Comparison of Genotypic and Expression Data to Determine Distinctness among Inbred Lines of Maize for Granting of Plant Variety Protection

Abstract

The Union Internationale pour la Protection des Obtentions Végétales (UPOV) currently relies on morphological characteristics to evaluate distinctness, uniformity, and stability (DUS) as eligibility requirements for the granting of Plant Variety Protection (PVP). We used 10 maize (Zea mays L.) inbred lines, including both unrelated and closely similar pairs, representing three heterotic groups to compare abilities of morphological, ribonucleic acid (RNA) transcription, metabolomic, and single nucleotide polymorphism (SNP) data to distinguish inbred lines. We used the range of variability and robustness as important factors to determine distinguishing power of each methodological approach. Using an index that ranged from 0 to 100 (useless to the perfect ideal), index scores for each methodology were: metabolomics (0), RNA transcription (18.2), morphology (19.6), and SNPs (35.7). The ability to distinguish among genotypes using RNA transcription expression data was concordant with SNP data for genotypes that were up to 97.2% similar according to SNPs. The SNP data alone could provide the basis for a determination of distinctness among inbred lines of maize with use of morphological, physiological, or agronomic performance data as supplementary information, if needed.

Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

“…Biological basis for seed testing with appearance Using 10 seeds of maize inbred, Hall et al 22 compared the identification ability based on the dataset of morphology, metabolism, ribonucleic acid (RNA) and single nucleotide polymorphisms (SNPs), and the result indicated that the morphological features ranked only second to SNP. By calculating the morphological similarity matrix of the maize inbred and the coefficient of genetic, Babić et al 23 compared the similarity between the morphology and gene, and found that the description of morphology was very helpful to estimate genetic similarity, the morphological information had great significance for maize breeding, especially in the treatment of a large number of germplasm resources or the case of unknown germplasm.…”

“…3) including size, shape, color and texture features. Size and shape features were extracted from edge image and binary image, texture features • Size/6: area (1), major axis length (2), minor axis length (3), perimeter (4), diameter (5), convex area (6); • Shape/5: eccentricity (7), extent (8), shape factor (9), compactness (10), area/convex area (11); • Texture/8: gray mean (12), gray variance (13), smoothness (14), three moments (15), consistency (16), entropy (17), contrast (18), correlation homogeneity (19); • Color/12: mean (20)(21)(22)(23)(24)(25) and deviation (26-31) of red, green, blue, hue, saturation, value of components of color image (RGB and HSV).…”

Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments

“…There have been some analyses that explore a maize plant's response to specific environmental conditions such as salinity, heat, and drought (Sun, Li, et al, 2016b;Witt et al, 2012), nitrogen (Amiour et al, 2012;Brusamarello-Santos et al, 2017;Simons et al, 2014) and low phosphorus (Ganie et al, 2015) by monitoring the relationships of these environmental factors to those of metabolites and transcripts. Further examinations have begun to characterize the breadth of diversity of various inbreds and hybrids across typical agronomic environments particularly emphasizing these effectors on grain and forage composition (Asiago, Hazebroek, Harp, & Zhong, 2012;Baniasadi, Vlahakis, Hazebroek, Zhong, & Asiago, 2014;Benevenuto et al, 2017;Hall et al, 2016;Tang et al, 2017;Venkatesh et al, 2016). There have also been several "omic" analyses to understand the relationship of transcripts and metabolites to critical phenotypic traits including: yield (Asiago et al, 2012;Hazebroek, Janni, & Lightner, 2012;Wang, Xue, & Wang, 2014;Xu, Xu, & Xu, 2017), dry matter accumulation (Westhues et al, 2017), and silking (Yesbergenova-Cuny et al, 2016).…”

Is plant variety registration keeping pace with speed breeding techniques?

“…SNPs are highly abundant in the genome of plants and can be easily assayed and automated for high-throughput sampling. Recent examples of SNP fingerprinting maize (Tian et al 2015;Hall et al 2016) and alfalfa (Annicchiarico et al 2016) accessions demonstrate the potential of SNPs for the molecular barcoding of new plant varieties. In wheat, a set of 43 SNPs has been shown to provide unique barcodes capable of discriminating 429 cultivars sourced from across China (Gao et al 2016).…”