Alanna C. Morrison

ScreenDMT identifies metabolites consistently linked to sleep disorders across three human cohorts, revealing replicable associations with insomnia, sleep duration, and daytime sleepiness.

The paper develops a model to predict NMD outcomes for protein-truncating variants using RNA-seq data, achieving ~78% ROC-AUC and identifying non-canonical determinants.

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.

This study identifies NMD-escape variants as a distinct class of disease-causing mutations, showing that altered C-terminal regions drive pathogenicity through phase separation.

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 introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

The paper identifies ancestry-specific and shared genetic regulators of plasma proteins in European and African populations, revealing novel causal relationships and shared pQTLs.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

The paper identifies novel genetic loci associated with systolic blood pressure that interact with the DASH diet score through genome-wide interaction analysis.

The paper identifies 83 rare lncRNA variant sets associated with blood lipid levels using WGS data from the TOPMed program and validates some in UK Biobank data.

StocSum generates stochastic summary statistics for whole genome sequencing studies without external reference panels, enabling various genetic analyses and improving utility for underrepresented populations.

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.

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.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

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 paper constructs a multi-ethnic polygenic risk score for hypertension and shows it is associated with hypertension prevalence, progression, and related clinical outcomes.

This study investigates how rare and common genetic variations contribute to QT interval duration, finding that both monogenic and polygenic factors influence QTc prolongation, with polygenic risk modulating the effect of rare variants.

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.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

A coronary artery disease GWAS of 181,522 cases among 1,165,690 participants yields 241 associations (54 new); an eight-predictor consensus nominates 185 candidate causal genes, 94 with ≥3 predictors.

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.

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

This paper performs a large-scale TWAS of blood cell phenotypes, identifies 239 gene-trait associations, and develops an R Shiny app to visualize TWAS results.

The paper evaluates methods to handle missing lifestyle data in gene-by-lifestyle interaction meta-analyses and recommends the Safe Approach for accuracy.