Biology subjects

Stamp, J.

Publications and source records attributed to Stamp, J..

4 recordsLinked to original sources

bioRxiv2025

Sparse modeling of interactions enables fast detection of genome-wide epistasis in biobank-scale studies

The lack of computational methods capable of detecting epistasis in biobanks has led to uncertainty about the role of non-additive genetic effects on complex trait variation. The marginal epistasis framework is a powerful approach because it estimates the likelihood of a SNP being involved in any interaction, thereby reducing the multiple testing burden. Current implementations of this approach have failed to scale to large human studies. To address this, we present the sparse marginal epistasis (SME) test, which concentrates the scans for epistasis to regions of the genome that have known functional enrichment for a trait of interest. By leveraging the sparse nature of this modeling setup, we develop a novel statistical algorithm that allows SME to run 10 to 90 times faster than state-of-the-art epistatic mapping methods. In a study of blood traits measured in 349,411 individuals from the UK Biobank, we show that reducing searches of epistasis to variants in accessible chromatin regions facilitates the identification of genetic interactions associated with regulatory genomic elements.

genetics↗

bioRxiv2024

Incorporation of Epstein-Barr viral variation implicates significance of LMP1 in survival prediction and prognostic subgrouping in Burkitt lymphoma

While Epstein-Barr virus (EBV) plays a role in Burkitt lymphoma (BL) tumorigenesis, it is unclear if EBV genetic variation impacts clinical outcomes. From 130 publicly available whole-genome tumor sequences of EBV-positive BL patients, we used least absolute shrinkage and selection operator (LASSO) regression and Bayesian variable selection models within a Cox proportional hazards framework to select the top EBV variants, putative driver genes, and clinical features associated with patient survival time. These features were incorporated into survival prediction and prognostic subgrouping models. Our model yielded 22 EBV variants including seven in LMP1 as most associated with patient survival time. Using the top EBV variants, driver genes, and clinical features, we defined three prognostic subgroups that demonstrated differential survival rates, laying the foundation for incorporating EBV variants such as those in LMP1 as predictive biomarker candidates in future studies.

genomics↗

bioRxiv2022

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT) -- a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact -- thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized GWA studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

genetics↗

bioRxiv2020

Tumor-infiltrating nerves create an electro-physiologically active microenvironment and contribute to treatment resistance

Patients with densely innervated tumors do poorly as compared to those with sparsely innervated disease. Why some tumors heavily recruit nerves while others do not, remains unknown as does the functional contribution of tumor-infiltrating nerves to cancer. Moreover, while patients receive chemotherapeutic treatment, whether these drugs affect nerve recruitment has not been tested. Using a murine model of ovarian cancer, we show that tumor-infiltrating sensory nerves potentiate tumor growth, decrease survival, and contribute to treatment resistance. Furthermore, matched patient samples show significantly increased tumor innervation following chemotherapy. In vitro analysis of tumor-released extracellular vesicles (sEVs) shows they harbor neurite outgrowth activity. These data suggest that chemotherapy may alter sEV cargo, endowing it with robust nerve recruiting capacity.

cancer biology↗