Detection of epistatic interactions in association mapping populations: an example from tetraploid potato

Stich, Benjamin; Gebhardt, Christiane

doi:10.1038/hdy.2011.40

Cited by 14 publications

(16 citation statements)

References 37 publications

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“…For each of the above mentioned statistical models, different genetic models were examined: Model M1 considered only additive effects, M2 additive and dominance effects, whereas model M3 considered additive, dominance, and the three types of epistatic effects (e.g., Stich and Gebhardt, 2011 ). We observed in a previous study that polymorphisms in the StAOS2 locus explained individually between 30 and 40% of the phenotypic variance (Pajerowska-Mukhtar et al, 2009 ).…”

Section: Methodsmentioning

confidence: 99%

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Stich

Inghelandt

2018

Front. Plant Sci.

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Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

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Section: Methodsmentioning

confidence: 99%

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Stich

Inghelandt

2018

Front. Plant Sci.

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“…They range between 0 and 1. DNA markers on high LD ( D' > 0.9) with trait-encoding loci may allow detecting epistatic interactions accounting 5-10 % of the phenotypic variance (Stich and Gebhardt 2011). A combined mixed model and analysis of variance approach does not increase the rate of false positives for detecting such epistatic interactions, which are often not distributed uniformly among the loci.…”

Section: Genome-wide Association and Quantitative Trait Variationmentioning

confidence: 99%

Population Improvement

Ríos

2015

Plant Breeding in the Omics Era

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Cyclical phenotypic selection methods such as mass selection in open-pollinated (OP) cultivars were used widely in plant breeding until the inbred-hybrid approach began in the first half of the twentieth century. Mass selection was very effective to breed distinct OP cultivars but not for selecting high-yielding cultivars. This failure was due to selecting individual plants for a low heritability trait such as edible yield, low correlation of traits used in selection with edible yield, inappropriate male gamete control resulting from weak plot isolation, and poor plot techniques unable to control local environmental effects. Hence, population improvement methods, such as recurrent selection aiming to increase the frequency of favorable alleles by selecting the superior individuals in a population, intermating them, and selecting the best among their offspring, were sought in plant breeding (Hallauer et al. 2010). Recurrent selection differs from mass selection because selected individuals from the base population undergo progeny testing, and thereafter those selected based on this progeny testing data are intermated with each other to form the new population. Selection ensuing from recurrent selection can be further used in pedigree breeding for recycling inbred lines.

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“…Even when tetraploid allele dosage is available, it may be disregarded (i.e., the marker data are “diploidized”) to facilitate the use of diploid software (Simko et al, 2006). The first use of tetraploid marker data and association analysis models in potato was in studies of candidate genes (Pajerowska‐Mukhtar et al, 2009; Stich and Gebhardt, 2011), and these methods were later extended to true genome‐wide analyses (Uitdewilligen et al, 2013). Thus far, however, no one has released polyploid GWAS software targeted to the plant breeding and genetics community.…”

mentioning

confidence: 99%

Software for Genome‐Wide Association Studies in Autopolyploids and Its Application to Potato

et al. 2016

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Genome-wide association studies (GWAS) are widely used in diploid species to study complex traits in diversity and breeding populations, but GWAS software tailored to autopolyploids is lacking. The objectives of this research were to (i) develop an R package for autopolyploids based on the Q + K mixed model, (ii) validate the software with simulated data, and (iii) analyze a diversity panel of tetraploid potatoes. A unique feature of the R package, called GWASpoly, is its ability to model different types of polyploid gene action, including additive, simplex dominant, and duplex dominant. Using a simulated tetraploid population, we confirmed our hypothesis that statistical power is higher when the assumed gene action in the GWAS model matches the gene action at unobserved quantitative trait loci (QTL). Thirteen traits were analyzed in the Solanaceae Coordinated Agricultural Project (SolCAP) potato diversity panel and, consistent with previous studies, significant QTL for tuber shape and eye depth colocalized on chromosome 10. For the other traits, only marginally significant QTL were detected, most likely due to insufficient statistical power: for simulated traits with a heritability (h 2 ) of 0.3, the median genome-wide power was only 0.01. Our results indicate that both marker density and population size were limiting factors for GWAS with the SolCAP panel.

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Detection of epistatic interactions in association mapping populations: an example from tetraploid potato

Cited by 14 publications

References 37 publications

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Population Improvement

Software for Genome‐Wide Association Studies in Autopolyploids and Its Application to Potato

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