Training Population Design and Resource Allocation for Genomic Selection in Plant Breeding

Lorenz, Aaron J.; Nice, Liana

doi:10.1007/978-3-319-63170-7_2

Cited by 14 publications

(15 citation statements)

References 49 publications

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“…Maintenance of prediction accuracy across selection cycles is critical for long-term genetic gain (Müller et al 2017). Prediction accuracies for GARS were initially high, likely due to the within-family prediction scheme, high genetic variance and similarity between the selection population and training population (Lorenz and Nice 2017). Average prediction accuracy after the first year of GARS showed substantial decline.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study

Muleta

Pressoir

Morris

2019

G3 Genes|Genomes|Genetics

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Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs’ unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12–88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26–165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33–39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.

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Section: Discussionmentioning

confidence: 99%

Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study

Muleta

Pressoir

Morris

2019

G3 Genes|Genomes|Genetics

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“…Marker‐independent groups are more effective than groups based on marker‐only information, probably because these groups provide redundant information that is already accessible from the relationship matrix. Other studies have shown that considering population structure improves prediction ability (Isidro et al, 2015; Rincent et al, 2017; Norman et al, 2018); therefore, population structure should play a key role in the strategies used to build OTRs for genomic selection (Asoro et al, 2011; Crossa et al, 2014; Isidro et al, 2015, Lorenz and Nice, 2017; Rincent et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Training Population Optimization for Genomic Selection

et al. 2019

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The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. G enomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). Genomic selection is preferred over marker-assisted selection approaches for complex traits (Habier et al., 2007; Lorenz et al., 2011) because it includes all molecular markers in the prediction model and because it considers the quantitative trait loci of both major and minor effects (Xu, 2003; Jannink et al., 2010; Poland and Rife, 2012; Smith et al., 2018). Simulated and empirical cross-validation studies in plants show that GS can accelerate progress in plant breeding compared with marker-assisted selection, resulting in higher genetic gains (

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“…Limited genetic relatedness between the training and test lines reveals that the extent of LD is shorter and unstable across individuals in the population (Tan et al, 2017). RRBLUP relies mainly on the strength of LD between markers and QTL, where an increase in marker-QTL LD is expected to improve predictions (Lorenz and Nice, 2017). In our case, as there is no strong marker-QTL LD observed due to low relatedness between TP and validation sets, the implementation of the RRBLUP model for GS resulted in inter-year prediction accuracies close to zero.…”

Section: Discussionmentioning

confidence: 99%

“…RRBLUP also performed poorly compared with its Bayesian counterparts in predicting flowering time and grain number using unrelated double haploid populations of wheat (Thavamanikumar et al, 2015). Similarly, the GBLUP model, which depends mainly on the genomic relationships between the training and selection set (Lorenz and Nice, 2017), had low accuracies, most likely also a consequence of the genetic relationships between the training and test populations. Low relatedness between the TP and selection candidates further suggests the presence of opposite linkage phases between markers and QTL (Haile et al, 2018), which negatively affects the accuracy of predictions.…”

Section: Discussionmentioning

confidence: 99%

Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat Through Genome-Wide Association Study and Genomic Selection

et al. 2019

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Snow mold is a yield-limiting disease of wheat in the Pacific Northwest (PNW) region of the US, where there is prolonged snow cover. The objectives of this study were to identify genomic regions associated with snow mold tolerance in a diverse panel of PNW winter wheat lines in a genome-wide association study (GWAS) and to evaluate the usefulness of genomic selection (GS) for snow mold tolerance. An association mapping panel (AMP; N = 458 lines) was planted in Mansfield and Waterville, WA in 2017 and 2018 and genotyped using the Illumina® 90K single nucleotide polymorphism (SNP) array. GWAS identified 100 significant markers across 17 chromosomes, where SNPs on chromosomes 5A and 5B coincided with major freezing tolerance and vernalization loci. Increased number of favorable alleles was related to improved snow mold tolerance. Independent predictions using the AMP as a training population (TP) to predict snow mold tolerance of breeding lines evaluated between 2015 and 2018 resulted in a mean accuracy of 0.36 across models and marker sets. Modeling nonadditive effects improved accuracy even in the absence of a close genetic relatedness between the TP and selection candidates. Selecting lines based on genomic estimated breeding values and tolerance scores resulted in a 24% increase in tolerance. The identified genomic regions associated with snow mold tolerance demonstrated the genetic complexity of this trait and the difficulty in selecting tolerant lines using markers. GS was validated and showed potential for use in PNW winter wheat for selecting on complex traits such tolerance to snow mold.

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Training Population Design and Resource Allocation for Genomic Selection in Plant Breeding

Cited by 14 publications

References 49 publications

Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study

Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study

Training Population Optimization for Genomic Selection

Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat Through Genome-Wide Association Study and Genomic Selection

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