Optimization of Selective Phenotyping and Population Design for Genomic Prediction

Heslot, Nicolas; Feoktistov, Vitaliy

doi:10.1007/s13253-020-00415-1

Cited by 22 publications

(19 citation statements)

References 39 publications

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“…Our results have focused on models with fixed genotypic effects but equivalence relations hold equally when genotypes are modelled as random, which is becoming increasingly common in breeding programs (Cullis et al 2020;Heslot and Feoktistov 2020). Note that our equivalence relations rely on a model reduction for fixed block effects, and the model reduction does not alter the estimates of any other effects in the model (De Hoog et al 1990).…”

Section: Discussionmentioning

confidence: 99%

Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?

Boer

Piepho

Williams

2020

JABES

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Nearest-neighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, P-splines have been proposed as a flexible alternative to spatial analysis of field trials. On the surface, P-splines may appear like a completely new type of method, but closer scrutiny reveals intimate ties with earlier proposals based on first differences and the linear variance model. This paper studies these relations in detail, first focussing on one-dimensional spatial models and then extending to the two-dimensional case. Two yield trial datasets serve to illustrate the methods and their equivalence relations. Parsimonious linear variance and random walk models are suggested as a good point of departure for exploring possible improvements of model fit via the flexible P-spline framework.

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Section: Discussionmentioning

confidence: 99%

Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?

Boer

Piepho

Williams

2020

JABES

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“…The process repeated until reaching a plateau. Akdemir et al (2015) and Heslot and Feoktistov (2020) also modified Rincent et al (2012) with improved computational efficiency. The efficacy of these methods was not compared in our study, but results from preliminary analysis show the strategy used in this study improved prediction accuracy compared to Rincent et al (2012) (results not shown).…”

Section: Methodsmentioning

confidence: 99%

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

et al. 2021

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To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

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“…In contrast, Roth et al 30 observed in apple a systematic increase of individual PA with an optimized TS in the same context (i.e., with a diversity panel as TS and bi-parental families as VS, and common optimization methods). To our knowledge, only a single study tested TS optimization for cross mean prediction, by Heslot and Feoktistov 35 , who implemented optimization of parent selection for hybrid crossing in sunflower while selecting individuals to phenotype, but did not calculate cross mean PA.…”

Section: Practical Consequences On Breeding Programsmentioning

confidence: 99%

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Brault

Segura

This

et al. 2021

Preprint

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Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

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Optimization of Selective Phenotyping and Population Design for Genomic Prediction

Cited by 22 publications

References 39 publications

Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?

Linear Variance, P-splines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related?

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

Across-population genomic prediction in grapevine opens up promising prospects for breeding

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