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Ma, C.* ; Boehnke, M.* ; Lee, S.* ; GoT2D Consortium (Gieger, C. ; Grallert, H. ; Hrabě de Angelis, M. ; Huth, C. ; Kriebel, J. ; Meisinger, C. ; Meitinger, T. ; Müller-Nurasyid, M. ; Peters, A. ; Ried, J.S. ; Strauch, K. ; Strom, T.M.)

Evaluating the calibration and power of three gene-based association tests of rare variants for the X chromosome.

Genet. Epidemiol. 39, 499-508 (2015)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Gene-based Association Tests ; Genome-wide Association Study ; Low-frequency Variants ; Rare Variants
ISSN (print) / ISBN 0741-0395
e-ISSN 1098-2272
Zeitschrift Genetic Epidemiology
Quellenangaben Band: 39, Heft: 7, Seiten: 499-508 Artikelnummer: , Supplement: ,
Verlag Wiley
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Experimental Genetics (IEG)
Institute of Human Genetics (IHG)
Institute of Epidemiology II (EPI2)
Institute of Genetic Epidemiology (IGE)