Predicting Additive and Non-additive Genetic Effects from Trials Where Traits Are Affected by Interplot Competition

Hunt, Colleen H.; Smith, Alison; Jordan, David R.; Cullis, Brian R

doi:10.1007/s13253-012-0117-7

Cited by 17 publications

(18 citation statements)

References 18 publications

(27 reference statements)

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“…They also considered genetic effects as random facilitating genotype selection. Pedigree information was used for assessing direct and competition effects by Hunt et al (2013) . Their competition modeling was based on the inference model suggested by Besag and Kempton (1986) , and its modification suggested by Draper and Guttman (1980) .…”

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confidence: 99%

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

Elias

Rabbi

Kulakow

et al. 2018

G3 Genes|Genomes|Genetics

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Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance.

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confidence: 99%

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

Elias

Rabbi

Kulakow

et al. 2018

G3 Genes|Genomes|Genetics

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“…To provide reliable predictions of test line performance across harvest or environments in analyzing Brazilian sugarcane experiments, we propose to follow Gilmour et al (1997) and to use mixed models that incorporate terms for local, global and extraneous variation. In addition, as Stringer et al (2011) and Hunt et al (2013), terms for genetic and residual competition will be included. Then, the general form of the model for n × 1 vector of yields, y (assumed ordered as rows within columns), where n is the total number of plots, can be written as:…”

Section: Methodsmentioning

confidence: 99%

“…Then, the autoregressive models for residual variation have become common for researchers (Stringer and Cullis, 2002;Atkin et al, 2009;Hunt et al, 2013;Liu et al, 2015). The variation may occur because of management practices impacting on the experiment, non-stationary spatial trend occurring across the field, and neighbouring plots being more similar than those further apart related to soil fertility or moisture levels (De Faveri et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers (Besag and Kempton, 1986;Stringer and Cullis, 2002;Stringer et al, 2011;Silva and Kerr, 2013;Hunt et al, 2013) argued that the interplot competition between plants of different varieties or treatments is one effect that can seriously bias the assessment of varietal performance and thus reduce the accuracy of the genetic prediction.…”

Section: Introductionmentioning

confidence: 99%

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Design and analysis of sugarcane breeding experiments: a case study

Santos¹

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Design and analysis of sugarcane breeding experiments: a case study One purpose of breeding programs is the selection of the better test lines. The accuracy of selection can be improved by using optimal design and using models that fit the data well. Finding this is not easy, especially in large experiments which assess more than one hundred lines without the possibility of replication due to the limited material, area and high costs. Thus, the large number of parameters in the complex variance structure that needs to be fitted relies on the limited number of replicated check varieties. The main objectives of this thesis were to model 21 trials of sugarcane provided by "Centro de Tecnologia Canavieira" (CTC-Brazilian company of sugarcane) and to evaluate the design employed, which uses a large number of unreplicated test lines (new varieties) and systematic replicated check (commercial) lines. The mixed linear model was used to identify the three major components of spatial variation in the plot errors and the competition effects at the genetic and residual levels. The test lines were assumed as random effects and check lines as fixed, because they came from different processes. The single and joint analyses were developed because the trials could be grouped into two types: (i) one longitudinal data set (two cuts) and (ii) five regional groups of experiment (each group was a region which had three sites). In a study of alternative designs, a fixed size trial was assumed to evaluate the efficiency of the type of unreplicated design employed in these 21 trials comparing to spatially optimized unreplicated and p-rep designs with checks and a spatially optimized p-rep design. To investigate models and design there were four simulation studies to assess mainly the i) fitted model, under conditions of competition effects at the genetic level, ii) accuracy of estimation in the separate versus joint analysis; iii) relation between the sugarcane lodging and the negative residual correlation, and iv) design efficiency. To conclude, the main information obtained from the simulation studies was: the convergence number of the algorithm model analyzed; the variance parameter estimates; the correlations between the direct genetic EBLUPs and the true direct genetic effects; the assertiveness of selection or the average similarity, where similarity was measured as the percentage of the 30 test lines with the highest direct genetic EBLUPs that are in the true 30 best test lines (generated); and the heritability estimates or the genetic gain.

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“…selecionar o material genético(Cullis et al, 2006;Smith, A. and Thompson, R. and Butler, D. and Cullis, 2011;Clarke e Stefanova, 2011;Stringer et al, 2011;Williams et al, 2011;Hunt et al, 2013;Moehring et al, 2014;Williams et al, 2014;Smith et al, 2015;Piepho e Williams, 2016;Santos, 2017).…”

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confidence: 99%

Delineamentos ótimos para experimentos com cana-de-açúcar

Goes¹

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Optimum designs in sugarcane experiments The main goal of the sucarcane breeding programs is to select new varieties with desirable characteristics in a short time period. In the early stage of the plant-breeding program there is a large number of treatments (test lines), resulting in limitations such as availability of genetic material for several replications and the size of the experimental area. In this sense, efficient designs are necessary in order to obtain as much information as possible about the treatments. The present study evaluates, using simulation studies, the genetic gain and the selection quality of 24 designs, distinct in relation to the type, whether grid-plot or partially replicated; the number of standard varieties for a fixed percentage of replicated test lines; the percentage of replicated test lines and optimality criteria, C or D, for different data scenarios and a given experimental area. From the results we concluded that the differences between the designs are small, being more evident in terms of genetic gain, but that in practice implies the selection of an additional test line, being the average percentage of correct test lines in the selection between 25 and 40% for the studied cases.

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Predicting Additive and Non-additive Genetic Effects from Trials Where Traits Are Affected by Interplot Competition

Cited by 17 publications

References 18 publications

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

Design and analysis of sugarcane breeding experiments: a case study

Delineamentos ótimos para experimentos com cana-de-açúcar

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