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      Genetic Diversity of Global Faba Bean Germplasm Resources Based on the 130K TNGS Genotyping Platform

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      Agronomy
      MDPI AG

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          Abstract

          Novel germplasm resources are the key to crop breeding, with their genetic diversity and population structure analysis being highly significant for future faba bean breeding. We genotyped 410 global faba bean accessions using the 130K targeted next-generation sequencing (TNGS) genotyping platform, resulting in a total of 38,111 high-quality SNP loci by high-standard filtering. We found the polymorphism information content (PIC) and Nei’s gene diversity were 0.0905–0.3750 and 0.0950–0.5000, with averages of 0.2471 and 0.3035, respectively. After evaluating the genetic diversity of 410 accessions using Nei’s gene diversity and PIC, on the basis of their geographical origin (continent) and structure-analysis-inferred subpopulations, we found that the faba bean accessions from Asia (except China) and Europe had rich genetic diversity, while those from the winter sowing area of China were low. The 410 faba bean accessions were divided into four subpopulations according to population structure analysis and clustering analysis based on Nei’s (1972) genetic distance using the neighbor-joining (NJ) method. However, the same subpopulation contained materials from different geographical origins, thereby indicating that the gene flow or introgression occurred among the accessions. Results from NJ clustering based on shared allele genetic distance indicated that the 410 accessions were divided into three groups according to their dissemination routes. The genetic diversity analysis results demonstrated that the genetic relationships among the faba bean groups with similar ecological environments and geographic origins in neighboring regions or countries were closer and frequently found within the same group, while genetic variation among individuals was the main source of their total genetic variation.

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          Fast model-based estimation of ancestry in unrelated individuals.

          Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
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            MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

            Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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              genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

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                Author and article information

                Contributors
                Journal
                ABSGGL
                Agronomy
                Agronomy
                MDPI AG
                2073-4395
                March 2023
                March 10 2023
                : 13
                : 3
                : 811
                Article
                10.3390/agronomy13030811
                9303a209-e2a3-4405-b730-9f331e642183
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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