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VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.
Kim, Juhyun; Shen, Judong; Wang, Anran; Mehrotra, Devan V; Ko, Seyoon; Zhou, Jin J; Zhou, Hua.
Afiliação
  • Kim J; Department of Biostatistics, University of California, Los Angeles.
  • Shen J; Biostatistics and Research Decision Sciences, Merck & Co., Inc.
  • Wang A; Biostatistics and Research Decision Sciences, Merck & Co., Inc.
  • Mehrotra DV; Biostatistics and Research Decision Sciences, Merck & Co., Inc.
  • Ko S; Department of Biostatistics, University of California, Los Angeles.
  • Zhou JJ; Department of Medicine, University of California, Los Angeles.
  • Zhou H; Department of Biostatistics, University of California, Los Angeles.
Ann Appl Stat ; 15(4): 1652-1672, 2021 Dec.
Article em En | MEDLINE | ID: mdl-35198092
Single nucleotide polymorphism (SNP) set analysis aggregates both common and rare variants and tests for association between phenotype(s) of interest and a set. However, multiple SNP-sets, such as genes, pathways, or sliding windows are usually investigated across the whole genome in which all groups are tested separately, followed by multiple testing adjustments. We propose a novel method to prioritize SNP-sets in a joint multivariate variance component model. Each SNP-set corresponds to a variance component (or kernel), and model selection is achieved by incorporating either convex or nonconvex penalties. The uniqueness of this variance component selection framework, which we call VCSEL, is that it naturally encompasses multivariate traits (VCSEL-M) and SNP-set-treatment or -environment interactions (VCSEL-I). We devise an optimization algorithm scalable to many variance components, based on the majorization-minimization (MM) principle. Simulation studies demonstrate the superiority of our methods in model selection performance, as measured by the area under the precision-recall (PR) curve, compared to the commonly used marginal testing and group penalization methods. Finally, we apply our methods to a real pharmacogenomics study and a real whole exome sequencing study. Some top ranked genes by VCSEL are detected as insignificant by the marginal test methods which emphasizes formal inference of individual genes with a strict significance threshold. This provides alternative insights for biologists to prioritize follow-up studies and develop polygenic risk score models.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article