Hui Liu1, Daniel Mullan2, Chi Zhang3, Shancen Zhao3, Xin Li4, Aimin Zhang4, Zhanyuan Lu5, Yong Wang6, Guijun Yan7. 1. School of Plant Biology, Faculty of Science and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia. 2. InterGrain Pty Ltd, 19 Ambitious Link, Bibra Lake, WA, 6163, Australia. 3. Beijing Genomics Institute, Shenzhen, 518053, China. 4. The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chicness Academy of Sciences, Beijing, 100101, China. 5. Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, No. 22, Yuquan Qu Zhaojun Lu, Hohhot, Inner Mongolia Autonomous Region, China. 6. Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China. 7. School of Plant Biology, Faculty of Science and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6009, Australia. guijun.yan@uwa.edu.au.
Abstract
MAIN CONCLUSION: Meta-QTL (MQTL) analysis was done for yield-related traits in wheat. Candidate genes were identified within the refined MQTL and further validated by genotype-phenotype association analysis. Extensive studies have been undertaken on quantitative trait locus/loci (QTL) for wheat yield and its component traits. This study conducted a meta-analysis of 381 QTL related to wheat yield under various environments, including irrigated, drought- and/or heat-stressed conditions. Markers flanking meta-QTL (MQTL) were mapped on the wheat reference genome for their physical positions. Putative candidate genes were examined for MQTL with a physical interval of less than 20 Mbp. A total of 86 MQTL were identified as responsible for yield, of which 34 were for irrigated environments, 39 for drought-stressed environments, 36 for heat-stressed environments, and 23 for both drought- and heat-stressed environments. The high-confidence genes within the physical positions of the MQTL flanking markers were screened in the reference genome RefSeq V1.0, which identified 210 putative candidate genes. The phenotypic data for 14 contrasting genotypes with either high or low yield performance-according to the Australian National Variety Trials-were associated with their genotypic data obtained through ddRAD sequencing, which validated 18 genes or gene clusters associated with MQTL that had important roles for wheat yield. The detected and refined MQTL and candidate genes will be useful for marker-assisted selection of high yield in wheat breeding.
MAIN CONCLUSION: Meta-QTL (MQTL) analysis was done for yield-related traits in wheat. Candidate genes were identified within the refined MQTL and further validated by genotype-phenotype association analysis. Extensive studies have been undertaken on quantitative trait locus/loci (QTL) for wheat yield and its component traits. This study conducted a meta-analysis of 381 QTL related to wheat yield under various environments, including irrigated, drought- and/or heat-stressed conditions. Markers flanking meta-QTL (MQTL) were mapped on the wheat reference genome for their physical positions. Putative candidate genes were examined for MQTL with a physical interval of less than 20 Mbp. A total of 86 MQTL were identified as responsible for yield, of which 34 were for irrigated environments, 39 for drought-stressed environments, 36 for heat-stressed environments, and 23 for both drought- and heat-stressed environments. The high-confidence genes within the physical positions of the MQTL flanking markers were screened in the reference genome RefSeq V1.0, which identified 210 putative candidate genes. The phenotypic data for 14 contrasting genotypes with either high or low yield performance-according to the Australian National Variety Trials-were associated with their genotypic data obtained through ddRAD sequencing, which validated 18 genes or gene clusters associated with MQTL that had important roles for wheat yield. The detected and refined MQTL and candidate genes will be useful for marker-assisted selection of high yield in wheat breeding.
Authors: C Anilkumar; Rameswar Prasad Sah; T P Muhammed Azharudheen; Sasmita Behera; Namita Singh; Nitish Ranjan Prakash; N C Sunitha; B N Devanna; B C Marndi; B C Patra; Sunil Kumar Nair Journal: Sci Rep Date: 2022-08-16 Impact factor: 4.996