Dmitri V. Zaykin

The paper introduces an exact alternative to the Shen and Faraway F-statistic for gene-based multi-phenotype analysis, showing it performs well in small sample sizes and identifies novel associations in neurodegenerative disease data.

DOT improves gene-set analysis by decorrelating SNP-level statistics, enhancing power in heterogeneous effect size scenarios.

The paper introduces a method to estimate odds ratios from P-values and sample size, showing it provides reliable effect size estimates for genetic associations.

The paper develops a method for joint analysis of correlated biomarkers and applies it to study inflammation and muscle mass in senior horses, finding an association between IFN-gamma positive lymphocytes and muscle condition.

The paper introduces a new method for combining P-values in genetic association studies to detect weak signals, demonstrating improved power in identifying associations of μ-opioid receptor variants with pain sensitivity.