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)...
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Autor*in:	Liu, Hai-Jun [verfasserIn] 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

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Population development Genome-wide association mapping Cross-omics Functional genomics

Anmerkung:	© The Author(s). 2020

Übergeordnetes Werk:	Enthalten in: Genome biology - London : BioMed Central, 2000, 21(2020), 1 vom: 24. Jan.
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:24 ; month:01

Links:	Volltext

DOI / URN:	10.1186/s13059-020-1930-x

Katalog-ID:	SPR030036968

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520			\|a 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.
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allfields	10.1186/s13059-020-1930-x doi (DE-627)SPR030036968 (SPR)s13059-020-1930-x-e DE-627 ger DE-627 rakwb eng Liu, Hai-Jun verfasserin aut CUBIC: an atlas of genetic architecture promises directed maize improvement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2020 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. Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213 Wang, Xiaqing aut Xiao, Yingjie aut Luo, Jingyun aut Qiao, Feng aut Yang, Wenyu aut Zhang, Ruyang aut Meng, Yijiang aut Sun, Jiamin aut Yan, Shijuan aut Peng, Yong aut Niu, Luyao aut Jian, Liumei aut Song, Wei aut Yan, Jiali aut Li, Chunhui aut Zhao, Yanxin aut Liu, Ya aut Warburton, Marilyn L. aut Zhao, Jiuran aut Yan, Jianbing (orcid)0000-0001-8650-7811 aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 24. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:24 month:01 https://dx.doi.org/10.1186/s13059-020-1930-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 24 01
spelling	10.1186/s13059-020-1930-x doi (DE-627)SPR030036968 (SPR)s13059-020-1930-x-e DE-627 ger DE-627 rakwb eng Liu, Hai-Jun verfasserin aut CUBIC: an atlas of genetic architecture promises directed maize improvement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2020 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. Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213 Wang, Xiaqing aut Xiao, Yingjie aut Luo, Jingyun aut Qiao, Feng aut Yang, Wenyu aut Zhang, Ruyang aut Meng, Yijiang aut Sun, Jiamin aut Yan, Shijuan aut Peng, Yong aut Niu, Luyao aut Jian, Liumei aut Song, Wei aut Yan, Jiali aut Li, Chunhui aut Zhao, Yanxin aut Liu, Ya aut Warburton, Marilyn L. aut Zhao, Jiuran aut Yan, Jianbing (orcid)0000-0001-8650-7811 aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 24. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:24 month:01 https://dx.doi.org/10.1186/s13059-020-1930-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 24 01
allfields_unstemmed	10.1186/s13059-020-1930-x doi (DE-627)SPR030036968 (SPR)s13059-020-1930-x-e DE-627 ger DE-627 rakwb eng Liu, Hai-Jun verfasserin aut CUBIC: an atlas of genetic architecture promises directed maize improvement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2020 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. Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213 Wang, Xiaqing aut Xiao, Yingjie aut Luo, Jingyun aut Qiao, Feng aut Yang, Wenyu aut Zhang, Ruyang aut Meng, Yijiang aut Sun, Jiamin aut Yan, Shijuan aut Peng, Yong aut Niu, Luyao aut Jian, Liumei aut Song, Wei aut Yan, Jiali aut Li, Chunhui aut Zhao, Yanxin aut Liu, Ya aut Warburton, Marilyn L. aut Zhao, Jiuran aut Yan, Jianbing (orcid)0000-0001-8650-7811 aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 24. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:24 month:01 https://dx.doi.org/10.1186/s13059-020-1930-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 24 01
allfieldsGer	10.1186/s13059-020-1930-x doi (DE-627)SPR030036968 (SPR)s13059-020-1930-x-e DE-627 ger DE-627 rakwb eng Liu, Hai-Jun verfasserin aut CUBIC: an atlas of genetic architecture promises directed maize improvement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2020 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. Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213 Wang, Xiaqing aut Xiao, Yingjie aut Luo, Jingyun aut Qiao, Feng aut Yang, Wenyu aut Zhang, Ruyang aut Meng, Yijiang aut Sun, Jiamin aut Yan, Shijuan aut Peng, Yong aut Niu, Luyao aut Jian, Liumei aut Song, Wei aut Yan, Jiali aut Li, Chunhui aut Zhao, Yanxin aut Liu, Ya aut Warburton, Marilyn L. aut Zhao, Jiuran aut Yan, Jianbing (orcid)0000-0001-8650-7811 aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 24. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:24 month:01 https://dx.doi.org/10.1186/s13059-020-1930-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 24 01
