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

Abstract

Optimum flowering time is the key to maximize canola production in order to meet global demand of vegetable oil, biodiesel and canola-meal. We reveal extensive variation in flowering time across diverse genotypes of canola under f ield, glasshouse and controlled environmental conditions. We conduct a genome-wide association study and identify 69 single nucleotide polymorphism (SNP) markers associated with flowering time, which are repeatedly detected across experiments. Several associated SNPs occur in clusters across the canola genome; seven of them were detected within 20 Kb regions of a priori candidate genes; FLOWERING LOCUS T, FRUIT-FUL, FLOWERING LOCUS C, CONSTANS, FRIGIDA, PHYTOCHROME B and an additional five SNPs were localized within 14 Kb of a previously identified quantitative trait loci for flowering time. Expression analyses showed that among FLC paralogs, BnFLC.A2 accounts for~23% of natural variation in diverse accessions. Genome-wide association analysis for FLC expression levels mapped not only BnFLC. C2 but also other loci that contribute to variation in FLC expression. In addition to revealing the complex genetic architecture of flowering time variation, we demonstrate that the identified SNPs can be modelled to predict flowering time in diverse canola germplasm accurately and hence are suitable for genomic selection of adaptative traits in canola improvement programmes.

A Polynucleotide Repeat Expansion Causing Temperature-Sensitivity Persists in Wild Irish Accessions of Arabidopsis thaliana

“…To determine whether the accessions that carry the repeat expansion are genetically similar to each other, we analyzed the genetic diversity among Irish accessions using DArT-seq (Raman et al, 2015). From the DArT-Seq data, we compiled data for 4556 SNPs for which genotypic information was available for over 90% of the accessions and utilized this data to analyze the genetic architecture of the samples that we collected in Ireland (Table S6).…”

Genome-Wide Association Mapping Reveals the Genetic Control Underlying Branch Angle in Rapeseed (Brassica napus L.)

“…A similar situation existed in the present study: when a GWAS was performed using the BLUP values in an MLM based on a modified Bonferroni threshold of p < 5.0 × 10 −5 [−log 10 ( p ) = 4.3, 1/19,945], only one significant SNP on the A5 chromosome was discovered (Figure S1). Thus, to detect as many association signals as possible for use in further research, the significance threshold of association analysis in the MLM was dropped to a less stringent value (i.e., p < 1.0 × 10 −3 , −log 10 ( p ) = 3.0, Figure 5, Figure S1), which has been widely used in association mapping in rapeseed (Cai et al, 2014; Hatzig et al, 2015; Raman et al, 2015). …”

“…An MLM can be described by the following matrix notation: y = X β + Zu + e , in which y is the phenotype; X is the genotype; β is a vector containing the fixed effects, including the genetic marker and the population structure (Q); Z is the relative kinship matrix; u is a vector of random additive genetic effects; and e is the unobserved vector of the random residual. The threshold of significant association between a trait and the SNPs in the MLM was p < 1.0 × 10 −3 [i.e., −log 10 ( p ) = 3.0], which has been broadly adopted in the literature (Cai et al, 2014; Hatzig et al, 2015; Raman et al, 2015). The GWAS results were visualized with Manhattan and quantile-quantile (Q-Q) plots that were yielded from the qqman package in R software (Turner, 2014).…”

Genome-Wide Association Study of Genetic Control of Seed Fatty Acid Biosynthesis in Brassica napus

“…To date, GWAS approaches using whole genome sequencing have allowed researchers to dissect genetic regulation of complex traits such as oil biosynthesis, carotenoid concentration and yield in well studied crops including maize and rice (Gao et al, 2013; Li H. et al, 2013; Suwarno et al, 2015). In oilseed rape, GWAS using DartSeq and Brassica 60K SNP array genotyping approaches allowed identification of alleles involved in regulation of flowering time, as well as seed quality traits including germination, vigor and seed weight (Li et al, 2014; Hatzig et al, 2015; Raman et al, 2016). …”