XWAS: A Software Toolset for Genetic Data Analysis and Association Studies of the X Chromosome.
XWAS is a new software suite for the analysis of the X chromosome in association studies and similar genetic studies. The X chromosome plays an important role in human disease and traits of many species, especially those with sexually dimorphic characteristics. Special attention needs to be given to its analysis due to the unique inheritance pattern, which leads to analytical complications that have resulted in the majority of genome-wide association studies (GWAS) either not considering X or mishandling it with toolsets that had been designed for non-sex chromosomes. We hence developed XWAS to fill the need for tools that are specially designed for analysis of X. Following extensive, stringent, and X-specific quality control, XWAS offers an array of statistical tests of association, including: 1) the standard test between a SNP (single nucleotide polymorphism) and disease risk, including after first stratifying individuals by sex, 2) a test for a differential effect of a SNP on disease between males and females, 3) motivated by X-inactivation, a test for higher variance of a trait in heterozygous females as compared with homozygous females, and 4) for all tests, a version that allows for combining evidence from all SNPs across a gene. We applied the toolset analysis pipeline to 16 GWAS datasets of immune-related disorders and 7 risk factors of coronary artery disease, and discovered several new X-linked genetic associations. XWAS will provide the tools and incentive for others to incorporate the X chromosome into GWAS and similar studies in any species with an XX/XY system, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Feng
F
Gao
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Diana
D
Chang
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Arjun
A
Biddanda
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Li
L
Ma
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Yingjie
Y
Guo
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo).
Zilu
Z
Zhou
From the Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 (Gao, Chang, Biddanda, Ma, Guo, Zhou, and Keinan); Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853 (Chang and Keinan); Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20740 (Ma); and School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (Guo). ak735@cornell.edu.
Alon
A
Keinan
D041321:Chromosomes, Human, X / Q000235:genetics*; D005260:Female; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008297:Male; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012984:Software; D049951:X Chromosome Inactivation
GWAS; chromosome X; complex diseases; genetic association study; sexual dimorphism; software