Thanneer Malai Perumal

The paper introduces a NanoString nCounter Mouse AD panel to assess LOAD mouse models by correlating gene expression with human AD modules, showing strong correlation with RNA-Seq data.

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

The paper develops a multi-view evidence aggregation method to identify and rank potential driver genes of Alzheimer's Disease using RNA-Seq data and GWAS summary statistics.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.

The paper analyzes allelic expression bias in schizophrenia using RNA-seq data to assess imprinted gene expression and its relationship with age, ancestry, and disease.

The paper develops gene-expression-based prognostic models for 30-day mortality in sepsis using community-onset and hospital-acquired sepsis cohorts, showing improved prediction when combined with clinical scores.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.