A Unified Framework for Mapping Quantitative Trait Loci in Bivalent Tetraploids Using Single-dose Restriction Fragments: A Case Study from Alfalfa

Abstract

The development of statistical methodologies for quantitative trait locus (QTL) mapping in polyploids is complicated by complex polysomic inheritance. In this article, we propose a statistical method for mapping QTL in tetraploids undergoing bivalent formation at meiosis by using single-dose restriction fragments. Our method is based on a unified framework, one that uses chromosome bivalent pairing configuration and gametic recombination to discern different mechanisms of gamete formation. Our bivalent polyploid model can not only provide a simultaneous estimation of the linkage and chromosome pairing configuration-a cytological parameter of evolutionary and systematic interest-but also enhances the precision of estimating QTL effects and position by correctly characterizing gene segregation during polyploid meiosis. By using our method and a linkage map constructed in a previous study, we successfully identify several QTL affecting winter hardiness in bivalent tetraploid alfalfa. Moreover, our results reveal significant preferential chromosome pairing at meiosis in an F1 hybrid population, which indicates the importance of reassessing the traditional view of random chromosome segregation in alfalfa.

Theoretical basis for genetic linkage analysis in autotetraploid species

“…Simulation examples demonstrated the feasibility of implementing the algorithm to analyze practical data, validated the adequacy of parameter estimation under various models of chromosomal pairing, and showed a sharp resolving power in diagnosing the most likely parental genotypes and their linkage phases from a large number of possible rivals. Moreover, the present method offers appropriate modeling of both bivalent and quadrivalent chromosomal pairing during autotetraploid meiosis, distinguished sharply from the methods that appeared in almost all recent literature and considered bivalent pairing only (14)(15)(16)(17)(18)(19)(20)(21). These methods cannot be used to cope with complexities in patterns of gene segregation and recombination due to double reduction.…”

“…However, these studies have been based on various assumptions that have avoided various degrees of complexity of the analyses, on the one hand, but ignored some essential features of autotetrasomic inheritance and practical data analysis on the other. The assumption of bivalent pairing of homologous chromosomes in autotetrasomic meiosis, which was made in almost all currently relevant literature (14)(15)(16)(17)(18)(19)(20)(21), remarkably reduces the challenges in modeling autotetrasomic linkage analysis.…”

Correct Estimation of Preferential Chromosome Pairing in Autotetraploids

“…A method for mapping quantitative trait loci (QTLs) in polyploids was described by Ma et al (2002) within the context of bivalent chromosome pairing. Their statistical model contained several parameters, that when estimated from experimental data, supplied associated QTL effects, including preferential pairing factor (p).…”

“…Their statistical model contained several parameters, that when estimated from experimental data, supplied associated QTL effects, including preferential pairing factor (p). Ma et al (2002) reanalyzed data from a study of autotetraploid alfalfa (Medicago sativa, 2n = 4x = 32; Brouwer and Osborn 1999;Brouwer et al 2000). They identified different associated QTL effects than were reported previously (Brouwer et al 2000), and they obtained numerous high estimates of preferential pairing (p = 0.60).…”

“…Their results disagree with results from previous studies showing tetrasomic inheritance in alfalfa based on genetic segregation data (for review, see McCoy and Bingham 1988). We question the estimates of preferential pairing provided in Ma et al (2002), because different values of p were obtained from the same set of genotypes that had been grown in different environments (different years listed in Tables 1 and 2 of Ma et al 2002). Estimates of p in mapping studies should be based only on marker genotype data and should not be influenced by environmental effects on phenotype.…”