A New Method for Detecting Associations with Rare Copy-Number Variants.

TitleA New Method for Detecting Associations with Rare Copy-Number Variants.
Publication TypeJournal Article
Year of Publication2015
AuthorsTzeng, Jung-Ying, Patrik K. E. Magnusson, Patrick F. Sullivan, and Jin P. Szatkiewicz
Corporate AuthorsSwedish Schizophrenia Consortium
JournalPLoS Genet
Date Published2015 Oct
KeywordsDNA Copy Number Variations, Genetic Heterogeneity, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Theoretical, Polymorphism, Single Nucleotide, Schizophrenia

Copy number variants (CNVs) play an important role in the etiology of many diseases such as cancers and psychiatric disorders. Due to a modest marginal effect size or the rarity of the CNVs, collapsing rare CNVs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare CNVs on disease risk. While a plethora of powerful collapsing methods are available for sequence variants (e.g., SNPs) in association analysis, these methods cannot be directly applied to rare CNVs due to the CNV-specific challenges, i.e., the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and etiological heterogeneity (e.g., heterogeneous effects of duplications and deletions that occur within a locus or in different loci). Existing CNV collapsing analysis methods (a.k.a. the burden test) tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a CNV feature. We introduce CCRET, a random effects test for collapsing rare CNVs when searching for disease associations. CCRET is applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. Multiple confounders can be simultaneously corrected. To evaluate the performance of CCRET, we conducted extensive simulations and analyzed large-scale schizophrenia datasets. We show that CCRET has powerful and robust performance under multiple types of etiological heterogeneity, and has performance comparable to or better than existing methods when there is no heterogeneity.

Alternate JournalPLoS Genet
Original PublicationA new method for detecting associations with rare copy-number variants.
PubMed ID26431523
PubMed Central IDPMC4592002
Grant ListR21 MH104831 / MH / NIMH NIH HHS / United States
R01 MH095034 / MH / NIMH NIH HHS / United States
R01 MH106611 / MH / NIMH NIH HHS / United States
C490/A10124 / CRUK_ / Cancer Research UK / United Kingdom
P01 CA142538 / CA / NCI NIH HHS / United States
K01 MH093517 / MH / NIMH NIH HHS / United States
FRCN-CCT-83028 / CAPMC / CIHR / Canada
R01 MH077139 / MH / NIMH NIH HHS / United States
C490/A10119 / CRUK_ / Cancer Research UK / United Kingdom