期刊
GENETIC EPIDEMIOLOGY
卷 39, 期 7, 页码 499-508出版社
WILEY-BLACKWELL
DOI: 10.1002/gepi.21935
关键词
rare variants; low-frequency variants; gene-based association tests; genome-wide association study
资金
- NHGRI NIH HHS [R01 HG000376, HG000376, R56 HG000376] Funding Source: Medline
- NHLBI NIH HHS [R00 HL113164, K99 HL113164, HL113164] Funding Source: Medline
- NIDDK NIH HHS [R56 DK062370, U01 DK062370, DK062370, R01 DK062370] Funding Source: Medline
Although genome-wide association studies (GWAS) have identified thousands of trait-associated genetic variants, there are relatively few findings on the X chromosome. For analysis of low-frequency variants (minor allele frequency <5%), investigators can use region- or gene-based tests where multiple variants are analyzed jointly to increase power. To date, there are no gene-based tests designed for association testing of low-frequency variants on the X chromosome. Here we propose three gene-based tests for the X chromosome: burden, sequence kernel association test (SKAT), and optimal unified SKAT (SKAT-O). Using simulated case-control and quantitative trait (QT) data, we evaluate the calibration and power of these tests as a function of (1) male:female sample size ratio; and (2) coding of haploid male genotypes for variants under X-inactivation. For case-control studies, all three tests are reasonably well-calibrated for all scenarios we evaluated. As expected, power for gene-based tests depends on the underlying genetic architecture of the genomic region analyzed. Studies with more (haploid) males are generally less powerful due to decreased number of chromosomes. Power generally is slightly greater when the coding scheme for male genotypes matches the true underlying model, but the power loss for misspecifying the (generally unknown) model is small. For QT studies, type I error and power results largely mirror those for binary traits. We demonstrate the use of these three gene-based tests for X-chromosome association analysis in simulated data and sequencing data from the Genetics of Type 2 Diabetes (GoT2D) study.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据