Xihong Lin

This paper defines and compares two INT-based association tests for non-normally distributed traits in GWAS, introduces an adaptive omnibus test, and shows it outperforms standard tests in power and type I error control.

The paper identifies two EBV variants in BALF2 associated with increased NPC risk through genome sequencing and association studies in China.

The paper identifies DNA methylation sites associated with daytime sleepiness in African Americans using epigenome-wide association analysis in the Multi-Ethnic Study of Atherosclerosis.

The paper evaluates adjusted spectral clustering methods for detecting gene clusters in eQTL data, considering regression errors in network construction.

The paper introduces SCANG, a dynamic scan statistic method for detecting rare-variant association regions in WGS data without pre-specifying window sizes, showing improved performance over existing methods.

This paper introduces the Generalized Berk-Jones statistic for gene set analysis in GWAS, showing it increases power by incorporating multiple signals per gene and addressing confounding from gene set lists.

ACAT improves power for rare-variant analysis by combining p-values using two tests, ACAT-V and ACAT-O, showing robustness and higher power than existing methods.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

The paper assesses the reproducibility and functional importance of eQTL network nodes by analyzing degree centrality across 21 GTEx tissues.