Abstract
BackgroundThis paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions.ResultsThe consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal.ConclusionsThis summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.
Highlights
This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20
GAW20 data The GAW20 data are derived from the Genetics of Lipid Lowering and Diet Network (GOLDN) study, which aims to identify genetic markers of lipid response to fenofibrate treatment
The 9 contributions from the genome-wide association studies (GWAS) group of GAW20 extend upon the current literature and reflect varied goals, including the creation of new statistic tests, development of phenotype imputation methods, and application of the empirical kinship matrices
Summary
This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. Genome-wide association studies (GWAS) have proven a useful systematic method to investigate the genetic complexities of hundreds of disease phenotypes and their associations with common genomic variations. More than 1000 GWAS have identified more than 4000 significant loci as associated with 500 human diseases and traits [1]. GWAS for common variants have far achieved substantial success, their findings generally only explain a modest fraction of disease heritability [2, 3]. Potential reasons of missing heritability could be the limited power of GWAS [3] or the contribution of genetic variation such as rare variants [4]. To be considered “‘GWAS significant,” only those associations with a p < 5 × 10− 8 are considered statistically significant with single-marker analysis [3, 5]
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