An approach to gene-based testing accounting for dependence of tests among nearby genes.
Brief Bioinform
; 22(6)2021 11 05.
Article
em En
| MEDLINE
| ID: mdl-34459489
ABSTRACT
In genome-wide association studies (GWAS), it has become commonplace to test millions of single-nucleotide polymorphisms (SNPs) for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive $P$-value thresholding, guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Testes Genéticos
/
Biologia Computacional
/
Predisposição Genética para Doença
/
Estudo de Associação Genômica Ampla
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos