(Proposal abstract): One of the main goals of human genetics is to identify the genetic variants that affect susceptibility to complex, non-Mendelian diseases. A common approach is association mapping, whereby researchers genotype many markers to find those correlated with the phenotype of interest. These markers may not affect disease susceptibility themselves, but are likely to be in strong linkage disequilibrium (LD) with causative markers. One essential tool in the planning and analysis of association studies is computer simulation. Simulations help researchers compare competing experimental designs, and aid in the interpretation of any associations that are found. Despite this importance, there is a lack of proven simulation methods that are appropriate for the genome-wide data sets now being produced. For those methods that do exist, no attempt has been made to test whether the data produced accurately reflects the properties of observed data, or whether their use for power studies introduces a bias in terms of the final estimates of power. In this proposal, we focus on developing methods for simulating whole chromosome genetic data and for analyzing whole-genome association study data. We will test the accuracy of these methods on publicly available data as well as on genotype data collected by our collaborators at the University of Southern California. We will concentrate on how to analyze data from admixed populations such as Latinos, where population stratification makes most existing analytical methods inappropriate. This work will also help us determine the marker density and sample size needed for future association studies.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG004049-03
Application #
7653629
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Brooks, Lisa
Project Start
2007-08-16
Project End
2011-06-30
Budget Start
2009-07-01
Budget End
2011-06-30
Support Year
3
Fiscal Year
2009
Total Cost
$301,737
Indirect Cost
Name
University of California San Francisco
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Kim, Sung K; Gignoux, Christopher R; Wall, Jeffrey D et al. (2012) Population genetic structure and origins of Native Hawaiians in the multiethnic cohort study. PLoS One 7:e47881
Wall, Jeffrey D; Jiang, Rong; Gignoux, Christopher et al. (2011) Genetic variation in Native Americans, inferred from Latino SNP and resequencing data. Mol Biol Evol 28:2231-7
Chen, Gary K; Marjoram, Paul; Wall, Jeffrey D (2009) Fast and flexible simulation of DNA sequence data. Genome Res 19:136-42
Wall, Jeffrey D; Lohmueller, Kirk E; Plagnol, Vincent (2009) Detecting ancient admixture and estimating demographic parameters in multiple human populations. Mol Biol Evol 26:1823-7
Shtir, Corina J; Marjoram, Paul; Azen, Stanley et al. (2009) Variation in genetic admixture and population structure among Latinos: the Los Angeles Latino eye study (LALES). BMC Genet 10:71
Padhukasahasram, Badri; Marjoram, Paul; Wall, Jeffrey D et al. (2008) Exploring population genetic models with recombination using efficient forward-time simulations. Genetics 178:2417-27