Statistics for X‐chromosome associations |
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| Authors: | Umut Özbek Hui‐Min Lin Yan Lin Daniel E. Weeks Wei Chen John R. Shaffer Shaun M. Purcell Eleanor Feingold |
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| Affiliation: | 1. Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York;2. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York;3. Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania;4. Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania;5. Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania;6. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York;7. Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York;8. Broad Institute of MIT and Harvard, Cambridge, Massachusetts;9. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts;10. Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts |
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| Abstract: | In a genome‐wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X‐chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X‐chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X‐chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X‐chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single‐marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X‐chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions. |
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| Keywords: | genetic association study GWAS X chromosome |
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