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      Mapping quantitative trait loci for biomass yield and yield-related traits in lowland switchgrass ( Panicum virgatum L.) multiple populations

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          Abstract

          Switchgrass can be used as an alternative source for bioenergy production. Many breeding programs focus on the genetic improvement of switchgrass for increasing biomass yield. Quantitative trait loci (QTL) mapping can help to discover marker-trait associations and accelerate the breeding process through marker-assisted selection. To identify significant QTL, this study mapped 7 hybrid populations and one combined of 2 hybrid populations (30–96 F1s) derived from Alamo and Kanlow genotypes. The populations were evaluated for biomass yield, plant height, and crown size in a simulated-sward plot with 2 replications at 2 locations in Tennessee from 2019 to 2021. The populations showed significant genetic variation for the evaluated traits and exhibited transgressive segregation. The 17,251 single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS) were used to construct a linkage map using a fast algorithm for multiple outbred families. The linkage map spanned 1,941 cM with an average interval of 0.11 cM between SNPs. The QTL analysis was performed on evaluated traits for each and across environments (year and location) that identified 5 QTL for biomass yield (logarithm of the odds, LOD 3.12–4.34), 4 QTL for plant height (LOD 3.01–5.64), and 7 QTL for crown size (LOD 3.0–4.46) ( P ≤ 0.05). The major QTL for biomass yield, plant height, and crown size resided on chromosomes 8N, 6N, and 8K explained phenotypic variations of 5.6, 5.1, and 6.6%, respectively. SNPs linked to QTL could be incorporated into marker-assisted breeding to maximize the selection gain in switchgrass breeding.

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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 statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

            Heng Li (2011)
            Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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              A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

              Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                May 2023
                22 March 2023
                22 March 2023
                : 13
                : 5
                : jkad061
                Affiliations
                Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA
                United States Department of Agriculture (USDA) Agricultural Research Service (ARS), Western Regional Research Center , 800 Buchanan Street, Albany, CA 94710, USA
                Plant Systems-Production, USDA National Institute of Food and Agriculture (NIFA) , Beacon Complex, USA
                Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA
                United States Department of Agriculture (USDA) Agricultural Research Service (ARS), Western Regional Research Center , 800 Buchanan Street, Albany, CA 94710, USA
                Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA
                USDA ARS, Crop Improvement and Protection Research Unit , 1636 E Alisal Street, Salinas, CA 93905, USA
                Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA
                Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA
                Author notes
                Corresponding author: Department of Plant Sciences, University of Tennessee , 112 Plant Biotechnology Building, Knoxville, TN 37996-4500, USA. Email: sshres18@ 123456utk.edu

                Conflicts of interest The author(s) declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-3044-2286
                https://orcid.org/0000-0002-7881-750X
                Article
                jkad061
                10.1093/g3journal/jkad061
                10151402
                36947434
                2c903b0f-5959-4c0c-9fd7-16a14bc50cdc
                © The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.

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

                History
                : 15 November 2022
                : 09 March 2023
                : 03 April 2023
                Page count
                Pages: 10
                Categories
                Plant Genetics and Genomics
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                bioenergy,biomass,environment,genome,hybrid,phenotype,plant genetics and genomics
                Genetics
                bioenergy, biomass, environment, genome, hybrid, phenotype, plant genetics and genomics

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