The use of MapPop1.0 for choosing a QTL mapping sample from an advanced backcross population

Abstract

QTL detection is a good way to assess the genetic basis of quantitative traits such as the plant response to its environment, but requires large mapping populations. Experimental constraints, however, may require a restriction of the population size, risking a decrease in the quality level of QTL mapping. The purpose of this paper was to test if an advanced backcross population sample chosen by MapPop 1.0 could limit the effect of size restriction and improve the QTL detection when compared to random samples. We used the genotypic and phenotypic data obtained for 280 genotypes, considered as the reference population. The "MapPop sample" of 100 genotypes was first compared to the reference population, and genetic maps, genotypic and phenotypic data and QTL results were analysed. Despite the increase in donor allele frequency in the MapPop sample, this did not lead to an increase of the genetic map length or a biased phenotypic distribution. Three QTL among the 10 QTL found in the reference population were also detected in the MapPop sample. Next, the MapPop sample results were compared to those from 500 random samples of the same size. The main conclusion was that the MapPop software avoided the selection of biased samples and the detection of false QTL and appears particularly interesting to select a sample from an unbalanced population.

Assessment of a new strategy for selective phenotyping applied to complex traits in <i>Brassica napus</i>

“…[16]. This was also observed by [10][11][12][13] who tested a SP strategy with the sampling method implemented in MapPop software. The benefits obtained with a selected sample, especially when specific genetic regions are targeted, were reported by Jin et al [14].…”

“…This selection should optimize the distribution of recombination points all over the genome. The effect of this selection procedure on QTL detection was evaluated in simulation [9] and empirical studies in different plants such as Arabidopsis [10], barley [11], maize [12] or pepper [13]. With the same objective, other authors proposed, through simulation studies, methods to select 1) individuals which maximize their genotypic dissimilarity using markers across the entire genome, markers on the chromosome that contained a known QTL or a single marker near the QTL [14]; 2) the individuals with a maximal number of recombination events considering (uniRec) or not (maxRec) the uniformity of their distribution across the genome [15].…”

QTL analysis of plant development and fruit traits in pepper and performance of selective phenotyping

“…the expected maximum distance between two points subjected to recombination. As previously reported by Birolleau-Touchard et al (2007), the best sample is the one with the most similar eMBL value compared to the original population obtained with a defined computational time and a chosen sample size. As a consequence, several iterations were run, until no further improvement in eMBL was achieved (data not shown).…”

“…This reduced subset is further submitted to phenotyping, reducing the field experiments while losing as little information as possible. Such strategies where evaluated by Vales et al (2005) and Birolleau-Touchard et al (2007). They showed that the MapPop sampling method permits to select population samples with balanced allele frequencies, and was superior to random sampling, particularly when QTL analysis has to be performed from unbalanced populations (doubled haploid or advanced backcross populations).…”

“…The use of subpopulations also led to the detection of new QTLs, which were significant only in the smallest subpopulation B. The reduction in population size seemed to have generated unbalanced (skewed) samples which were responsible for detecting spurious QTLs(Birolleau-Touchard et al 2007…”