Andrew P. Morris

This study evaluates when nonlinear machine learning methods outperform standard polygenic risk scores, finding their effectiveness depends on genetic architecture and sample size.

This study performs ancestry-specific TWAS across six tissues to refine T2D GWAS loci, identifying 289-322 independent signals and highlighting tissue and ancestry-specific effects.

PANACEA improves GWAS meta-analysis by using a continuous ancestry representation to include more diverse participants and increase statistical power.

The paper conducts a multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycemic traits, identifying novel loci and highlighting limited pregnancy-specific genetic effects.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

This study uses genomic structural equation modeling to identify latent genetic factors underlying T2D and its comorbidities, showing distinct risk profiles and broader associations with complications.

The paper identifies 508 CKD-associated genetic loci, prioritizes 651 kidney genes, and highlights novel CKD genes like CA12 with functional consequences.

The paper identifies HTRA1-AS1 as a genetic risk factor for AMD through transcriptome-wide association and fine-mapping analyses.

The paper introduces two fine-mapping methods to account for ancestral and environmental heterogeneity in GWAS, improving resolution of causal variants across diverse populations.

This paper combines multi-trait fine-mapping and functional annotation to improve the identification of causal variants at glycaemic trait loci, reducing the number of likely causal variants significantly.

This study investigates causal links between T2D genetic clusters and non-cardiovascular comorbidities, identifying potential drug repurposing opportunities.

This study identifies 80 genetic loci and 61 effector genes associated with dilated cardiomyopathy through GWAS and integrates transcriptomic data to uncover biological pathways and cellular states involved in the disease.

This paper introduces env-MR-MEGA, a meta-regression model that adjusts for environmental and ancestral heterogeneity in GWAS meta-analysis, improving association power.

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 investigates the genetic and phenotypic associations between endometriosis and 31 immunological diseases using UK Biobank data, finding significant genetic correlations and a causal link with rheumatoid arthritis.

This study conducts a GWAS on urinary albumin-to-creatinine ratio in Sub-Saharan African and non-resident African-ancestry populations, identifying two genome-wide significant loci and highlighting limited transferability of polygenic scores across populations.

This study investigates the genetic relationship between type 2 diabetes and schizophrenia, finding no strong causal link but identifying shared genomic loci and biological pathways.

This paper compares ML methods for predicting T2D status from GWAS data using imbalanced learning techniques, finding that MLP with SMOTE achieves the highest accuracy.

This paper introduces MGflashfm and MGfm, methods that improve fine-mapping resolution by integrating multiple traits and population groups using summary statistics.

The paper integrates genetic fine-mapping with multi-omics data to identify candidate effector genes and new drug targets for hypertension.

This study identifies genetic correlations and colocalizing signals between type 2 diabetes and osteoarthritis, highlighting shared pathways and effector genes.

The paper introduces an omics-informed quality score to improve CNV detection and association analysis by reducing false positives and increasing statistical power.

The paper introduces rareSurvival, a software tool for analyzing rare variant associations with time-to-event outcomes, combining survival and gene-based methods.

This study uses multivariable Mendelian randomization to show that higher bone mineral density causally increases osteoarthritis risk independently of body mass index.

This study identifies 592 genetic loci associated with 184 plasma proteins in 26,494 individuals and finds causal relationships between protein levels and disease.

This study conducts the first GWAS of eGFR in Africans, identifies two genome-wide significant loci, and highlights the need for more African-specific genetic research on kidney function.

This paper identifies 56 new type 2 diabetes loci in 433,540 East Asian individuals through a meta-analysis of GWAS data.

The paper identifies pQTLs for cardiovascular proteins and uses them to evaluate drug targets and suggest new candidates for precision medicine.

This study identifies three new genetic loci associated with idiopathic pulmonary fibrosis and confirms 11 previously reported ones, highlighting mTOR signaling and mitotic spindle-assembly genes in disease susceptibility.