GMStool: GWAS-based marker selection tool for genomic prediction from genomic data

Jeong, Seongmun; Kim, Jaeyoon; Kim, Namshin

doi:10.1038/s41598-020-76759-y

Cited by 31 publications

(34 citation statements)

References 33 publications

(42 reference statements)

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“…Finally, the utilization of highly significant SNPs from ssGWAS can give comparative prediction accuracies for the two traits studied, for both un-imputed and imputed genotype data, as compared to the full set of 6470 SNPs in our present study. However, previous reports showed that using significant SNP panels selected from ssGWAS had higher accuracy in the estimated breeding values for disease resistance traits in Litopeneaus vannamei ( Luo et al 2021 ) or fish species ( Jeong et al 2020 ). On the one hand, these SNPs can be used to develop a small SNP panel to genotype a large number of animals in both the training and validation populations.…”

Section: Discussionmentioning

confidence: 95%

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e.,, machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e.,, pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e.,, ML-KAML) and deep learning (i.e.,, DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e.,, survival status coded as 0 and 1 and survival time, i.e.,, days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease resistant traits studied in this population of striped catfish P. hypophthalmus.

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Section: Discussionmentioning

confidence: 95%

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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“…For the model GBLUP_GMS (GWAS Marker Selection GBLUP), genomic additive matrix (G) was obtained using only the significant SNPs from the GWAS analysis for each treatment (B+ and B-), except for Delta that we used all significant SNPs. A similar approach for marker selection based on GWAS for genomic prediction is described by Jeong et al (2020). The covered of the significant SNPs from GWAS analysis of each treatment (B+, B-, and Delta) were obtained using a windows range upstream and downstream, based on LD decay of respective chromosomes.…”

Section: Genomic Predictionmentioning

confidence: 99%

On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience

et al. 2021

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Exploring the symbiosis between plants and plant-growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits, improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience.

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“…The major limitation of incorporating TAMs into GS models depended on the accuracy of GWAS results. Marker selection strategies based on p values or marker effects might produce an improper marker set with low accuracies if the GWAS was incorrect (Jeong et al, 2020). GWAS results from the full data set that included the training set and testing sets might produce an overfitted markers set.…”

Section: Discussionmentioning

confidence: 99%

“…Besides, the marker information and training population will be used to obtain an optimum breeding design and improve genetic gains through reducing costs. Recently, GMStool is developed to present the best prediction model with the optimal marker set based on GWAS results (Jeong et al, 2020), which provides a useful tool for breeders. As GBS, SNP array technology, and other high-output genotyping strategies arise, the genotyping costs are likely to continue to decrease, whereas the phenotyping costs are usually steady or increasing (Spindel et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

Cao

2021

Front. Plant Sci.

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High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.

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GMStool: GWAS-based marker selection tool for genomic prediction from genomic data

Cited by 31 publications

References 33 publications

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience

Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

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