Xiaowei Hu

This study improves proteome-wide association study results by using multi-ancestry data, finding more protein-phenotype associations and better prediction models.

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

This study performs a multi-ancestry GWAS on lung function and COPD, identifying 559 genes and 29 pathways, and showing genetic risk scores are associated with COPD across ancestries.

This paper develops a genomic selection framework using weighted kernels to improve multi-environment prediction by incorporating genetic associations and population characteristics, achieving up to 31% higher predictability in winter wheat.

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.