Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton

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Abstract

Background

Verticillium wilt (VW) and Fusarium wilt (FW), caused by the soil-borne fungi Verticillium dahliae and Fusarium oxysporum f. sp. vasinfectum, respectively, are two most destructive diseases in cotton production worldwide. Root-knot nematodes ( Meloidogyne incognita, RKN) and reniform nematodes ( Rotylenchulus reniformis, RN) cause the highest yield loss in the U.S. Planting disease resistant cultivars is the most cost effective control method. Numerous studies have reported mapping of quantitative trait loci (QTLs) for disease resistance in cotton; however, very few reliable QTLs were identified for use in genomic research and breeding.

Results

This study first performed a 4-year replicated test of a backcross inbred line (BIL) population for VW resistance, and 10 resistance QTLs were mapped based on a 2895 cM linkage map with 392 SSR markers. The 10 VW QTLs were then placed to a consensus linkage map with other 182 VW QTLs, 75 RKN QTLs, 27 FW QTLs, and 7 RN QTLs reported from 32 publications. A meta-analysis of QTLs identified 28 QTL clusters including 13, 8 and 3 QTL hotspots for resistance to VW, RKN and FW, respectively. The number of QTLs and QTL clusters on chromosomes especially in the A-subgenome was significantly correlated with the number of nucleotide-binding site (NBS) genes, and the distribution of QTLs between homeologous A- and D- subgenome chromosomes was also significantly correlated.

Conclusions

Ten VW resistance QTL identified in a 4-year replicated study have added useful information to the understanding of the genetic basis of VW resistance in cotton. Twenty-eight disease resistance QTL clusters and 24 hotspots identified from a total of 306 QTLs and linked SSR markers provide important information for marker-assisted selection and high resolution mapping of resistance QTLs and genes. The non-overlapping of most resistance QTL hotspots for different diseases indicates that their resistances are controlled by different genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1682-2) contains supplementary material, which is available to authorized users.

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Most cited references 67

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A modified algorithm for the improvement of composite interval mapping.

Huihui Li, Guoyou Ye, Jiankang Wang (2007)

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.

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BioMercator: integrating genetic maps and QTL towards discovery of candidate genes.

Anne Arcade, Aymeric Labourdette, Alain Charcosset … (2004)

Breeding programs face the challenge of integrating information from genomics and from quantitative trait loci (QTL) analysis in order to identify genomic sequences controlling the variation of important traits. Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. BioMercator has been developed to automate map compilation and QTL meta-analysis, and to visualize co-locations between genes and QTL through a graphical interface. Available upon request (http://moulon/~bioinfo/BioMercator/). Free of charge for academic use.

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Evolving disease resistance genes.

Blake Meyers, Shail Kaushik, Raja Nandety (2005)

Defenses against most specialized plant pathogens are often initiated by a plant disease resistance gene. Plant genomes encode several classes of genes that can function as resistance genes. Many of the mechanisms that drive the molecular evolution of these genes are now becoming clear. The processes that contribute to the diversity of R genes include tandem and segmental gene duplications, recombination, unequal crossing-over, point mutations, and diversifying selection. Diversity within populations is maintained by balancing selection. Analyses of whole-genome sequences have and will continue to provide new insight into the dynamics of resistance gene evolution.

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Author and article information

Contributors

Jinfa Zhang: 575-646-3438 , jinzhang@nmsu.edu

Jiwen Yu: yujw666@hotmail.com

Wenfeng Pei: peiwenfeng1988@163.com

Xingli Li: liyd@cricaas.com.cn

Joseph Said: joesaid@nmsu.edu

Mingzhou Song: joemsong@cs.nmsu.edu

Soum Sanogo: ssanogo@nmsu.edu

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 5 August 2015

Publication date PMC-release: 5 August 2015

Publication date Collection: 2015

Volume: 16

Issue: 1

Electronic Location Identifier: 577

Affiliations

[ ]Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003 USA

[ ]State Key Laboratory of Cotton Biology, Institute of Cotton Research of China, Chinese Academy of Agricultural Science, Anyang, Henan 455000 China

[ ]Department of Computer Science, New Mexico State University, Las Cruces, NM 88003 USA

[ ]Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM 88003 USA

Article

Publisher ID: 1682

DOI: 10.1186/s12864-015-1682-2

PMC ID: 4524102

SO-VID: 4cb2e23d-85bc-4bed-8966-af27df2dcd64

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 4 February 2015

Date accepted : 1 June 2015

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: cotton,verticillium wilt,fusarium wilt,root-knot nematodes,reniform nematodes,resistance,quantitative trait loci,meta-analysis

Data availability:

ScienceOpen disciplines: Genetics

Keywords: cotton, verticillium wilt, fusarium wilt, root-knot nematodes, reniform nematodes, resistance, quantitative trait loci, meta-analysis

Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton

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Background

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Electronic supplementary material

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G3: Genes|Genomes|Genetics

Most cited references 67

A modified algorithm for the improvement of composite interval mapping.

BioMercator: integrating genetic maps and QTL towards discovery of candidate genes.

Evolving disease resistance genes.

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