Detection and use of QTL for complex traits in multiple environments

Eeuwijk, F.A. van; Bink, M.C.A.M.; Chenu, Karine; Chapman, Scott C.

doi:10.1016/j.pbi.2010.01.001

Cited by 166 publications

(143 citation statements)

References 71 publications

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“…This allowed the use of markers of one parent as co-factors while searching for QTL in the other parent, thereby increasing the power to detect QTL. The QTL library of Genstat 15 (VSNi 2012) was used for the multi-trait QTL analysis by fitting the models as described by van Eeuwijk et al (2010) and Alimi et al (2013). The analysis started by fitting QTL models using simple interval mapping, SIM (Lander and Botstein 1989).…”

Section: Multi-trait Qtl Analysismentioning

confidence: 99%

Understanding the genetic basis of potato development using a multi-trait QTL analysis

et al. 2015

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Understanding the genetic basis of plant development in potato requires a proper characterization of plant morphology over time. Parameters related to different aging stages can be used to describe the developmental processes. It is attractive to map these traits simultaneously in a QTL analysis; because the power to detect a QTL will often be improved and it will be easier to identify pleiotropic QTLs. We included complex, agronomic traits together with plant development parameters in a multi-trait QTL analysis. First, the results of our analysis led to coherent insight into the genetic architecture of complex traits in potato. Secondly, QTL for parameters related to plant development were identified. Thirdly, pleiotropic regions for various types of traits were identified. Emergence, number of main stems, number of tubers and yield were explained by 9, 5, 4 and 6 QTL, respectively. These traits were measured once during the growing season. The genetic control of flowering, senescence and plant height, which were measured at regular time intervals, was explained by 9, 10 and 12 QTL, respectively. Genetic relationships between aboveground and belowground traits in potato were observed in 14 pleiotropic QTL. Some of our results suggest the presence of QTL-byEnvironment interactions. Therefore, additional studies comparing development under different photoperiods are required to investigate the plasticity of the crop.

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Section: Multi-trait Qtl Analysismentioning

confidence: 99%

Understanding the genetic basis of potato development using a multi-trait QTL analysis

et al. 2015

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“…The methods for QTL analysis in diploid species have become increasingly convoluted (van Eeuwijk et al, 2010); in polyploid species such theoretical complexities have yet to be attempted, given the more immediate difficulties in accurately genotyping as well as modelling polyploid inheritance. Just like for linkage mapping and GWAS, the range of software tools available for QTL analysis in polyploids remains rather limited, although there are a number of recent developments that are helping transform the field.…”

Section: Qtl Analysismentioning

confidence: 99%

“…In our treatment of genetic co-factors we do not currently compare between models, but a model comparison framework such as the Akaike or Bayesian Information Criterion could be quite instructive here. There is also currently no provision for investigating genotype x environment (GxE) interactions, despite these interactions being relatively common (van Eeuwijk et al, 2010). One reason we have not (yet) implemented GxE in polyqtlR is the difficulty of including an interaction term for a polyploid population.…”

Section: Future Directions Of Polyqtlrmentioning

confidence: 99%

Genetic mapping in polyploids

Bourke¹

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“…Increasing attention has been devoted to the use of crop modeling for elucidating the genetic basis of genotype × management × environment (G × M × E) interaction at the level of the entire genotype and, more recently, at the level of single loci (Ludwig and Asseng 2010 ;Richards et al 2010 ;Tardieu and Tuberosa 2010 ;van Eeuwijk et al 2010 ;Parent and Tardieu 2014 ). The objective is to predict, via modeling, yield differences among genotypes grown under different environmental conditions (Cooper et al 2009 ;Tardieu and Tuberosa 2010 ).…”

Section: Future Perspectivesmentioning

confidence: 99%

Genomics Approaches to Dissect the Genetic Basis of Drought Resistance in Durum Wheat

Tuberosa

Maccaferri

2015

Advances in Wheat Genetics: From Genome to Field

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A better knowledge of the genetic basis of the mechanisms underlying the adaptive response to drought will be instrumental to more effectively deploy marker-assisted selection (MAS) to improve yield potential while optimizing wateruse effi ciency. Genomics approaches allow us to identify and clone the genes and QTLs that underlie the adaptive response of durum wheat to drought. Linkage and association mapping have allowed us to identify QTLs for traits that infl uence drought resistance and yield in durum and bread wheat. Once major genes and QTLs that affect yield under drought conditions are identifi ed, their cloning provides a more direct path for mining and manipulating benefi cial alleles. While QTL analysis and cloning addressing natural variation will increasingly shed light on mechanisms of adaptation to drought and other adverse conditions, more emphasis on approaches relying on resequencing, candidate gene identifi cation, 'omics' platforms and reverse genetics will accelerate the pace of gene/QTL discovery. Genomic selection provides a valuable option to improve wheat performance under drought conditions without prior knowledge of the relevant QTLs. Modeling crop growth and yield based on the effects of major QTLs offers an additional opportunity to leverage genomics information. Although it is expected that genomics-assisted breeding will enhance the pace of durum wheat improvement, major limiting factors are how to (i) phenotype genetic materials in an accurate, relevant and highthroughput fashion and (ii) more effectively translate the deluge of molecular and phenotypic data into improved cultivars. A multidisciplinary effort will be instrumental to meet these challenges.

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Detection and use of QTL for complex traits in multiple environments

Cited by 166 publications

References 71 publications

Understanding the genetic basis of potato development using a multi-trait QTL analysis

Understanding the genetic basis of potato development using a multi-trait QTL analysis

Genetic mapping in polyploids

Genomics Approaches to Dissect the Genetic Basis of Drought Resistance in Durum Wheat

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