Patterns of variation at molecular markers provide exciting insights into the evolution of natural populations. Variation at the two most commonly used molecular markers, single nucleotide polymorphisms (SNPs) and short tandem repeat polymorphisms (STRPs), arises from different mutational processes, leading to complementary inferences about evolutionary forces. Because of higher mutation rates, STRPs better reveal recent evolutionary events, whereas slowly mutating SNPs are more powerfully applied to reconstructing older evolutionary events. Despite this crucial difference, patterns of polymorphism at SNPs and STRPs have rarely been compared directly in empirical or theoretical studies. The proposed research will address this important challenge by jointly examining genomic patterns of variation at SNPs and STRPs in human populations and by conducting computer simulations that explicitly model the contrasting mutational processes at the two marker classes. Specifically, this project will (1) compare expected patterns of polymorphism at SNPs, STRPs, and SNPs+STRPs generated under different evolutionary processes, (2) compare the performance of SNPs, STRPs, and SNPs+STRPs as markers for association studies of complex traits, (3) characterize expected linkage disequilibrium and other diversity correlations between linked SNPs and STRPs, and (4) measure patterns of co-variation between linked SNPs and STRPs across the human genome. This combined empirical and theoretical effort will yield the first systematic comparison of patterns of SNP and STRP polymorphism. Evolutionary scenarios that produce patterns of polymorphism that are relatively robust to variation in the STRP mutational model will be specifically sought as promising avenues for inference. Important outcomes of the proposed research include: objective guidance on marker choice based on timescale and evolutionary process of interest, a framework for integrating polymorphism at neighboring SNPs and STRPs throughout the human genome, and a foundation for the development of statistical methods that combine these two markers to enhance population genetic inference. By directly examining linkage disequilibrium between SNPs and STRPs across the human genome and quantitatively comparing the power of analyses using different or combined marker types, this research will also yield improved strategies for association mapping of complex human diseases. This project will examine the properties of different classes of human DNA variation. The results will help reveal the causes of the genetic variation that underlies complex inherited diseases. Project Relevance: This project will examine the properties of different classes of human DNA variation. The results will help reveal the causes of the genetic variation that underlies complex inherited diseases.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Genetic Variation and Evolution Study Section (GVE)
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Brooks, Lisa
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University of Wisconsin Madison
Schools of Earth Sciences/Natur
United States
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Haasl, Ryan J; Payseur, Bret A (2014) Remarkable selective constraints on exonic dinucleotide repeats. Evolution 68:2737-44
Haasl, Ryan J; Johnson, Ross C; Payseur, Bret A (2014) The effects of microsatellite selection on linked sequence diversity. Genome Biol Evol 6:1843-61
Haasl, Ryan J; McCarty, Catherine A; Payseur, Bret A (2013) Genetic ancestry inference using support vector machines, and the active emergence of a unique American population. Eur J Hum Genet 21:554-62
Haasl, Ryan J; Payseur, Bret A (2013) Microsatellites as targets of natural selection. Mol Biol Evol 30:285-98
Cutter, Asher D; Payseur, Bret A (2013) Genomic signatures of selection at linked sites: unifying the disparity among species. Nat Rev Genet 14:262-74
Payseur, Bret A; Jing, Peicheng; Haasl, Ryan J (2011) A genomic portrait of human microsatellite variation. Mol Biol Evol 28:303-12
Haasl, R J; Payseur, B A (2011) Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity (Edinb) 106:158-71
Haasl, Ryan J; Payseur, Bret A (2010) The number of alleles at a microsatellite defines the allele frequency spectrum and facilitates fast accurate estimation of theta. Mol Biol Evol 27:2702-15
Wong, Aaron K; Ruhe, Alison L; Dumont, Beth L et al. (2010) A comprehensive linkage map of the dog genome. Genetics 184:595-605
Shim, Heejung; Chun, Hyonho; Engelman, Corinne D et al. (2009) Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium. BMC Proc 3 Suppl 7:S35

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