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      The genetic architecture of the dynamic changes in grain moisture in maize

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          Summary

          Low grain moisture at harvest is crucial for safe production, transport and storage, but the genetic architecture of this trait in maize ( Zea mays) remains elusive. Here, we measured the dynamic changes in grain moisture content in an association‐mapping panel of 513 diverse maize inbred lines at five successive stages across five geographical environments. Genome‐wide association study (GWAS) revealed 71 quantitative trait loci (QTLs) that influence grain moisture in maize. Epistatic effects play vital roles in the variability in moisture levels, even outperforming main‐effect QTLs during the early dry‐down stages. Distinct QTL–environment interactions influence the spatio‐temporal variability of maize grain moisture, which is primarily triggered at specific times. By combining genetic population analysis, transcriptomic profiling and gene editing, we identified GRMZM5G805627 and GRMZM2G137211 as candidate genes underlying major QTLs for grain moisture in maize. Our results provide insights into the genetic architecture of dynamic changes in grain moisture, which should facilitate maize breeding.

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          TASSEL: software for association mapping of complex traits in diverse samples.

          P J Bradbury, Z. Zhang, D. E. Kroon … (2007)
          Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
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            Mixed linear model approach adapted for genome-wide association studies.

            Zhiwu ZHANG, Elhan Ersoz, Chao-Qiang Lai … (2010)
            Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.
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              Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

              Miao-Xin Li, Juilian M Y Yeung, Stacey S Cherny … (2011)
              Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M e) for the adjustment of multiple testing, but current methods of calculation for M e are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M e. Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M e, and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10−7 as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10−8 for current or merged commercial genotyping arrays, ~10−8 for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10−8 for the common SNPs only within genes. Electronic supplementary material The online version of this article (doi:10.1007/s00439-011-1118-2) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Yingjie Xiao:
                ORCID: https://orcid.org/0000-0001-7276-653X
                yxiao25@mail.hzau.edu.cn
                Jianbing Yan:
                ORCID: https://orcid.org/0000-0001-8650-7811
                yjianbing@mail.hzau.edu.cn
                Journal
                Journal ID (nlm-ta): Plant Biotechnol J
                Journal ID (iso-abbrev): Plant Biotechnol J
                Journal ID (doi): 10.1111/(ISSN)1467-7652
                Journal ID (publisher-id): PBI
                Title: Plant Biotechnology Journal
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 1467-7644
                ISSN (Electronic): 1467-7652
                Publication date (Electronic): 11 February 2021
                Publication date (Print): June 2021
                Volume: 19
                Issue: 6 ( doiID: 10.1111/pbi.v19.6 )
                Pages: 1195-1205
                Affiliations
                [ 1 ] National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan China
                [ 2 ] Instutute of Agricultural Biotechnology Jilin Academy of Agricultural Sciences Changchun China
                Author notes
                [*] [* ] Correspondence (Tel +86 27 87280110; fax +86 27 87384670; email yjianbing@ 123456mail.hzau.edu.cn (J.Y.), Tel +86 27 87280169; fax +86 27 87384670; email yxiao25@ 123456mail.hzau.edu.cn (Y.X.))

                Author information
                https://orcid.org/0000-0001-7276-653X
                https://orcid.org/0000-0001-8650-7811
                Article
                Publisher ID: PBI13541
                DOI: 10.1111/pbi.13541
                PMC ID: 8196655
                PubMed ID: 33386670
                SO-VID: c8fcbdff-731d-46f0-b389-b69da12131bf
                Copyright © © 2021 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                Date revision received : 14 December 2020
                Date received : 23 October 2020
                Date accepted : 26 December 2020
                Page count
                Figures: 6, Tables: 0, Pages: 11, Words: 8998
                Funding
                Funded by: National Natural Science Foundation of China , open-funder-registry 10.13039/501100001809;
                Award ID: 31525017
                Award ID: 31961133002
                Categories
                Subject: Research Article
                Subject: Research Articles
                Custom metadata
                source-schema-version-number 2.0
                cover-date June 2021
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:12.06.2021

                ScienceOpen disciplines: Biotechnology
                Keywords: grain moisture,gwas,qtl,genetic basis,moisture plasticity
                Data availability:
                ScienceOpen disciplines: Biotechnology
                Keywords: grain moisture, gwas, qtl, genetic basis, moisture plasticity

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