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Drake-Stowe K, Bakaher N, Goepfert S, Philippon B, Mark R, Peterson P, Lewis RS. Multiple Disease Resistance Loci Affect Soilborne Disease Resistance in Tobacco (Nicotiana tabacum). PHYTOPATHOLOGY 2017; 107:1055-1061. [PMID: 28581342 DOI: 10.1094/phyto-03-17-0118-r] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Phytophthora nicotianae and Ralstonia solanacearum are two of the most important pathogens affecting tobacco worldwide. Greater insight regarding genetic systems controlling resistance to these two soilborne pathogens, as well as identification of DNA markers associated with genomic regions controlling this resistance, could aid in variety development. An evaluation of 50 historical tobacco lines revealed a high positive correlation between resistances to the two pathogens, preliminarily suggesting that some genomic regions may confer resistance to both pathogens. A quantitative trait loci (QTL) mapping experiment designed to investigate the genetic control of soilborne disease resistance of highly resistant 'K346' tobacco identified four QTL significantly associated with resistance to P. nicotianae (explaining 60.0% of the observed phenotypic variation) and three QTL to be associated with R. solanacearum resistance (explaining 50.3% of the observed variation). The two QTL with the largest effect on Phytophthora resistance were also found to be the QTL with the greatest effects on resistance to Ralstonia. This finding partially explains previously observed associations between resistances to these two pathogens among U.S. current cultivars and within breeding populations. Further study is needed to determine whether these relationships are due to the same genes (i.e., pleiotropy) or favorable coupling-phase linkages that have been established over time.
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Affiliation(s)
- Katherine Drake-Stowe
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Nicolas Bakaher
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Simon Goepfert
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Berangere Philippon
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Regis Mark
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Paul Peterson
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
| | - Ramsey S Lewis
- First and seventh authors: Crop and Soil Science Department, North Carolina State University, Raleigh; second, third, fourth, and fifth authors: Philip Morris International R&D, Philip Morris Products S.A., Neuchatel, Switzerland (part of Philip Morris International group of companies); and sixth author: Pee Dee Research and Education Center, Clemson University, 2200 Pocket Rd., Florence, SC
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Schandry N. A Practical Guide to Visualization and Statistical Analysis of R. solanacearum Infection Data Using R. FRONTIERS IN PLANT SCIENCE 2017; 8:623. [PMID: 28484483 PMCID: PMC5401893 DOI: 10.3389/fpls.2017.00623] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 04/06/2017] [Indexed: 05/11/2023]
Abstract
This paper describes and summarizes approaches for visualization and statistical analysis using data from Ralstonia solanacearum infection experiments based on methods and concepts that are broadly applicable. Members of the R. solanacearum species complex cause bacterial wilt disease. Bacterial wilt is a lethal plant disease and has been studied for over 100 years. During this time various methods to quantify disease and different ways to analyze the generated data have been employed. Here, I aim to provide a general background on three distinct and commonly used measures of disease: the area under the disease progression curve, longitudinal recordings of disease severity and host survival. I will discuss how one can proceed with visualization, statistical analysis, and interpretation using different datasets while revisiting the general concepts of statistical analysis. Datasets and R code to perform all analyses discussed here are included in the supplement.
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