Trial heterogeneity and variance models in the genetic evaluation of potato tuber yield

Paget, M. F.; Alspach, P. A.; Anderson, John A.; Genet, R. A.; Apiolaza, Luis A.

doi:10.1111/pbr.12251

Cited by 6 publications

(9 citation statements)

References 39 publications

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“…On average, spatial autocorrelation values were medium and low in magnitude, which corroborates the findings of Paget et al. (2015) with potato and in Stringer and Cullis (2002) with sugarcane ( Saccharum officinarum L.) but were much smaller than other studies (Dutkowski et al., 2002; Chen et al., 2018). The spatial autocorrelation values were influenced by the trait, that is, for TTY and MTY being higher than SG.…”

Section: Discussionsupporting

confidence: 87%

“…This improvement in error control varied according to the trait studied and was more pronounced for TTY and MTY and slightly smaller for SG. Dependence between different efficiencies and different traits has also been detected in similar studies (Chen, Helmersson, Westin, Karlsson, & Wu, 2018; Dutkowski, Costa e Silva, Gilmour, Wellendorf, & Aguiar, 2006; Paget et al., 2015).…”

Section: Discussionsupporting

confidence: 68%

“…Paget et al. (2015) reported a similar pattern in early stage trials of a potato breeding program and so did Dutkowski et al. (2002) in forest breeding trials.…”

Section: Discussionmentioning

confidence: 72%

“…The efficiency of AR1 models in controlling the variations that may occur in field trials has been reported in different studies for various crops: wheat, sorghum, cotton, beet, barley, and potato (Borges et al., 2018; Gilmour et al., 1997; Liu et al., 2015; Müller et al., 2010; Paget et al., 2015; Qiao et al., 2000; Velazco et al., 2017). Models that consider spatial variation in two dimensions, such as those used in the present study, are more efficient for the analysis of experiments (Grondona et al., 1996).…”

Section: Discussionmentioning

confidence: 99%

“…Paget, Alspach, Anderson, Genet, and Apiolaza (2015) analyzed 21 trials from the early stages of a potato breeding program that verified the efficiency of the AR1 model in controlling heterogeneity within these experiments. They concluded that AR1 model was efficient in controlling the variations despite not presenting the best fit for all scenarios considering different traits.…”

Section: Introductionmentioning

confidence: 99%

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Accounting for spatial trends to increase the selection efficiency in potato breeding

et al. 2020

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A crucial point in agricultural experimentation is to compare treatments with high accuracy. However, agricultural experimentation is prone to field heterogeneity, and a common source of error is the spatial variation between the plots used in an experiment. With plant breeding experiments, the high number of tested genotypes requires breeders to use large areas, which invariably increases the likelihood of spatial variation. The use of models that do not address this variation can lead to errors in selecting the best genotypes. Our goal was to evaluate the effects of two spatial models—first‐order autoregressive (AR1) and spatial analysis of field trials with splines (SpATS)—to control the spatial variation in 30 experiments from potato (Solanum tuberosum L.) breeding programs. Specifically, we sought to control for three traits: total tuber yield (TTY), marketable tuber yield (MTY), and tuber specific gravity (SG). The results obtained with the use of spatial models were compared with the base model (independent errors) based on precision, heritability, and the impact on the selection of the best clones. Spatial models were effective in controlling local and global errors and achieved greater accuracy and efficiency over the base model. The spatial approach also showed greater heritability for all analyzed traits. The spatial models led to differences in the clone ranking and consequently in the selection of the best clones. Thus, spatial analysis has the power to make more precise analyses, which leads to more accurate selections and should be used to analyze phenotype data of potato breeding programs.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 68%

“…Paget et al. (2015) reported a similar pattern in early stage trials of a potato breeding program and so did Dutkowski et al. (2002) in forest breeding trials.…”

Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accounting for spatial trends to increase the selection efficiency in potato breeding

et al. 2020

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Computation of the inverse additive relationship matrix for autopolyploid and multiple-ploidy populations

Hamilton

Kerr

2017

Theor Appl Genet

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Rules to generate the inverse additive relationship matrix (A ) are defined to enable the adoption restricted maximum likelihood (REML) and best linear unbiased prediction (BLUP) in autopolyploid populations with multiple ploidy levels. Many important agronomic, horticultural, ornamental, forestry, and aquaculture species are autopolyploids. However, the adoption of restricted maximum likelihood (REML), for estimating co/variance components, and best linear unbiased prediction (BLUP), for predicting breeding values, has been hampered in autopolyploid breeding by the absence of an appropriate means of generating the inverse additive relationship matrix (A ). This paper defines rules to generate the A of autopolyploid populations comprised of individuals of the same or different ploidy-levels, including populations exhibiting (1) odd-numbered ploidy levels (e.g. triploids), (2) sex-based differences in the probability that gametic genes are identical by descent and (3) somatic chromosome doubling. Inbreeding, due to double reduction, in autopolyploid founders in the absence of mating among relatives is also accounted for. A previously defined approach is modified, whereby rules are initially defined to build an inverse matrix of kinship coefficients (K ), which is then used to generate A . An R package (polyAinv; https://github.com/mghamilton/polyAinv ) to implement these rules has been developed and examples of analyses provided. The adoption of REML and BLUP methods made possible by these new rules has the potential to provide further insights into the quantitative genetic architecture of autopolyploid and multiple-ploidy populations, improve estimates of breeding values, and increase genetic gains made through recurrent selection.

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Replicate allocation to improve selection efficiency in the early stages of a potato breeding scheme

et al. 2017

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Trial heterogeneity and variance models in the genetic evaluation of potato tuber yield

Cited by 6 publications

References 39 publications

Accounting for spatial trends to increase the selection efficiency in potato breeding

Accounting for spatial trends to increase the selection efficiency in potato breeding

Computation of the inverse additive relationship matrix for autopolyploid and multiple-ploidy populations

Replicate allocation to improve selection efficiency in the early stages of a potato breeding scheme

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