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Genome-Wide Association Studies and Meta-Analyses for Congenital Heart Defects.

ABSTRACT: BACKGROUND:Maternal and inherited (ie, case) genetic factors likely contribute to the pathogenesis of congenital heart defects, but it is unclear whether individual common variants confer a large risk. METHODS AND RESULTS:To evaluate the relationship between individual common maternal/inherited genotypes and risk for heart defects, we conducted genome-wide association studies in 5 cohorts. Three cohorts were recruited at the Children's Hospital of Philadelphia: 670 conotruncal heart defect (CTD) case-parent trios, 317 left ventricular obstructive tract defect (LVOTD) case-parent trios, and 406 CTD cases (n=406) and 2976 pediatric controls. Two cohorts were recruited through the Pediatric Cardiac Genomics Consortium: 355 CTD trios and 192 LVOTD trios. We also conducted meta-analyses using the genome-wide association study results from the CTD cohorts, the LVOTD cohorts, and from the combined CTD and LVOTD cohorts. In the individual genome-wide association studies, several genome-wide significant associations (P?5×10-8) were observed. In our meta-analyses, 1 genome-wide significant association was detected: the case genotype for rs72820264, an intragenetic single-nucleotide polymorphism associated with LVOTDs (P=2.1×10-8). CONCLUSIONS:We identified 1 novel candidate region associated with LVOTDs and report on several additional regions with suggestive evidence for association with CTD and LVOTD. These studies were constrained by the relatively small samples sizes and thus have limited power to detect small to moderate associations. Approaches that minimize the multiple testing burden (eg, gene or pathway based) may, therefore, be required to uncover common variants contributing to the risk of these relatively rare conditions.

SUBMITTER: Agopian AJ

PROVIDER: S-EPMC5539991 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Genome-Wide Association Studies and Meta-Analyses for Congenital Heart Defects.

Agopian A J AJ Goldmuntz Elizabeth E Hakonarson Hakon H Sewda Anshuman A Taylor Deanne D Mitchell Laura E LE

Circulation. Cardiovascular genetics 20170601 3

<h4>Background</h4>Maternal and inherited (ie, case) genetic factors likely contribute to the pathogenesis of congenital heart defects, but it is unclear whether individual common variants confer a large risk.<h4>Methods and results</h4>To evaluate the relationship between individual common maternal/inherited genotypes and risk for heart defects, we conducted genome-wide association studies in 5 cohorts. Three cohorts were recruited at the Children's Hospital of Philadelphia: 670 conotruncal hea ...[more]

PMID: 28468790

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Project description:IntroductionGene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher's inverse ?2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.Simulation and powerWe investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon's rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.ApplicationWe applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 "transmembrane transporter activity" as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 "acetylcholine receptor activity" but only when not corrected for multiple testing (all GSA-methods applied; p ? 0.02).

Dataset Information

Genome-Wide Association Studies and Meta-Analyses for Congenital Heart Defects.

Publications

Genome-Wide Association Studies and Meta-Analyses for Congenital Heart Defects.

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