Arjun Bhattacharya

The paper shows that RNA-seq quantification methods and reference annotations significantly impact eQTL analysis, colocalization, and TWAS results across multiple tissues and phenotypes.

ICONIC is an R package that integrates instrumental variable and negative-control methods for causal inference in multi-omic studies, offering diagnostics and benchmarks for mediation analysis.

The paper uses long-read RNA-seq to create a comprehensive placental transcriptome reference, revealing high transcriptional complexity and identifying novel isoforms associated with metabolic and endocrine functions in gestational diabetes.

The paper shows PFAS affect birth weight and gestational age through distinct transcriptional network architectures, with network position, not fold-change, determining mediator importance.

This study performs a cell-type-aware transcriptome-wide association study (TWAS) of mammographic density phenotypes, identifying novel genes and prioritizing known GWAS loci through cell-type-specific gene expression analysis.

This paper introduces a formal statistical replication analysis to reduce false positives in lung cancer GWAS, finding it significantly lowers false discovery rate and improves polygenic risk score efficiency.

This study identifies genetic risk mechanisms for six cancers by analyzing isoform-level expression data, finding more associations and greater heritability explained compared to gene-level analysis.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

This study identifies ancestry-specific and sex-specific genetic loci for primary open-angle glaucoma through a meta-analysis of 15 biobanks, highlighting shared biology with vascular mechanisms and cell proliferation.

This paper evaluates a European-derived polygenic score for tobacco use disorder in a diverse biobank, finding inconsistent performance across ancestries and associations with systemic health risks.

This paper quantifies how differences in allelic effect sizes across ancestry backgrounds impact GWAS power in admixed populations and finds modeling these differences does not significantly improve results.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

The paper describes a biobank initiative that links genomic data with electronic health records to enable genome-wide and phenome-wide association studies across diverse populations.

This paper shows that meta-analysis fine-mapping is often miscalibrated due to cohort heterogeneity and introduces a QC method to identify suspicious loci.

The paper introduces a new statistical method for multivariate variance components models to analyze brain isoform expression and identifies novel psychiatric disease mechanisms.

This paper uses proteome-wide Mendelian randomization across multiple ancestries to identify causal protein-disease associations and drug targets in over 1.26 million individuals.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

The paper outlines best practices for conducting multi-ancestry transcriptome-wide association studies (TWAS) using the Global Biobank Meta-analysis Initiative data, addressing challenges in ancestry and tissue specificity.

The paper explores the relationship between genomic ancestry and health outcomes using a biobank linked to electronic health records, finding disease stratification across ancestry clusters.

This paper introduces isoTWAS, a method that improves discovery of genetic risk mechanisms for neuropsychiatric disorders by analyzing isoform-level transcriptome-wide associations.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

The paper identifies non-coding RNAs that mediate gene expression regulation in breast and prostate tissue and overlap with genetic signals for cancer susceptibility.

The paper investigates race-specific germline genetic associations with breast cancer recurrence scores using GReX and finds differential associations between germline variants and CRS in Black and White patients.

The paper identifies placental gene-trait associations across the life course using multi-omics data, finding genetic effects on early-life traits but not later ones.

MOSTWAS improves transcriptome-wide association studies by incorporating distal genetic variants and multi-omic regulatory biomarkers to enhance gene-trait association detection.

DeCompress improves deconvolution of targeted mRNA panels by expanding feature space with reference data, achieving better cell-type proportion estimation and identifying a tumor-specific cis-eQTL for CCR3.

The paper uses multi-omic kernel aggregation to predict intellectual and social impairment in extremely preterm children, finding strong predictive power for IQ and SRS scores.