Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Abstract

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

“…These MQTLs were unevenly distributed on seven chromosomes, with the highest number of MQTLs on chromosome 2H (18) and the lowest on chromosome 6H (9), which was generally consistent with the distribution of the initial QTL on the chromosomes (Fig 3C). The distribution was inconsistent with the previously reported MQTLs for yield-related traits in barley, which were similar in form to the initial QTL [24].…”

“…Recently, QTL meta-analysis has been widely used for QTL integration of complex quantitative traits in different crops, such as ionome-related traits in Arabidopsis thaliana [ 14 ], grain water content, grain dehydration rate and yield-related traits in maize [ 15 , 16 ], grain weight and yield-related traits in rice [ 17 , 18 ], grain zinc and iron contents, flag leaf morphology, quality and yield-related traits in wheat [ 19 – 21 ]. In addition, QTL meta-analysis has also been reported for several traits in barley, such as three studies on abiotic stress tolerance traits [ 11 , 22 , 23 ], and one on yield and yield-related traits [ 24 ], in which a total of 31 MQTLs were identified, but limited by the number of QTL mapping populations and the initial QTL, the results have certain limitation and need further refinement.…”

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

“…Meta-analyses of QTLs associated with a variety of traits have been recently conducted in different crops such as wheat ( Kumar et al, 2021 ; Kumar et al, 2022 A. C. ; Kumar et al, 2023 S. ; Saini et al, 2021 ; Saini et al, 2022 ; Tanin et al, 2022 ), rice ( Sandhu et al, 2021 ; Kumari et al, 2023 ), barley ( Akbari et al, 2022 ), common bean ( Shafi et al, 2022 ), pigeon pea ( Halladakeri et al, 2023 ), including maize ( Kaur et al, 2021 ; Sheoran et al, 2022 ; Wang et al, 2022 ; Gupta et al, 2023 ; Karnatam et al, 2023 ), for diverse traits, including both yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) and quality traits ( Jin et al, 2013 ; Dong et al, 2015 ). However, there is currently no comprehensive study on the genomic regions influencing both grain quality and yield in maize.…”

Conventional and contemporary approaches for drought tolerance rice breeding: Progress and prospects

“…Several MQTL analysis studies have been carried out for drought tolerance in different field crops like maize (Semagn et al, 2013), wheat (Acuña-Galindo et al, 2015 and barley (Zhang, Shabala, et al, 2017). Recently, MQTL analysis studies have been reported in rice focusing on yield and other parameters under unstressed conditions (Anilkumar, Sah, Muhammed Azharudheen, et al, 2022;Khahani et al, 2020)…”

“…Several MQTL analysis studies have been carried out for drought tolerance in different field crops like maize (Semagn et al, 2013), wheat (Acuña‐Galindo et al, 2015) and barley (Zhang, Shabala, et al, 2017). Recently, MQTL analysis studies have been reported in rice focusing on yield and other parameters under unstressed conditions (Anilkumar, Sah, Muhammed Azharudheen, et al, 2022; Khahani et al, 2020), whereas Swamy et al (2011) and Trijatmiko et al (2014) made significant contribution in detecting MQTL for yield and related traits under water deficit conditions in rice. Selamat and Nadarajah (2021) complied the 653 QTL identified for drought‐related traits in rice across the globe and developed a consensus map to identify 70 MQTL for drought traits over all chromosomes of rice.…”