To stratify or not to stratify: power considerations for population-based genome-wide association studies of quantitative traits

2011 ◽  
Vol 35 (8) ◽  
pp. 867-879 ◽  
Author(s):  
Gundula Behrens ◽  
Thomas W. Winkler ◽  
Mathias Gorski ◽  
Michael F. Leitzmann ◽  
Iris M. Heid
Keyword(s):  
Quantitative Traits ◽  
Association Studies ◽  
Population Based ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

2017 ◽  
Vol 60 (3) ◽  
pp. 335-346 ◽  
Author(s):  
Markus Schmid ◽  
Jörn Bennewitz
Keyword(s):  
Statistical Models ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Livestock Breeding ◽  
Genome Wide

Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Baolin Wu ◽  
James S. Pankow
Keyword(s):  
Quantitative Traits ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Competitive Performance ◽  
Multiple Traits ◽  
Atherosclerosis Risk In Communities ◽  
Genome Wide

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.

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