Marker-assisted selection and QTL mapping for yield, root morphology and agronomic traits using MASARB25 (aerobic) × Pusa Basmati 1460 F3mapping populations

Effectiveness of marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTL depends on the extent of LD and how it declines with distance in a population. Because marker–QTL LD cannot be observed directly, the objective of this study was to evaluate alternative measures of observable LD between multi-allelic markers as predictors of usable LD of multi-allelic markers with presumed biallelic QTL. Observable LD between marker pairs was evaluated using eight existing measures and one new measure. These consisted of two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy–Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. In simulated populations with a range of LD generated by drift and a range of marker polymorphism, marker–marker LD measured by a standardized chi-square statistic (denoted χ2′) was found to be the best predictor of useable marker–QTL LD for a group of multi-allelic markers. Estimates of the level and decline of marker–marker LD with distance obtained from χ2′ were linearly and highly correlated with usable LD of those markers with QTL across population structures and marker polymorphism. Corresponding relationships were poorer for the other marker–marker LD measures. Therefore, when LD is generated by drift, χ2′ is recommended to quantify the amount and extent of usable LD in a population for QTL mapping and MAS based on multi-allelic markers.

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QTL mapping of agronomic traits using an RIL population derived from a cross between temperate japonica cultivars in rice (Oryza sativa L.)

Breeding Science ◽

10.1270/jsbbs.58.271 ◽

2008 ◽

Vol 58 (3) ◽

pp. 271-279 ◽

Cited By ~ 11

Author(s):

Soo-Jin Kwon ◽

Young-Chan Cho ◽

Soon-Wook Kwon ◽

Chang-Sik Oh ◽

Jung-Pil Suh ◽

...

Keyword(s):

Oryza Sativa ◽

Qtl Mapping ◽

Agronomic Traits ◽

Oryza Sativa L ◽

Temperate Japonica ◽

Ril Population

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Application Techniques of Molecular Marker and Achievement of Marker Assisted Selection (MAS) in Three Major Crops Rice, Wheat and Maize

International Journal for Research in Applied Sciences and Biotechnology ◽

10.31033/ijrasb.8.1.10 ◽

2021 ◽

Vol 8 (1) ◽

pp. 82-93

Author(s):

Muhammad Gul Arabzai ◽

Hameed Gul

Keyword(s):

Molecular Markers ◽

Molecular Marker ◽

Abiotic Stresses ◽

Marker Assisted Selection ◽

Agronomic Traits ◽

Linkage Drag ◽

Conventional Breeding ◽

Phenotype Data ◽

Application Techniques ◽

New Varieties

With the discovery of new genetic technology, the researcher focuses on using DNA molecular markers to improve new varieties worldwide. Such as resistance to biotic and abiotic stresses and enhancing quality and quantity at different plant breeding fields. Conventional breeding selection is based on phenotype data selection, time-consuming, and has a high chance of linkage drag. Thus, DNA molecular marker method usage is faster, easy, and not expensive than conventional breeding programs. This review focused on applying molecular markers such as genetic diversity analysis, the genotype of identification and fingerprinting, gene tagging and mapping, QTL analysis, and marker-assisted selection. In another part of this review, we focused on MAS's achievements related to improving agronomic traits, quality traits, and biotic/abiotic stresses for three major cereal crops like Wheat, Rice, and Maize.

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An automated, high-throughput image analysis pipeline enables genetic studies of shoot and root morphology in carrot (Daucus carota L.)

10.1101/384974 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarah D. Turner ◽

Shelby L. Ellison ◽

Douglas A. Senalik ◽

Philipp W. Simon ◽

Edgar P. Spalding ◽

...

Keyword(s):

Image Analysis ◽

High Throughput ◽

Root Morphology ◽

Agronomic Traits ◽

Automated Analysis ◽

Root Characteristics ◽

Analysis Pipeline ◽

Genetic Studies ◽

Daucus Carota L ◽

Size And Shape

AbstractCarrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

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