title: Detecting associated single-nucleotide polymorphisms on the X chromosome in case control genome-wide association studies.
abstract: In the past decade, hundreds of genome-wide association studies have been conducted to detect the significant single-nucleotide polymorphisms that are associated with certain diseases. However, most of the data from the X chromosome were not analyzed and only a few significant associated single-nucleotide polymorphisms from the X chromosome have been identified from genome-wide association studies. This is mainly due to the lack of powerful statistical tests. In this paper, we propose a novel statistical approach that combines the information of single-nucleotide polymorphisms on the X chromosome from both males and females in an efficient way. The proposed approach avoids the need of making strong assumptions about the underlying genetic models. Our proposed statistical test is a robust method that only makes the assumption that the risk allele is the same for both females and males if the single-nucleotide polymorphism is associated with the disease for both genders. Through simulation study and a real data application, we show that the proposed procedure is robust and have excellent performance compared to existing methods. We expect that many more associated single-nucleotide polymorphisms on the X chromosome will be identified if the proposed approach is applied to current available genome-wide association studies data.
authors.affiliation: 1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA.
authors.forename: Zhongxue
authors.initials: Z
authors.lastname: Chen
authors.affiliation: 2 Department of Statistical Science, Southern Methodist University, Dallas, TX, USA.
authors.forename: Hon Keung Tony
authors.initials: HK
authors.lastname: Ng
authors.affiliation: 1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: 3 Department of Computer Science, Sam Houston State Uiversity, Huntsville, TX, USA.
authors.forename: Qingzhong
authors.initials: Q
authors.lastname: Liu
authors.affiliation: 4 Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA.
authors.forename: Hanwen
authors.initials: H
authors.lastname: Huang
mesh_terms: D000483:Alleles; D000544:Alzheimer Disease / Q000201:enzymology / Q000235:genetics; D056808:Biostatistics / Q000379:methods; D016022:Case-Control Studies; D041321:Chromosomes, Human, X / Q000235:genetics*; D003198:Computer Simulation; D019175:DNA Methylation / Q000235:genetics; D005260:Female; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D005838:Genotype; D006801:Humans; D008297:Male; D008957:Models, Genetic; D009954:Ornithine Carbamoyltransferase / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D011401:Promoter Regions, Genetic
chemical_list: D005819:Genetic Markers; D009954:Ornithine Carbamoyltransferase
keywords: Chromosome X; GWAS; combining p values; generalized genetic model; single-nucleotide polymorphism; trend test