Nicholas L. Smith

This study identifies genetic loci associated with residual pulmonary vascular obstruction and links inflammation to its risk, while showing limited role of impaired fibrinolysis.

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

The paper identifies genetic loci and proteins associated with venous thromboembolism recurrence risk using GWAS and subgroup analyses across different thrombotic subtypes.

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

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

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 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 analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

This study conducts a multi-ancestry GWAS for venous thromboembolism, identifies novel loci, and validates their function in zebrafish.

The paper uses bidirectional Mendelian randomization to show LTL lengthening increases CHIP risk, while CHIP accelerates LTL shortening, with modest mediation between CHIP and CAD via LTL.

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.

This paper introduces RUTH, a new HWE test that accounts for population structure and genotype uncertainty, showing it reduces false positives compared to traditional methods.

This study identifies 59 sentinel variants and two novel genes associated with telomere length using whole genome sequences from 109,122 trans-ethnic individuals.

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 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 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.

The paper identifies germline genetic variants associated with clonal hematopoiesis of indeterminate potential (CHIP) using whole genome sequences from the TOPMed program.

This study conducts a large GWAS on heart failure, identifying 12 genetic loci with associations to CAD, atrial fibrillation, and reduced left ventricular function, and provides causal evidence for several risk factors.

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.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

This paper conducts a genome-wide meta-analysis of chronic back pain in 158,025 individuals, identifying three genetic loci associated with the condition.