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This article is part of the supplement: Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)

Open Access Proceedings

Genome-wide association analyses of the 15th QTL-MAS workshop data using mixed model based single locus regression analysis

Wei-Xuan Fu1, Chong-Long Wang12, Xiang-Dong Ding1, Zhe Zhang1, Pei-Pei Ma1, Zi-Qing Weng1, Jian-Feng Liu1* and Qin Zhang1*

Author Affiliations

1 Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China

2 Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China

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BMC Proceedings 2012, 6(Suppl 2):S5  doi:10.1186/1753-6561-6-S2-S5

Published: 21 May 2012



The mixed model based single locus regression analysis (MMRA) method was used to analyse the common simulated dataset of the 15th QTL-MAS workshop to detect potential significant association between single nucleotide polymorphisms (SNPs) and the simulated trait. A Wald chi-squared statistic with df =1 was employed as test statistic and the permutation test was performed. For adjusting multiple testing, phenotypic observations were permutated 10,000 times against the genotype and pedigree data to obtain the threshold for declaring genome-wide significant SNPs. Linkage disequilibrium (LD) in term of D' between significant SNPs was quantified and LD blocks were defined to indicate quantitative trait loci (QTL) regions.


The estimated heritability of the simulated trait is approximately 0.30. 82 genome-wide significant SNPs (P < 0.05) on chromosomes 1, 2 and 3 were detected. Through the LD blocks of the significant SNPs, we confirmed 5 and 1 QTL regions on chromosomes 1 and 3, respectively. No block was detected on chromosome 2, and no significant SNP was detected on chromosomes 4 and 5.


MMRA is a suitable method for detecting additive QTL and a fast method with feasibility of performing permutation test. Using LD blocks can effectively detect QTL regions.