David Bennett

This study uses eHiCA to identify regulatory interactions at the PICALM-EED locus, showing distinct chromatin interactions for SNPs within a segregating haplotype.

The paper identifies cell type-specific gene modules associated with Alzheimer's Disease traits using snRNASeq data from human brain tissues.

This paper investigates how APOEe4 influences molecular changes in Alzheimer's disease through proteomic, epigenetic, and genetic analyses of post-mortem brain samples, identifying key proteins and epigenetic markers associated with risk and resilience.

This study integrates multi-omic data from aged human brains to identify immunometabolic and sex-related aging patterns.

fSuSiE improves fine-mapping of QTLs by modeling spatial genetic effects on molecular traits, achieving higher resolution in identifying causal variants and their molecular targets.

This study examines the association between APOE genotype and cognitive resilience in SuperAgers, finding lower APOE-ε4 and higher APOE-ε2 frequencies in non-Hispanic White SuperAgers compared to controls and Alzheimer's cases.

This study identifies blood-based biomarkers associated with Alzheimer's disease genetic risk variants in a large cohort of healthy adults, showing effects from early adulthood and sex-specific patterns.

The paper develops a machine learning model to predict COVID-19 severity using exonic variants from whole exome sequencing data, finding that variants across all frequency categories contribute to severity.

The paper identifies shorter androgen receptor polyQ alleles as protective against severe COVID-19 in males using machine learning on whole-exome sequencing data from the GEN-COVID cohort.

The paper investigates the role of non-coding RNAs, specifically miRNAs and lincRNAs, in Alzheimer’s disease pathology by analyzing their expression in brain samples and integrating with mRNA data.