Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Abstract

Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.KEYWORDS QTL detection; parental contribution; recombinant population; selection detection; experimental evolution; multiparental populations; Multiparent Advanced Generation Inter-Cross (MAGIC); MPPA LTHOUGH the recent development of genome sequencing in crop species has strongly increased capacity for gene discovery, the accurate mapping of genes controlling the genetic variation of complex traits (QTL) remains a key objective. The principal mapping methods rely on the detection of statistical association between polymorphisms at molecular markers and quantitative variation of phenotypic traits and were historically developed for progenies of biparental crosses with parents chosen for their extreme phenotypes at a trait of interest. These populations are powerful resources for QTL detection, but have low precision as few effective meioses (and thus subsequent recombinations) occur during their development. As a result, large parental haplotype blocks are preserved in the segregating population. In a...

In-Depth Characterisation of Common Bean Diversity Discloses Its Breeding Potential for Sustainable Agriculture

“…The second method is to create segregating Composite-Cross Populations (CCPs), in which, carefully selected genetically diverse "founder" genotypes are crossed with each other-rather than physically mixed [85]-and bulked progenies are then let to evolve for a number of successive generations applying or not an active selection [86]. Recently, this method has generated interest for its application to cereals (i.e., wheat and barley) in sustainable farming systems [80,87]. In both cases, previous knowledge of genetic diversity of a wide panel of candidate components (in the case of mixtures) or founders (CCPs) is crucial for a wise and more conscious exploitation of their diversity.…”

Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature?

“…Recent and ongoing advances in sequencing technologies make this kind of repeated sampling design more and more logistically (and financially) feasible (Thépot et al . ) even in nonmodel organisms with no reference genome (Jones & Good ). Furthermore, a range of methods are being developed to distinguish selection and drift using these temporal designs (Goldringer & Bataillon ; Feder et al .…”

Interest of a Multiparental and Outcrossing Wheat Population for Fine Mapping

“…With its high diversity, absence of structure and low LD (Thépot et al 2015 ), this population appears as a new QTL mapping resource, allowing the discovery of original genomic regions controlling traits of interest. Ongoing studies will better explore the potential of this population for detection, using the 1,000 SSD lines.…”

“…38.1 ). First we inferred allelic frequencies in the initial population on the basis of the 56 parental lines (including Probus) still available in seed banks (four missing ones), estimating their contributions to the global pool using a Bayesian method (Thépot et al 2015 ). The evolved population was studied through a subset of 380 SSD lines, representative of the phenotypic diversity of the 1,000 lines.…”

“…Evolution of growth habit was tested through a comparison of spring/winter ratio in the initial and the evolved populations (Chi square test). Strong shifts in allelic frequencies were used to detect markers under selection, using a new method (Thépot et al 2015 ). Q-values were estimated to cope with the multiple tests (Storey and Tibshirani 2003 ).…”