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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…

Overview of attention for article published in BMC Genomics, May 2015
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Title
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
Published in
BMC Genomics, May 2015
DOI 10.1186/s12864-015-1559-4
Pubmed ID
Authors

Guangqin Cai, Qingyong Yang, Bin Yi, Chuchuan Fan, Chunyu Zhang, David Edwards, Jacqueline Batley, Yongming Zhou

Abstract

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. 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. 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.

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Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sri Lanka 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 30%
Student > Ph. D. Student 7 26%
Student > Master 3 11%
Other 2 7%
Unspecified 1 4%
Other 2 7%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 59%
Biochemistry, Genetics and Molecular Biology 4 15%
Unspecified 1 4%
Economics, Econometrics and Finance 1 4%
Medicine and Dentistry 1 4%
Other 0 0%
Unknown 4 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 February 2016.
All research outputs
#5,411,380
of 7,191,979 outputs
Outputs from BMC Genomics
#4,271
of 5,368 outputs
Outputs of similar age
#149,818
of 213,790 outputs
Outputs of similar age from BMC Genomics
#241
of 265 outputs
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