Kenneth Ross Muir

This study uses Mendelian randomisation and causal network analysis to evaluate the causal effects of phenotypic age acceleration and metabolic health on dementia, finding no evidence of a causal effect but identifying key biomarkers.

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

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

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

The paper assesses how the performance of an African-ancestry-specific polygenic hazard score varies with local ancestry in 8q24 using genetic data from 6,253 men with African ancestry.

This study evaluates if adding polygenic hazard scores to clinical variables improves association with fatal prostate cancer in the Cohort of Swedish Men.

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

The paper improves a polygenic hazard score for prostate cancer age at diagnosis by adding SNPs specific to African ancestry, enhancing performance in this population.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.