Biology subjects

Bradford, Y.

Publications and source records attributed to Bradford, Y..

3 recordsLinked to original sources

bioRxiv2018

Cross-cancer pleiotropic associations with lung cancer risk in African Americans

BackgroundIdentifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European descent individuals. Yet population-specific genetic variation can occur and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.\n\nMethodsWe conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.\n\nResultsWe identified three novel genomic regions significantly associated (FDR-corrected p-value < 0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with increased risk (OR=1.24, 95% CI: 1.12-1.38) and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The risk allele at rs336958 on 5q14.3 was associated with reduced lung cancer risk (OR=0.68, 95% CI: 0.56-0.82), while the risk allele at rs11658063 on 17q12 was associated with increased risk (OR=1.24, 95% CI: 1.11-1.39).\n\nConclusionWe identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.\n\nImpactOur findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

genetics↗

bioRxiv2018

Genome-wide association meta-analysis of PR interval identifies 47 novel loci associated with atrial and atrioventricular electrical activity

Electrocardiographic PR interval measures atrial and atrioventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. We performed a genome-wide association study in over 92,000 individuals of European descent and identified 44 loci associated with PR interval (34 novel). Examination of the 44 loci revealed known and novel biological processes involved in cardiac atrial electrical activity, and genes in these loci were highly over-represented in several cardiac disease processes. Nearly half of the 61 independent index variants in the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with one or more missense variants. Cardiac regulatory regions of the genome as measured by cardiac DNA hypersensitivity sites were enriched for variants associated with PR interval, compared to non-cardiac regulatory regions. Joint analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation identified additional new pleiotropic loci. The majority of associations discovered in European-descent populations were also present in African-American populations. Meta-analysis examining over 105,000 individuals of African and European descent identified additional novel PR loci. These additional analyses identified another 13 novel loci. Together, these findings underscore the power of GWAS to extend knowledge of the molecular underpinnings of clinical processes.

genetics↗

bioRxiv2017

A simulation study investigating power estimates in Phenome-Wide Association Studies

BackgroundPhenome-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.\n\nResultsWe 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 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.\n\nConclusionsThis 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.

genomics↗