Association studies provide a powerful approach for locating alleles that contribute to disease susceptibility and phenotypic variation. Since population-genetic processes play a central role in generating patterns of statistical association between diseases and their causal alleles, an understanding of human population- genetic history and its consequences for association is important for the development of methods to map disease susceptibility alleles. We propose four specific aims that will augment and capitalize on theoretical and empirical population genetics knowledge of human populations to advance the prospects for identifying disease susceptibility loci by association mapping. This work will be performed through a combination of mathematical theory, computer simulation, and analysis of human population-genetic data. First, we will extend methods for analysis of the production by population structure of spurious associations between genotypes and disease to accommodate clinal or spatially distributed populations. The new approaches will make it possible to reduce the occurrence of the false positive associations that arise from population structure or stratification in a broader set of scenarios than is currently possible. Second, we will develop population-genetic models of human evolution that use approximate Bayesian computation to account for patterns of haplotype variation among diverse worldwide populations. Third, we will develop a framework for statistical analysis of replication studies of genetic association that takes into account the fact that all humans are related by descent from shared ancestors. Fourth, we will compare properties of genetic association statistics computed from genotypes and from estimated haplotypes and will identify scenarios in which haplotype statistics provide more accurate association information than methods that do not require haplotype estimation. This work will enable more accurate estimation of the linkage disequilibrium important in association study design and analysis. The long-term goal of the project is to make optimal use of knowledge of human variation and evolutionary history for the design and analysis of association mapping studies. Our efforts will make use of genome-wide microsatellite and single-nucleotide polymorphism data that we have gathered in a worldwide collection of populations. As part of the project, we will be developing new statistical methods and implementing them in software tools that we will make publicly available.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
7R01GM081441-06
Application #
8369808
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Krasnewich, Donna M
Project Start
2007-05-01
Project End
2013-04-30
Budget Start
2011-09-01
Budget End
2013-04-30
Support Year
6
Fiscal Year
2011
Total Cost
$265,880
Indirect Cost
Name
Stanford University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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