A history of studies of reproductive isolation between and .

and are a sister species pair that have been used as a model for studies of reproductive isolation and speciation for almost 100 years owing to their close evolutionary history, well characterized genetic differences, and overlapping geographic distribution. There are extensive analyses of both pre- and post-zygotic isolation, including studies of courtship divergence, conspecific sperm precedence (CSP) and how reinforcement by natural selection may or may not act to strengthen isolation in sympatry. Post-zygotic analyses explore the underlying mechanics of reproductive isolation; how inversions may give rise to initial speciation events and misexpression of key genes typically found within inversion regions render hybrid offspring unfit or inviable.

Polygenic risk scores for cardiometabolic traits demonstrate importance of ancestry for predictive precision medicine.

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups.

Integrated exposomic analysis of lipid phenotypes: Leveraging GE.db in environment by environment interaction studies.

Gene-environment interaction (GxE) studies provide insights into the interplay between genetics and the environment but often overlook multiple environmental factors' synergistic effects. This study encompasses the use of environment by environment interaction (ExE) studies to explore interactions among environmental factors affecting lipid phenotypes (e.g.

Connecting intermediate phenotypes to disease using multi-omics in heart failure.

Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities.

Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease.

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model leverages both the Alzheimer's knowledge base and the Drug-Gene interaction database (DGIdb) to identify a list of biologically plausible novel gene-drug targets for further investigation.