Kuldeep Kumar

The paper identifies polygenic hierarchies in macroscale brain structural organisation using genome-wide association studies of imaging-derived phenotypes.

gVCF2CNV converts gVCF files into CNV-compatible signals for large-scale WGS cohorts, achieving high precision in detecting deletions and duplications.

The paper develops FunBurd to analyze gene dosage effects on 43 traits in UK-Biobank, finding pleiotropy and non-monotonic responses linked to genetic constraint and brain-specific mechanisms.

MCNV2 assesses CNV quality using Mendelian Precision, offering tools to compute, explore, and optimize CNV call accuracy in parent-offspring trios.

This study identifies genetic maps of human sulcal complexity using rare and common variants, revealing new mechanistic pathways for cortical morphogenesis.

The paper identifies shared and divergent genetic mechanisms across psychiatric disorders using gene-set burden analysis of copy number variants.

The paper develops a method to analyze genomic deletions and duplications across the cerebral cortex and finds opposing effects on cognitive ability depending on gene expression patterns in different cortical regions.

The paper identifies a CNV duplication associated with higher cognitive ability and shows gene-dosage effects differ between deletions and duplications across biological processes.

The paper compares functional brain measures between 22qDel and CHR cohorts, finding distinct patterns of connectivity and variability disruptions.

The paper investigates the impact of rare high-effect genetic variants on brain morphology and related behavioral traits in adolescents using the ABCD cohort.

The paper investigates how rare genetic mutations affect brain asymmetry by analyzing CNVs in a cohort of individuals and linking genetic alterations to brain structure and function.

This study examines subcortical brain alterations in CNV carriers across multiple neuropsychiatric disorders, finding shared and distinct patterns of structural changes.

The paper investigates how eight rare CNVs affect brain structure and phenotypes across 534 carriers, finding structural divergence and phenotypic convergence despite different genetic origins.

The paper examines how rare and common genomic variants affect brain functional connectivity, revealing how polygenicity and pleiotropy influence psychiatric conditions and brain networks.

Neuropsychiatric CNVs reorganize brain connectivity along shared patterns linked to cognitive traits and disorder severity.

The paper examines how CNVs in 16p11.2 and 22q11.2 affect functional brain connectivity in neuropsychiatric disorders, identifying shared dysconnectivity patterns across ASD, SZ, and deletions.

The paper examines the effects of neuropsychiatric CNVs on brain structure using neuroimaging data and identifies shared anatomical patterns across multiple CNVs.

This paper introduces a manifold approximation framework for generating brain fingerprints from multi-modal data, showing that multi-modal fingerprints are more discriminative and genetically informative than single-modal ones.