allfieldsSound	10.1186/s13059-020-1930-x doi (DE-627)SPR030036968 (SPR)s13059-020-1930-x-e DE-627 ger DE-627 rakwb eng Liu, Hai-Jun verfasserin aut CUBIC: an atlas of genetic architecture promises directed maize improvement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2020 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. Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213 Wang, Xiaqing aut Xiao, Yingjie aut Luo, Jingyun aut Qiao, Feng aut Yang, Wenyu aut Zhang, Ruyang aut Meng, Yijiang aut Sun, Jiamin aut Yan, Shijuan aut Peng, Yong aut Niu, Luyao aut Jian, Liumei aut Song, Wei aut Yan, Jiali aut Li, Chunhui aut Zhao, Yanxin aut Liu, Ya aut Warburton, Marilyn L. aut Zhao, Jiuran aut Yan, Jianbing (orcid)0000-0001-8650-7811 aut Enthalten in Genome biology London : BioMed Central, 2000 21(2020), 1 vom: 24. Jan. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:21 year:2020 number:1 day:24 month:01 https://dx.doi.org/10.1186/s13059-020-1930-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 1 24 01
language	English
source	Enthalten in Genome biology 21(2020), 1 vom: 24. Jan. volume:21 year:2020 number:1 day:24 month:01
sourceStr	Enthalten in Genome biology 21(2020), 1 vom: 24. Jan. volume:21 year:2020 number:1 day:24 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Population development Genome-wide association mapping Cross-omics Functional genomics
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authorswithroles_txt_mv	Liu, Hai-Jun @@aut@@ Wang, Xiaqing @@aut@@ Xiao, Yingjie @@aut@@ Luo, Jingyun @@aut@@ Qiao, Feng @@aut@@ Yang, Wenyu @@aut@@ Zhang, Ruyang @@aut@@ Meng, Yijiang @@aut@@ Sun, Jiamin @@aut@@ Yan, Shijuan @@aut@@ Peng, Yong @@aut@@ Niu, Luyao @@aut@@ Jian, Liumei @@aut@@ Song, Wei @@aut@@ Yan, Jiali @@aut@@ Li, Chunhui @@aut@@ Zhao, Yanxin @@aut@@ Liu, Ya @@aut@@ Warburton, Marilyn L. @@aut@@ Zhao, Jiuran @@aut@@ Yan, Jianbing @@aut@@
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author	Liu, Hai-Jun
spellingShingle	Liu, Hai-Jun misc Population development misc Genome-wide association mapping misc Cross-omics misc Functional genomics CUBIC: an atlas of genetic architecture promises directed maize improvement
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topic_title	CUBIC: an atlas of genetic architecture promises directed maize improvement Population development (dpeaa)DE-He213 Genome-wide association mapping (dpeaa)DE-He213 Cross-omics (dpeaa)DE-He213 Functional genomics (dpeaa)DE-He213
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title	CUBIC: an atlas of genetic architecture promises directed maize improvement
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author_browse	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
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title_sort	cubic: an atlas of genetic architecture promises directed maize improvement
title_auth	CUBIC: an atlas of genetic architecture promises directed maize improvement
abstract	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. © The Author(s). 2020
abstractGer	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. © The Author(s). 2020
abstract_unstemmed	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. © The Author(s). 2020
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title_short	CUBIC: an atlas of genetic architecture promises directed maize improvement
url	https://dx.doi.org/10.1186/s13059-020-1930-x
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author2	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
author2Str	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
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up_date	2024-07-03T13:29:32.206Z
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