Accumulation of beneficial haplotypes in Huang-Huai-Hai wheat region and its application in molecular breeding

doi:10.1016/j.jia.2024.12.003

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Chengzhi Jiao¹^*, Mingxing Wen¹^*, Xin Jing², Vanika Garg³, Chuanqing Zhou², Liyang Chen², Fengfeng Xu², Chenyang Hao⁴, Jin Xiao¹, Haiyan Wang¹, Rajeev K. Varshney³, Xueyong Zhang⁴, Xiue Wang¹^#

¹State Key Laboratory of Crop Genetics and Germplasm Enhancement & Utilization/Zhongshan Biological Breeding Laboratory/CIC-MCP, Nanjing Agricultural University, Nanjing 210095, China

²Smartgenomics Technology Institute, Tianjin 301700, China

³Centre for Crop & Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch 6150, Australia

⁴The National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Highlights

1. The HHH wheat region showed superior yield and disease resistance, with beneficial haplotypes more frequently than in other regions.

2. The MFP-a gene may affect both grain development and powdery mildew resistance.

3. IBD analysis identified fixed genomic segments in HHHR, aiding breeding targets.

Abstract
References

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摘要

黄淮海麦区是我国种植面积最大、产量最高的小麦主产区。在我国70年的育种历史中，黄淮海麦区小麦育种成绩突出，育成一系列高产、稳产、抗病品种，但目前对该区域小麦育种进程中组学层面的研究还不够系统。本研究利用55K SNP芯片（Affymetrix® Axiom® Wheat55K）对387份来自不同麦区的小麦种质进行基因型扫描，并整合了476份种质660K SNP芯片（Affymetrix® Axiom® Wheat660K）基因型数据和145份种质的重测序基因型数据，系统地分析了黄淮麦区小麦品种的产量、抗白粉病等性状的表型以及优异等位基因的分布规律。结果发现，黄淮麦区小麦品种的产量相关性状和白粉病抗性显著高于其他麦区品种；通过全基因组关联分析（genome-wide association study，GWAS）鉴定到了与产量和抗白粉病相关的数量性状遗传位点（quantitative trait nucleotides，QTN），这些性状的优异单倍型在黄淮麦区的累计频率显著高于其他麦区。发现产量和抗性共定位的一个位点，其中一个茉莉酸合成MFP-a基因与小麦籽粒发育和抗白粉病性均显著关联。通过同源一致性分析（identity by descent，IBD）鉴定到了黄淮麦区育种正向选择、固定下来的基因组区段，可用来定向改良其他麦区的产量和抗病性状，以矮孟牛育种系谱为例，解析了重要育种骨干亲本形成的遗传学基础，为小麦分子育种提供了参考。

Abstract

The Huang-Huai-Hai wheat region (HHHR) is characterized by the largest cultivation area and yield among all the major wheat-producing regions in China. Over the past 70 years, significant advances in wheat breeding have been achieved in this region, resulting in high and stable yields as well as improved disease resistance. However, there is a notable deficiency in the systematic molecular-level analyses of wheat breeding advantages in HHHR. To bridge this gap, we used a Wheat 55K SNP array to evaluate 384 accessions from a core collection of wheat germplasms across China to systematically analyze the distribution patterns of beneficial haplotypes associated with traits related to yield and powdery mildew resistance specific to HHHR. Our findings indicate that varieties from HHHR demonstrate significantly superior performance in terms of yield-related traits and powdery mildew resistance compared to those from other wheat regions. Using genome-wide association studies (GWAS) analysis, we identified the QTNs associated with both grain yield and powdery mildew resistance. Importantly, beneficial haplotypes were found at significantly higher frequencies in the HHHR than in other wheat-growing regions. Based on these haplotypes, the MFP-a gene was identified as potentially regulating jasmonic acid synthesis while also playing a role in grain development and conferring powdery mildew resistance. Furthermore, identity by descent (IBD) analysis revealed specific conserved genomic segments that have become fixed through selective breeding practices in HHHR, which may serve as invaluable resources for the targeted enhancement of yield and disease resistance traits in other wheat-growing areas. Finally, using the Aimengniu breeding lineage as a case study, we elucidated the genetic basis underlying the key founder parental formations utilized in breeding programs. This study not only provides essential references and guidance for future molecular breeding initiatives in China but also has implications for enhancing wheat production worldwide.

