Feasibility assessment of phenotyping cotton fiber maturity using infrared spectroscopy and algorithms for genotyping analyses

[Background] Cotton fiber maturity is an important property that partially determines the processing and performance of cotton. Due to difficulties of obtaining fiber maturity values accurately from every plant of a genetic population, cotton geneticists often use micronaire (MIC) and/or lint percentage for classifying immature phenotypes from mature fiber phenotypes although they are complex fiber traits. The recent development of an algorithm for determining cotton fiber maturity (MIR) from Fourier transform infrared (FT-IR) spectra explores a novel way to measure fiber maturity efficiently and accurately. However, the algorithm has not been tested with a genetic population consisting of a large number of progeny plants.

[Results] The merits and limits of the MIC- or lint percentage-based phenotyping method were demonstrated by comparing the observed phenotypes with the predicted phenotypes based on their DNA marker genotypes in a genetic population consisting of 708 F2 plants with various fiber maturity. The observed MIC-based fiber phenotypes matched to the predicted phenotypes better than the observed lint percentage-based fiber phenotypes. The lint percentage was obtained from each of F2 plants, whereas the MIC values were unable to be obtained from the entire population since certain F2 plants produced insufficient fiber mass for their measurements. To test the feasibility of cotton fiber infrared maturity (MIR) as a viable phenotyping tool for genetic analyses, we measured FT-IR spectra from the second population composed of 80 F2 plants with various fiber maturities, determined MIR values using the algorithms, and compared them with their genotypes in addition to other fiber phenotypes. The results showed that MIR values were successfully obtained from each of the F2 plants, and the observed MIR-based phenotypes fit well to the predicted phenotypes based on their DNA marker genotypes as well as the observed phenotypes based on a combination of MIC and lint percentage.

[Conclusions] The MIR value obtained from FT-IR spectra of cotton fibers is able to accurately assess fiber maturity of all plants of a population in a quantitative way. The technique provides an option for cotton geneticists to determine fiber maturity rapidly and efficiently.

[Authors] KIM Hee Jin, LIU Yongliang, FANG David D. and DELHOM Christopher D.

Journal of Cotton Research2019; 2:8

https://doi.org/10.1186/s42397-019-0027-0

Development of better naturally colored cotton lines (gossypium hirsutum l.) regarding seed cotton yield, ginning outturn and fiber technological properties under kahramanmaras conditions

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Author

Lale Efe

Kahramanmaras Sutcu Imam University, Faculty of Agriculture, Field Crops Department

Abstract

The aim of this project carried out in 2011-2013 under Kahramanmaras conditions was to develop superior lines for seed cotton yield, ginning outturn and fiber technological properties from four naturally coloured cotton populations (Gossypium hirsutum L.) crossing naturally and having genetic variation using method  of  pedigree selection. Naturally coloured cotton populations used as materials had fibers coloured light brown, dark brown, green and creamy.

In 2011 year, 100 individual plants were selected from each coloured population according to field observations such as fiber colour, plant form, boll and leaf form, plant height, number of monopodia, number of sympodia, boll number per plant (for four colour 4×100=400 plants). Each  plant was harvested separately. In  100 plants (for each colour) seed cotton yield, ginning outturn, 100 seed weight, boll weight, seed cotton weight per boll, fiber length, fiber fineness, fiber strength, fiber elongation, fiber uniformity, short fiber index, trash area, trash count, trash degree were recorded. According to seed cotton yield, ginning outturn, 100 seed weight, boll and fiber traits 50 individual plants were selected to be sown in next year. In 2012 year, self pollinated seeds of 50 plants selected from four populations having different fiber colours were sown in the separate rows 5 m in length. Thus, 200 progeny rows were formed in total (50 dark brown, 50 light brown, 50 green, 50 creamy). The all plants in rows were self pollinated during flowering period. Individual plant selection were repeated in progeny rows according to field observations. 2 or 3 plants selected according to field observations in each row were harvested  separately (For each colour 50×2=100 plants, in total 100×4=400 plants). For each colour in 100 plants seed cotton yield, ginning outturn, 100 seed weight, boll and fiber traits were recorded. According to these traits 50 individual plants were selected. Self pollinated seeds of these plants were sown in the next year. In 2013 year these steps were replicated.

As a result, in each colour individual plants higher yielding and having higher lint properties were obtained. Second part of this project continuous to obtain homozygous and pure lines using self pollination from 2014 to 2016.

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Association mapping for seed cotton yield, its contributing and fiber quality traits in Upland Cotton (G. hirsutum L.) germplasm lines.

