Alisa Knodle Manning

The paper estimates whole-genome heritability of complex traits across diverse populations, finding rare and non-coding variants significantly contribute to heritability.

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

The paper examines proteomic changes caused by MODY mutations in HNF1A and HNF1B using cell line models, finding suppressed energy metabolism and interferon signaling.

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

The paper develops a pipeline to compare polygenic scores (PGS) for detecting genetic interactions between adiposity and cardiometabolic risk factors, finding that interaction-based PGS (iPGS) most consistently identifies these interactions.

This paper improves type 2 diabetes polygenic risk scores by including rare and population-specific variants, showing better risk prediction in diverse populations.

The paper introduces a robust mixed model association test (RoM) for gene-environment interactions in related samples, showing improved type I error control compared to existing methods.

The paper identifies ancestry-specific T2D risk variants in South Asian populations through exome-wide association study and functional validation.

The paper introduces a decomposition approach to detect genetic modifiers of the relationship between dietary omega-3 fatty acids, plasma biomarkers, and hsCRP in the UK Biobank, improving statistical power.

The paper refines a gene-physical activity interaction affecting HDL-cholesterol by analyzing lipoprotein subfractions and exploring sex differences across multiple cohorts.

The paper conducts a large-scale rare variant GWAS meta-analysis of type 2 diabetes, identifying novel variants in monogenic diabetes genes and assessing their pathogenicity.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

The paper introduces two tools, REGEM and METAGEM, to analyze gene-environment interaction summary statistics from GWIS studies, enabling efficient re-analysis and meta-analysis.

This study identifies genetic subtypes of type 2 diabetes across diverse ancestries, revealing ancestry-specific risk profiles and BMI thresholds.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

This paper develops an automated pipeline to collect, unify, and translate genetic variants associated with monogenic diabetes into protein sequences, creating a database for proteomics and structural biology analysis.

This paper clusters T2D genetic loci into ten groups using bNMF, identifying distinct biological mechanisms and clinical associations.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper identifies variance-quantitative trait loci (vQTLs) and gene-environment interactions (GEIs) for cardiometabolic serum biomarkers using UK Biobank data.

The paper introduces a method to generate summary statistics for gene-environment interaction tests and a meta-analysis approach to combine results across studies, demonstrating its effectiveness in large-scale sequencing data.

The paper introduces a cloud-based workflow for rare variant analysis in whole genome sequencing studies, enabling scalable and reproducible analysis with functional annotations.

This study evaluates a polygenic risk score for breast cancer in women aged 70 and older, finding it improves risk prediction beyond conventional factors.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

The paper identifies five novel gene-diet interaction effects on HbA1c using UK Biobank data, highlighting interactions with dietary patterns and traits.

GEM enables scalable gene-environment interaction analysis in large samples, identifying novel loci in a gene-sex interaction study.

The paper introduces MAGEE, a method for efficient gene-environment interaction testing in large biobank-scale sequencing studies, reducing computational burden and enabling analysis of hundreds of thousands of individuals.

The paper identifies LOC157273 as a negative regulator of PPP1R3B and glycogen deposition in human hepatocytes, linking it to type 2 diabetes risk.