Christopher A. Odhams

The paper uses blood-based RNA-Seq on 5,412 individuals with rare diseases to identify new candidate diagnoses and shows its utility in prioritizing genetic variants.

The paper introduces DeNovoLOEUF, a method to identify diagnostic variants in genome sequencing data, achieving 96% positive predictive value and uncovering 39 missed diagnoses in the 100,000 Genomes Project.

This study uses whole-genome sequencing to identify regulatory and novel genic variants contributing to childhood cardiomyopathy.

This study evaluates genetic risk scores for predicting SLE susceptibility and severity using GWAS data from European and Chinese populations, finding moderate predictive performance and associations with renal disease and age of onset.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.

This study identifies de novo mutations and rare variant burdens in SLE candidate genes, supporting their role in disease pathogenesis.

This paper shows that analyzing RNA-Seq data at multiple resolutions increases the detection of causal eQTLs in autoimmune diseases, revealing more loci with shared genetic effects than gene-level analysis alone.