scholarly journals Expected benefit of genomic selection over forward selection in conifer breeding and deployment

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208232 ◽  
Author(s):  
Yongjun Li ◽  
Heidi S. Dungey
Keyword(s):  
Genomic Selection ◽  
Forward Selection

scholarly journals Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yongjun Li ◽  
Jaroslav Klápště ◽  
Emily Telfer ◽  
Phillip Wilcox ◽  
Natalie Graham ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Predictive Ability ◽  
Radiata Pine ◽  
Exome Capture ◽  
Forward Selection ◽  
Generation Interval ◽  
Stem Straightness ◽  
Cluster Frequency

Abstract Background Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. Results Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. Conclusion It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

scholarly journals Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer

2020 ◽  
Author(s):  
Ruidong Li ◽  
Shibo Wang ◽  
Yanru Cui ◽  
Han Qu ◽  
John M Chater ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genomic Selection ◽  
Agronomic Traits ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Omics Data ◽  
Forward Selection ◽  
Prognostic Tests ◽  
Cancer Genome Atlas

Abstract Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

scholarly journals Extended Application of Genomic Selection to Screen Multi-Omics Data for Prognostic Signatures of Prostate Cancer

2020 ◽  
Author(s):  
Ruidong Li ◽  
Shibo Wang ◽  
Yanru Cui ◽  
Han Qu ◽  
John M. Chater ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genomic Selection ◽  
Agronomic Traits ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Omics Data ◽  
Forward Selection ◽  
Prognostic Tests ◽  
Cancer Genome Atlas

AbstractPrognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e., miRNAs) significantly increased outcome predictability when compared with panels consisting of small numbers of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multi-omics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multi-omics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

Strategy of whole genomic selection breeding and its application prospect in aquaculture

2013 ◽  
Vol 18 (4) ◽  
pp. 936-943
Author(s):  
Yang YU ◽  
Xiaojun ZHANG ◽  
Fuhua LI ◽  
Jianhai XIANG
Keyword(s):  
Genomic Selection ◽  
Selection Breeding

scholarly journals Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Emmanuel A. Lozada-Soto ◽  
Christian Maltecca ◽  
Duc Lu ◽  
Stephen Miller ◽  
John B. Cole ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Beef Cattle ◽  
Genomic Selection ◽  
Inbreeding Depression ◽  
Growth Traits ◽  
Effective Population ◽  
Angus Cattle ◽  
Genomic Inbreeding ◽  
Chromosome Segments ◽  
The Impact

Abstract Background While the adoption of genomic evaluations in livestock has increased genetic gain rates, its effects on genetic diversity and accumulation of inbreeding have raised concerns in cattle populations. Increased inbreeding may affect fitness and decrease the mean performance for economically important traits, such as fertility and growth in beef cattle, with the age of inbreeding having a possible effect on the magnitude of inbreeding depression. The purpose of this study was to determine changes in genetic diversity as a result of the implementation of genomic selection in Angus cattle and quantify potential inbreeding depression effects of total pedigree and genomic inbreeding, and also to investigate the impact of recent and ancient inbreeding. Results We found that the yearly rate of inbreeding accumulation remained similar in sires and decreased significantly in dams since the implementation of genomic selection. Other measures such as effective population size and the effective number of chromosome segments show little evidence of a detrimental effect of using genomic selection strategies on the genetic diversity of beef cattle. We also quantified pedigree and genomic inbreeding depression for fertility and growth. While inbreeding did not affect fertility, an increase in pedigree or genomic inbreeding was associated with decreased birth weight, weaning weight, and post-weaning gain in both sexes. We also measured the impact of the age of inbreeding and found that recent inbreeding had a larger depressive effect on growth than ancient inbreeding. Conclusions In this study, we sought to quantify and understand the possible consequences of genomic selection on the genetic diversity of American Angus cattle. In both sires and dams, we found that, generally, genomic selection resulted in decreased rates of pedigree and genomic inbreeding accumulation and increased or sustained effective population sizes and number of independently segregating chromosome segments. We also found significant depressive effects of inbreeding accumulation on economically important growth traits, particularly with genomic and recent inbreeding.

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

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.

Optical sorter‐augmented genomic selection lowers DON accumulation in wheat

Crop Science ◽  
2021 ◽  
Author(s):  
W. Jesse Carmack ◽  
Anthony J. Clark ◽  
H. Jeanette Lyerly ◽  
Yanhong Dong ◽  
Gina Brown‐Guedira ◽  
...  
Keyword(s):  
Genomic Selection

scholarly journals Genome-Wide Association and Prediction of Male and Female Floral Hybrid Potential Traits in Elite Spring Bread Wheat Genotypes

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 895
Author(s):  
Samira El Hanafi ◽  
Souad Cherkaoui ◽  
Zakaria Kehel ◽  
Ayed Al-Abdallat ◽  
Wuletaw Tadesse
Keyword(s):  
Genomic Selection ◽  
Bread Wheat ◽  
Genome Wide Association ◽  
Hybrid Breeding ◽  
Floral Traits ◽  
Phenotypic Variance ◽  
Spring Bread Wheat ◽  
Genome Wide ◽  
Anther Length ◽  
Anther Extrusion

Hybrid wheat breeding is one of the most promising technologies for further sustainable yield increases. However, the cleistogamous nature of wheat displays a major bottleneck for a successful hybrid breeding program. Thus, an optimized breeding strategy by developing appropriate parental lines with favorable floral trait combinations is the best way to enhance the outcrossing ability. This study, therefore, aimed to dissect the genetic basis of various floral traits using genome-wide association study (GWAS) and to assess the potential of genome-wide prediction (GP) for anther extrusion (AE), visual anther extrusion (VAE), pollen mass (PM), pollen shedding (PSH), pollen viability (PV), anther length (AL), openness of the flower (OPF), duration of floret opening (DFO) and stigma length. To this end, we employed 196 ICARDA spring bread wheat lines evaluated for three years and genotyped with 10,477 polymorphic SNP. In total, 70 significant markers were identified associated to the various assessed traits at FDR ≤ 0.05 contributing a minor to large proportion of the phenotypic variance (8–26.9%), affecting the traits either positively or negatively. GWAS revealed multi-marker-based associations among AE, VAE, PM, OPF and DFO, most likely linked markers, suggesting a potential genomic region controlling the genetic association of these complex traits. Of these markers, Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 deserve particular attention. The consistently significant markers with large effect could be useful for marker-assisted selection. Genomic selection revealed medium to high prediction accuracy ranging between 52% and 92% for the assessed traits with the least and maximum value observed for stigma length and visual anther extrusion, respectively. This indicates the feasibility to implement genomic selection to predict the performance of hybrid floral traits with high reliability.

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