Ajay Nadig

The paper estimates the contribution of de novo coding mutations to autism using data from 38,680 trios, finding they explain 3.4% of autism variance.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

The paper uses village editing to study how genetic backgrounds affect the impact of schizophrenia-associated mutations in human neurons.

This study performs a meta-analysis of brain transcriptomes from genetic mouse models of schizophrenia, identifying dysregulation in striatum and thalamus with distinct patterns in different models.

The paper identifies chr22q as a regulatory hub where genetic risk for neuropsychiatric conditions is associated with coordinated gene expression downregulation in brain tissue.

The paper introduces TRADE, a statistical framework to detect transcriptome-wide differential expression effects from noisy Perturb-seq data, showing that many true effects are not statistically significant but detectable in aggregate.

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

This study identifies shared genetic determinants between type 1 diabetes, coronary artery disease, and leukocyte counts, showing genetic associations between leukocyte counts and both diseases.

This study identifies genetic variants associated with cellular morphology using high-content imaging and genomic data from iPSCs.

The paper quantifies the heritability explained by rare coding variants and compares it to common variants across 22 traits using 400,000 UK Biobank exomes, finding rare variants explain less phenotypic variance but are more concentrated in constrained genes.

This study maps resting-state functional connectivity changes in Klinefelter syndrome and links them to brain anatomy and psychopathology.

The paper links genetic risks to spatial brain vulnerabilities in neurodevelopmental disorders using neuroimaging and gene expression data.

The paper analyzes longitudinal MRI data to identify sex-biased growth trajectories of the amygdala and hippocampus across human development, revealing differences in volume expansion and shape changes between males and females.

This study examines how childhood socioeconomic status relates to brain morphology changes from age 5 to 25, finding consistent positive associations with brain volume and specific cortical and subcortical regions.

This study examines how supernumerary sex chromosomes affect limbic structure volume and shape in patients compared to controls, finding reduced amygdala volume but no hippocampal volume changes after brain size correction.