Comparison of multiple single-nucleotide variant association tests in a meta-analysis of Genetic Analysis Workshop 19 family and unrelated data. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Wang, Shuai; Fisher, Virginia; Chen, Yuning; Dupuis, Josée
• Is Part Of: BMC proceedings. Volume 10:Issue 7(2016)
• Page Start: 187
• Page End: 191
• Publication Date: 2016-10
• Subjects: Medicine -- Congresses; Biology -- Congresses; 610.5
• DOI: 10.1186/s12919-016-0028-7
• Languages: English
• ISSNs: 1753-6561

Abstract

Abstract Background Meta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis approaches for haplotype analysis have not been extensively developed and used, and have not been compared with other ways of jointly analysing multiple genetic variants. Methods We propose a novel meta-analysis approach for a gene-based haplotype association test, and compare it with an existing meta-analysis approach of the sequence kernel association test (SKAT), using the unrelated samples and family samples of the Genetic Analysis Workshop 19 data sets. We performed association tests with diastolic blood pressure and restricted our analyses to all variants in exonic regions on all odd chromosomes. Results Meta-analysis of haplotype results and SKAT identified different genes. The most significantly associated gene identified by SKAT was theALCAM gene on chromosome 3 with ap value of 7.0 × 10− 5 . Two of the most associated genes identified by the haplotype method wereFPGT (p = 6.7 × 10− 8 ) on chromosome 1 andSPARC (p = 3.3 × 10− 7 ) on chromosome 5. Both genes were previously implicated in blood pressure regulation and hypertension. Conclusion We compared two meta-analysis approaches to jointly analyze multiple variants: SKAT and haplotype tests. The difference in observed results may be because the haplotype method considered all observed haplotypes, whereas SKAT weighted variants inversely to their minor allele frequency, masking the effects of common variants. The two approaches identified different top genes, and appear to be complementary.