Empirical and deterministic accuracies of across-population genomic prediction

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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Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP

BMC Genetics ◽

10.1186/s12863-015-0305-x ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 11

Author(s):

S. van den Berg ◽

M. P. L. Calus ◽

T. H. E. Meuwissen ◽

Y. C. J. Wientjes

Keyword(s):

Variable Selection ◽

Genomic Prediction ◽

Bayesian Variable Selection ◽

Population Genomic

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A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices

Genetics Selection Evolution ◽

10.1186/s12711-020-00540-y ◽

2020 ◽

Vol 52 (1) ◽

Author(s):

Biaty Raymond ◽

Yvonne C. J. Wientjes ◽

Aniek C. Bouwman ◽

Chris Schrooten ◽

Roel F. Veerkamp

Keyword(s):

Genomic Prediction ◽

Genomic Relationship ◽

Deterministic Equation ◽

Population Genomic

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Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs

Genetics Selection Evolution ◽

10.1186/s12711-019-0500-8 ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 5

Author(s):

Hailiang Song ◽

Shaopan Ye ◽

Yifan Jiang ◽

Zhe Zhang ◽

Qin Zhang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genomic Prediction ◽

Prior Information ◽

Reference Population ◽

Genomic Feature ◽

Whole Genome ◽

Single Population ◽

Population Genomic ◽

Snp Panel

Abstract Background For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs. Results In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations. Conclusions The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

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Faculty Opinions recommendation of Population genomic analysis of base composition evolution in Drosophila melanogaster.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717982880.793472060 ◽

2013 ◽

Author(s):

Hiroshi Akashi

Keyword(s):

Drosophila Melanogaster ◽

Base Composition ◽

Genomic Analysis ◽

Population Genomic

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Faculty Opinions recommendation of Morphological and population genomic evidence that human faces have evolved to signal individual identity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718877905.793508760 ◽

2015 ◽

Author(s):

Adam Jones

Keyword(s):

Individual Identity ◽

Population Genomic ◽

Human Faces

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Population genomic data in spider mites point to a role for local adaptation in shaping range shifts

Evolutionary Applications ◽

10.1111/eva.13086 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2821-2835

Author(s):

Lei Chen ◽

Jing‐Tao Sun ◽

Peng‐Yu Jin ◽

Ary A. Hoffmann ◽

Xiao‐Li Bing ◽

...

Keyword(s):

Local Adaptation ◽

Genomic Data ◽

Spider Mites ◽

Range Shifts ◽

Population Genomic

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Genomic prediction ability for carcass composition indicator traits in Nellore cattle

Livestock Science ◽

10.1016/j.livsci.2021.104421 ◽

2021 ◽

Vol 245 ◽

pp. 104421

Author(s):

Rosiane P. Silva ◽

Rafael Espigolan ◽

Mariana P. Berton ◽

Raysildo B. Lôbo ◽

Cláudio U. Magnabosco ◽

...

Keyword(s):

Genomic Prediction ◽

Carcass Composition ◽

Prediction Ability ◽

Nellore Cattle ◽

Indicator Traits

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The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

Scientific Reports ◽

10.1038/s41598-021-83567-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fatemeh Amini ◽

Felipe Restrepo Franco ◽

Guiping Hu ◽

Lizhi Wang

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

In Planta ◽

Data Set ◽

Look Ahead ◽

Trace Back ◽

New Variant ◽

The Look ◽

Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

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