Kai Yuan

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

The paper introduces Tractor-Mix, a mixed model method for GWAS in admixed cohorts with high relatedness, improving ancestry-specific association detection and controlling false positives.

This study uses exome and genome sequencing to identify 68 genes directly associated with inflammatory bowel disease, providing clearer genetic insights and therapeutic targets.

The paper identifies CFTR loss-of-function variants as protective against IBD using large-scale exome sequencing data and evaluates variant prioritization methods.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

The paper introduces a cost-efficient sequencing method called Blended Genome Exome (BGE) that combines low-coverage whole genome and high-coverage exome data, achieving >99% concordance with 30x WGS for variant imputation and rare coding variant calling.

The paper identifies ten new genes with coding variants linked to Crohn's disease risk using sequencing data from over 30,000 patients and 80,000 controls.

The paper introduces MultiWaver, a method to infer multiple-wave admixture histories from ancestral tracks, validated on real datasets.