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      Genome-wide association study of cooking-caused grain expansion in rice ( Oryza sativa L.)

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          Abstract

          Cooking-caused rice grain expansion (CCRGE) is a critical trait for evaluating the cooking quality of rice. Previous quantitative trait locus (QTL) mapping studies on CCRGE have been limited to bi-parental populations, which restrict the exploration of natural variation and mapping resolution. To comprehensively and precisely dissect the genetic basis of CCRGE, we performed a genome-wide association study (GWAS) on three related indices: grain breadth expansion index (GBEI), grain length expansion index (GLEI), and grain length-breadth ratio expansion index (GREI), using 345 rice accessions grown in two years (environments) and 193,582 SNP markers. By analyzing each environment separately using seven different methods (3VmrMLM, mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO), we identified a total of 32, 19 and 27 reliable quantitative trait nucleotides (QTNs) associated with GBEI, GLEI and GREI, respectively. Furthermore, by jointly analyzing the two environments using 3VmrMLM, we discovered 19, 22 and 25 QTNs, as well as 9, 5 and 7 QTN-by-environment interaction (QEIs) associated with GBEI, GLEI and GREI, respectively. Notably, 12, 9 and 15 QTNs for GBEI, GLEI and GREI were found within the intervals of previously reported QTLs. In the vicinity of these QTNs or QEIs, based on analyses of mutation type, gene ontology classification, haplotype, and expression pattern, we identified five candidate genes that are related to starch synthesis and endosperm development. The five candidate genes, namely, LOC_Os04g53310 ( OsSSIIIb, near QTN qGREI-4.5s), LOC_Os05g02070 ( OsMT2b, near QTN qGLEI-5.1s), LOC_Os06g04200 ( wx, near QEI qGBEI-6.1i and QTNs qGREI-6.1s and qGLEI-6.1t), LOC_Os06g12450 ( OsSSIIa, near QTN qGLEI-6.2t), and LOC_Os08g09230 ( OsSSIIIa, near QTN qGBEI-8.1t), are predicted to be involved in the process of rice grain starch synthesis and to influence grain expansion after cooking. Our findings provide valuable insights and will facilitate genetic research and improvement of CCRGE.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)
          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.

            Pablo E. Cingolani, Adrian E. Platts, Le Lily Wang … (2012)
            We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5'UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5' and 3' UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
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              Genomic variation in 3,010 diverse accessions of Asian cultivated rice

              Wensheng Wang, Ramil Mauleon, Zhiqiang Hu … (2018)
              Here we analyse genetic variation, population structure and diversity among 3,010 diverse Asian cultivated rice (Oryza sativa L.) genomes from the 3,000 Rice Genomes Project. Our results are consistent with the five major groups previously recognized, but also suggest several unreported subpopulations that correlate with geographic location. We identified 29 million single nucleotide polymorphisms, 2.4 million small indels and over 90,000 structural variations that contribute to within- and between-population variation. Using pan-genome analyses, we identified more than 10,000 novel full-length protein-coding genes and a high number of presence–absence variations. The complex patterns of introgression observed in domestication genes are consistent with multiple independent rice domestication events. The public availability of data from the 3,000 Rice Genomes Project provides a resource for rice genomics research and breeding.
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Plant Sci
                Journal ID (iso-abbrev): Front Plant Sci
                Journal ID (publisher-id): Front. Plant Sci.
                Title: Frontiers in Plant Science
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1664-462X
                Publication date (Electronic): 30 August 2023
                Publication date Collection: 2023
                Volume: 14
                Electronic Location Identifier: 1250854
                Affiliations
                [1] 1 College of Life Sciences, Fujian Agriculture and Forestry University , Fuzhou, Fujian, China
                [2] 2 Fujian Provincial Key Laboratory of Crop Breeding by Design, Fujian Agriculture and Forestry University , Fuzhou, Fujian, China
                [3] 3 Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University , Fuzhou, Fujian, China
                [4] 4 Department of Botany, Mawlamyine University , Mawlamyine, Myanmar
                Author notes

                Edited by: Yuan-Ming Zhang, Huazhong Agricultural University, China

                Reviewed by: Xiangqian Zhao, Zhejiang Agriculture and Forestry University, China; Yang-Jun Wen, Nanjing Agricultural University, China; Li Mei, Huazhong Agricultural University, China

                *Correspondence: Weiren Wu, wuwr@ 123456fafu.edu.cn

                †These authors have contributed equally to this work

                Article
                DOI: 10.3389/fpls.2023.1250854
                PMC ID: 10498926
                PubMed ID: 37711286
                SO-VID: 24137e7b-87a9-4906-b6ec-0b6c2dd73387
                Copyright © Copyright © 2023 Zheng, Thi, Lin, Xie, Khine, Nyein, Lin, New, Aye and Wu
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 30 June 2023
                Date accepted : 14 August 2023
                Page count
                Figures: 8, Tables: 7, Equations: 0, References: 62, Pages: 14, Words: 6271
                Funding
                This work was supported by Natural Science Foundation of Fujian Province (CN) (2020I0009, 2022J01596), Cooperation Project on University Industry-Education-Research of Fujian Provincial Science and Technology Plan (CN) (2022N5011), Lancang-Mekong Cooperation Special Fund (2017-2018), International Sci-Tech Cooperation and Communication Program of Fujian Agriculture and Forestry University (KXGH17014).
                Categories
                Subject: Plant Science
                Subject: Original Research
                Custom metadata
                section-in-acceptance Technical Advances in Plant Science

                ScienceOpen disciplines: Plant science & Botany
                Keywords: rice,grain breadth expansion index (gbei),grain length expansion index (glei),grain length-breadth relative expansion index (grei),genome-wide association study (gwas)
                Data availability:
                ScienceOpen disciplines: Plant science & Botany
                Keywords: rice, grain breadth expansion index (gbei), grain length expansion index (glei), grain length-breadth relative expansion index (grei), genome-wide association study (gwas)

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