Abstract
Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.
Highlights
Genome-wide association studies (GWAS) have identified genetic variants associated with a number of complex diseases or phenotypes [1], [2], [3] and some of these variants had confirmations from several studies [1]
Single-locus analysis may not be the best approach in the presence of confirmed single-nucleotide polymorphism (SNP) effects, because confirmed effects become a component of random residuals and decrease statistical power for detecting new effects if those true effects are omitted in the analysis
We propose a multi-locus test (MLT) that tests each candidate SNP conditional on confirmed SNPs for GWAS analysis to increase the statistical power for detecting new SNP effects, and we demonstrate the MLT method had increased statistical power relative to single-locus test (SLT) using analytical formulae derived in this study and using simulation, case studies, and the Framingham Heart Study (FHS) data
Summary
Genome-wide association studies (GWAS) have identified genetic variants associated with a number of complex diseases or phenotypes [1], [2], [3] and some of these variants had confirmations from several studies [1]. We propose a multi-locus test (MLT) that tests each candidate SNP conditional on confirmed SNPs for GWAS analysis to increase the statistical power for detecting new SNP effects, and we demonstrate the MLT method had increased statistical power relative to SLT using analytical formulae derived in this study and using simulation, case studies, and the Framingham Heart Study (FHS) data. For SLT, the statistical model is the same as Equation 1 except that the residual term is a summation of confirmed effects and random residuals.
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.
