Mark J. Caulfield

This study shows that HTT CAG repeat length within the normal and intermediate range is associated with brain structure and neuropsychiatric risk, challenging the view of CAG repeats as purely categorical.

The paper estimates the frequency of repeat expansion mutations across populations using whole genome sequencing data and finds higher expected prevalence than current reports.

This paper builds a haplotype reference panel from Genomics England data and imputes UK Biobank data, revealing more rare variants and suggesting limitations in detecting rare variant associations with current sample sizes.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

The paper identifies 88 novel rare disease-gene associations using a burden analysis framework on whole genome sequencing data from the 100,000 Genomes Project, with potential clinical impact for undiagnosed cases.

This study identifies genetic differences between pediatric and adult cardiomyopathy, finding more constrained variants in childhood cases and female patients.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

This study identifies TMEM63B variants in patients with developmental and epileptic encephalopathy and neurodegeneration, showing altered cation currents and hematological abnormalities.

GROFFFY filters whole genome sequence data to identify non-coding and polyadenylation site variants, improving detection of disease-causing variants in unsolved cases.

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.

The paper identifies novel Mendelian genes by analyzing mouse embryo lethality and linking them to human disease phenotypes and genetic variants.

The paper identifies an intermediate-effect variant in UMOD associated with chronic kidney disease risk and shows it has reduced disease severity compared to canonical ADTKD mutations.

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

The paper analyzes whole genome sequencing data from endothelial vasculopathy patients to identify rare variants in clinically relevant genes and their association with disease severity.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

This study demonstrates that whole genome sequencing can effectively detect repeat expansions in neurological disorders with high sensitivity and specificity, enabling diagnosis in previously undiagnosed patients.

This study uses whole-genome sequencing in a national healthcare system to diagnose rare diseases and discover new aetiological variants, identifying 95 Mendelian associations and 4 novel non-coding disease-causing variants.

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 210 genetic loci associated with the PR interval through multi-ancestry GWAS, highlighting their role in cardiac conduction and cardiovascular disease.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

This paper uses Mendelian Randomisation to show that height reduces coronary artery disease risk independently of BMI, lipid, and blood pressure, but has no direct effect on type 2 diabetes risk after adjusting for BMI.