No abstract

In 2001, entries from the peanut core collection, a subset of the USDA peanut germplasm collection, were planted in non-replicated plots in a field with a history of Sclerotinia blight caused by Sclerotinia minor. Variability existed among entries for reaction to Sclerotinia blight. Of the 744 entries evaluated, 11% had no disease, nearly 30% had ,10% disease incidence, and only 21% had 50% disease incidence or more. Most of the resistant entries had an upright growth habit and were in early and mid-maturity groups. Many of the early maturing entries were susceptible to the foliar disease pepper spot which occurred throughout the study. Entries were selected for further evaluation in replicated plots based on a nil to low (,10% 208, 128, 804, 582, and 273 combined resistance to Sclerotinia blight, pepper spot, and web blotch with less than erect growth habits. Entry 103 had good Sclerotinia blight resistance and yield, but an upright growth habit. Entry 92 had an upright growth habit and low yield, but good Sclerotinia blight resistance. Entries 92 and 103 had upright growth habits but were among the best entries for resistant to pepper spot and web blotch. Entries 426, 184, and 562 were upright and susceptible to pepper spot, but had resistance to web blotch and the best resistance to Sclerotinia blight. These entries appear to be useful sources of resistance to Sclerotinia blight for breeding programs and for increasing the probability of finding additional sources of resistance in clusters of germplasm identified within the entire USDA collection.

Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture.

Peanut varieties with high oleic/linoleic acid ratios have become preferred by the peanut industry due to their increased shelf life and improved health benefits. Many peanut breeding programs are trying to incorporate the high oleic trait into new and improved varieties and are in need of diagnostic tools to track its inheritance early in development and at the single seed level. Traditionally, gas chromatography has been used to accurately determine the properties of peanut oil. Recently a method was developed to carry out this analysis by capillary elecrophoresis providing researchers with an alternative analytical platform. In this study, the use of capillary electrophoresis and gas chromatography for analysis of oleic/linoleic acid ratios are compared. Oil was extracted from approximately 0.10 g of peanut seed tissue taken from the distal end, leaving the embryonic end of the seed intact for subsequent germination. Over 100 samples inclusive of runner, Spanish and Virginia market types were processed. Oil extracts were analyzed for oleic/ linoleic acid ratio using (1) capillary electrophoresis (CE) and (2) gas chromatography (GC). Results showed that the two methods are 100% in agreement in determining whether a peanut seed is ''high-oleic'' or ''normal oleic'' in oil content. Furthermore, the two methods are highly correlated (r 5 0.96; p , 0.0001) with respect to determining the exact oleic/linoleic acid ratio from each sample. Results from this study validate the use of CE as a diagnostic tool for breeding programs to identify individual high oleic peanut seed for further testing and development.

Single-nucleotide polymorphisms, which can be identified in the thousands or millions from comparisons of transcriptome or genome sequences, are ideally suited for making high-resolution genetic maps, investigating population evolutionary history, and discovering marker-trait linkages. Despite significant results from their use in human genetics, progress in identification and use in plants, and particularly polyploid plants, has lagged. As part of a long-term project to identify and use SNPs suitable for these purposes in cultivated peanut, which is tetraploid, we generated transcriptome sequences of four peanut cultivars, namely OLin, New Mexico Valencia C, Tamrun OL07 and Jupiter, which represent the four major market classes of peanut grown in the world, and which are important economically to the US southwest peanut growing region. CopyDNA libraries of each genotype were used to generate 2 × 54 paired-end reads using an Illumina GAIIx sequencer. Raw reads were mapped to a custom reference consisting of Tifrunner 454 sequences plus peanut ESTs in GenBank, compromising 43,108 contigs; 263,840 SNP and indel variants were identified among four genotypes compared to the reference. A subset of 6 variants was assayed across 24 genotypes representing four market types using KASP chemistry to assess the criteria for SNP selection. Results demonstrated that transcriptome sequencing can identify SNPs usable as selectable DNA-based markers in complex polyploid species such as peanut. Criteria for effective use of SNPs as markers are discussed in this context.