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      Genome-Wide Association Studies Provide Insights into the Genetic Determination of Flower and Leaf Traits of Actinidia eriantha


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          Kiwifruit ( Actinidia eriantha) is a dioecious vine, and the pollen of its male cultivars has a direct effect on the quality of its fruits. In this study, to facilitate molecular breeding and gene identification, we performed genome-wide association studies (GWAS) on 11 traits of flower and leaf. A total of 946,337 highly consistent SNP markers were obtained in the whole genome. Phylogenetic tree analysis and population structure analysis showed that the 143 germplasms can be divided into two groups. The linkage disequilibrium analysis showed that A. eriantha have a relatively fast attenuation rate, and that the average attenuation distance of LD was 0.1–0.3 Kb. The MLM (QK) model was determined as best for correlation analysis, and eight and three SNPs associated with flower- and leaf-related traits were identified, respectively, at 0.01 significance level. However, SNP markers associated with stamen number per flower, pollen viability, total chlorophyll content, and total flavonoid content were not identified at the 0.01 significant level, although it is worth noting that one, one, five, and two SNPs were identified to be associated with these traits at the 0.05 significant level. This study provides insights into the complex flower- and leaf-related biology, and identifies genes controlling important traits in A. eriantha through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in kiwifruits.

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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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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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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.

                Author and article information

                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                20 August 2021
                : 12
                : 730890
                College of Agronomy, Jiangxi Agricultural University/Kiwifruit institute of Jiangxi Agricultural University , Nanchang, China
                Author notes

                Edited by: Ahmad Arzani, Isfahan University of Technology, Iran

                Reviewed by: Mohammad Mahdi Majidi, Isfahan University of Technology, Iran; Bing Zhang, Yangzhou University, China

                *Correspondence: Chunhui Huang lindahch@ 123456163.com

                This article was submitted to Plant Biotechnology, a section of the journal Frontiers in Plant Science

                †These authors have contributed equally to this work and share first authorship

                Copyright © 2021 Liao, Zhong, Jiang, Tao, Jia, Qu, Huang, Liu and Xu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 25 June 2021
                : 23 July 2021
                Page count
                Figures: 6, Tables: 4, Equations: 0, References: 39, Pages: 11, Words: 5882
                Funded by: Foundation for Innovative Research Groups of the National Natural Science Foundation of China 10.13039/501100012659
                Plant Science
                Original Research

                Plant science & Botany
                actinidia eriantha,flower,leaf,male vines,snps,gwas
                Plant science & Botany
                actinidia eriantha, flower, leaf, male vines, snps, gwas


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