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      Genome-wide association studies of mineral and phytic acid concentrations in pea ( Pisum sativum L.) to evaluate biofortification potential


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          Pea ( Pisum sativum L.) is an important cool season food legume for sustainable food production and human nutrition due to its nitrogen fixation capabilities and nutrient-dense seed. However, minimal breeding research has been conducted to improve the nutritional quality of the seed for biofortification, and most genomic-assisted breeding studies utilize small populations with few single nucleotide polymorphisms (SNPs). Genomic resources for pea have lagged behind those of other grain crops, but the recent release of the Pea Single Plant Plus Collection (PSPPC) and the pea reference genome provide new tools to study nutritional traits for biofortification. Calcium, phosphorus, potassium, iron, zinc, and phytic acid concentrations were measured in a study population of 299 different accessions grown under greenhouse conditions. Broad phenotypic variation was detected for all parameters except phytic acid. Calcium exhibited moderate broad-sense heritability (H 2) estimates, at 50%, while all other minerals exhibited low heritability. Of the accessions used, 267 were previously genotyped in the PSPPC release by the USDA, and we mapped the genotyping data to the pea reference genome for the first time. This study generated 54,344 high-quality SNPs used to investigate the population structure of the PSPPC and perform a genome-wide association study to identify genomic loci associated with mineral concentrations in mature pea seed. Overall, we were able to identify multiple significant SNPs and candidate genes for iron, phosphorus, and zinc. These results can be used for genetic improvement in pea for nutritional traits and biofortification, and the candidate genes provide insight into mineral metabolism.

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          Fast and accurate long-read alignment with Burrows–Wheeler transform

          Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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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.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                September 2021
                08 July 2021
                08 July 2021
                : 11
                : 9
                : jkab227
                Plant and Environmental Sciences, Clemson University , Clemson, SC 29634, USA
                Author notes
                Corresponding author: Email: dthavar@ 123456clemson.edu
                Author information
                © 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.

                : 05 May 2021
                : 27 June 2021
                Page count
                Pages: 10
                Funded by: Organic Agriculture Research and Extension Initiative;
                Award ID: 2018-51300-28431
                Award ID: 2018-02799
                Funded by: United States Department of Agriculture, DOI 10.13039/100000199;
                Funded by: National Institute of Food and Agriculture, DOI 10.13039/100005825;
                Funded by: USDA-ARS Pulse Health Initiative;

                pea,biofortification,genome-wide association study,iron,phosphorus,zinc,phytic acid
                pea, biofortification, genome-wide association study, iron, phosphorus, zinc, phytic acid


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