نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی تولید و ژنتیک گیاهی، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران
2 گروه زراعت و اصلاح نباتات، دانشکده علوم کشاورزی دانشگاه گیلان. رشت ، ایران.
3 پژوهشکده آبزی پروری آبهای داخلی، موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، بندرانزلی، ایران.
4 گروه بیوتکنولوژی، دانشکده کشاورزی، دانشگاه گیلان
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
Improving yield-related traits in major crops such as rice requires accurate identification of the genomic regions controlling these traits. Although many studies have reported QTLs for photosynthesis-related traits in rice, differences in mapping populations and experimental conditions have made it difficult to combine their results for practical applications. Meta-analysis provides an effective approach to integrate QTL data from different studies, identify consensus genomic regions (Meta-QTLs, MQTLs), and overcome the limitations of individual QTL mapping studies.
Material and methods
In this study, studies reporting QTLs associated with photosynthesis-related traits in rice were first collected and reviewed. Among the 27 identified studies, 17 studies comprising 129 eligible QTLs were finally selected for the meta-analysis. A consensus map was then constructed, and MQTL regions were identified in centiMorgans (cM) according to the software guidelines. Subsequently, genes and molecular markers located within or adjacent to the MQTL regions were identified. Finally, the functional pathways of the candidate genes were analyzed using relevant bioinformatics databases and tools.
Results
A total of 32 MQTLs were identified. Chromosomes 1 and 3 each contained five MQTLs, representing the highest number, whereas chromosome 9 contained only two MQTLs. No MQTLs were detected on chromosomes 7, 8, 10, or 11. Candidate genes within the MQTL regions were identified using online databases and bioinformatics tools. Overall, 131 gene IDs were found in 29 MQTLs. MQTL6.3 contained the largest number of candidate genes (59), while MQTL1.2, MQTL3.2, and MQTL4.3 each contained only one gene. No candidate genes were identified in MQTL12.3 or MQTL3.1. Several important genes, including OsCML19, OsSTA36, OsRDCP4, OsPP2C60, OsSEC18, OsbZIP54, and OsUCL18, were identified as promising candidates. Gene ontology and pathway analyses indicated that environmental stresses such as cold, heat, and salinity affect photosynthesis and chlorophyll content by activating biological processes related to protein folding, cell wall metabolism, hydrogen peroxide response, and root development. In addition, molecular functions associated with transcription factor binding, nitrate reductase activity, and molybdenum cofactor binding were significantly enriched.
Conclusion
Although QTL mapping in individual studies is challenged by environmental effects, differences among parental genotypes, genetic background, and chromosome map characteristics, the QTL meta-analysis conducted in the present study provided a comprehensive chromosomal map, enabling the simultaneous analysis of the collected QTLs and the identification of significant meta-QTLs (MQTLs). Investigation of the genomic regions underlying these MQTLs led to the identification of key genes involved in stress responses, stress-responsive pathways, and their encoded protein products, ultimately revealing important biological and functional pathways at the cellular and molecular levels. These findings may improve our understanding of the molecular mechanisms underlying photosynthesis and provide a basis for more effective crop breeding. However, the results of QTL meta-analysis depend on the quality and diversity of the primary studies, including population type, marker density, statistical methods, environmental conditions, and developmental stage. In the present study, due to the limited number of studies addressing photosynthesis-related traits, all available data were included without accounting for variation in environmental conditions and developmental stages. Therefore, the identified MQTLs and candidate genes require validation in independent populations and environments through fine mapping, gene expression analysis, and molecular approaches.
کلیدواژهها [English]
)