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      QTL × environment interactions underlie ionome divergence in switchgrass

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          Abstract

          Ionomics measures elemental concentrations in biological organisms and provides a snapshot of physiology under different conditions. In this study, we evaluate genetic variation of the ionome in outbred, perennial switchgrass in three environments across the species’ native range, and explore patterns of genotype-by-environment interactions. We grew 725 clonally replicated genotypes of a large full sib family from a four-way linkage mapping population, created from deeply diverged upland and lowland switchgrass ecotypes, at three common gardens. Concentrations of 18 mineral elements were determined in whole post-anthesis tillers using ion coupled plasma mass spectrometry (ICP-MS). These measurements were used to identify quantitative trait loci (QTL) with and without QTL-by-environment interactions (QTLxE) using a multi-environment QTL mapping approach. We found that element concentrations varied significantly both within and between switchgrass ecotypes, and GxE was present at both the trait and QTL level. Concentrations of 14 of the 18 elements were under some genetic control, and 77 QTL were detected for these elements. Seventy-four percent of QTL colocalized multiple elements, half of QTL exhibited significant QTLxE, and roughly equal numbers of QTL had significant differences in magnitude and sign of their effects across environments. The switchgrass ionome is under moderate genetic control and by loci with highly variable effects across environments.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            A new look at the statistical model identification

            IEEE Transactions on Automatic Control, 19(6), 716-723
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              Phosphorus Uptake by Plants: From Soil to Cell

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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press
                2160-1836
                July 2021
                29 April 2021
                29 April 2021
                : 11
                : 7
                : jkab144
                Affiliations
                [1 ] Department of Integrative Biology, University of Texas at Austin , Austin, TX 78712, USA
                [2 ] Division of Plant Sciences, University of Missouri , Columbia, MO 65211, USA
                [3 ] Department of Plant Biology and DOE Great Lakes Bioenergy Research Center, Michigan State University , East Lansing, MI 48824, USA
                Author notes
                Corresponding authors: Department of Integrative Biology, University of Texas at Austin, 1 University Station C0930, Austin, TX 78712, USA. Emails: lz5943@ 123456utexas.edu ; alice.macqueen@ 123456utexas.edu ; tjuenger@ 123456austin.utexas.edu
                Author information
                https://orcid.org/0000-0001-8625-8042
                https://orcid.org/0000-0003-0825-6855
                Article
                jkab144
                10.1093/g3journal/jkab144
                8495926
                33914881
                25191496-e3dc-4549-bb12-7ae540bb14cd
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 January 2021
                : 23 April 2021
                Page count
                Pages: 13
                Funding
                Funded by: National Science Foundation Plant Genome Research Program ;
                Award ID: IOS-1444533
                Funded by: US Department of Energy, Office of Science, Office of Biological and Environmental Research Award;
                Award ID: DESC0014156
                Funded by: Great Lakes Bioenergy Research Center, DOI 10.13039/100015814;
                Funded by: U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research;
                Award ID: DE-SC0018409
                Award ID: DE-FC02-07ER64494
                Funded by: National Science Foundation Long-term Ecological Research Program;
                Award ID: DEB 1832042
                Funded by: Kellogg Biological Station and by Michigan State University AgBioResearch;
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960

                Genetics
                allelic effects,antagonistic pleiotropy,bioenergy,conditional neutrality,differential sensitivity,gxe,ionome,qtlxe,reaction norm,switchgrass

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