Exposure to specific environmental factors plays an impactful role in the development of asthma. Understanding how these environmental exposures interact with genetic predispositions is essential for the prevention of this chronic condition and to alleviate health disparities. Thus, our lab aims to uncover how genetic ancestry and environmental factors contribute to health disparities, especially in asthma and allergy-related conditions. To do so, we utilize admixture mapping (AM) techniques to dissect the genetic ancestry of admixed populations, which occurs by intermixing of previously separated populations. These techniques help identify genetic loci that contribute to differences in disease prevalence between different ancestral populations.

Understanding the intricate molecular mechanisms underlying allergic diseases is crucial for developing effective treatments and improving patient outcomes. Our lab integrates various omics data—genomics, epigenomics, transcriptomics, microbiome analyses, proteomics, and metabolomics—to unravel the molecular architecture of these diseases. This comprehensive approach enables us to identify potential functional variants and understand disease mechanisms at a deeper level.

Since establishing and leading the Functional Genomics Laboratory, in collaboration with a team of investigators, we have pioneered investigations to identify functional variants within loci identified from genome-wide association studies (GWAS). We use machine learning algorithms to integrate multi-omics data, pinpointing specific genetic changes within regions of risk loci, identified by GWAS, that functionally contribute to disease. Recently, we have begun using massive parallel reporter assays (MPRA) and CRISPR/Cas9 editing to identify these functional variants in a high-throughput manner. Furthermore, we analyze gene networks and pathways to identify 'hub' genes, which are key genes with significant interactions and functional similarities. By exploring these networks, we aim to develop targeted therapeutics that address the underlying causes of allergic diseases, ultimately improving patient outcomes and advancing precision medicine.

Immigrants have a higher risk of developing chronic diseases, such as asthma, due to health disparities and stress, which alter the epigenetic profiles of their immune system towards a proinflammatory state. Our lab is working to understand how migration and environmental changes affect disease susceptibility and progression, ultimately aiming to improve healthcare outcomes for immigrant communities.

Furthermore, with the discovery of more polymorphic markers across the genome, examining population structure using dense loci has become a common practice in evolutionary biology and human genetics. Our goal is to understand human variation and utilize this information to dissect traits associated with ancestry using population and statistical genetics methods. As such, we have created a publicly available SNP data tool, AncestrySNPminer, to identify ancestry-informative markers (AIMs) and localize variants and pathways with ancestry-specific effects.

One of our goals in the Mersha lab is to aid genetic research by creating bioinformatics tools that can be widely adopted by the scientific community. As such, we have developed a variety of algorithms to mine omics databases, including AncestrsySNPminer, GENEASE, and AllergyGenDB. As of today, AncestrsySNPminer alone has been accessed freely by 22,000 registered users world-wide.