Leslie A. Lange

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.

This study identifies novel genetic signals associated with severe obesity through GWAS and shows strong genetic overlap with BMI, highlighting shared comorbidities and lifestyle interactions.

This study calibrates polygenic risk scores (PRS) for African American adults based on genetic ancestry, improving risk stratification and effect estimation for type 2 diabetes and height.

The paper identifies proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

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 improves multi-omics integration by enhancing orthogonality in canonical vectors and demonstrates transferability across cohorts for proteomics and methylomics data.

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 analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

This paper uses gradient boosted trees to improve polygenic risk prediction by capturing non-linear genetic effects and SNP interactions, showing significant increases in variance explained for multiple complex traits.

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.

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.

The paper investigates genetic risk scores for adiposity traits in African Americans and finds they predict subcutaneous but not visceral adiposity.

Over 400 million variants, 97% below 1% frequency, emerge from deep whole-genome sequencing of 53,831 diverse TOPMed participants; TOPMed haplotypes enable imputation down to ~0.01% frequency.

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

This study identifies 5,106 new genetic variants associated with blood cell traits using large-scale GWAS, highlighting the polygenic and monogenic basis of hematopoiesis.

The paper introduces a fully-adjusted two-stage method for rank normalization in genetic association studies to reduce Type I errors and improve power by adjusting for covariates in both stages.

This study identifies over 50 new genetic loci associated with pulmonary function through a multiethnic meta-analysis of 90,715 individuals.