Multivariate genomic predictions for age at puberty in tropically adapted beef heifers1

Engle, Bailey; Corbet, N. J.; Allen, Jack; Laing, A. R.; Fordyce, Geoffry; McGowan, Michael; Burns, B. M.; Lyons, Russell E.; Hayes, Ben J.

doi:10.1093/jas/sky428

Cited by 14 publications

(16 citation statements)

References 39 publications

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“…A remarkable improvement of prediction accuracy was achieved in this study when the multivariate model was applied to perform the GS. Several previous studies have shown that using correlated traits as auxiliary variates in the GS model can efficiently enhance the prediction accuracy and is obviously superior to the univariate model [67][68][69][70][71]. The increase of prediction accuracy estimated by the FIXED and multivariate models is mainly attributed to the higher proportion of genetic variance captured by these models than in the univariate model, as shown in the present study and previous researches [67,72].…”

Section: Discussionsupporting

confidence: 73%

Genetic mapping and genomic selection for maize stalk strength

Liu

Xiu

et al. 2020

BMC Plant Biol

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Background: Maize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages. Results: The optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects. Conclusions: The genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.

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Section: Discussionsupporting

confidence: 73%

Genetic mapping and genomic selection for maize stalk strength

Liu

Xiu

et al. 2020

BMC Plant Biol

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“…Several previous studies have shown that using correlated traits as auxiliary variates in the GS model can efficiently enhance the prediction accuracy and is obviously superior to the univariate model [66][67][68][69][70]. The increase of prediction accuracy estimated by the FIXED and multivariate models is mainly attributed to the higher proportion of genetic variance captured by these models than in the univariate model, as shown in the present study and previous researches [66,71].…”

Section: Discussionsupporting

confidence: 73%

Genetic mapping and genomic selection for maize stalk strength

Liu

Xiu

et al. 2019

Preprint

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Background Maize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages.Results The optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects.Conclusions The genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.

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confidence: 99%

“…In contrast to AGECL, reproductive maturity score (RMS), a proxy trait for AGECL, is a categorical trait measured on a 0 to 5 scale where 0 = infantile reproductive tract, 1 = small ovarian follicles, 2 = ovarian follicles with a diameter larger than 10 mm, 3 = presence of corpus luteum, 4 = pregnancy to 10 weeks, and 5 = pregnancy longer than 10 weeks [12,13]. Unlike AGECL, for which multiple measurements are taken, RMS is measured only once, at approximately 600 days of age [12,13]. Recent studies have shown that RMS is moderately heritable (h 2 = 0.23) and is highly genetically correlated (r g = − 0.83) to AGECL in tropically-adapted heifers [13], which suggests that it could be used as a proxy for AGECL in genomic evaluations.…”

mentioning

confidence: 99%

“…Unlike AGECL, for which multiple measurements are taken, RMS is measured only once, at approximately 600 days of age [12,13]. Recent studies have shown that RMS is moderately heritable (h 2 = 0.23) and is highly genetically correlated (r g = − 0.83) to AGECL in tropically-adapted heifers [13], which suggests that it could be used as a proxy for AGECL in genomic evaluations. The advantage of using RMS as a proxy indicator of age at puberty, compared to the highly heritable AGECL trait, is that it is a single measurement that is taken at a similar age as many scanned carcase traits [14].…”

mentioning

confidence: 99%

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Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers

et al. 2020

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Background: In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Methods: Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. Results: In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. Conclusions: While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

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Multivariate genomic predictions for age at puberty in tropically adapted beef heifers1

Cited by 14 publications

References 39 publications

Genetic mapping and genomic selection for maize stalk strength

Genetic mapping and genomic selection for maize stalk strength

Genetic mapping and genomic selection for maize stalk strength

Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers

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