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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #232743
Title: Simulation appraisal of the adequacy of numbers of background markers for relationship estimation in association mapping

Author
item Buckler, Edward - Ed
item YU, JIANMING - KANSAS STATE UNIVERSITY
item ZHANG, ZHIWU - CORNELL UNIVERSITY
item TABANAO, DINDO - KANSAS STATE UNIVERSITY
item GAIL, PRESSOIR - CORNELL UNIVERSITY
item KRESOVICH, STEPHEN - CORNELL UNIVERSITY
item TODHUNTER, RORY - CORNELL UNIVERSITY

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2009
Publication Date: 3/18/2009
Citation: Buckler Iv, E.S., Yu, J., Zhang, Z., Tabanao, D.A., Gail, P., Kresovich, S., Todhunter, R.J. 2009. Simulation appraisal of the adequacy of numbers of background markers for relationship estimation in association mapping. The Plant Genome. 2:63-77.

Interpretive Summary: Complex trait dissection through association mapping provides a powerful complement to traditional linkage analysis. The genetic structure of an association mapping panel can be estimated by genome-wide background markers and subsequently accounted for in association analysis. The number of background markers and sample size are two common issues that need to be addressed in many association mapping studies. This study provides guidelines at to the number of markers needed for estimating relatedness used in association mapping.

Technical Abstract: The number of background markers and sample size are two common issues that need to be addressed in many association mapping studies. Our objectives were (1) to investigate the robustness of genetic relatedness estimates based on different numbers of background markers via model testing and variance components estimation of a mixed model for phenotypic trait values, and (2) to examine the statistical power of association mapping with different sample sizes and genetic effects. We showed that the adequacy of markers in relationship estimation influences the maximum likelihood of the model. A series of analyses and computer simulations was conducted using two different data sets: one from a diverse set of maize inbred lines with a complex population structure and familial relatedness, and the other from a group of crossbred dogs. We found that kinship estimation was more sensitive to the number of markers used than population structure estimation in terms of model fitting and a robust estimate of kinship for association mapping with diverse germplasm requires a certain amount of background markers. Our results suggested that kinship construction with subsets of the whole marker panel and subsequent model testing could provide information on whether the number of markers is sufficient to quantify genetic relationships among individuals. A moderate sample size increases the statistical power to detect causative polymorphisms with relatively small to moderate genetic effects explaining 1% to 12% phenotypic variation, which are likely to be common mapping targets for many quantitative traits in plants and animals.