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Author

Suresh handi and I.S.Katageri*

*Professor and Head,

Institute of Agribiotechnology, University of Agricultural Sciences, Dharwad, Karnataka, INDIA, katageriis@uasd.in

Abstract

Determination of the genetic  basis of complex quantitative traits has been one of the major scientific challenges in the process of crop improvement. To assist in this effort, an increasing number of genomic and genetic resources are today exploitable,  including genome sequences, germplasm collections and public databases of genomic information. The availability of these resources, the recent advances in high-throughput genomic platforms and the increasing interest in exploring natural genetic diversity, make association mapping an appealing and affordable approach to identify genes responsible for quantitative variation of complex traits. Association mapping requires high-density oligonucleotide arrays to efficiently identify SNPs distributed across the genome at a density that accurately reflects genome wide LD structure and haplotype diversity. For Cotton, a high-density infinium array (63K SNP array) was recently built (Hulse-Kemp et al., 2015), with 63058 SNPs developed from different species which resulted in suitability for genome wide association analysis.

Association or linkage disequilibrium (LD) mapping revolutionized genetic mapping in humans, and is increasingly used to examine in plant genetics; it is an efficient way of determining the genetic basis of complex traits. In the present study, association mapping was examined with the use 201 germplasm of G. hirsutum lines evaluated for yield, yield components and fiber quality traits. Results from fastSTRCUTURE identified 12 subgroups in the population.  The critical value of R2 was set to 0.243 was taken as a threshold to claim LD between two loci. About 3.13 % marker pairs showed significant high LD (R2=1) and about 82.72 percent pairs of loci were in linkage equilibrium with R2 values less than 0.3. Mixed linear model accounting for population structure and kinship has identified 349 significant marker trait associations for yield, yield components and fiber quality traits effectively controlling false positives reported in GLM (642 markers). More number of markers showing significant association were situated on D genome indicates than ‘A’ genome indicates detection of diverse SNP markers than ‘D’ genome or this may also because of the dense marker coverage in the D genome. The phenotypic variation explained by makers in this study was smaller suggesting minor QTLs or polygenic nature of these traits.

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Molecular breeding: cotton transcriptome analysis, characterisation and validation of fibre strength genes assistive in marker assisted selection

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Author

B.R.Patil,I.S. Katageri,B.M Khadi, G. Balasubramani, K.P.Raghvendra, J.Amudha, S.K.Deshpande

Abstract

The relative gene expression of GhcesA1, GhcesA2, and GhcesA7 orthologus of AtcesA8, AtcesA4, and AtcesA7 respectively, Ghcobl4, Ghfla3 and GhMT1genes using Recombinant Inbred Lines mapping population was studied through q PCR. The results showed that GhcesA1, GhcesA2, Ghfla3 and Ghcobl4 were strongly associated with secondary wall synthesis and hence the plan is to prepare the gene construct with an appropriate fibre specific promoter to transform a suitable genotype. To validate the q PCR analysis, Scanning Electron Microscope study was conducted to confirm that cellulose is a key entity for conferring high fibre strength. The high fiber strength line HBS144 (28.0 g/tex) and low fiber strength line, HBS 187 (20.0 g/tex ) fiber’s micrograph showed that HBS 144 had strong series of fibrillar structure which was found less in HBS 187.A fibre diameter of 17µm was observed in HBS144 while ,a 10 µm fiber diameter was recorded in HBS 187.The fibrils which relate to deposition of cellulose had a diameter of 0.2 µm for HBS 144 and 0.1 µm for HBS 187 respectively. The RNA sequence analysis of HBS 144 and 187 revealed 74.6 million and 53.4 million raw reads respectively through Illumina. The number of unigenes expressed for genotype HBS-144 were 11328 while , 6866 unigenes were observed for HBS-187. A total of 14828 unigenes were up regulated while, a total of 13468 unigenes were down regulated in both genotypes employed for the study.The total number of identified SSR’s for HBS 144 were 29868 while, 21680 SSR’s were identified for HBS 187.The total number of variants (SNP) were 90857 for HBS 144 while, 74161 variants were observed for HBS 187.The plan is to utilize these SSR’ s and SNP ‘s for Marker assisted selection after validation by Gold standard linkage map.

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Honeydew and Seed Coat Fragments: Identifying and Counting Two Major Cotton Fiber Contaminants

ABSTRACT
Spinning techniques are constantly progressing. Equipment is getting faster and more automated. Spinners are less and less tolerant of fiber contaminants. Honeydew and seedcoat fragments (SCF) are a major problem that cannot be detected by HVI systems CIRAD developed techniques for honeydew and SCF detection and quantified them for use by researchers, producers and spinners. Thermodetection detects cotton entomological stickiness, results being expressed as the number of sticky spots in the specimen, providing a sample of stickiness potential. High Speed Stickiness Detector (H2SD) is fully automated and allows a bale by bale classification for stickiness at speeds comparable to HVI speed (30 seconds per sample). Results correlate well with the reference Stickiness Cotton Detector. Sticky spot size distribution is available. TRASHCAM image analysis on a card web detects seedcoat fragments. Results are expressed as an SCF total count in the specimen. Very small SCF are detected so the results can be considered as samples of SCF potential. TRASHCAM uses a scanning device and a specific algorithm. SCF size distribution is available. Yarn SCF assessment is possible without spinning any yarn. TRASHCAM can count SCF in yarn.

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