SNP markers‐based map construction and genome‐wide linkage analysis in Brassica napus

Abstract

An Illumina Infinium array comprising 5306 single nucleotide polymorphism (SNP) markers was used to genotype 175 individuals of a doubled haploid population derived from a cross between Skipton and Ag-Spectrum, two Australian cultivars of rapeseed (Brassica napus L.). A genetic linkage map based on 613 SNP and 228 non-SNP (DArT, SSR, SRAP and candidate gene markers) covering 2514.8 cM was constructed and further utilized to identify loci associated with flowering time and resistance to blackleg, a disease caused by the fungus Leptosphaeria maculans. Comparison between genetic map positions of SNP markers and the sequenced Brassica rapa (A) and Brassica oleracea (C) genome scaffolds showed several genomic rearrangements in the B. napus genome. A major locus controlling resistance to L. maculans was identified at both seedling and adult plant stages on chromosome A07. QTL analyses revealed that up to 40.2% of genetic variation for flowering time was accounted for by loci having quantitative effects. Comparative mapping showed Arabidopsis and Brassica flowering genes such as Phytochrome A/D, Flowering Locus C and agamous-Like MADS box gene AGL1 map within marker intervals associated with flowering time in a DH population from Skipton/Ag-Spectrum. Genomic regions associated with flowering time and resistance to L. maculans had several SNP markers mapped within 10 cM. Our results suggest that SNP markers will be suitable for various applications such as trait introgression, comparative mapping and high-resolution mapping of loci in B. napus.

Genome‐wide association analyses reveal complex genetic architecture underlying natural variation for flowering time in canola

“…QTLs and the potential candidate genes for flowering time and vernalization response such as FT (BnFT.A2a, BnFT.A2b, BnFT.C6a and BnFT.C6b) , FLC3 ( BnFLC.A3a , BnFLC.A3b and BnFLC.C3) (S5 Fig.) and FRIGIDA homologues ( BnFri.A3a ) were localized previously on A2/C2, A3/C3 and A10/C9, in genetic mapping populations (Raman et al , , Raman et al , , Zou et al , ). This suggests that these SNP associations may represent a priori candidate genes controlling natural variation in flowering time in canola.…”

Genetic architecture of growth traits in Populus revealed by integrated quantitative trait locus (QTL) analysis and association studies

“…Therefore, more co-dominant markers, such as SNPs, InDels, gene copy number variants, and even epigenetic variants, can be employed in linkage mapping; more genomic information from model species also can be transferred to related species. In most cases, the linkage map and physical sequence reciprocally validate marker order and position (Hyten et al, 2010;Raman et al, 2014). Improving the integration of the linkage map and the assembled sequence will require more assignable markers designed to target poorly aligned regions.…”

“…Quantitative traits probably have complex genetic architectures, including many coordinated genes and dynamic interactions (Du et al, 2015). Mapping such traits requires dense linkage maps, large mapping populations, and thorough, dynamic phenotypic data from multiple field experiments (Wu & Lin, 2006;Raman et al, 2014). Also, common QTL regions identified in diverse populations should be subjected to further dissection using association mapping, and diverse genomic, epigenomic and transcriptomic data.…”

Mapping of homoeologous chromosome exchanges influencing quantitative trait variation in Brassica napus

“…Most genetic linkage maps constructed using high-density SNP array data do not consider CNV or PAV (e.g. Delourme et al, 2013;Fopa et al, 2014;Liu et al, 2013;Raman et al, 2014) although both are inherent phenomena in chromosome regions shaped by HEs or deletions. The consequence may be that genome regions affected by genomic rearrangements including deletions, which sometimes span entire chromosomes in synthetic B. napus (Chalhoub et al, 2014), may not be incorporated into genetic maps and result in large gaps in linkage groups.…”