Naisha Shah

This paper develops a model combining polygenic risk scores and quantitative MRI to predict dementia onset up to eight years earlier than traditional methods.

This study identifies metabolic and genetic signatures of obesity using metabolome analysis and whole genome sequencing in twin and volunteer cohorts, revealing clinically meaningful heterogeneity.

The paper integrates multimodal health data to identify novel biomarker signatures for health and disease, showing better predictive power for diabetes and hypertension than traditional biomarkers.

This paper screens adults using whole genome sequencing and imaging to detect age-related chronic disease risks, finding unrecognized diseases in 8% of participants and genomic correlations in 25%.

The paper analyzes genetic diversity across 11,257 human genomes to identify context-dependent conserved regions and their association with disease variants.

This study sequences 10,545 human genomes at 30-40x coverage, identifying 150 million variants and showing 84% of the genome can be confidently sequenced, with implications for clinical use.

The paper identifies misclassified variants in ClinVar by analyzing population prevalence and genetic risk patterns in whole genome sequencing data.