Characterizing the Genomic Basis of Immune-Mediated Resilience Against Tuberculosis in an Admixed Peruvian Population

Project Summary/Abstract Tuberculosis (TB) remains the global leading cause of death from an infectious agent. Understanding why some patients present with early symptoms while others develop latent infections is critical for combating this illness. While environmental factors are known to elevate the risk of early progression, a recent Genome

Wide Association Study revealed that host genetic factors also play a role, with early progression heritability near 20%. However, TB genetics studies focusing on Europeans, which have dominated the field, suffer from linkage disequilibrium obscuring causal loci and fail to center on the populations who suffer most from TB. By

contrast, this project will focus on a population from Lima, Peru with high TB burden and admixed ancestry, which will reveal a broader range of genetic variants and enable more specific mapping of impactful loci. Early progression risk is likely dictated by the immune response, the primary host-pathogen interface. Therefore,

using transcriptional profiling of monocytes (in bulk) and T cells (as single cells) in combination with genotyping and environmental covariate data, the objective of this project is to characterize immune phenotypes altered by genomic background that influence TB progression risk. Specifically, this project will: Aim 1) identify gene

expression immune phenotypes influenced by genetic ancestry, Aim 2) create a novel single-cell analysis method and use it to identify cell population immune phenotypes influenced by genetic ancestry, and Aim 3) identify specific genomic variants that alter immune phenotypes and impact TB progression risk. If successful,

this work will elucidate genomic mechanisms influencing TB infection outcomes and deepen our understanding of immune physiology in a Peruvian population. Knowledge of genetic factors that prevent early progression can inform the development of therapeutics and vaccines. This work will also produce a powerful method for

association testing in single cell datasets, Covarying Neighborhood Analysis, that can define with great flexibility and granularity cell populations whose abundance is altered by a clinical phenotype of interest. Through the fellowship training plan, the applicant will develop: expertise in complex trait genetics,

fluency in the application of and development of bioinformatic methodologies, an understanding of how social and environmental factors also contribute to infectious disease outcomes, and familiarity with immune physiology and clinical aspects of infectious disease management. An ideal training environment of close

mentorship by an expert in complex trait genetics of immune-mediated diseases and single-cell methods development, plus support from a network of advisors with complementary scientific expertise and career experience in academic medicine, in combination with exposure to relevant coursework and meetings, will

enable the applicant to thrive in this program of study. At the conclusion of this fellowship, the applicant will be poised to tackle critical problems in computational immune genetics with applications to infectious diseases and global health, and to bridge the clinical and computational worlds through a career in academic medicine.