Genetic analysis and gain from selection of thrips resistance in cotton

Wann, Dylan Q.; Dever, Jane K.; Arnold, Mark D.; Elkins, Heather D.

doi:10.1007/s10681-017-1861-0

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Identifying such resistance in A. hypogaea provides tetraploid sources for introgressing smut resistance into new, elite peanut cultivars or breeding populations, without requiring pre-breeding of resistant diploid germplasm. Additionally, using A. hypogaea sources of resistance can help reduce potential linkage drag of undesirable alleles from resistant wild Arachis species, as evidenced in other crops (Wann et al, 2017). However, depending on the source, using a tetraploid source of resistance may make it more complicated to pyramid resistance from both Table 4.…”

Section: Discussionmentioning

confidence: 99%

Phenotypic Variation of Peanut Smut (Thecaphora frezii) Incidence and Severity in the U.S. Peanut Mini-Core Collection

Wann

Falco²,

Cavigliasso³

et al. 2020

Peanut Science

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Peanut smut is an emergent soilborne disease of peanut in South America that has significantly impacted the commercial peanut industry in Argentina. In response, plant breeders are in need of information about potential sources of smut resistance in cultivated germplasm for the rapid development of resistant cultivars. Available U.S. peanut mini-core accessions were evaluated under naturally-infested soil conditions in 2016-2019 near General Cabrera, Córdoba, Argentina, in addition to three susceptible breeding lines and four local commercial controls. Over three years, 18 mini-core accessions and two germplasm collection accessions exhibited no smut incidence in a 100-pod sample. Of those, 12 mini-core accessions and one germplasm collection accession (PI 153323) exhibited no smut incidence when all available pods were opened and examined in the 2016-2017 and 2017-2018 crop years. These 13 accessions were collected from a variety of origins across the Americas, Africa, and Asia; only three were collected from origins in South America. These results suggest that resistance mechanisms may be well-conserved across various groups within Arachis hypogaea L. The 13 identified accessions appear to be sources of resistance to peanut smut in A. hypogaea and would likely be good parent material for the development of new, resistant commercial peanut cultivars.

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Section: Discussionmentioning

confidence: 99%

Phenotypic Variation of Peanut Smut (Thecaphora frezii) Incidence and Severity in the U.S. Peanut Mini-Core Collection

Wann

Falco²,

Cavigliasso³

et al. 2020

Peanut Science

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“…Visual thrips injury ratings were also conducted on the aforementioned evaluations at the fifth‐true‐leaf stage, using the 1‐to‐9 rating scale reported by Wann et al (2017), where 1 = no observable damage and 9 = complete plant necrosis. The evaluations were conducted under natural thrips infestations at the field level.…”

Section: Methodsmentioning

confidence: 99%

“…An important component of resistance in G. barbadense is a thicker outer epidermal layer on the underside of the cotton leaf (Arif et al, 2004; Bowman and McCarty, 1997; Gawaad and Soliman, 2009). Introgressing this resistance into G. hirsutum germplasm, however, could prove difficult across species boundaries and given the photoperiodic nature of some of the most thrips‐resistant G. barbadense accessions (Arnold et al, 2010; Wann et al, 2017; Zhang et al, 2013). Therefore, selecting within G. hirsutum , directly or indirectly, for similar morphological characteristics may aid in developing breeding populations with elevated tolerance to thrips feeding, without the confounding aspects and background variation characteristic of many interspecific breeding populations.…”

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confidence: 99%

Registration of CA 4005 and CA 4006 Cotton Germplasm Lines with Partial Resistance to Feeding Injury from Thrips

Wann

Dever²,

Arnold³

et al. 2017

J of Plant Registrations

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Thrips (Thysanoptera: Thripidae) are common pests of cotton (Gossypium spp.) worldwide, typically causing economic damage within the first few weeks after planting of a cotton crop. With the loss of granular aldicarbs and resistance development to other insecticides in recent years, host plant resistance could play an important role in the integrated management of thrips. Two breeding lines originally developed for cold tolerance in the Texas A&M AgriLife Research cotton improvement program at Lubbock, TX, exhibited characteristics associated with thrips tolerance. These G. hirsutum lines, CA 4005 (Reg. No. GP‐1023, PI 681593) and CA 4006 (Reg. No. GP‐1024, PI 681594), are germplasm lines that were subsequently evaluated for potential resistance to thrips feeding injury. Both lines were evaluated in field and greenhouse tests from 2012 to 2014. CA 4005 and CA 4006 consistently displayed greater resistance to thrips feeding injury than the commercial and susceptible checks at the field level. Greenhouse evaluations, however, revealed that these lines are only partially resistant and can be overcome at high thrips densities. Yield potential, high‐volume instrument (HVI) fiber quality, and other agronomic characteristics of CA 4005 and CA 4006 were comparable to the evaluated commercial checks. These germplasm lines exhibited improved thrips resistance and competitive agronomic potential for the Texas High Plains and would be valuable sources of partial thrips resistance in adapted G. hirsutum germplasm.

