Wenjian Bi

LKOM identifies genetic variants associated with longitudinal phenotypes by jointly modeling mean and variability signals while controlling the false discovery rate.

The paper improves rare variant association testing by introducing SAIGE-GENE+ which reduces type I error and computational costs, identifying 551 gene-phenotype associations in UK Biobank WES data.

The paper introduces a method to improve genetic association studies by incorporating family disease history and addressing case-control imbalance, showing improved power and calibration.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

The paper introduces POLMM, a mixed model for ordinal categorical phenotypes in GWAS, showing it is computationally efficient, controls type I error, and identifies more significant variants than existing methods.

SAIGE-GENE is a scalable generalized linear mixed model for region-based association tests in large cohorts, efficiently handling over 400,000 samples with controlled type I error rates.

The paper analyzes de novo mutations in 1,465 genomes to show similar mutation rates across ancestries but reduced rates in the Amish population, with environmental factors possibly influencing mutation rates.

The paper develops a region-based rare variant test for unbalanced binary phenotypes, showing improved type I error control and scalability, and identifies 10 rare variant associations in UK Biobank whole exome data.