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

Hurtado-Lopez, P.X.; Tessema, B.B.; Schnabel, Sabine; Maliepaard, Chris; Linden, C. Gerard van der; Eilers, Paul H.C.; Jansen, J.; Eeuwijk, F.A. van; Visser, Richard G. F.

doi:10.1007/s10681-015-1431-2

Cited by 10 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QTL for tuber number under well-watered conditions was also detected on chromosome 3. Similarly, QTL for tuber number under short photoperiod conditions was reported at the same chromosome in [23].…”

Section: Qtlsmentioning

confidence: 75%

“…However QTL detected on chromosome 12 for tuber fresh weight under drought stress conditions showed an overlap with QTL detected for onset and inflection point of plant height under normal growing conditions in [23]. Also, QTL identified on chromosome 8 for plant height specific to drought stress condition co-located with QTL identified (single trait QTL analysis) on chromosome 8 for a parameter controlling inflection point of senescence under short photoperiod conditions [23].…”

Section: Qtlsmentioning

confidence: 94%

“…[13] also reported QTLs on these two different positions on chromosome 5, where out of 21 QTLs detected three QTLs found under drought stress conditions were located in the interval range of 47 to 71 cM, while the rest of the QTLs for well-watered, stress and recovery were located between 20 and 48 cM.These results suggest there may be two different, independent QTL present on chromosome 5. For this same potato population, 31 QTLs controlling developmental (plant height, flowering and senescence) and 24 QTLs affecting agronomic traits were reported under short day conditions[23].…”

mentioning

confidence: 90%

See 2 more Smart Citations

Unraveling the genetic basis of drought tolerance in potato

Tessema

Gebregiorgis

Visser

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: Potato is highly valued as a food security crop but is sensitive to drought stress. A relatively small reduction of the optimum amount of water can already result in a significant reduction in tuber yield. Therefore, unravelling the genetic basis of drought tolerance is important in order to enhance tolerance to drought in potato. We have evaluated a diploid potato backcross mapping population for drought tolerance under field conditions. Water application was withheld at the stage of tuber initiation. At the end of the drought stress period, we collected data on a number of morphological and physiological traits.Results: The potato population showed significant reduction in several growth traits measured, as well as in tuber yield. We applied quantitative trait locus (QTL) analysis to find genomic regions underlying these drought tolerance traits. We found 60 QTL for several of the traits measured under well-watered and drought stress conditions. From the total number of QTLs identified, 21 were detected under water stress conditions while 39 QTLs were identified under well-watered conditions. Drought specific QTLs for tuber fresh yield was detected on chromosome 7, 9 and 12.Conclusions: There is genetic variation in potato for drought tolerance and QTLs discovered for drought tolerance traits in this study can aid in improving potato for drought tolerance.

show abstract

Section: Qtlsmentioning

confidence: 75%

Section: Qtlsmentioning

confidence: 94%

mentioning

confidence: 90%

See 1 more Smart Citation

Unraveling the genetic basis of drought tolerance in potato

Tessema

Gebregiorgis

Visser

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In a 3 to 5 day course on statistical approaches to GxE, little attention can be given to explicit dynamic (Malosetti et al, 2006;Wu and Lin, 2006) and multi-trait modelling approaches. However, simultaneous univariate analyses of traits, especially yield and phenology, can shed light on trait dependencies, while the dynamic behavior of traits can be represented by slope and curvature parameters of reaction norms (Van Eeuwijk et al, 2007;van Eeuwijk et al, 2010;Hurtado-Lopez et al, 2015).…”

Section: Crop Growth Models Multi-trait Reaction Norms and Networkmentioning

confidence: 99%

“…In both scenarios, intermediate traits can be measured at a single time point, or they can be monitored at multiple time points during the season to describe their dynamics. Characterizing trait dynamics allows to better capture the genotypic response to the environmental conditions integrated over the growing season and therefore might be more informative about genotypic performance than single-time point measurements (Malosetti et al, 2006;van Eeuwijk et al, 2010;Hurtado et al, 2012;Hurtado-Lopez et al, 2015). Simultaneous modelling of data points over time is also a strategy to reduce the measurement error and to increase the heritability of traits measured with HTP (Rutkoski et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics

Bustos-Korts

View full text Add to dashboard Cite

Bustos -Korts, D. (2017). Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics. PhD thesis, Wageningen University, the Netherlands.The main objective of plant breeders is to create and identify genotypes that are well-adapted to the target population of environments (TPE). The TPE corresponds to the future growing conditions in which the varieties produced by a breeding program will be grown. All possible genotypes that could be considered as selection candidates for a specific TPE are said to belong to the target population of genotypes, TPG.Genotypes commonly show different sensitivities to environmental gradients and then genotype by environment interaction (GxE) is observed. GxE can lead to changes in genotypic ranking, complicating the breeding process. The main aim of this thesis was to investigate statistical models and the combination of statistical and crop growth models to improve phenotype prediction across multiple environments. One aspect that determines the quality of phenotype prediction is the set of genotypes used to train the prediction model, especially when the TPG is structured. We proposed a method that uniformly covers the genetic space of the TPG, leading to a larger prediction accuracy than random sampling. We produced positive results for wheat, maize and rice. A second aspect that influences the accuracy of phenotype predictions is the choice of environments used to train the prediction model, which should capture the heterogeneity in the TPE. When accounting for heterogeneity in environmental quality, it is important to distinguish between repeatable and well predictable elements in the environmental conditions from those that are badly predictable. We proposed statistical methods based on the AMMI model and on mixed models to identify groups of environments that show repeatable GxE, illustrating our ideas with multienvironment wheat data in North-Western Europe. The importance of training set construction strategies and multi-environment genomic prediction models was also demonstrated for barley data. If breeders are interested in identifying the genetic basis of the target traits, it is advantageous to have a higher SNP density. In this thesis, we used exome sequence data of the EU-Whealbi-barley germplasm, which corresponds to a unique set of genotypes with a diverse origin, growth habit and breeding history.vi For this diverse data, we assessed the effects of QTLs and haplotypes across multiple environments for awn length, grain weight, heading date and plant height. Our results show that the EU-Whealbi-barley collection possesses a large diversity of promising alleles regulating the four traits we analysed. The last major topic addressed in this thesis is the use of a combination of statistical-genetic models and crop growth models (APSIM) as a strategy to assess the traits and phenotyping schemes to improve the prediction accuracy of a target trait like yield...

show abstract