Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Abstract

Transcriptome-wide association studies (TWAS) are a powerful approach to identify genes whose expression associates with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium patterns (LD) and eQTL pleiotropy at genomic risk regions which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample size across multiple metrics. We perform 15 blood trait TWAS using genome-wide summary statistics (average NEA=511k, NAA=13k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries compared with SNP-level effects. We perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries rather than ancestry-specific. Using multiple lines of evidence to validate our findings, we find gene sets produced by MA-FOCUS are more enriched in hematopoietic categories compared to alternative approaches (P=1.73e-16). Our work demonstrates that including, and appropriately accounting for, genetic diversity can drive deeper insights into the genetic architecture of complex traits.

Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits

“…Analyses only used GWAS from European samples. Recent work suggests that gene-level findings from expression-based methods may be more replicable across ancestries than SNP-level effects (54). Thus, expression-based methods may have superior performance in contexts that could not be evaluated in the present study, owing to the lack of well-powered GWAS for brain-related traits in non-European samples.…”

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

“…Third, gene-level effects can be more stably shared across populations, as compared to SNP-level effects. A recent study [52] suggests that the correlation of gene-level effects is 20% stronger than SNP-level effects across populations. Therefore, leveraging the genetic diversity at the gene-level for fine-mapping can be also an interesting direction.…”

Transcriptome‐Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

“…Leveraging phenotypic data in a multi-biobank setting requires paying attention to case-control definition, data harmonization, and ascertainment and selection bias. Implementing JTI (Zhou et al, 2020) and MOSTWAS (Bhattacharya et al, 2021), the Global Biobank Meta-analysis Initiative (GBMI), a network of 24 biobanks consisting of 2.2 million patients of diverse ancestries, investigates these challenges and others, including the portability of the gene expression prediction across ancestries (Bhattacharya et al, 2022;Li et al, 2022;Lu et al, 2022).…”