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“…; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017). ; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017).…”

Section: New Proposals To Estimate Unbiased Selection Gain and Coeffimentioning

confidence: 99%

“…L. ; Wann et al, 2017); and disease severity in wheat (Iqbal et al, 2016;Saintenac et al, 2018), sugar beet (Beta vulgaris L.; Kakueinezhad et al, 2018;Vogel et al, 2018), common bean (Amaro et al, 2007), and coffee (Coffea arabica L.; Botelho et al, 2017). Usually, these score scales are treated as interval scales.…”

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confidence: 99%

New Proposals to Estimate Unbiased Selection Gain and Coefficient of Variation in Traits Evaluated Using Score Scales

et al. 2019

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937 RESEARCHR andom variables can be classified as qualitative, discrete quantitative, or continuous quantitative, whose scales of measurement can be nominal, ordinal, interval, or ratio (Resende et al., 2014). The nominal scale classifies the data into distinct categories, assigning names and arbitrary numerical correspondence to the variable's categories (e.g., gender: 1 = female and 2 = male). The ordinal scale classifies data into distinct categories, and in this case, the order or position is assigned to the categories (e.g., water quality level: 1 = good, 2 = intermediate, and 3 = bad). Either nominal or ordinal scales show no relation of difference or ratio between the numerical values of the scale (Levine et al., 2014;Resende et al., 2014). The interval scale is an ordered scale in which the difference between any two numbers in the scale is known; however, 0 is included as an arbitrary point (e.g., temperature in °C). Finally, a ratio scale is an ordered scale in which the distances between any two numbers in the scale are known, and their measurements include the true zero as the point of origin (e.g., height in centimeters) (Levine et al., 2014;Resende et al., 2014).In plant evaluations, several multicategorical variables are visually evaluated by score scales, such as plant architecture and grain appearance in common bean (Phaseolus vulgaris L.; Batista et al., 2017); lodging in common bean (Soltani et al., 2016), soybean [Glycine max (L.) Merr.; Akpertey et al., 2018], and wheat (Triticum aestivum L.; Iqbal et al., 2016); vigor in chickpea (Cicer arietinum L.; Sivasakthi et al., 2018); leaf senescence and leaf rolling in maize (Zea mays L.; Soni et al., 2018); herbicide tolerance in lentils (Lens culinaris Medik.; Sharma et al., 2018); salt tolerance in soybean (Do et al., 2018); injury caused by pests in cotton (Gossypium hirsutum ABSTRACTIn plant breeding, several multicategorical variables are evaluated using score scales that are treated as interval scales. However, statistics that use ratio operations, such as the experimental CV and the selection gain, are only appropriate for ratio scales data. Thus, this work aimed to propose strategies to mitigate the inconveniences faced in the data analysis of score scales, especially those involving CV and selection gain obtained from different scales. This work proposes the following strategies: (i) the use of a standard score scale with the properties of a ratio scale (ascending scale with 0 as the point of origin); (ii) the conversion of the scores from different scales into a common ratio scale; (iii) the adjustment of the data from an interval scale to a ratio scale using the Delta Scale (Scale d ui ), and (iv) the use of unbiased estimator for CV and selection gain in score scales data. The proposed strategies resulted in unbiased estimates of CV and selection gain from data of different score scales. These strategies have the potential to be used in the meta-analysis of data within and between plant breeding programs.

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Genetic analysis and gain from selection of thrips resistance in cotton

Cited by 7 publications

References 16 publications

Phenotypic Variation of Peanut Smut (Thecaphora frezii) Incidence and Severity in the U.S. Peanut Mini-Core Collection

Phenotypic Variation of Peanut Smut (Thecaphora frezii) Incidence and Severity in the U.S. Peanut Mini-Core Collection

Registration of CA 4005 and CA 4006 Cotton Germplasm Lines with Partial Resistance to Feeding Injury from Thrips

New Proposals to Estimate Unbiased Selection Gain and Coefficient of Variation in Traits Evaluated Using Score Scales

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