Abstract
Genome-wide association studies often collect multiple phenotypes for complex diseases. Multivariate joint analyses have higher power to detect genetic variants compared with the marginal analysis of each phenotype and are also able to identify loci with pleiotropic effects. We extend the unified score-based association test to incorporate family structure, apply different approaches to analyze multiple traits in GAW20 real samples, and compare the results. Through simulation studies, we confirm that the Type I error rate of the pedigree-based unified score association test is appropriately controlled. In marginalanalysis of triglyceride levels, we found 1 subgenome-wide significant variant on chromosome 6. Joint analyses identified several suggestive genome-wide significant signals, with the pedigree-based unified score association test yielding the greatest number of significant results.
Highlights
The increasing availability of high-density genomic data with thousands of samples enables the identification of single-nucleotide polymorphisms (SNPs) contributing to complex traits on a genome-wide scale
We found that the Type I error rate of pUSATwas well preserved through simulations
TheType I error rate is well controlled for the pedigree-based USAT (pUSAT) approach (Table 1) slightly conservative
Summary
The increasing availability of high-density genomic data with thousands of samples enables the identification of single-nucleotide polymorphisms (SNPs) contributing to complex traits on a genome-wide scale. Research studies often collect data on multiple related phenotypes to better understand disease structure; genome-wide association studies (GWAS) commonly analyze each trait independently. The standard approach usually analyzes each phenotype separately and reports the corresponding findings of each analysis, ignoring the dependency among traits. Approaches considering joint analyses have been proposed to tackle multiple phenotypes. The sum of squared score (SSU) test does not explicitly incorporate trait correlation, and multivariate analysis of variance (MANOVA) could fail to detect pleiotropy when a strong trait correlation exists and the traits have thesame direction of association [3]. Considered to be an optimally weighted combination of MANOVA and SSU, the unified score-based association test (USAT) by Ray et al [3] may provide higher power, especially for detecting pleiotropy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
