SeminarsA random effects test for collapsing analysis of rare copy number variants
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Copy number variants (CNVs) play an important role in the etiology of multiple psychiatric disorders. Due to modest marginal effect size or rarity of the CNVs, collapsing approaches could be important to study how CNVs impact disease risk. While a plethora of powerful methods are available for collapsing analysis of sequence variants (e.g., SNPs), these methods could not be directly applied to CNVs due to the CNV-specific challenges: (1) the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and (2) etiological heterogeneity (e.g., heterogeneous effects of duplications/deletions). Existing burden tests tends to have suboptimal performance due to ignoring heterogeneity and evaluating only marginal effects of a CNV feature. We introduce a random effect test for collapsing analysis of rare CNVs; it collectively examines the effects of multiple CNV features and is robust to multiple types of heterogeneity. Multiple confounders can be simultaneously corrected. We demonstrate the robustness, validity and utility of the proposed approaches using real data applications and simulations.
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Jung-Ying Tzeng
2014-07-04
10:00:00 - 11:00:00
308 , Mathematics Research Center Building (ori. New Math. Bldg.)
Copy number variants (CNVs) play an important role in the etiology of multiple psychiatric disorders. Due to modest marginal effect size or rarity of the CNVs, collapsing approaches could be important to study how CNVs impact disease risk. While a plethora of powerful methods are available for collapsing analysis of sequence variants (e.g., SNPs), these methods could not be directly applied to CNVs due to the CNV-specific challenges: (1) the multi-faceted nature of CNV polymorphisms (e.g., CNVs vary in size, type, dosage, and details of gene disruption), and (2) etiological heterogeneity (e.g., heterogeneous effects of duplications/deletions). Existing burden tests tends to have suboptimal performance due to ignoring heterogeneity and evaluating only marginal effects of a CNV feature. We introduce a random effect test for collapsing analysis of rare CNVs; it collectively examines the effects of multiple CNV features and is robust to multiple types of heterogeneity. Multiple confounders can be simultaneously corrected. We demonstrate the robustness, validity and utility of the proposed approaches using real data applications and simulations.