden modify the association between periodontitis and NAFLD. Data came from 2,481 participants of the Study of Health in Pomerania who attended baseline examination that occurred between 1997 and 2001. Periodontitis was defined as the percentage of sites (0%, <30%, ≥30%) with probing pocket depth (PD) ≥4 mm, and NAFLD status was determined using liver ultrasound assessment. Serum CRP levels were assayed at a central laboratory, and single-nucleotide polymorphisms previously identified through genome-wide association studies as robustly associated with serum CRP were combined into a weighted genetic CRP score (wGSCRP). Logistic regression models estimated the association between periodontitis and NAFLD within strata of serum CRP and separately within strata of the wGSCRP. The prevalence of NAFLD was 26.4% (95% confidence interval [CI], 24.6, 28.1) while 17.8% (95% CI, 16.0-19.6) had ≥30% of sites with PD ≥4 mm. Whereas the wGSCRP was not a modifier ( Pinteraction = 0.8) on the multiplicative scale, serum CRP modified the relationship between periodontitis and NAFLD ( Pinteraction = 0.01). The covariate-adjusted prevalence odds ratio of NAFLD comparing participants with ≥30% of sites with PD ≥4 mm to those with no site affected was 2.39 (95% CI, 1.32-4.31) among participants with serum CRP <1 mg/L. The corresponding estimate was 0.97 (95% CI, 0.57-1.66) for participants with serum CRP levels of 1 to 3 mg/L and 1.12 (95% CI, 0.65-1.93) for participants with serum CRP >3 mg/L. Periodontitis was positively associated with higher prevalence odds of NAFLD, and this relationship was modified by serum CRP levels.

Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.

Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the 'missing heritability' of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 × 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.

Common genetic variation near the connexin-43 gene is associated with resting heart rate in African Americans: a genome-wide association study of 13,372 participants.

BACKGROUND: Genome-wide association studies have identified several genetic loci associated with variation in resting heart rate in European and Asian populations. No study has evaluated genetic variants associated with heart rate in African Americans. OBJECTIVE: To identify novel genetic variants associated with resting heart rate in African Americans. METHODS: Ten cohort studies participating in the Candidate-gene Association Resource and Continental Origins and Genetic Epidemiology Network consortia performed genome-wide genotyping of single nucleotide polymorphisms (SNPs) and imputed 2,954,965 SNPs using HapMap YRI and CEU panels in 13,372 participants of African ancestry. Each study measured the RR interval (ms) from 10-second resting 12-lead electrocardiograms and estimated RR-SNP associations using covariate-adjusted linear regression. Random-effects meta-analysis was used to combine cohort-specific measures of association and identify genome-wide significant loci (P≤2.5×10(-8)). RESULTS: Fourteen SNPs on chromosome 6q22 exceeded the genome-wide significance threshold. The most significant association was for rs9320841 (+13 ms per minor allele; P = 4.98×10(-15)). This SNP was approximately 350 kb downstream of GJA1, a locus previously identified as harboring SNPs associated with heart rate in Europeans. Adjustment for rs9320841 also attenuated the association between the remaining 13 SNPs in this region and heart rate. In addition, SNPs in MYH6, which have been identified in European genome-wide association study, were associated with similar changes in the resting heart rate as this population of African Americans. CONCLUSIONS: An intergenic region downstream of GJA1 (the gene encoding connexin 43, the major protein of the human myocardial gap junction) and an intragenic region within MYH6 are associated with variation in resting heart rate in African Americans as well as in populations of European and Asian origin.

The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.

The field of phenomics has been investigating network structure among large arrays of phenotypes, and genome-wide association studies (GWAS) have been used to investigate the relationship between genetic variation and single diseases/outcomes. A novel approach has emerged combining both the exploration of phenotypic structure and genotypic variation, known as the phenome-wide association study (PheWAS). The Population Architecture using Genomics and Epidemiology (PAGE) network is a National Human Genome Research Institute (NHGRI)-supported collaboration of four groups accessing eight extensively characterized epidemiologic studies. The primary focus of PAGE is deep characterization of well-replicated GWAS variants and their relationships to various phenotypes and traits in diverse epidemiologic studies that include European Americans, African Americans, Mexican Americans/Hispanics, Asians/Pacific Islanders, and Native Americans. The rich phenotypic resources of PAGE studies provide a unique opportunity for PheWAS as each genotyped variant can be tested for an association with the wide array of phenotypic measurements available within the studies of PAGE, including prevalent and incident status for multiple common clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers. The results of PheWAS can be used to discover novel relationships between SNPs, phenotypes, and networks of interrelated phenotypes; identify pleiotropy; provide novel mechanistic insights; and foster hypothesis generation. The PAGE network has developed infrastructure to support and perform PheWAS in a high-throughput manner. As implementing the PheWAS approach has presented several challenges, the infrastructure and methodology, as well as insights gained in this project, are presented herein to benefit the larger scientific community.

Genotype-by-sex interaction in the aetiology of type 2 diabetes mellitus: support for sex-specific quantitative trait loci in Hypertension Genetic Epidemiology Network participants.

AIMS/HYPOTHESIS: While there are sex-related differences in both the prevalence of type 2 diabetes mellitus and disease risk factors, there is only limited research on sex-specific influences on type 2 diabetes aetiology within the same study population. Thus, we assessed genotype-by-sex interaction using a liability threshold model in an attempt to localise sex-specific type 2 diabetes quantitative trait loci (QTLs). SUBJECTS, MATERIALS AND METHODS: Hypertensive siblings and their offspring and/or parents in the Hypertension Genetic Epidemiology Network of the Family Blood Pressure Program were recruited from five field centres. The diabetic phenotype was adjusted for race, study centre, age and non-linear age effects. In total, 567 diabetic individuals were identified in 385 families. Variance component linkage analyses in the combined sample and stratified by sex and race were performed (SOLAR program) using race-specific marker allele frequencies derived from a random sample of participants at each centre. RESULTS: We observed a QTL-specific genotype-by-sex interaction (p=0.009) on chromosome 17 at 31 cM, with females displaying a robust adjusted logarithm of odds (LOD) of 3.0 compared with 0.2 in males and 1.3 in the combined sample. Three additional regions demonstrating suggestive evidence for linkage were detected: chromosomes 2 and 5 in the female sample and chromosome 22 (adjusted LOD=1.9) in the combined sample. CONCLUSIONS/INTERPRETATION: These findings suggest that multiple genes may regulate susceptibility to type 2 diabetes, demonstrating the importance of considering the interaction of genes and environment in the aetiology of common complex traits.

Efficient semiparametric estimation of haplotype-disease associations in case-cohort and nested case-control studies.

Estimating the effects of haplotypes on the age of onset of a disease is an important step toward the discovery of genes that influence complex human diseases. A haplotype is a specific sequence of nucleotides on the same chromosome of an individual and can only be measured indirectly through the genotype. We consider cohort studies which collect genotype data on a subset of cohort members through case-cohort or nested case-control sampling. We formulate the effects of haplotypes and possibly time-varying environmental variables on the age of onset through a broad class of semiparametric regression models. We construct appropriate nonparametric likelihoods, which involve both finite- and infinite-dimensional parameters. The corresponding nonparametric maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Consistent variance-covariance estimators are provided, and efficient and reliable numerical algorithms are developed. Simulation studies demonstrate that the asymptotic approximations are accurate in practical settings and that case-cohort and nested case-control designs are highly cost-effective. An application to a major cardiovascular study is provided.