Melissa C. Southey

PRANA adapts polygenic risk scores to non-European populations, improving predictive accuracy by 5%-20% in South Asian, East Asian, and Ashkenazi Jewish populations.

This study identifies novel genetic risk loci for pancreatic ductal adenocarcinoma in African ancestry individuals through admixture mapping and GWAS.

The paper identifies a causal epigenetic link between TRIM61 methylation and age at menarche through genome-wide DNA methylation analysis and Mendelian randomisation.

The paper uses case-control data from over 400,000 women to classify BRCA1 and BRCA2 variants, finding high consistency with ClinVar annotations for 785 previously unclassified variants.

The paper introduces SAM-DNMT3A, a CRISPR-based method to induce global DNA methylation, and shows it exploits a vulnerability in ER-positive breast cancers.

The paper refines ACMG/AMP variant classification criteria for BRCA1 and BRCA2 by calibrating evidence strength and improving classification consistency through expert panel review.

This study analyzes 17,425 breast cancer families to assess genetic factors contributing to familial risk, finding a recessive risk component and polygenic factors explain residual familial variance.

The paper assesses breast cancer risks linked to rare missense variants in five susceptibility genes using logistic and mixture models, finding varying risk contributions across genes.

This study evaluates a polygenic risk score for breast cancer in women aged 70 and older, finding it improves risk prediction beyond conventional factors.

In 159,701 siblings from 17 cohorts, population GWAS overstate direct effects on height, education, smoking, cognition, depressive symptoms and age at first birth; clinical traits mostly agree across models.

This study investigates the causes of variation in DNA methylation age across the lifespan, finding that environmental factors, particularly shared ones, are the main drivers.

This study identifies 137 genome-wide significant loci associated with DNA methylation biomarkers of ageing using DNAm and SNP data from over 41,000 individuals.

This study identifies novel DNA methylation associations with smoking, replicates previous findings, and shows significant reversibility of methylation changes after smoking cessation.