Multi-ancestry gene expression models amplify transcriptome-wide association study discovery and validation.

Abstract

Our understanding of the influence of ancestral background on genetically determined expression remains limited, especially when gene expression models are applied to studies from different or multiple populations. We perform transcriptome-wide association studies of 6 psychiatric conditions, leveraging gene expression models trained in cohorts with different proportions of African, European, and Indigenous American genetic ancestries. For comparison, we repeat each transcriptome-wide association study using a model trained in individuals of predominantly European ancestry. We identify 1416 statistically significant gene-level associations (false discovery rate adjusted p < 0.05) across the 6 diagnoses, of which 62% are uniquely detected by the admixed gene models. Notably, we observe high correlation (<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>ρ</mi><mo>></mo><mn>0.92</mn></math>) in the gene-level effects on disease risk across ancestries, a statistic that remains robust for results that only reach statistical significance in one population. The genes identified by the admixed models implicate more neurophysiological features (as measured by brain imaging) associated with diagnostic symptoms. Overall, admixed gene expression models greatly extend the yield of transcriptome-wide association studies and substantially enhance validation, enabling more precise mapping of genetic effects to underlying pathophysiological mechanisms and highlighting potential avenues for therapeutic development.