| 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 genomewide background markers and subsequently accounted for in association analysis. Deciding the number of background markers is a common issue that needs to be addressed in many association mapping studies. We first showed that the adequacy of markers in relationship estimation influences the maximum likelihood of the model explaining phenotypic variation and demonstrated this influence with a series of computer simulations with different trait architectures. Analyses and computer simulations were then conducted using two different data sets: one from a diverse set of maize ( L.) inbred lines with a complex population structure and familial relatedness, and the other from a group of crossbred dogs. Our results showed that the likelihood-based model-fitting approach can be used to quantify the robustness of genetic relationships derived from molecular marker data. We also 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 (e.g., 300–600 biallelic markers for the simulated pedigree materials, >1000 single nucleotide polymorphisms or 100 simple sequence repeats [SSRs] for the diverse maize panel, and about 100 SSRs for the canine panel). Kinship construction with subsets of the whole marker panel and subsequent model testing with multiple phenotypic traits could provide ad hoc information on whether the number of markers is sufficient to quantify genetic relationships among individuals.
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