Most of the burden in our health care system comes from complex human diseases, whose onset and outcome are influenced by multiple genomic variants (e.g., cardiovascular disease, cancer, and diabetes). For the past decade, human geneticists have conducted genome-wide association studies, scanning for risk alleles associated with complex disease phenotypes. These studies have generally identified variants that confer relatively small increments in disease risk. We propose an alternative explanation for the unrealized promise of GWA studies in humans: rather than lacking the correct data with which to study medically-relevant traits, human genomics suffers from a lack of theoretical models that accurately characterize the population-level history of our species and the concomitant effects of natural selection. Overlooking population histories in the search for disease-associated variants leads to both spurious correlations between genotype and disease status, as well as the identification of disease-associated variants in one population that are not reproduced across multiple ancestral genomic backgrounds. Many common diseases vary in incidence across ethnicities and/or sexes, but association studies offer no framework to identify risk alleles for such diseases. The objective of this application is to incorporate the shared demographic history of human populations and models of variable dominance into genome-wide association studies. The central hypothesis is that human demographic history drives the incidence of common diseases across ethnicities and sexes.
The aims of the proposal are to: 1) develop computational methods to identify risk alleles for diseases with disparities in incidence across ethnicities; 2) develop population-genetic methods to infer the fitness effects of mutations associated with diseases occurring differentially across sexes; and 3) apply newly developed methods to dbGAP association-study data from diseases with ethnic and sex-based disparities in incidence. The methods developed will be applicable to genome-wide, whole-genome and exome studies of disease association.

Public Health Relevance

For the past decade, human geneticists have been studying associations between common genetic mutations and disease status in diseases like type II diabetes, schizophrenia, and heart disease. 96% of subjects in these studies are of European descent, and association studies of diseases that differ across the sexes rarely compare male patients and female patients. The proposed research is to develop new methods to study disease susceptibility in individuals from various ethnic backgrounds and from both sexes.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM109035-04
Application #
9644055
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
2019-03-01
Budget End
2020-02-29
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Brown University
Department
Type
DUNS #
001785542
City
Providence
State
RI
Country
United States
Zip Code
02912
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Cabral, Damien J; Wurster, Jenna I; Belenky, Peter (2018) Antibiotic Persistence as a Metabolic Adaptation: Stress, Metabolism, the Host, and New Directions. Pharmaceuticals (Basel) 11:
Sugden, Lauren Alpert; Atkinson, Elizabeth G; Fischer, Annie P et al. (2018) Localization of adaptive variants in human genomes using averaged one-dependence estimation. Nat Commun 9:703
Cabral, Damien J; Penumutchu, Swathi; Norris, Colby et al. (2018) Microbial competition between Escherichia coli and Candida albicans reveals a soluble fungicidal factor. Microb Cell 5:249-255
Hurley, Edward; Zabala, Valerie; Boylan, Joan M et al. (2018) Hepatic Gene Expression during the Perinatal Transition in the Rat. Gene Expr :
Grizotte-Lake, Mayara; Vaishnava, Shipra (2018) Autophagy: Suicide Prevention Hotline for the Gut Epithelium. Cell Host Microbe 23:147-148
Atkinson, Elizabeth Grace; Audesse, Amanda Jane; Palacios, Julia Adela et al. (2018) No Evidence for Recent Selection at FOXP2 among Diverse Human Populations. Cell 174:1424-1435.e15

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