Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank.
Am J Hum Genet
; 110(5): 762-773, 2023 05 04.
Article
em En
| MEDLINE
| ID: mdl-37019109
ABSTRACT
The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estudo de Associação Genômica Ampla
/
Exoma
Tipo de estudo:
Risk_factors_studies
País/Região como assunto:
Europa
Idioma:
En
Revista:
Am J Hum Genet
Ano de publicação:
2023
Tipo de documento:
Article