title: X-inactivation informs variance-based testing for X-linked association of a quantitative trait.
abstract: BACKGROUND
  The X chromosome plays an important role in human diseases and traits. However, few X-linked associations have been reported in genome-wide association studies, partly due to analytical complications and low statistical power.
  
  
  RESULTS
  In this study, we propose tests of X-linked association that capitalize on variance heterogeneity caused by various factors, predominantly the process of X-inactivation. In the presence of X-inactivation, the expression of one copy of the chromosome is randomly silenced. Due to the consequent elevated randomness of expressed variants, females that are heterozygotes for a quantitative trait locus might exhibit higher phenotypic variance for that trait. We propose three tests that build on this phenomenon: 1) A test for inflated variance in heterozygous females; 2) A weighted association test; and 3) A combined test. Test 1 captures the novel signal proposed herein by directly testing for higher phenotypic variance of heterozygous than homozygous females. As a test of variance it is generally less powerful than standard tests of association that consider means, which is supported by extensive simulations. Test 2 is similar to a standard association test in considering the phenotypic mean, but differs by accounting for (rather than testing) the variance heterogeneity. As expected in light of X-inactivation, this test is slightly more powerful than a standard association test. Finally, test 3 further improves power by combining the results of the first two tests. We applied the these tests to the ARIC cohort data and identified a novel X-linked association near gene AFF2 with blood pressure, which was not significant based on standard association testing of mean blood pressure.
  
  
  CONCLUSIONS
  Variance-based tests examine overdispersion, thereby providing a complementary type of signal to a standard association test. Our results point to the potential to improve power of detecting X-linked associations in the presence of variance heterogeneity.
authors.affiliation: Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20740, USA. lima@umd.edu.|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA. lima@umd.edu.
authors.forename: Li
authors.initials: L
authors.lastname: Ma
authors.affiliation: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA. gh258@cornell.edu.|Present address: Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA. gh258@cornell.edu.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Hoffman
authors.affiliation: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14850, USA. ak735@cornell.edu.
authors.forename: Alon
authors.initials: A
authors.lastname: Keinan
mesh_terms: D000465:Algorithms; D000483:Alleles; D050197:Atherosclerosis / Q000209:etiology / Q000235:genetics; D005260:Female; D050172:Genes, X-Linked; D055106:Genome-Wide Association Study; D006579:Heterozygote; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D012307:Risk Factors; D049951:X Chromosome Inactivation