Michelle R. Jones

This paper introduces S4-Oligo, an optimized polygenic score method for clinical use, which matches or exceeds existing models' accuracy while using fewer SNPs.

The paper evaluates EHR-based phenotyping accuracy in the All of Us Research Program to optimize statistical power for genetic association testing, finding disparities in accuracy across racial groups.

S4-Multi improves polygenic score prediction across diverse ancestries by reducing genetic variants while maintaining accuracy in non-European populations.

This study profiles the proteome of ovarian cancer tumors before and after chemotherapy, revealing how HRD status affects immune pathways and identifies potential therapeutic targets.

This study shows that mutational and clonality profiles in HGSOC are established early and remain stable through therapy resistance.

This study uses exome sequencing to identify HELB as a novel susceptibility gene for non-high grade serous epithelial ovarian cancer, with a strong association found in genetic analyses.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

The paper introduces an improved polygenic score method, S4, which enhances accuracy across multiple ancestries and phenotypes using summary statistics.

This study evaluates imputation methods for proteomic datasets, finding that local and global methods perform best in different contexts and that fragment-level imputation improves accuracy.

This study shows that DNA methylation and gene expression patterns are preserved in recurrent high grade serous ovarian cancers, with BRCA1/2 carriers showing more shared methylation changes and improved survival.

This study develops a polygenic and phenotypic risk score for PCOS using EHR data, showing improved detection and identifying shared metabolic and endocrine comorbidities.

This study performs a large-scale genome-wide meta-analysis of PCOS cases and identifies genetic loci associated with PCOS and related traits, showing shared genetic architecture across different diagnostic criteria.

The paper critiques the use of p-values and confidence intervals in statistical analysis.