Dataset Information

Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.).

ABSTRACT: Genomic selection is a promising molecular breeding strategy enhancing genetic gain per unit time. The objectives of our study were to (1) explore the prediction accuracy of genomic selection for plant height and yield per plant in soybean [Glycine max (L.) Merr.], (2) discuss the relationship between prediction accuracy and numbers of markers, and (3) evaluate the effect of marker preselection based on different methods on the prediction accuracy. Our study is based on a population of 235 soybean varieties which were evaluated for plant height and yield per plant at multiple locations and genotyped by 5361 single nucleotide polymorphism markers. We applied ridge regression best linear unbiased prediction coupled with fivefold cross-validations and evaluated three strategies of marker preselection. For plant height, marker density and marker preselection procedure impacted prediction accuracy only marginally. In contrast, for grain yield, prediction accuracy based on markers selected with a haplotype block analyses-based approach increased by approximately 4 % compared with random or equidistant marker sampling. Thus, applying marker preselection based on haplotype blocks is an interesting option for a cost-efficient implementation of genomic selection for grain yield in soybean breeding.

SUBMITTER: Ma Y

PROVIDER: S-EPMC4965486 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Potential of marker selection to increase prediction accuracy of genomic selection in soybean (<i>Glycine max</i> L.).

Ma Yansong Y Reif Jochen C JC Jiang Yong Y Wen Zixiang Z Wang Dechun D Liu Zhangxiong Z Guo Yong Y Wei Shuhong S Wang Shuming S Yang Chunming C Wang Huicai H Yang Chunyan C Lu Weiguo W Xu Ran R Zhou Rong R Wang Ruizhen R Sun Zudong Z Chen Huaizhu H Zhang Wanhai W Wu Jian J Hu Guohua G Liu Chunyan C Luan Xiaoyan X Fu Yashu Y Guo Tai T Han Tianfu T Zhang Mengchen M Sun Bincheng B Zhang Lei L Chen Weiyuan W Wu Cunxiang C Sun Shi S Yuan Baojun B Zhou Xinan X Han Dezhi D Yan Hongrui H Li Wenbin W Qiu Lijuan L

Molecular breeding : new strategies in plant improvement 20160728

Genomic selection is a promising molecular breeding strategy enhancing genetic gain per unit time. The objectives of our study were to (1) explore the prediction accuracy of genomic selection for plant height and yield per plant in soybean [<i>Glycine max</i> (L.) Merr.], (2) discuss the relationship between prediction accuracy and numbers of markers, and (3) evaluate the effect of marker preselection based on different methods on the prediction accuracy. Our study is based on a population of 23 ...[more]

PMID: 27524935

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Project description:Background: Pollen, the male partner in the reproduction of flowering plants, comprises either two or three cells at maturity. The current knowledge of the pollen transcriptome is limited to the model plant Arabidopsis thaliana, which has tri-cellular pollen grains at maturity. Comparative studies on pollen of other genera, particularly crop plants, are needed to understand the pollen gene networks that are subject to functional and evolutionary conservation. In this study, we used the Affymetrix Soybean GeneChip® to perform transcriptional profiling on mature bi-cellular soybean pollen. Results: Compared to the sporophyte transcriptome, the soybean pollen transcriptome revealed a restricted and unique repertoire of genes, with a significantly greater proportion of specifically expressed genes than is found in the sporophyte tissue. Comparative analysis shows that, among the 37,500 soybean unique transcripts addressed in this study, 10,299 genes (27.46%) are expressed in pollen. Of the pollen-expressed genes, about 9,489 (92.13%) are also expressed in sporophytic tissues, and 810 (7.87%) are selectively expressed in pollen. Overall, the soybean pollen transcriptome shows an enrichment of transcription factors (mostly zinc finger family proteins), cell cycle-related transcripts, signal recognition receptors, ethylene responsive factors, chromatin remodeling factors, and members of the ubiquitin proteasome proteolytic pathway. Moreover, we identify several new pollen-specific candidate genes that might play a significant role in pollen biology. Conclusion: This is the first report of a soybean pollen transcriptional profile. These data extend our current knowledge regarding regulatory pathways that govern the gene regulation and development of pollen. We also demonstrate that pollen is a rich store of regulatory proteins that are essential and sufficient for de novo gene expression. A comparison between transcription factors up-regulated in soybean and those upregulated in Arabidopsis revealed some divergence in the numbers and kinds of regulatory proteins expressed in both species.

Dataset Information

Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.).

Publications

Potential of marker selection to increase prediction accuracy of genomic selection in soybean (<i>Glycine max</i> L.).

Similar Datasets

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