Keywords: wheat breeding HHHR yield powdery mildew resistance GWAS IBD

Received: 28 October 2024 Online: 04 December 2024

Fund:

This project was supported by the Zhongshan Biological Breeding Laboratory, Jiangsu Province, China (ZSBBL-KY2023-02-2), the National Key Research and Development Program of China (2023YFF1000603), the Jiangsu Provincial Key Research and Development Program, China (BE2022346 and BE2023313), the Seed Industry Revitalization Project of Jiangsu Province, China (JBGS (2021) 006, JBGS (2021) 047), the Jiangsu Agricultural Technology System, China (JATS[2023]422), and the Joint Research of Wheat Variety Improvement of Anhui Province, China (2021-2025).

About author: #Correspondence Xiue Wang, E-mail: xiuew@njau.edu.cn *These authors contributed equally to this work.

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Chengzhi Jiao, Mingxing Wen, Xin Jing, Vanika Garg, Chuanqing Zhou, Liyang Chen, Fengfeng Xu, Chenyang Hao, Jin Xiao, Haiyan Wang, Rajeev K. Varshney, Xueyong Zhang, Xiue Wang. 2024. Accumulation of beneficial haplotypes in Huang-Huai-Hai wheat region and its application in molecular breeding. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2024.12.003

Beecher B S, Carter A H, See D R. 2012. Genetic mapping of new seed-expressed polyphenol oxidase genes in wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 124, 1463-1473.

Boden S A, Cavanagh C, Cullis B R, Ramm K, Greenwood J, Jean Finnegan E, Trevaskis B, Swain S M. 2015. Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat. Nature Plants, 1, 14016.

Browning S R, Browning B L. 2007. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Genetics, 81, 1084-1097.

Chen Y, Yan Y, Wu T T, Zhang G L, Yin H, Chen W, Wang S, Chang F, Gou J Y. 2020. Cloning of wheat keto-acyl thiolase 2B reveals a role of jasmonic acid in grain weight determination. Nature Communications, 11, 6266.

Danecek P, Auton A, Abecasis G, Albers C A, Banks E, DePristo M A, Handsaker R E, Lunter G, Marth G T, Sherry S T, McVean G, Durbin R; 1000 Genomes Project Analysis Group. 2011. The variant call format and VCFtools. Bioinformatics 27, 2156-2158.

Deng W, Casao M C, Wang P, Sato K, Hayes P M, Finnegan E J, Trevaskis B. 2015. Direct links between the vernalization response and other key traits of cereal crops. Nature Communications, 6, 5882.

Hao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zheng J, Liu H, Bi Z, Xu F, et al. 2020. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Molecular Plant, 13, 1733-1751.

He Z H, Rajaram S, Xin Z Y, Huang G Z. 2001. A History of Wheat Breeding in China. International Maize and Wheat Improvement Center, Mexico.

Hou J, Jiang Q, Hao C, Wang Y, Zhang H, Zhang X. 2014. Global selection on sucrose synthase haplotypes during a century of wheat breeding. Plant Physiology, 164, 1918-1929.

Hu T X, Zhang X Z, Khanal A, Wilson R, Leng G Y, Toman E M, Wang X H, Li Y, Zhao K G. 2024. Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods. Environmental Modelling & Software, 179, 1364-8152.

Huang H, Han Y, Song J, Zhang Z, Xiao H. 2016. Impacts of climate change on water requirements of winter wheat over 59 years in the Huang-Huai-Hai Plain. Soil and Water Research, 11, 11-19.

The International Wheat Genome Sequencing Consortium. 2018. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361, eaar7191.

Jiang Q, Hou J, Hao C, Wang L, Ge H, Dong Y, Zhang X. 2010. The wheat (T. aestivum) sucrose synthase 2 gene (TaSus2) active in endosperm development is associated with yield traits. Functional & Integrative Genomics, 11, 49-61.

Jiao C, Hao C, Li T, Bohra A, Wang L, Hou J, Liu H, Liu H, Zhao J, Wang Y, Liu Y, Wang Z, Jing X, Wang X, Varshney RK, Fu J, Zhang X. 2023. Fast integration and accumulation of beneficial breeding alleles through an AB–NAMIC strategy in wheat. Plant Communications, 4, 100549

Jiao Y, Zhao H, Ren L, Song W, Zeng B, Guo J, Wang B, Liu Z, Chen J, Li W, Zhang M, Xie S, Lai J. 2014. Corrigendum: Genome-wide genetic changes during modern breeding of maize. Nature Genetics, 46, 1039-1040.

Jin S B. 1983. Chinese Wheat Varieties and Their Pedigrees. Agricultural Publishing House, Beijing.

