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Community Resources and Strategies for Association Mapping in Sorghum

^a Inst. for Genomic Diversity, Cornell Univ., Ithaca, NY 14853
^b Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX 77843
^c Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506
^d USDA-ARS Plant Stress and Germplasm Development Unit, Cropping Systems Research Lab., Lubbock, TX 79415
^e current address: Nature Source Genetics, Ithaca, NY 14850
^f contributed equally to this work

View larger version (11K): [in this window] [in a new window]   Figure 1. Posterior probability, ln P(D), of the data as a function of the number of subpopulations (k), where k was allowed to range from 1 to 12. Diamonds represent independent runs for each value of k.

View larger version (18K): [in this window] [in a new window]   Figure 2. Influence of the number of subpopulations (k) on the performance of the QK model, a mixed model that accounts for both population structure and kinship, measured by the Bayesian information criterion (BIC), for selected phenotypic traits. Lower BIC values indicate best likelihood.

View larger version (15K): [in this window] [in a new window]   Figure 3. Simulation of Type I error under the naive, simple, and mixed models using 45 random simple sequence repeat (SSR) loci and all traits combined. Cumulative distribution of P values is presented for a naive model (not accounting for multiple levels of relatedness) and Q (accounting for population structure), K' (an alternative kinship-based model), QK' (with the alternative kinship estimates), and QK (accounting for both population structure and kinship) mixed models. Assuming that these markers are unlinked to the polymorphisms controlling these traits, methods that appropriately control for Type I error should show a uniform distribution of P values (e.g., the diagonal line in this cumulative plot). The K model (controlling for familial relatedness or kinship), is not shown as it exhibited poor convergence properties.