Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers

AbstractSuccessful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

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Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

Theoretical and Applied Genetics ◽

10.1007/s00122-017-2944-y ◽

2017 ◽

Vol 130 (10) ◽

pp. 2091-2108 ◽

Cited By ~ 36

Author(s):

Elsa Sverrisdóttir ◽

Stephen Byrne ◽

Ea Høegh Riis Sundmark ◽

Heidi Øllegaard Johnsen ◽

Hanne Grethe Kirk ◽

...

Keyword(s):

Genomic Prediction ◽

Starch Content ◽

Genotyping By Sequencing ◽

Tetraploid Potato ◽

Chipping Quality

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Genomic prediction accuracies and abilities for growth and wood quality traits of Scots pine, using genotyping-by-sequencing (GBS) data

10.1101/607648 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ainhoa Calleja-Rodriguez ◽

Jin Pan ◽

Tomas Funda ◽

Zhi-Qiang Chen ◽

John Baison ◽

...

Keyword(s):

Scots Pine ◽

Genomic Prediction ◽

Prediction Models ◽

Wood Quality ◽

Genotyping By Sequencing ◽

Tree Breeding ◽

Snp Markers ◽

Progeny Testing ◽

Quality Traits ◽

The Impact

ABSTRACTHigher genetic gains can be achieved through genomic selection (GS) by shortening time of progeny testing in tree breeding programs. Genotyping-by-sequencing (GBS), combined with two imputation methods, allowed us to perform the current genomic prediction study in Scots pine (Pinus sylvestrisL.). 694 individuals representing 183 full-sib families were genotyped and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic prediction models. In addition, the impact on the predictive ability (PA) and prediction accuracy to estimate genomic breeding values was evaluated by assigning different ratios of training and validation sets, as well as different subsets of SNP markers. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed higher PAs and prediction accuracies than Bayesian LASSO (BL). A subset of approximately 4000 markers was sufficient to provide the same PAs and accuracies as the full set of 8719 markers. Furthermore, PAs were similar for both pedigree- and genomic-based estimations, whereas accuracies and heritabilities were slightly higher for pedigree-based estimations. However, prediction accuracies of genomic models were sufficient to achieve a higher selection efficiency per year, varying between 50-87% compared to the traditional pedigree-based selection.

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Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs

Frontiers in Plant Science ◽

10.3389/fpls.2021.672525 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shiliang Cao ◽

Junqiao Song ◽

Yibing Yuan ◽

Ao Zhang ◽

Jiaojiao Ren ◽

...

Keyword(s):

Genetic Diversity ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Genetic Relationships ◽

Genotyping By Sequencing ◽

Genotypic Diversity ◽

Complex Trait ◽

Training Set ◽

Tar Spot ◽

Training Sets

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

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Genotyping by sequencing for genomic prediction in a soybean breeding population

BMC Genomics ◽

10.1186/1471-2164-15-740 ◽

2014 ◽

Vol 15 (1) ◽

pp. 740 ◽

Cited By ~ 105

Author(s):

Diego Jarquín ◽

Kyle Kocak ◽

Luis Posadas ◽

Katie Hyma ◽

Joseph Jedlicka ◽

...

Keyword(s):

Genomic Prediction ◽

Genotyping By Sequencing ◽

Breeding Population ◽

Soybean Breeding

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Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

Frontiers in Plant Science ◽

10.3389/fpls.2017.01916 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 45

Author(s):

Ao Zhang ◽

Hongwu Wang ◽

Yoseph Beyene ◽

Kassa Semagn ◽

Yubo Liu ◽

...

Keyword(s):

Population Size ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Marker Density ◽

Tropical Maize ◽

Training Population ◽

Accuracy Estimation ◽

Maize Populations

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Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

Genes ◽

10.3390/genes11111270 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1270 ◽

Cited By ~ 1

Author(s):

Jia Guo ◽

Jahangir Khan ◽

Sumit Pradhan ◽

Dipendra Shahi ◽

Naeem Khan ◽

...

Keyword(s):

Deep Learning ◽

Genomic Prediction ◽

Genotyping By Sequencing ◽

Triticum Aestivum L ◽

Yield Component ◽

Soft Wheat ◽

Response To Selection ◽

Best Linear Unbiased ◽

Trait Model ◽

Variable Water

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

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