Kenneth S. Kendler

This study investigates rare copy number variants in PTSD using whole-genome sequencing and biobank data, finding significant associations in specific ancestry groups and gene-sets.

LT-FGRS estimates genetic liabilities from large-scale family data, unifying methods into an R package for efficient population-scale analysis.

The paper shows genotype- and phenotype-based genetic instruments explain largely independent components of liability to psychiatric disorders, supported by theoretical analysis and simulations.

This study shows that as ASD and ADHD diagnoses have increased, the genetic risk associated with these conditions has decreased over time.

This study identifies synergistic sets of genetic risk variants in psychiatric disorders, suggesting subtypes and reinterpreting shared genetic effects.

The paper identifies five genomic factors explaining most genetic variance across 14 psychiatric disorders and finds shared and disorder-specific genetic signals with distinct biological functions.

The paper identifies distinct functional SNP classes associated with psychiatric disorders, behavioral traits, and neurological disorders, highlighting differences in genomic regions involved.

The paper shows that PHQ9 and CIDI-SF MDD symptoms have low to moderate genetic correlations, with distinct associations to dysphoria and symptom heterogeneity.

AutoComplete improves phenotype imputation in biobank data, enhancing GWAS power and genetic discovery.

This study uses machine learning and functional weights to identify rare variants associated with AUDIT-P, finding significant associations in ADH1C and THRA genes.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

The paper analyzes rare functional variation in genes related to ethanol metabolism in severe alcohol dependence cases using a case-only design and WES data from the island of Ireland.

This paper shows that cross-trait assortative mating explains genetic correlations between traits, suggesting many reported genetic similarities may be overestimated.

This paper improves genetic studies of MDD by integrating shallow and deep phenotypes, enhancing GWAS power and PRS accuracy while preserving specificity.

This study uses BMI GWAS and genomic structural equation modeling to show that genetic influences on BMI remain stable from age 40 to 73 in men and women.

This study performs a GWAS on creativity using 241,736 individuals, identifying 25 loci and showing genetic overlap with several psychiatric disorders.

This paper conducts a multi-ancestry GWAS of major depression, identifying 190 loci and 354 genes, with findings showing increased fine-mapping power and bidirectional BMI relationship in European ancestry samples.

This study performs a multivariate GWAS on psychiatric disorders and symptoms to identify two genetic liabilities distinguishing common mood disturbances from serious mental illnesses.

This study identifies four genetic factors underlying 11 psychiatric disorders and finds limited evidence for a single dimension of genetic risk across them.

The paper profiles lncRNA and protein coding gene expression in nucleus accumbens from alcohol-dependent and control subjects, identifying differentially expressed genes and their functional roles in alcohol dependence.

The paper introduces JEPEGMIX2-P, a TWAS pathway method that improves computational efficiency and performance for psychiatric disorder GWAS, showing increased power over existing methods like MAGMA.

This study identifies 308 genetic loci associated with mood disorder and schizophrenia across East Asian and European populations, revealing shared and distinct genetic mechanisms.

This paper improves psychiatric GWAS by re-imputing summary statistics using a larger, more diverse reference panel, revealing new signals from rare variants.

This study finds that subthreshold psychosis symptoms in a population-based cohort are associated with genetic risks for schizophrenia, ADHD, autism, and neuroticism, with notable sex differences.

The paper compares genome-wide data from opioid use and dependence cases to identify genetic associations and differences between exposure and dependence.

This study shows that using unscreened or super-normal controls in GWA studies biases genetic correlation estimates between disorders upward, with stronger effects at higher prevalence and lower true genetic correlation.

This paper shows that GWAS using minimal phenotyping for depression identifies non-specific genetic factors shared with other psychiatric conditions, not specific to major depressive disorder.

This study uses Mendelian randomization to find a causal link between major depression and alcohol dependence using large genetic datasets.

Cross-disorder meta-analysis of eight psychiatric GWAS (232,964 cases, 494,162 controls) reports 109 loci shared by two or more disorders and three groups of correlated disorders.

This study presents a comprehensive catalog of genetic variation in Han Chinese using whole-genome sequencing, revealing population structure, admixture, and loci under local adaptation.