Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Abstract

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Keywords: PrediXcan; genetics; genomics; human genetics; multi-ancestry GWAS; multivarite adaptive shrinkage; transcriptome prediction; transcriptome-wide association studies.

Figure 1

Overall study methodology Using TOPMed MESA as a training dataset, we built population-based transcriptome prediction models using five different methods (elastic net [EN], joint-tissue imputation [JTI], multivariate adaptive shrinkage in R [MASHR], Matrix eQTL, and transcriptome-integrated genetic association resource [TIGAR]). With these transcriptome models, we evaluated their out-of-sample transcriptome prediction accuracy using the GEUVADIS dataset. Additionally, we assessed their applicability in multiethnic TWASs using GWAS summary statistics from the PAGE Study and the PanUKBB. AFA, African American; CHN, Chinese; EUR, European; HIS, Hispanic/Latino; Mono, CD14+ monocytes; PBMC, peripheral blood mononuclear cells; Tcell, CD4+ T cells.

Figure 2

Design of the methodology implemented to make MASHR models (A) Using effect sizes estimated using Matrix eQTL within each population dataset, we combined them across genes, with the different populations as conditions, to use as input for MASHR. The output matrixes contain adjusted effect sizes. (B) For each population, we selected the top SNP (lowest local false sign rate) per gene. Then, we concatenated the gene-top SNP pairs across populations to determine which SNPs would end up in the final models. Lastly, to make our population-based transcriptome prediction models, we used population-specific effect sizes taken from the corresponding MASHR output matrices. AFA, African American; CHN, Chinese; EUR, European; HIS, Hispanic/Latino.

Figure 3

PBMC gene expression cis-heritability estimates across MESA populations (A) Gene expression cis-heritability (h²) estimated for different genes across different MESA population datasets in PBMCs. Only genes with significant estimated h² (p < 0.05) are shown. Gray bars represent the standard errors (2∗SE). Genes are ordered on the x axis in ascending h² order and colored according to the h² lower bound (h² − 2∗SE). (B) Number of significant heritable genes (p < 0.05 and h² lower bound > 0.01) within each PBMC population dataset by sample size. AFA, African American; CHN, Chinese; EUR, European; HIS, Hispanic/Latino.

Figure 4

Comparison of MESA population transcriptome prediction models (A) The number of genes in each MESA population model by method and tissue. (B) Prediction performance (Spearman’s rho) of EN, JTI, MASHR, Matrix eQTL, and TIGAR PBMC MESA population models in Geuvadis GBR and YRI populations. Only the intersections of genes with expression predicted by all methods for each MESA-Geuvadis population pair are shown. MASHR performed better than or the same as all other methods (see Table S2 for all pairwise comparisons).

Figure 5

Number of significant S-PrediXcan gene-trait pairs in PAGE and PanUKBB GWAS summary statistics (A) Total number of significant gene-trait pairs discovered by each MESA population model (considering the union of the three tissues) by method. (B) Number of significant gene-trait pairs discovered with individual or multiple MESA populations colored by method (considering the union of the three tissues). Population set intersections are indicated on the x axis in color.