Nested Association Mapping (NAM) Populations: Present Status and Future Prospects in the Genomics Era

Gireesh, C.; Sundaram, R. M.; Anantha, Siddaiah M.; Pandey, Manish K.; Maganti, Sheshu Madhav; Rathod, Santosha; Yathish, K. R.; Senguttuvel, P.; Kalyani, Barbadikar M.; Ranjith, Ellur; Subbarao, L. V.; Mondal, Tapan Kumar; Swamy, Mallikarjuna; Rakshit, Sujay

doi:10.1080/07352689.2021.1880019

Cited by 12 publications

(7 citation statements)

References 94 publications

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“…Alternatively, other popular population designs for QTL analysis in plants can be used, namely, nested association mapping (NAM) and multi-parent advanced generation inter-cross (MAGIC) populations ( Huang et al, 2015 ; Scott et al, 2020 ). The NAM design consists of a series of bi-parental crosses against a common founder, from which RILs are typically generated through selfing ( Scott et al, 2020 ; Gireesh et al, 2021 ). In the MAGIC design, a series of equally balanced crosses are made between founders before RILs are developed ( Huang et al, 2015 ; Scott et al, 2020 ).…”

Section: Studying Local Adaptation Of Plant-arthropod Interactions Us...mentioning

confidence: 99%

Evolutionary Ecology of Plant-Arthropod Interactions in Light of the “Omics” Sciences: A Broad Guide

et al. 2022

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Aboveground plant-arthropod interactions are typically complex, involving herbivores, predators, pollinators, and various other guilds that can strongly affect plant fitness, directly or indirectly, and individually, synergistically, or antagonistically. However, little is known about how ongoing natural selection by these interacting guilds shapes the evolution of plants, i.e., how they affect the differential survival and reproduction of genotypes due to differences in phenotypes in an environment. Recent technological advances, including next-generation sequencing, metabolomics, and gene-editing technologies along with traditional experimental approaches (e.g., quantitative genetics experiments), have enabled far more comprehensive exploration of the genes and traits involved in complex ecological interactions. Connecting different levels of biological organization (genes to communities) will enhance the understanding of evolutionary interactions in complex communities, but this requires a multidisciplinary approach. Here, we review traditional and modern methods and concepts, then highlight future avenues for studying the evolution of plant-arthropod interactions (e.g., plant-herbivore-pollinator interactions). Besides promoting a fundamental understanding of plant-associated arthropod communities’ genetic background and evolution, such knowledge can also help address many current global environmental challenges.

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Section: Studying Local Adaptation Of Plant-arthropod Interactions Us...mentioning

confidence: 99%

Evolutionary Ecology of Plant-Arthropod Interactions in Light of the “Omics” Sciences: A Broad Guide

et al. 2022

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“…Phenotyping is therefore becoming the main bottleneck for breeding. It is particularly challenging when assessment of very large collection of several thousand lines which is the typical size of nested association mapping (NAM) populations [ 21 – 23 ]. It is therefore urgent to improve and optimize the available methods of phenotyping.…”

Section: Introductionmentioning

confidence: 99%

RGB image-based method for phenotyping rust disease progress in pea leaves using R

Osuna-Caballero,

Olivoto,

Jiménez-Vaquero

et al. 2023

Plant Methods

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Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method’s optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin’s concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

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“…Yu et al, 2008). The nested association mapping (NAM) design is an integrative mapping strategy that combines the principles of both linkage analysis and association mapping and provides the high power of quantitative trait locus (QTL) detection and better mapping resolution (Gireesh et al, 2021). Nested association mapping populations are developed by crossing multiple diverse parental or founder lines with a recurrent parent.…”

Section: Introductionmentioning

confidence: 99%

“…As a multiparent population, the carbon‐partitioning nested association mapping (CP_NAM) panel represents an ideal genomic resource to identify favorable alleles from diverse parental lines that can dissect the genetic variation underlying complex quantitative traits (J. Yu et al., 2008). The nested association mapping (NAM) design is an integrative mapping strategy that combines the principles of both linkage analysis and association mapping and provides the high power of quantitative trait locus (QTL) detection and better mapping resolution (Gireesh et al., 2021). Nested association mapping populations are developed by crossing multiple diverse parental or founder lines with a recurrent parent.…”

Section: Introductionmentioning

confidence: 99%

Registration of the sorghum carbon‐partitioning nested association mapping (CP‐NAM) population

Kumar

Brenton

Myers

et al. 2022

J of Plant Registrations

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The sorghum [Sorghum bicolor (L.) Moench] carbon-partitioning nested association mapping (CP_NAM) (Reg. no. MP-4, NSL 542189 MAP) population was developed at Clemson University, SC, using 11 diverse, male founder accessions, each crossed with a recurrent female parent 'Grassl'. The male parents represent all five major botanical races and the four major agronomic types: cellulosic (5), sweet (3), grain (2) and forage (1). A set of 11 recombinant inbred line (RIL) families CP_NAM01 to CP_NAM011 were maintained, which consisted of 2,484 (F 6 ) individuals. Each RIL family contained a minimum of 193 individuals (CP_NAM01) and a maximum of 287 individuals (CP_NAM06). For the development of this population, the founder lines were judiciously selected from the sorghum Bioenergy Association Panel based on carbon-partitioning phenotypes that make this population an ideal genetic resource for dissecting a wide range of agronomic and compositional traits for basic and applied research. The founder accessions of the CP_NAM were phenotypically characterized for various traits, including agronomic, biomass and related components, and additional compositional components. Each of the 11 F 6 RIL families of the CP_NAM were genotyped using genotyping-by-sequencing analysis, and 144,087 single nucleotide polymorphisms were generated for each individual. Genotypic information along with phenotypic data were used for the characterization of this population and to explore the range of phenotypes that permits the understanding of carbon-partitioning dynamics. This population is a unique resource for researchers to study a wide range of contrasting carbon-partitioning characteristics in sorghum to understand the genetic architecture underlying whole-plant carbon partitioning and allocation.

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Nested Association Mapping (NAM) Populations: Present Status and Future Prospects in the Genomics Era

Cited by 12 publications

References 94 publications

Evolutionary Ecology of Plant-Arthropod Interactions in Light of the “Omics” Sciences: A Broad Guide

Evolutionary Ecology of Plant-Arthropod Interactions in Light of the “Omics” Sciences: A Broad Guide

RGB image-based method for phenotyping rust disease progress in pea leaves using R

Registration of the sorghum carbon‐partitioning nested association mapping (CP‐NAM) population

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