Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.
LASSO
bipolar disorder
elastic net
genetic covariance
heritability models
multivariate linear mixed model
risk prediction
schizophrenia
Journal
HGG advances
ISSN: 2666-2477
Titre abrégé: HGG Adv
Pays: United States
ID NLM: 101772885
Informations de publication
Date de publication:
13 07 2023
13 07 2023
Historique:
received:
26
10
2022
accepted:
17
05
2023
medline:
20
6
2023
pubmed:
19
6
2023
entrez:
19
6
2023
Statut:
epublish
Résumé
Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.
Identifiants
pubmed: 37333772
doi: 10.1016/j.xhgg.2023.100209
pii: S2666-2477(23)00041-6
pmc: PMC10276147
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100209Subventions
Organisme : CIHR
ID : MOP-74430
Pays : Canada
Organisme : CIHR
ID : MOP-114988
Pays : Canada
Organisme : CIHR
ID : PCG-155471
Pays : Canada
Organisme : CIHR
ID : PJT-175122
Pays : Canada
Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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