Veera Manikandan Rajagopal

This study identifies 64 new risk loci for major depression, refines its genetic architecture, and shows strong genetic overlap with other psychiatric disorders and educational attainment.

This paper presents the largest catalog of human protein-coding variation from 985,830 individuals, identifying rare variants and their functional implications for precision medicine.

This study identifies genetic overlaps between psychiatric disorders, language ability, and creativity through a GWAS of school grades.

The paper identifies rare coding variants in CHRNB2 associated with reduced smoking likelihood and links them to downstream smoking-related phenotypes.

The paper investigates genome-wide interactions between stressful life events and depression using a case-cohort study, identifying three genome-wide significant loci but lacking replication in an independent sample.

The paper identifies sex-differential participation bias in genetic studies by performing GWAS on sex in 3.3 million individuals, finding 158 loci associated with sex and highlighting traits like BMI that may influence participation.

The paper examines how a polygenic score for educational attainment predicts school performance in children with and without psychiatric disorders, finding reduced predictive power in ADHD but not ASD.

This study identifies risk variants and characterizes the polygenic architecture of ADHD comorbid with disruptive behavior disorders through a large GWAS meta-analysis.

This study identifies three novel genes associated with ADHD by linking genetic variants to differential DNA methylation and gene expression in brain tissues.

This study integrates transcriptome data with genetic variation to identify gene-trait associations and uncover biological mechanisms underlying complex traits in humans and mice.

This paper identifies a genome-wide significant risk locus for cannabis use disorder, implicating CHRNA2 and linking it to cognitive performance.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.