Genome-wide association studies (GWAS) in African-ancestry populations have yet to provide a large payoff in identifying robust novel associations to infectious diseases such as malaria and tuberculosis, even though resistance to these diseases is known to include a substantial heritable component. Genetic variants that affect the risk of infectious disease are often under natural selection, leading to strong signals of unusual population differentiation between closely related populations that experienced different selective pressures. We and others have previously applied this approach to detect signals of selection at risk variants for malaria and other infectious diseases, and have shown that this approach improves power to identify disease associations. This approach is optimally powered when genome-wide genetic differences between populations are small, so that differences at the risk variants of interest lie outside the genome-wide distribution. However, when analyzing closely related populations, very large sample sizes are needed to minimize sampling noise. Previous work in this area has been limited by the minimal availability of genotype data from closely related African-ancestry populations in large sample size. Now, GWAS data for malaria, tuberculosis and other traits in multiple closely related African-ancestry populations with thousands of samples provides an appealing opportunity to proceed with this research. Here, we will analyze West African and African-American data sets to identify signals of natural selection via unusual population differentiation, while addressing the complication of European admixture in African-American samples. Furthermore, we will combine these signals of selection with those produced by independent approaches, to increase power to identify and localize selected variants. Our findings will be of high interest to investigators aiming to identify the genetic basis for malaria, tuberculosis, and other infectious diseases.

Public Health Relevance

Resistance to infectious diseases such as malaria, tuberculosis and HIV/AIDS is known to include a substantial genetically heritable component in the host individual, but association studies have had limited success in identifying the underlying genetic risk variants. We and others have previously shown that genes affecting resistance to infectious disease are often under natural selection, producing strong signals of unusual population differentiation between closely related populations, and that these signals can be used to improve the success of efforts to identify disease-associated variants. In this proposal, we will search for signals of unusual population differentiation in multiple African populations using genome-wide data in large sample size, to identify signals of natural selection that will aid the search for variants associated to infectious disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Small Research Grants (R03)
Project #
1R03HG006170-01A1
Application #
8242257
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Ramos, Erin
Project Start
2012-03-09
Project End
2013-12-31
Budget Start
2012-03-09
Budget End
2012-12-31
Support Year
1
Fiscal Year
2012
Total Cost
$80,750
Indirect Cost
$30,750
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Gokcumen, Omer; Zhu, Qihui; Mulder, Lubbertus C F et al. (2013) Balancing selection on a regulatory region exhibiting ancient variation that predates human-neandertal divergence. PLoS Genet 9:e1003404
Bhatia, Gaurav; Patterson, Nick; Sankararaman, Sriram et al. (2013) Estimating and interpreting FST: the impact of rare variants. Genome Res 23:1514-21