Peanut composition is influenced by several groups of factors: environmental, genetic, and their interaction. This study evaluated the relative contributions of these factors using data from the USDA-ARS quality testing program using samples from the multi-state Uniform Peanut Performance Tests (UPPT). Data were subjected to restricted maximum likelihood estimation of variance components reflecting the main effects of year, production region, location within region, genotype (cultivar or breeding line), and kernel grade (''seed size'') within genotype, and the interactions among these main effects. Genetic variation in oil content was low (9% of total variation); however, fatty acid composition of the oil was highly influenced by genotype (34-77%) with the exception of lignoceric acid (1%). Genetic influence on tocopherols was generally less than that of fatty acids. Environmental variation of tocopherols was greater than the variation attributable to genotype-by-environment interaction. The lowest genetic variation was observed in sugar content; however, environmental variation was high (68%). The magnitude of genetic influence on oil content and fatty acid concentrations suggests that these traits are amenable to improvement through breeding.

The sensory attributes that make up roasted peanut flavor quality are important traits to evaluate in the development of new cultivars. Recent publications have characterized the variation in sensory attributes in U.S. peanuts (Arachis hypogaea L.), however, no estimates of the effects of lines asparents in a breeding program have been calculated. Best linear unbiased prediction (BLUP) is a method for predicting the breeding value of a parent based on the performance of its relatives. Commonly used in animal and tree breeding, the method is rarely applied in annual crop species. The method was applied to a set of data collected on the three sensory attributes roasted peanut, sweet, and bitter for 250 peanut genotypes evaluated in 53 environments. BLUP solutions computed usingdifferent estimates ofnarrow-sense heritability (h 2) were highly correlated (r > 0.9), suggesting that precise estimates ofh 2 are not necessary. Correlations of values predicted by BLUP with observed values were moderate (0.63 < r < 0.71) for individual lines, but strong (0.85 < r < 0.92) for means of crosses. BLUPs ofbreeding "The research reported in this publication was a cooperative effort of the Agric. Res. Servo ofthe U.S. Dept. of Agric. and the North Carolina Agric. Res. Serv., Raleigh, NC 27695-7643. The use of trade names in this publication neitherimplies endorsement by the USDAor the NCARS of the products named nor criticism of similar ones not mentioned.

Early and late leaf spots are the major foliar diseases of peanut responsible for severely decreased yield in the absence of intensive fungicide spray programs. Pyramiding host resistance to leaf spots in elite cultivars is a sustainable solution to mitigate the diseases. In order to determine the genetic control of leaf spot disease resistance in peanut, a recombinant inbred line population (Florida-07 x GP-NC WS16) segregating for resistance to both diseases was used to construct a SNP-based linkage map consisting of 855 loci. QTL mapping revealed three resistance QTLs for late leaf spot qLLSA05 (phenotypic variation explained, PVE=7-10%), qLLSB03 (PVE=5-7%), and qLLSB05 (PVE=15-41%) that were consistently expressed over multi-year analysis. Two QTL, qLLSA05 and qLLSB05, confirmed our previously published QTL-seq results. For early leaf spot, three resistance QTLs were identified in multiple years, two on chromosome A03 (PVE=8-12%) and one on chromosome B03 (PVE=13-20%), with the locus qELSA03_1.1 coinciding with the previously published genomic region for LLS resistance in GPBD4. Comparative analysis of the genomic regions spanning the QTLs suggests that resistance to early and late leaf spots are largely genetically independent. In addition, QTL analysis on yield showed that the presence of resistance allele in qLLSB03 and qLLSB05 loci might result in protection from yield loss caused by LLS disease damage. Finally, post hoc analysis of the RIL subpopulation that was not utilized in the QTL mapping revealed that the flanking markers for these QTLs can successfully select for resistant and susceptible lines, confirming the effectiveness of pyramiding these resistance loci to improve host-plant resistance in peanut breeding programs using marker-assisted selection.