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      Linkage QTL Mapping and Genome-Wide Association Study on Resistance in Chickpea to Pythium ultimum

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          Abstract

          The soilborne oomycete plant pathogen Pythium ultimum causes seed rot and pre-emergence damping-off of chickpea ( Cicer arietinum L .). The pathogen has been controlled for several decades using the fungicide metalaxyl as seed treatment but has re-emerged as a severe problem with the detection of metalaxyl-resistant isolates of the pathogen from infested fields in the United States Pacific Northwest. The objective of this study was to identify genetic markers and candidate genes associated with resistance to P. ultimum in an interspecific recombinant inbred line population (CRIL-7) derived from a cross between C. reticulatum (PI 599072) x C. arietinum (FLIP 84-92C) and conduct genome-wide association studies (GWAS) for disease resistance using a chickpea diversity panel consisting of 184 accessions. CRIL-7 was examined using 1029 SNP markers spanning eight linkage groups. A major QTL, “qpsd4-1,” was detected on LG 4 that explained 41.8% of phenotypic variance, and a minor QTL, “qpsd8-1,” was detected on LG8 that explained 4.5% of phenotypic variance. Seven candidate genes were also detected using composite interval mapping including several genes previously associated with disease resistance in other crop species. A total of 302,902 single nucleotide polymorphic (SNP) markers were used to determine population structure and kinship of the diversity panel. Marker–trait associations were established by employing different combinations of principal components (PC) and kinships (K) in the FarmCPU model. Genome-wide association studies detected 11 significant SNPs and seven candidate genes associated with disease resistance. SNP Ca4_1765418, detected by GWAS on chromosome 4, was located within QTL qpsd4-1 that was revealed in the interspecific CRIL-7 population. The present study provides tools to enable MAS for resistance to P. ultimum and identified genomic domains and candidate genes involved in the resistance of chickpea to soilborne diseases.

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          Most cited references63

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.

            We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5'UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5' and 3' UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
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              GAPIT: genome association and prediction integrated tool.

              Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                15 August 2022
                2022
                : 13
                : 945787
                Affiliations
                [1] 1 Department of Plant Pathology , Washington State University , Pullman, WA, United States
                [2] 2 USDA-ARS , Grain Legume Genetics and Physiology Research Unit , Pullman, WA, United States
                [3] 3 Centre for Crop and Food Innovation, State Agricultural Biotechnology Centre, Murdoch University , Murdoch, WA, Australia
                Author notes

                Edited by: Waltram Ravelombola, Texas A&M University, United States

                Reviewed by: Tuanjie Zhao, Nanjing Agricultural University, China

                Eugenio López-Cortegano, University of Edinburgh, United Kingdom

                *Correspondence: George Vandemark, george.vandemark@ 123456usda.gov

                This article was submitted to Plant Genomics, a section of the journal Frontiers in Genetics

                Article
                945787
                10.3389/fgene.2022.945787
                9420999
                36046237
                66ec8cb3-700b-4277-b17c-b580317a3fbe
                Copyright © 2022 Agarwal, Chen, Varshney and Vandemark.

                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.

                History
                : 16 May 2022
                : 20 June 2022
                Categories
                Genetics
                Original Research

                Genetics
                chickpea,disease,pulses,pythium,resistance
                Genetics
                chickpea, disease, pulses, pythium, resistance

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