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CUBIC: an atlas of genetic architecture promises directed maize improvement
BACKGROUND: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC)...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979394/ https://www.ncbi.nlm.nih.gov/pubmed/31980033 http://dx.doi.org/10.1186/s13059-020-1930-x |
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author | Liu, Hai-Jun Wang, Xiaqing Xiao, Yingjie Luo, Jingyun Qiao, Feng Yang, Wenyu Zhang, Ruyang Meng, Yijiang Sun, Jiamin Yan, Shijuan Peng, Yong Niu, Luyao Jian, Liumei Song, Wei Yan, Jiali Li, Chunhui Zhao, Yanxin Liu, Ya Warburton, Marilyn L. Zhao, Jiuran Yan, Jianbing |
author_facet | Liu, Hai-Jun Wang, Xiaqing Xiao, Yingjie Luo, Jingyun Qiao, Feng Yang, Wenyu Zhang, Ruyang Meng, Yijiang Sun, Jiamin Yan, Shijuan Peng, Yong Niu, Luyao Jian, Liumei Song, Wei Yan, Jiali Li, Chunhui Zhao, Yanxin Liu, Ya Warburton, Marilyn L. Zhao, Jiuran Yan, Jianbing |
author_sort | Liu, Hai-Jun |
collection | PubMed |
description | BACKGROUND: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. RESULTS: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. CONCLUSIONS: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits. |
format | Online Article Text |
id | pubmed-6979394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69793942020-01-29 CUBIC: an atlas of genetic architecture promises directed maize improvement Liu, Hai-Jun Wang, Xiaqing Xiao, Yingjie Luo, Jingyun Qiao, Feng Yang, Wenyu Zhang, Ruyang Meng, Yijiang Sun, Jiamin Yan, Shijuan Peng, Yong Niu, Luyao Jian, Liumei Song, Wei Yan, Jiali Li, Chunhui Zhao, Yanxin Liu, Ya Warburton, Marilyn L. Zhao, Jiuran Yan, Jianbing Genome Biol Research BACKGROUND: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. RESULTS: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. CONCLUSIONS: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits. BioMed Central 2020-01-24 /pmc/articles/PMC6979394/ /pubmed/31980033 http://dx.doi.org/10.1186/s13059-020-1930-x Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Liu, Hai-Jun Wang, Xiaqing Xiao, Yingjie Luo, Jingyun Qiao, Feng Yang, Wenyu Zhang, Ruyang Meng, Yijiang Sun, Jiamin Yan, Shijuan Peng, Yong Niu, Luyao Jian, Liumei Song, Wei Yan, Jiali Li, Chunhui Zhao, Yanxin Liu, Ya Warburton, Marilyn L. Zhao, Jiuran Yan, Jianbing CUBIC: an atlas of genetic architecture promises directed maize improvement |
title | CUBIC: an atlas of genetic architecture promises directed maize improvement |
title_full | CUBIC: an atlas of genetic architecture promises directed maize improvement |
title_fullStr | CUBIC: an atlas of genetic architecture promises directed maize improvement |
title_full_unstemmed | CUBIC: an atlas of genetic architecture promises directed maize improvement |
title_short | CUBIC: an atlas of genetic architecture promises directed maize improvement |
title_sort | cubic: an atlas of genetic architecture promises directed maize improvement |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979394/ https://www.ncbi.nlm.nih.gov/pubmed/31980033 http://dx.doi.org/10.1186/s13059-020-1930-x |
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