Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.
PLoS Genet
; 18(11): e1010447, 2022 11.
Article
en En
| MEDLINE
| ID: mdl-36342933
ABSTRACT
We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Polimorfismo de Nucleótido Simple
/
Estudio de Asociación del Genoma Completo
Tipo de estudio:
Risk_factors_studies
/
Systematic_reviews
Idioma:
En
Revista:
PLoS Genet
Asunto de la revista:
GENETICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos