Comparison of Marker-based Pairwise Relatedness Estimators on a Pedigreed Plant Population

Several estimators have been proposed that use molecular marker data to infer the degree of relatedness for pairs of individuals. The objective of this study was to evaluate the performance of seven estimators when applied to marker data of a set of 33 key individuals from a large complex apple pedigree. The evaluation considered different scenarios of allele frequencies and different numbers of marker loci. The method of moments estimators were Similarity, Queller-Goodknight, Lynch-Ritland and Wang. The maximum likelihood estimators were Thompson, Anderson-Weir and Jacquard. The pedigree-based coancestry coefficients were taken as the point of reference in calculating correlations and root mean square error (RMSE). The marker data comprised 86 multi-allelic SSR markers on 17 linkage groups, covering 11 Morgans. Additionally, we simulated 10 datasets conditional on the real pedigree to support the results on the real dataset. None of the estimators outperformed the others. Knowledge of allele frequencies appeared to be the most influential, i.e., the highest correlations and lowest RMSE were found when frequencies from the founder population were available. When equal allele frequencies were used, all estimators resulted in very similar, but on average lower, correlations. The use of allele frequencies estimated from the set of 33 individuals gave, on average, the poorest results. The maximum likelihood estimators and the Lynch-Ritland estimator were the most sensitive to allele frequencies. The results from the simulation study fully supported the trends in results of the real dataset. This study indicated that high correlations (up to 0.90) and small RMSE (below 0.03), may be obtained when population allelic frequencies are available. In this scenario, the performances of the various estimators were similar, but seemed to favor the maximum likelihood estimators. In the absence of reliable allele frequencies the method of moments estimators were shown to be more robust. The number of marker loci influenced the average performance of the estimators; however, the ranking was not affected. Correlations up to 0.80 were obtained when two markers per chromosome and appropriate allele frequencies were available. Adding more markers to the current dataset may lead to marginal improvements

Detecting QTLs and putative candidate genes involved in budbreak and flowering time in an apple multiparental population

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresIn temperate trees, growth resumption in spring time results from chilling and heat requirements, and is an adaptive trait under global warming. Here, the genetic determinism of budbreak and flowering time was deciphered using five related full-sib apple families. Both traits were observed over 3 years and two sites and expressed in calendar and degree-days. Best linear unbiased predictors of genotypic effect or interaction with climatic year were extracted from mixed linear models and used for quantitative trait locus (QTL) mapping, performed with an integrated genetic map containing 6849 single nucleotide polymorphisms (SNPs), grouped into haplotypes, and with a Bayesian pedigree-based analysis. Four major regions, on linkage group (LG) 7, LG10, LG12, and LG9, the latter being the most stable across families, sites, and years, explained 5.6–21.3% of trait variance. Co-localizations for traits in calendar days or growing degree hours (GDH) suggested common genetic determinism for chilling and heating requirements. Homologs of two major flowering genes, AGL24 and FT, were predicted close to LG9 and LG12 QTLs, respectively, whereas Dormancy Associated MADs-box (DAM) genes were near additional QTLs on LG8 and LG15. This suggests that chilling perception mechanisms could be common among perennial and annual plants. Progenitors with favorable alleles depending on trait and LG were identified and could benefit new breeding strategies for apple adaptation to temperature increase

Using complex plant pedigrees to map valuable genes

Statistical methods pioneered by human and animal geneticists use marker and pedigree information to detect quantitative trait loci within complex pedigrees. These methods, adapted to plants, promise to expand the range of data useful for identifying the genetic factors influencing plant growth, development and evolutionary responses, and to increase the relevance and cost effectiveness of quantitative trait loci mapping in applied contexts.

Analysis of genetic control of fruit size in apple using both multiple, pedigree-related and single full-sib families

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Detection and use of QTL for complex traits in multiple environments

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls

Identification of bloom date QTLs and haplotype analysis in tetraploid sour cherry (Prunus cerasus)

Bloom date is an important production trait in sour cherry (Prunus cerasus L.) as the risk of crop loss to floral freeze injury increases with early bloom time. Knowledge of the major loci controlling bloom date would enable breeders to design crosses and select seedlings with late bloom date. As sour cherry is a segmental allotetraploid, quantitative trait locus (QTL) analysis for bloom date was performed based on haplotype reconstruction by identifying the parental origins of marker alleles in sour cherry. A total of 338 sour cherry individuals from five F1 populations were genotyped using the cherry 6K Illumina Infinium® SNP array and phenotyped for bloom date in 3 years. A total of four QTLs were identified on linkage group (G)1, G2, G4, and G5, respectively. For these QTLs, 14 haplotypes constructed for the QTL regions were significantly associated with bloom date, accounting for 10.1–27.9% of the bloom date variation within individual populations. The three most significant haplotypes, which were identified for the G4 (G4-k), G2 (G2-j), and G1 (G1-c) QTLs, were associated with 2.8, 1.8, and 1.0 days bloom delay, respectively. These three haplotypes were also demonstrated to have additive effects on delaying bloom date for both individual and multiple QTLs. These results demonstrate that bloom date is under polygenic control in sour cherry; yet, pyramiding late blooming haplotypes for single and multiple QTLs would be an effective strategy to obtain later blooming offspring

Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering

Let be a given nn square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters. In additive fuzzy clustering, the nK fuzzy memberships matrix is found by least-squares approximation of the off-diagonal elements of by inner products of rows of . By contrast, kernelized fuzzy c-means is not least-squares and requires an additional fuzziness parameter. The aim is to popularize additive fuzzy clustering by interpreting it as a latent class model, whereby the elements of are modeled as the probability that two individuals share the same class on the basis of the assignment probability matrix . Two new algorithms are provided, a brute force genetic algorithm (differential evolution) and an iterative row-wise quadratic programming algorithm of which the latter is the more effective. Simulations showed that (1) the method usually has a unique solution, except in special cases, (2) both algorithms reached this solution from random restarts and (3) the number of clusters can be well estimated by AIC. Additive fuzzy clustering is computationally efficient and combines attractive features of both the vector model and the cluster model.