Kenneth S. Kendler

The paper develops a method to estimate sample size needed for a specified number of significant GWAS signals in polygenic traits, accounting for prevalence using noncentrality measures.

The paper constructs a polygenic risk atlas for 33 traits in emerging adults and finds genetic risks for schizophrenia, neuroticism, and depression predict various phenotypes.

The paper uses GWAS data to identify polygenic risk scores for drug metabolism pathways, finding that the GO:17144 pathway is significantly predictive of recurrent depressive disorder.

JEPEGMIX2 improves gene-level eQTL analysis by using updated cis-eQTL SNPs and adjusting for background enrichment, reducing false positives in cosmopolitan cohorts.

The paper confirms a polygenic effect on negative symptoms in schizophrenia using GWAS, identifies novel loci, and highlights the need for larger samples.

This paper investigates how global polygenic risk scores, sex, and their interaction predict schizophrenia risk using a classification model, finding sex and polygenic risk are the strongest predictors.

The paper shows that haplotypes of common SNPs explain more heritability of complex diseases than individual SNPs alone.

FIQT estimates Z-score means from genome scans more accurately and efficiently than existing methods, improving risk prediction in schizophrenia studies.