Abhishek Sarkar

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

This paper introduces GoM DE, a differential expression analysis method that allows cells to have partial membership in multiple groups, improving the interpretation of structure in single-cell sequencing data.

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

The paper introduces a flexible empirical Bayes method for multivariate multiple regression to improve prediction of multi-tissue gene expression from genotypes.

Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis

The paper clarifies confusion in scRNA-seq analysis by distinguishing between measurement error and true gene expression, advocating for a Poisson measurement model.

Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes

The paper identifies dynamic eQTLs in cardiomyocytes under hypoxic stress, showing how genetic variation affects gene expression differently across conditions.

Gene expression variability in human and chimpanzee populations share common determinants

This study compares gene expression variability and eQTLs in humans and chimpanzees, finding shared genetic and non-genetic determinants of inter-individual expression differences.

A simple new approach to variable selection in regression, with application to genetic fine-mapping

SuSiE writes sparse regression coefficients as a sum of single-effect vectors; its IBSS algorithm returns a credible set per effect and outperformed existing fine-mapping methods in numerical experiments.

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

This study identifies variance QTLs (vQTLs) in human iPSCs using scRNA-seq, finding that genetic variants affect gene expression variance but with smaller phenotypic impact than mean expression QTLs.

A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease

This paper introduces a Bayesian method to identify causal genes in Alzheimer’s disease by distinguishing mediation from pleiotropy using summary statistics, finding 206 causal genes.

Modeling prediction error improves power of transcriptome-wide association studies

This paper improves TWAS power by incorporating prediction uncertainty, finding 45 TWAS genes with 17 novel associations and replicating only 2 of 40 previously reported genes.

Multi-tissue polygenic models for transcriptome-wide association studies

The paper introduces fQTL, a multi-tissue model for eQTL mapping and gene expression prediction, improving TWAS results for Alzheimer's and schizophrenia.