Multivariate genome-wide analyses of the well-being spectrum

Abstract

We introduce two novel methods for multivariate genome-wide association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N obs = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMA, and in a ~ 57% increase in the predictive power of polygenic risk scores. Supporting transcriptome -and methylome-wide analyses (TWAS/MWAS) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the wellbeing spectrum.In the past decade, genome-wide association studies (GWAS) have provided insights into the genetic basis of quantitative variation in complex traits 1 . With summary statistics of these GWASs becoming public and the development of linkage disequilibrium score regression (LDSC) 2,3 , genetic correlations between traits can be systematically estimated (e.g. Brainstorm consortium 4 ). Levering this widely observed genetic overlap between traits, we introduce two novel methods for multivariate genome-wide association metaanalysis, where we define a multivariate model as a model where the effect of a single SNP is considered for multiple traits: 1) N-weighted multivariate GWAMA (N-GWAMA), with a unitary effect of the SNP on all traits, and 2) model averaging GWAMA (MA-GWAMA), where we relaxed the assumption of a unitary effect of the SNP on all traits. Both methods are well equipped to deal with (unknown) sample overlap. The dependence between effect sizes (error correlation) induced by possible sample overlap is estimated from the univariate GWAMA using LDSC 2,3 . Furthermore, the univariate LDSC intercept is used to correct for population stratification and cryptic relatedness. Both methods have advantages over existing methods. In contrast to MultiPhen 5 , CCA (mv-PLINK) 6 , Combined-PC 7 , and mv-BIMBAM 8 , both our methods can be applied without the need of individual-level genotypic data as only GWAS/GWAMA summary-statistics are required. Additionally, in contrast to S Hom 9 , N -and MA-GWAMA take a more precise estimate of the error correlation into account. In contrast to MTAG 10 , MA-GWAMA , similar to S Het 9 , generates trait specific estimates for each SNP allowing for a certain degree of heterogeneity (see online methods). Finally, in contrast to TATES 11 , both N-GWAMA and MA-GWAMA generate effect sizes for the multivariate effect where TATES only generates a P-value. The absence of a signed statistic in TATES complicates or even prohibits polygenic prediction.

Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

“…Multi-trait analysis is different from cross phenotype or pleiotropy effect analysis [3], where the null hypothesis is at most one trait is associated with the SNP. Of note, the proposed adaptive test aMAT is a general framework and can be easily extended to incorporate other multi-trait methods such as MTAG [13], N-GWAMA [34], and HIPOn [35]. To be specific, we can treat any other powerful tests as an individual MTA test and then apply aMAT to combine its results to further improve the power.…”

Genetic Association Study of Childhood Aggression across raters, instruments and age

“…Despite a heritability of roughly 50% [5, 23], genome-wide association studies (GWASs) on AGG have not identified genome-wide significant loci that replicated [1]. Childhood cohorts often have rich longitudinal data and assessments from multiple informants and we aimed to increase power to detect genomic loci via multivariate genome-wide association meta-analysis (GWAMA) across genetically correlated traits [24, 25]. In AGG, twin studies have reported moderate to high genetic correlations among instruments, raters, and age [26–29].…”

Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus

“…We show that ASPHD1 is co-expressed with genes involved in neurotransmission and synaptic vesicle location and function. In particular, CELF4 is involved in neurodevelopmental regulation [66] and is associated with educational attainment [67], neuroticism and mood related alterations [68][69][70]. Notably, the potential contribution of ASPHD1 to reproductive function via a putative role in synaptic vesicle integrity is consistent with the biological roles of some Mendelian GnRH deficiency disorder genes.…”