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      Meta-analysis of QTLs associated with popping traits in maize ( Zea mays L.)

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          Abstract

          The rising demand for popcorn necessitates improving the popping quality with higher yield of popcorn cultivars. Towards this direction several Quantitative Traits Loci (QTLs) for popping traits have been identified. However, identification of accurate and consistent QTLs across different genetic backgrounds and environments is necessary to effectively utilize the identified QTLs in marker-assisted breeding. In the current study, 99 QTLs related to popping traits reported in 8 different studies were assembled and projected on the reference map "Genetic 2005" using BioMercator v4.2 to identify metaQTLs with consistent QTLs. Total ten metaQTLs were identified on chromosome 1 (7 metaQTLs) and 6 (3 metaQTLs) with physical distance ranging between 0.43 and 12.75 Mb, respectively. Four identified metaQTLs, viz., mQTL1_1, mQTL1_5, mQTL1_7 and mQTL6_2 harboured 5–8 QTL clusters with moderately high R 2 value. The clustered QTLs were from two or more experiments. Based on the expression pattern in endosperm and pericarp tissues, a total of 229 genes were selected. Nineteen of these genes are involved in carbohydrate metabolism. Of the 19 genes specifically involved in carbohydrate metabolism, 11 of them were in these regions, implying the importance of these clustered QTLs. MetaQTL1_1 at bin location 1.01 coincided with the reported QTLs related to various agronomic traits like stalk diameter, tassel length, leaf area and plant height. The identified metaQTLs can be further explored for fine mapping and candidate gene identification, which can be validated by loss or gain of function. Identified metaQTLs can be used for introgression of popping traits towards enhancing the popping ability.

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          Most cited references26

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          Quantitative trait loci: a meta-analysis.

          This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few "actual" QTL locations. An application of the method is given using data from the maize database available online at http://www. agron.missouri.edu/.
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            BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms

            Summary: Compilation of genetic maps combined to quantitative trait loci (QTL) meta-analysis has proven to be a powerful approach contributing to the identification of candidate genes underlying quantitative traits. BioMercator was the first software offering a complete set of algorithms and visualization tool covering all steps required to perform QTL meta-analysis. Despite several limitations, the software is still widely used. We developed a new version proposing additional up to date methods and improving graphical representation and exploration of large datasets. Availability and implementation: BioMercator V3 is implemented in JAVA and freely available (http://moulon.inra.fr/biomercator) Contact: joets@moulon.inra.fr
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              MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments

              Background Integration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection). Results In this article, we present a new computational and statistical package, called MetaQTL, for carrying out whole-genome meta-analysis of QTL mapping experiments. Contrary to existing methods, MetaQTL offers a complete statistical process to establish a consensus model for both the marker and the QTL positions on the whole genome. First, MetaQTL implements a new statistical approach to merge multiple distinct genetic maps into a single consensus map which is optimal in terms of weighted least squares and can be used to investigate recombination rate heterogeneity between studies. Secondly, assuming that QTL can be projected on the consensus map, MetaQTL offers a new clustering approach based on a Gaussian mixture model to decide how many QTL underly the distribution of the observed QTL. Conclusion We demonstrate using simulations that the usual model choice criteria from mixture model literature perform relatively well in this context. As expected, simulations also show that this new clustering algorithm leads to a reduction in the length of the confidence interval of QTL location provided that across studies there are enough observed QTL for each underlying true QTL location. The usefulness of our approach is illustrated on published QTL detection results of flowering time in maize. Finally, MetaQTL is freely available at .
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                Author and article information

                Contributors
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysis
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 August 2021
                2021
                : 16
                : 8
                : e0256389
                Affiliations
                [1 ] Department of Plant Breeding & Genetics, Punjab Agricultural University, Ludhiana, India
                [2 ] ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, India
                [3 ] Division of Biochemistry, Indian Agricultural Research Institute, Pusa, New Delhi, India
                North Dakota State University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6139-7943
                Article
                PONE-D-20-37524
                10.1371/journal.pone.0256389
                8376040
                34411180
                301f3baa-6a5b-49a1-9770-5de617ba21e0
                © 2021 Kaur et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 December 2020
                : 5 August 2021
                Page count
                Figures: 3, Tables: 3, Pages: 14
                Funding
                The authors received no specific funding for this work. However, the work was carried out under general financial assistance from the Indian Council of Agricultural Research, New Delhi (India) to support research activities key in carrying out this work.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Quantitative Trait Loci
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Maize
                Research and Analysis Methods
                Model Organisms
                Maize
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Maize
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Plant and Algal Models
                Maize
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Fruit and Seed Anatomy
                Pericarp
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Gene Mapping
                Research and Analysis Methods
                Molecular Biology Techniques
                Gene Mapping
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Fruit and Seed Anatomy
                Endosperm
                Biology and life sciences
                Biochemistry
                Proteins
                DNA-binding proteins
                Transcription Factors
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Transcription Factors
                Biology and Life Sciences
                Biochemistry
                Proteins
                Regulatory Proteins
                Transcription Factors
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Metaanalysis
                Biology and Life Sciences
                Genetics
                Gene Expression
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

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