Amanda Elswick Gentry

This study investigates rare copy number variants in PTSD using whole-genome sequencing and biobank data, finding significant associations in specific ancestry groups and gene-sets.

This study conducts trans-ancestry GWAS on UK Biobank data to identify novel MDD risk loci, finding 61 loci with increased sample size and ancestry inclusivity.

This study identifies prevalence and disease risks for male and female sex chromosome trisomies using registry data from 1.5 million participants, finding shared phenotypic associations across trisomy subtypes.

This study uses machine learning and functional weights to identify rare variants associated with AUDIT-P, finding significant associations in ADH1C and THRA genes.

The paper analyzes rare functional variation in genes related to ethanol metabolism in severe alcohol dependence cases using a case-only design and WES data from the island of Ireland.

This study compares three methods to estimate time-since-deposition from cell morphology and autofluorescence data in forensic samples, finding that gradient boosting machines and GLMM-LASSO provide the most accurate predictions.

This study uses BMI GWAS and genomic structural equation modeling to show that genetic influences on BMI remain stable from age 40 to 73 in men and women.

The paper develops MAGIC-LASSO to predict missing data in biobanks with non-random missingness, achieving high phenotypic and genetic correlations in UK Biobank AUDIT score predictions.