Genome-Wide SNP Markers Based on SLAF-Seq Uncover Breeding Traces in Rapeseed (Brassica napus L.)

Abstract

Single Nucleotide Polymorphisms (SNPs) are the most abundant and richest form of genomic polymorphism, and hence make highly favorable markers for genetic map construction and genome-wide association studies. In this study, a total of 300 rapeseed accessions (278 representative of Chinese germplasm, plus 22 outgroup accessions of different origins and ecotypes) were collected and sequenced using Specific-Locus Amplified Fragment Sequencing (SLAF-seq) technology, obtaining 660.25M reads with an average sequencing depth of 6.27 × and a mean Q30 of 85.96%. Based on the 238,711 polymorphic SLAF tags a total of 1,197,282 SNPs were discovered, and a subset of 201,817 SNPs with minor allele frequency >0.05 and integrity >0.8 were selected. Of these, 30,877 were designated SNP “hotspots,” and 41 SNP-rich genomic regions could be delineated, with 100 genes associated with plant resistance, vernalization response, and signal transduction detected in these regions. Subsequent analysis of genetic diversity, linkage disequilibrium (LD), and population structure in the 300 accessions was carried out based on the 201,817 SNPs. Nine subpopulations were observed based on the population structure analysis. Hierarchical clustering and principal component analysis divided the 300 varieties roughly in accordance with their ecotype origins. However, spring-type varieties were intermingled with semi-winter type varieties, indicating frequent hybridization between spring and semi-winter ecotypes in China. In addition, LD decay across the whole genome averaged 299 kb when r2 = 0.1, but the LD decay in the A genome (43 kb) was much shorter than in the C genome (1,455 kb), supporting the targeted introgression of the A genome from progenitor species B. rapa into Chinese rapeseed. This study also lays the foundation for genetic analysis of important agronomic traits using this rapeseed population.

WITHDRAWN: Genome-wide association analysis of inguinal/scrotal hernia in pigs using specific length amplified fragment (SLAF) sequencing

“…The SLAF-seq strategy, a novel NGS-based method, has been applied widely in de novo SNP discovery and genotyping of large populations [18], such as highdensity genetic map construction of cucumbers [32], candidate genes analysis of cotton [33], and high-quality SNP identification of rapeseed [34]. However, there have been few reports about the application of SLAFseq technology in resistance breeding of livestock, and to our best knowledge, this study is the first report of pig inguinal/scrotal hernias at genome-wide significance using SLAF-seq technology.…”

A high-density genetic map developed by specific-locus amplified fragment (SLAF) sequencing and identification of a locus controlling anthocyanin pigmentation in stalk of Zicaitai (Brassica rapa L. ssp. chinensis var. purpurea)

“…With the development of NGS-based methods, high-throughput marker discovery and genotyping is easily achieved and has been successfully used in Brassica [5,36,37,[41][42][43][44][45][46][47]. The SLAF-seq strategy is a suitable method for large-scale SNP development and genotyping that combines high-throughput sequencing and locus-specific amplification [15,[46][47][48]. Several advantages including high success rate, accuracy, uniformity, stability, and low cost make SLAF-seq well suited for large-scale SNP marker discovery and genotyping [15].…”

Plant genetic resources management and pre-breeding in genomics era

“…have been used extensively for utilization of PGR and pre-breeding (Riar et al 2012;Neelam et al 2016;Zhou et al 2017). In genomics era, availability of quality reference genomes, highthroughput sequencing and re-sequencing platforms, automated and cost-effective high throughput genotyping platforms has made utilization of PGR and pre-breeding more productive and efficient (Kim et al 2016;Zhou et al 2017). Whole genome provides information required for genomics-assisted prebreeding in the form of best haplotypes or combinations of alleles, optimal gene networks, and specific genomic regions (Xu et al 2012).…”