Yu Jiang

This study identifies 447 new genetic variants associated with prostate-specific antigen levels across multiple ancestries and shows improved cross-ancestry prediction of PSA, with implications for personalized prostate cancer screening.

This study develops a polygenic score to adjust PSA levels, improving the detection of aggressive prostate cancer while reducing unnecessary biopsies.

The paper analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.

The paper develops a method to handle missing data in genetic meta-analysis, identifying novel low-frequency variants in nicotine dependence genes with improved accuracy.

The paper develops methods to analyze multi-allelic variants in large sequence datasets, showing improved power and unbiased estimates in association tests.