title: A weighted U statistic for association analyses considering genetic heterogeneity.
abstract: Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, U.S.A.
authors.forename: Changshuai
authors.initials: C
authors.lastname: Wei
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, U.S.A.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Elston
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, U.S.A.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
mesh_terms: D004194:Disease / Q000235:genetics; D004777:Environment; D005819:Genetic Markers; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype
chemical_list: D005819:Genetic Markers
keywords: high-dimensional data; nicotine dependence; non-parametric statistic
references.citation: Nat Genet. 2010 May;42(5):441-7
references.pmid: 20418890
references.citation: Genet Epidemiol. 2011 Feb;35(2):125-32
references.pmid: 21254220
references.citation: Hum Mol Genet. 2010 May 15;19(10):2059-67
references.pmid: 20176734
references.citation: Cell. 2010 Apr 16;141(2):210-7
references.pmid: 20403315
references.citation: Am J Hum Genet. 2005 May;76(5):780-93
references.pmid: 15786018
references.citation: J Am Stat Assoc. 2010 Jun;105(490):473-481
references.pmid: 20711441
references.citation: Genet Epidemiol. 2013 Apr;37(3):267-75
references.pmid: 23471868
references.citation: Trends Genet. 2004 Dec;20(12):640-7
references.pmid: 15522460
references.citation: Am J Hum Genet. 2007 Nov;81(5):927-38
references.pmid: 17924336
references.citation: Addiction. 2003 Jan;98(1):23-31
references.pmid: 12492752
references.citation: Nat Rev Genet. 2010 Jun;11(6):446-50
references.pmid: 20479774
references.citation: Trends Genet. 2010 Mar;26(3):132-41
references.pmid: 20106545
references.citation: PLoS Genet. 2007 Sep;3(9):1724-35
references.pmid: 17907809
references.citation: Ann Hum Genet. 2009 Nov;73(Pt 6):614-30
references.pmid: 19725835