Shengying Qin

Abstract Here we developed and deployed the blended genome exome (BGE) method, a DNA library approach that generates low-pass whole-genome (1–4× mean depth) and deep whole-exome (30–40× mean depth) data in a single sequencing run. BGE is cost-effective, empowers most genomic discoveries possible with deep whole-genome sequencing and captures global common single-nucleotide polymorphism diversity.

Correction to: Nature Genetics https://doi.org/10.1038/s41588-022-01054-7, published online 5 May 2022. In the version of this article initially published, affiliation 24—Digital China Health Technologies Corp. Ltd., Beijing, China—was presented incorrectly and has been amended in the HTML and PDF versions of the article.

Abstract Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) have been conducted predominantly in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations, and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most remain underpowered.