Generalization of cortical MOSTest genome-wide associations within and across samples.
Neuroimage
; 263: 119632, 2022 11.
Article
in En
| MEDLINE
| ID: mdl-36115590
ABSTRACT
Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci 242-496, replication rate 96-97%) in independent data when compared with the established min-P approach (# replicated loci 26-55, replication rate 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cognition
/
Genome-Wide Association Study
Type of study:
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Neuroimage
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2022
Document type:
Article