Nathan Pankratz

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

The paper estimates mCA fitness in the NHLBI TOPMed cohort and identifies genetic variants associated with clonal expansion rates.

The paper analyzes inflammation biomarkers using whole-genome sequencing from the TOPMed consortium, identifying both single-variant and rare variant associations with inflammatory traits.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

This study uses whole-genome sequencing to analyze mosaic chromosomal alterations across diverse ancestries, finding higher rates in certain populations and associations with germline variants.

This study identifies 135 genomic loci associated with venous thromboembolism risk through cross-ancestry GWAS and provides insights into biological pathways and potential therapeutic targets.

This study identifies that heteroplasmic mitochondrial mutations increase mortality risk and are linked to cancer incidence and outcomes, particularly leukemia.

This study identifies 144 genetic loci associated with abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target through Mendelian Randomization and pre-clinical validation.

This paper presents a bioinformatics pipeline to estimate mitochondrial DNA copy number and heteroplasmy levels from whole genome sequencing data, showing improved accuracy in capturing mitochondrial genetic features.

The paper introduces BinomiRare, a test for rare variant association with binary outcomes in mixed models, showing it controls type 1 error in simulations and applies it to study genetic variants in disease traits.

The paper describes a system for harmonizing phenotype data across multiple studies in the TOPMed program, enabling reproducible and shared phenotype data for over 80 studies.

This study identifies 100 loci associated with mitochondrial DNA copy number and finds causal links between mitochondrial function and mortality.

This study identifies CpGs associated with mtDNA-CN and shows they influence disease-related phenotypes through nDNA methylation and gene expression changes.

This study investigates the association between mitochondrial DNA copy number and risk of atrial fibrillation in three cohorts, finding an inverse relationship after adjustment for traditional risk factors.

This study identifies 5,552 blood-cell trait-variant associations across 746,667 individuals from 5 global populations, including 71 novel loci and ancestry-specific variants not found in European ancestry populations.

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 identifies a genetic variant in PROCR that influences protein C and factor VII levels, linking it to both reduced coronary artery disease risk and increased venous thromboembolism risk through opposing inflammatory and thrombotic mechanisms.

This study evaluates methods for estimating mitochondrial DNA copy number, finding that whole genome sequencing provides more accurate associations with known correlates than qPCR or microarray platforms.

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.

This paper introduces BridGE, a method to detect pathway-level genetic interactions from GWAS data, revealing significant interactions in multiple diseases.

This paper evaluates the causal effect of fibrinogen on coronary heart disease using Mendelian randomization, finding a small effect with multi-variant methods but not with single variant approaches.