Ontology highlight
ABSTRACT:
SUBMITTER: Sesia M
PROVIDER: S-EPMC8501795 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Sesia Matteo M Bates Stephen S Candès Emmanuel E Marchini Jonathan J Sabatti Chiara C
Proceedings of the National Academy of Sciences of the United States of America 20211001 40
We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, enc ...[more]