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

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Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage, length and details of gene disruptions. Rare CNVs have been shown to be associated with neuropsychiatric disorders both collectively and at specific loci. To evaluate the collective effects of rare CNVs on disease risk, sophisticated association methods are needed to pool information across CNV loci while handling CNV-specific properties; however, such methods are under-developed. To address these challenges, we have developed a new collapsing method for rare CNVs named CCRET. CCRET is a random effects approach 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 simulation and analyzed large-scale schizophrenia datasets. We demonstrate the robustness, validity and utility of CCRET under a variety of scenarios.

Vyšlo v časopise: A New Method for Detecting Associations with Rare Copy-Number Variants. PLoS Genet 11(10): e32767. doi:10.1371/journal.pgen.1005403
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005403

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