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数量性状基因座定位：对控制小麦在高温和干旱胁迫综合作用下产量构成性状的基因组区域的见解。

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat.

作者信息

Manjunath Karthik Kumar, Krishna Hari, Devate Narayana Bhat, Sunilkumar V P, Patil Sahana Police, Chauhan Divya, Singh Shweta, Kumar Sudhir, Jain Neelu, Singh Gyanendra Pratap, Singh Pradeep Kumar

机构信息

Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India.

Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi, India.

出版信息

Front Genet. 2024 Jan 10;14:1282240. doi: 10.3389/fgene.2023.1282240. eCollection 2023.

DOI:10.3389/fgene.2023.1282240
PMID:38269367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10805833/
Abstract

Drought and heat frequently co-occur during crop growth leading to devastating yield loss. The knowledge of the genetic loci governing component traits of yield under combined drought and heat stress is essential for enhancing the climate resilience. The present study employed a mapping population of 180 recombinant inbred lines (RILs) derived from a cross between GW322 and KAUZ to identify quantitative trait loci (QTLs) governing the component traits of yield under heat and combined stress conditions. Phenotypic evaluation was conducted across two consecutive crop seasons (2021-2022 and 2022-2023) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at the Indian Council of Agricultural Research Institute-Indian Agricultural Research Institute (ICAR-IARI), New Delhi. Various physiological and agronomic traits of importance were measured. Genotyping was carried out with 35K SNP Axiom breeder's genotyping array. The linkage map spanned a length of 6769.45 cM, ranging from 2.28 cM/marker in 1A to 14.21 cM/marker in 5D. A total of 35 QTLs were identified across 14 chromosomes with 6B containing the highest (seven) number of QTLs. Out of 35 QTLs, 16 were major QTLs explaining the phenotypic variance greater than 10%. The study identified eight stable QTLs along with two hotspots on chromosomes 6B and 5B. Five QTLs associated with traits thousand-grain weight (TGW), normalized difference vegetation index (NDVI), and plant height (PH) were successfully validated. Candidate genes encoding antioxidant enzymes, transcription factors, and growth-related proteins were identified in the QTL regions. expression analysis highlighted higher expression of transcripts TraesCS2D02G021000.1, TraesCS2D02G031000, TraesCS6A02G247900, and TraesCS6B02G421700 under stress conditions. These findings contribute to a deeper understanding of the genetic architecture underlying combined heat and drought tolerance in wheat, providing valuable insights for wheat improvement strategies under changing climatic conditions.

摘要

干旱和高温在作物生长期间经常同时出现，导致产量严重损失。了解在干旱和高温联合胁迫下控制产量构成性状的基因位点对于提高气候适应性至关重要。本研究利用由GW322和KAUZ杂交产生的180个重组自交系（RIL）构建的作图群体，来鉴定在高温和联合胁迫条件下控制产量构成性状的数量性状位点（QTL）。在印度农业研究理事会-印度农业研究所（ICAR-IARI）新德里分所，于两个连续作物季节（2021 - 2022年和2022 - 2023年）的晚播灌溉（LSIR）和晚播限水灌溉（LSRI）条件下进行了表型评估。测量了各种重要的生理和农艺性状。使用35K SNP Axiom育种家基因分型阵列进行基因分型。连锁图谱长度为6769.45 cM，范围从1A染色体上的2.28 cM/标记到5D染色体上的14.21 cM/标记。在14条染色体上共鉴定出35个QTL，其中6B染色体上的QTL数量最多（7个）。在35个QTL中，有16个是主效QTL，解释的表型变异大于10%。该研究在6B和5B染色体上鉴定出8个稳定的QTL以及两个热点区域。成功验证了与千粒重（TGW）、归一化植被指数（NDVI）和株高（PH）性状相关的5个QTL。在QTL区域鉴定出了编码抗氧化酶、转录因子和生长相关蛋白的候选基因。表达分析突出了转录本TraesCS2D02G021000.1、TraesCS2D02G031000、TraesCS6A02G247900和TraesCS6B02G421700在胁迫条件下的较高表达。这些发现有助于更深入地了解小麦中高温和干旱联合耐受性的遗传结构，为气候变化条件下的小麦改良策略提供了有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/56a0257fa4bb/fgene-14-1282240-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/bae7a38500b0/fgene-14-1282240-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/f044c8c0219b/fgene-14-1282240-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/83a24112ea60/fgene-14-1282240-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/48671d3253fc/fgene-14-1282240-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/03ba28da0d6c/fgene-14-1282240-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/a7f2ec6afbbd/fgene-14-1282240-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/41a68e0f3cf2/fgene-14-1282240-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/56a0257fa4bb/fgene-14-1282240-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/bae7a38500b0/fgene-14-1282240-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/f044c8c0219b/fgene-14-1282240-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/83a24112ea60/fgene-14-1282240-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/48671d3253fc/fgene-14-1282240-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/03ba28da0d6c/fgene-14-1282240-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/a7f2ec6afbbd/fgene-14-1282240-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/41a68e0f3cf2/fgene-14-1282240-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6951/10805833/56a0257fa4bb/fgene-14-1282240-g008.jpg

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