Association mapping of six yield-related traits in rapeseed (Brassica napus L.)

Abstract

Yield is one of the most important traits for rapeseed (Brassica napus L.) breeding, but its genetic basis remains largely ambiguous. Association mapping has provided a robust approach to understand the genetic basis of complex agronomic traits in crops. In this study, a panel of 192 inbred lines of B. napus from all over the world was genotyped using 451 single-locus microsatellite markers and 740 amplified fragment length polymorphism markers. Six yield-related traits of these inbred lines were investigated in three consecutive years with three replications, and genome-wide association studies were conducted for these six traits. Using the model controlling both population structure and relative kinship (Q + K), a total of 43 associations (P < 0.001) were detected using the means of the six yield-related traits across 3 years, with two to fourteen markers associated with individual traits. Among these, 18 markers were repeatedly detected in at least 2 years, and 12 markers were located within or close to QTLs identified in previous studies. Six markers commonly associated with correlated traits. Conditional association analysis indicated that five of the associations between markers and correlated traits are caused by one QTL with pleiotropic effects, and the remaining association is caused by linked but independent QTLs. The combination of favorable alleles of multiple associated markers significantly enhances trait performance, illustrating a great potential of utilization of the associations in rapeseed breeding programs.

Association mapping for frost tolerance using multi-parent advanced generation inter-cross (MAGIC) population in faba bean (Vicia faba L.)

“…For association mapping, at least 100-500 individuals are needed and the use of co-dominant SSRs and SNPs is more informative (no allelic ambiguity) than other dominant markers (Kumar et al 2011). The markertrait association was tested against a false discovery rate (Benjamini and Hochberg1995;Kraakman et al 2004;Honsdorf et al 2010;Cai et al 2014). However, an attention should be paid for marker-trait associating detected at a threshold of 20 % FDR because it may lead to identify false positive QTL.…”

“…The general linear model (GLM) procedure of TASSEL and mixed linear model (MLM) with kinship matrix (Yu et al 2006) were used. A false discovery rate of 20 % (FDR = 0.20) was used to test the statistical significance of marker-trait associations as described previously in Benjamini and Hochberg (1995), , and Cai et al (2014). In addition, we presented the marker-trait association at 5 % FDR.…”

Estimation of additive and epistatic gene effects of doubled haploid lines of winter oilseed rape (Brassica napus L.)

“…To determine the genetic background of quantitative traits in segregating populations of Brassica napus, quantitative trait loci (QTLs) analyses were applied (Cai et al 2014). In the genome of oilseed rape a lot of QTLs and epistatic interactions were discovered, many of which had pleiotropic effects (Udall et al 2006;Basunanda et al 2010).…”

BnaC9.SMG7b functions as a positive regulator of number of seeds per silique in rapeseed (Brassica napus L.) by regulating the formation of functional female gametophytes

“…Thus, it is highly desirable to identify the genes responsible for each component and subsequently, understand the genetic mechanisms underlying the natural variations. Toward this goal, a large number of quantitative trait loci (QTLs) associated with these yield components in B. napus has been identified in different mapping populations Udall et al, 2006;Chen et al, 2007Chen et al, , 2011Li et al, 2007;Radoev et al, 2008;Shi et al, 2009;Basunanda et al, 2010;Fan et al, 2010;Zhang et al, 2011Zhang et al, , 2012bYang et al, 2012;Cai et al, 2014;Qi et al, 2014). Among these QTLs, 24 are reported to be associated with NSS, and they are distributed on the majority of B. napus chromosomes (A1, A2, A5, A7-A9, C1-C4, C6, C7, and C9), explaining phenotypic variances ranging from 0.78% to 57.77% of each mapping population (Radoev et al, 2008;Shi et al, 2009;Chen et al, 2011;Zhang et al, 2011Zhang et al, , 2012bQi et al, 2014).…”