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Background  Single nucleotide polymorphism (SNP) markers have a wide range of applications in crop genetics and genomics. Due to their polyploidy nature, many important crops, such as wheat, cotton and rapeseed contain a large amount of repeat and homoeologous sequences in their genomes, which imposes a huge challenge in high-throughput genotyping with sequencing and/or array technologies. Allotetraploid  Brassica napus  (AACC, 2n = 4x = 38) comprises of two highly homoeologous sub-genomes derived from its progenitor species  B. rapa  (AA, 2n = 2x = 20) and  B. oleracea  (CC, 2n = 2x = 18), and is an ideal species to exploit methods for reducing the interference of extensive inter-homoeologue polymorphisms (mHemi-SNPs and Pseudo-simple SNPs) between closely related sub-genomes.    Results  Based on a recent  B. napus  6K SNP array, we developed a bi-filtering procedure to identify unauthentic lines in a DH population, and mHemi-SNPs and Pseudo-simple SNPs in an array data matrix. The procedure utilized both monomorphic and polymorphic SNPs in the DH population and could effectively distinguish the mHemi-SNPs and Pseudo-simple SNPs that resulted from superposition of the signals from multiple SNPs. Compared with conventional procedure for array data processing, the bi-filtering method could minimize the pseudo linkage relationship caused by the mHemi-SNPs and Pseudo-simple SNPs, thus improving the quality of SNP genetic map. Furthermore, the improved genetic map could increase the accuracies of mapping of QTLs as demonstrated by the ability to eliminate non-real QTLs in the mapping population.    Conclusions  The bi-filtering analysis of the SNP array data represents a novel approach to effectively assigning the multi-loci SNP genotypes in polyploid  B. napus  and may find wide applications to SNP analyses in polyploid crops.    Electronic supplementary material  The online version of this article (doi:10.1186/s12864-015-1559-4) contains supplementary material, which is available to authorized users.

bmc genomics

A bi-filtering method for processing single nucleotide polymorphism array data improves the quality of genetic map and accuracy of quantitative trait locus mapping in doubled haploid populations of polyploid Brassica napus