Whole-genome mining of abiotic stress gene loci in rice
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ORIGINAL ARTICLE
Whole‑genome mining of abiotic stress gene loci in rice Luomiao Yang1 · Lei Lei1 · HuaLong Liu1 · Jingguo Wang1 · Hongliang Zheng1 · Detang Zou1 Received: 27 January 2020 / Accepted: 1 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Main conclusion We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained. Abstract Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) metaQTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between indica and japonica rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice. Keywords Rice · Abiotic stress · QTL · Meta-analysis · GWAS Abbreviations QTL Quantitative trait loci GWAS Genome-wide association analysis AIC Akaike’s Information Criterion CI Confidence interval Communicated by Anastasios Melis. Luomiao Yang, Lei Lei have equally contributed to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00425-020-03488-x) contains supplementary material, which is available to authorized users.
CT Cold tolerance DT Drought tolerance IT Heavy metal ion tolerance MAS Marker-assisted selection M-CTQTL Meta-quantitative trait loci for cold tolerance M-DTQTL Meta-quantitative trait loci for drought tolerance MM-QTL Multiple-tolerance meta-QTL M-ITQTL Meta-quantitative trait loci for ion tolerance M-STQTL Meta-quantitative trait loci for salt tolerance MQTL Meta-quantitati
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