Kang H M, Sul J H, Service S K, Zaitlen N A, Kong S Y, Freimer N B, Sabatti C, Eskin E. 2010. Variance component model to account for sample structure in genome-wide association studies. Nature Genetics, 42, 348-354.

Li Z, Huang B, Rampling L, Wang J, Yu J, Morell M, Rahman S. 2004. Detailed comparison between the wheat chromosome group 7 short arms and the rice chromosome arms 6S and 8L with special reference to genes involved in starch biosynthesis. Functional & Integrative Genomics, 4, 231–240.

Liu E, Zhu S, Du M, Lyu H, Zeng S, Liu Q, Wu G, Jiang J, Dang X, Dong Z, Hong D. 2023. LAX1, functioning with MADS-box genes, determines normal palea development in rice. Gene, 883, 147635.

Liu J, Luo W, Qin N, Ding P, Zhang H, Yang C, Mu Y, Tang H, Liu Y, Li W, Jiang Q, Chen G, Wei Y, Zheng Y, Liu C, Lan X, Ma J. 2018. A 55 K SNP array-based genetic map and its utilization in QTL mapping for productive tiller number in common wheat. Theoretical and Applied Genetics, 131, 2439-2450.

Ma S, Wang M, Wu J, Guo W, Chen Y, Li G, Wang Y, Shi W, Xia G, Fu D, Kang Z, Ni F. 2021. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Molecular Plant, 14, 1965-1968.

Mao X, Cai T, Olyarchuk J G, Wei L. 2005. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics, 21, 3787-3793.

Ortiz R, Sayre K D, Govaerts B, Gupta R, Subbarao G V, Ban T, Hodson D, Dixon J M, Iván Ortiz-Monasterio J, Reynolds M. 2008. Climate change: Can wheat beat the heat? Agriculture, Ecosystems & Environment, 126, 46-58.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A R, Bender D, Maller J, Sklar P, de Bakker P I W, Daly M J, Sham P C. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81, 559-575.

Ru Z G, Feng F W, Li G. 2015. High-yield potential and effective ways of wheat in Yellow & Huai River Valley facultative winter wheat region. Scientia Agricultura Sinica, 48, 3388-3393. (in Chinese)

Shin J H, Blay S, McNeney B, Graham J. 2006. LDheatmap: An R Function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. Journal of Statistical Software, 16, 1-9.

Sun H W, Wang Y W, Wang L. 2024. Impact of climate change on wheat production in China. European Journal of Agronomy, 153, 127066.

Vilella A J, Blanco-Garcia A, Hutter S, Rozas J. 2005. VariScan: Analysis of evolutionary patterns from large-scale DNA sequence polymorphism data. Bioinformatics, 21, 2791-2793.

Vilella A J, Severin J, Ureta-Vidal A, Heng L, Durbin R, Birney E. 2009. EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates. Genome Research, 19, 327-335.

Wang K, Li M, Hakonarson H. 2010. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38, e164.

Yang J, Lee S H , Goddard M E, Visscher P M. 2011. GCTA: A tool for genome-wide complex trait analysis. The American Journal of Human Genetic, 88, 76-82.

Zhang B, Liu X, Xu W, Chang J, Li A, Mao X, Zhang X, Jing R. 2015. Novel function of a putative MOC1 ortholog associated with spikelet number per spike in common wheat. Scientific Reports, 5, 12211.

Zhang J, Dell B, Biddulph B, Drake-Brockman F, Walker E, Khan N, Wong D, Hayden M, Appels R. 2013. Wild-type alleles of Rht-B1 and Rht-D1 as independent determinants of thousand-grain weight and kernel number per spike in wheat. Molecular Breeding, 32, 771-783.

Zhao Y X, Tao H Y, He P, Yao X, Cheng T, Zhu Y, Cao W X, Tian Y C. 2023. Annual 30 m winter wheat yield mapping in the Huang-Huai-Hai plain using crop growth model and long-term satellite images. Computers and Electronics in Agriculture, 214, 0168-1699.

Zhu T, Wang L, Rimbert H, Rodriguez J C, Deal K R, De Oliveira R, Choulet F, Keeble-Gagnère G, Tibbits J, Rogers J, Eversole K, Appels R, Gu Y Q, Mascher M, Dvorak J, Luo M C. 2021. Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly. The Plant Journal, 107, 303-314.

Zhuang Q S. 2003. Chinese Wheat Improvement and Pedigree Analysis. Agricultural Press, Beijing. (in Chinese)

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