Mark I. McCarthy

GEMS associates GWAS variants with cell types using sequence-to-function predictions, revealing cell-type-specific mechanisms for disease risk variants.

This study identifies three novel genetic signals associated with idiopathic pulmonary fibrosis using GWAS and clinically-curated datasets, highlighting potential drug targets like MUC1 and NTN4.

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

The paper benchmarks five machine learning-based variant annotation methods for rare variant association testing across 350,377 UK Biobank participants, finding CADD provides best signal separation and GPN-MSA highest enrichment for loss-of-function intolerant genes.

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

The paper uses single-cell multiome profiling to identify gene regulatory changes in pancreatic islets linked to diabetes risk factors like age, sex, and genetic variants.

This study integrates multi-omics data to identify genetic variants and molecular mechanisms associated with type 2 diabetes and highlights potential drug targets.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

The paper maps genetic associations with plasma proteins in 54,306 UK Biobank participants, identifying 10,248 new protein quantitative trait loci (pQTLs) and expanding the genetic architecture of the plasma proteome.

The paper identifies shared genetic regulation across mRNA, proteins, and metabolites in blood, revealing networks and mediators of GWAS variants.

This study conducts a GWAS of preeclampsia and hypertensive disorders of pregnancy, identifying 18 genome-wide significant loci, including novel associations with blood pressure and placental function.

This study identifies genetic loci associated with gestational duration and preterm delivery, showing maternal and fetal genetic effects and antagonistic pleiotropy with birth weight.

This study identifies 43 genomic loci associated with reproductive success in 785,604 individuals, showing ongoing natural selection at the FADS locus.

This study identifies 400+ genetic loci associated with circulating metabolic traits and highlights pleiotropy and novel disease pathways.

The paper uses targeted chromatin interaction profiling to identify causal genes at type 2 diabetes GWAS loci with higher sensitivity and resolution than genome-wide methods.

This paper improves genetic scores for adiposity traits in East Asian populations by using trans-ancestry meta-analysis, increasing variance explained compared to European-derived scores.

The paper introduces the Effector Index, an algorithm to predict causal genes at GWAS loci using genomic features, achieving 85% AUC in type 2 diabetes validation.

This study identifies rare loss of function variants in four genes associated with beneficial changes in body fat distribution using genome-wide and exome-sequence data from UK Biobank.

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.

ClinGen and the PGS Catalog expand the GRIPS guideline into a 22-item Polygenic Risk Score Reporting Statement covering study populations, model construction, evaluation, and clinical interpretation.

This study evaluates if African American or multi-ethnic polygenic risk scores improve type 2 diabetes prediction in continental Africans, finding that African American-derived scores are more predictive.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

The paper develops a framework to determine tissue-of-action for type 2 diabetes GWAS signals by integrating gene expression and epigenomic data, revealing key tissues and enhancing functional interpretation.

The paper introduces a multivariate GWAS framework that identifies functional variants and disease associations by transforming multivariate results into univariate phenotypes for follow-up analysis.

The paper investigates how genetic variants affect gene expression in human pancreatic islets and identifies candidate genes linked to type 2 diabetes.

This study identifies 48 genetic variants associated with handedness using a large GWAS, highlighting polygenic nature and genetic links to neuropsychiatric traits.

This study develops machine learning models to predict liver fat content using multi-omic and clinical data from NAFLD patients, achieving higher accuracy than existing tools.