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Heather L. Merk, Shawn C. Yarnes, Allen Van Deynze, Nankui Tong, Naama Menda, Lukas A. Mueller, Martha A. Mutschler, Steven A. Loewen, James R. Myers, and David M. Francis

unbiased predictors were estimated for each line for each trait using the same models used to estimate variance components. The random effect “ranef” command in the lme4 package was used to estimate BLUPs for all terms in the model ( Bates et al., 2011

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Michelle L. Paynter, Joanne De Faveri, and Mark E. Herrington

genotypes and using them in breeding programs. Best linear unbiased predictions [BLUPs ( Henderson, 1984 )] of breeding values have been used in many breeding programs to increase the frequency of desired phenotypes in progeny ( Davik and Honne, 2005

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Steven J. McKay, James M. Bradeen, and James J. Luby

panelists were incorporated as random effects as were interaction terms, yielding best linear unbiased predictions [BLUP (e.g., Piepho et al., 2008 )]. Before analysis, the data were re-parameterized to reflect the unbalanced nesting of genotypes within

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Desalegn D. Serba, Osman Gulsen, Bekele G. Abeyo, Keenan L. Amundsen, Donald J. Lee, P. Stephen Baenziger, Tiffany M. Heng-Moss, Kent M. Eskridge, and Robert C. Shearman

(SAS Version 9.1; SAS Institute, Cary, NC) was used to detect differences among the half-sibs and their parents and to estimate the BLUPs ( Henderson, 1975 ) for the progeny in full-sibs. In the mixed model analysis, the parents and the crosses were

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Hongzhan Huang, James Harding, and Thomas Bvrne

The effects of long-term genetic improvement are measured by selection response predicted from estimates of narrow-sense heritability. However, changes of population mean must be partitioned into genetic and environmental components-in order to accurately estimate selection response.

A long-term selection experiment for cut-flower yield in the Davis population of gerbera (Gerbera hybrida, Compositae) was conducted for sixteen generations. Breeding value was estimated for individual plants in the population using Best Linear Unbiased Prediction (BLUP). Genetic change was calculated from breeding values of individual plants in each generation. The results of this study indicate: the long-term selection experiment was successful and necessary for genetic improvement. Genetic change over sixteen generations was 33 flowers. Mean breeding values increased monotonously with an “S” shape pattern. Environmental effects fluctuated from generation to generation. Cut-flower yield in the Davis population of gerbera will continuously respond to selection.

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Ulrik Bräuner Nielsen and Gary A. Chastagner

Needle retention is an important trait when selecting for high quality Christmas trees. Nordmann fir [Abies nordmanniana (Stev.) Spach.] is generally considered to have good needle retention, but recent research has shown that when cut trees are allowed to dry, significant needle loss problems can develop. This has the potential to limit the use of this species in situations where trees are harvested early, shipped long distances, sold in warm weather markets and displayed for extended periods of time. A set of 39 provenances where tested to identify provenance differences in needle retention. Branches where collected in two consecutive years in October in 1999 and 2000 and November 2000. Small branch samples where cut and displayed indoors under controlled conditions and allowed to dry. Strong provenance differences in needle loss were seen for all three test dates. No significant interactions were seen among the October collections, but significant rank changes occurred from October to November. Predicted (BLUP) provenance mean values ranged between 11% and 27% for needle loss when branches where allowed to dry, averaging all three tests. Despite only one test location, the study clearly indicates that it should be possible to select for provenances with generally better needle retention characteristics.

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Valdomiro A.B. de Souza, David H. Byrne, and Jeremy F. Taylor

Heritability estimates are useful to predict genetic progress among offspring when the parents are selected on their performance, but they also provide information about major changes in the amount and nature of genetic variability through generations. Genetic and phenotypic correlations, on the other hand, are useful for better planning of selection programs. In this research, seedlings of 39 families resulting from crosses among 27 peach [Prunus persica (L.) Batsch] cultivars and selections were evaluated for date of full bloom (DFB), date of ripening (DR), fruit period development (FDP), flower density (FD), node density (ND), fruit density (FRD), fruit weight (WT), soluble solids content (SS), apical protuberance (TIP), red skin color (BLUSH), and shape (SH) in 1993 and 1994. The data were analyzed using the mixed linear model. The best linear unbiased prediction (BLUP) was used to estimate fixed effects and predict breeding values (BV). Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple-trait model to estimate genetic and phenotypic covariances between traits. The data indicates high heritability for DFB, DR, FDP, and BLUSH, intermediate heritability for WT, TIP, and SH, and low heritability for FD, ND, FRD, and SS. They also indicate year effect as a major environmental component affecting seedling performance. High correlation estimates were found between some traits, but further analysis is needed to determine their significance.

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Valdomiro A.B. de Souza, David H. Byrne, and Jeremy F. Taylor

Seedlings of 108 families from crosses among 42 peach [Prunus persica (L.) Batsch] cultivars and selections were evaluated for six plant characteristics in 1993, 1994, and 1995. The data were analyzed by using a mixed linear model, with years treated as fixed and additive genotypes as random factors. Best linear unbiased prediction (BLUP) was used to estimate fixed effects. Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple trait model was used to estimate genetic and phenotypic covariances among traits. The narrow-sense heritability estimates were 0.41, 0.29, 0.48, 0.47, 0.43, and 0.23 for flower density, flowers per node, node density, fruit density, fruit set, and blind node propensity, respectively. Most genetic correlations among pairs of traits were ≥0.30 and were, in general, much higher than the corresponding phenotypic correlations. Flower density and flowers per node (ra = 0.95), fruit density and fruit set (ra = 0.84) and flower density and fruit density (ra = 0.71) were the combinations of traits that had the highest genetic correlation estimates. Direct selection practiced solely for flower density (either direction) is expected to have a greater effect on fruit density than direct selection for fruit density.

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Valdomiro A.B. de Souza, David H. Byrne, and Jeremy F. Taylor

Breeding values (BVs) for four plant (bloom date, fruit development period, fruit density, and blind node propensity) and five fruit (weight, blush, shape, soluble solids, and titratable acidity) traits of 28 peach [Prunus persica (L.) Batsch (Peach Group)] genotypes used as parents in the Texas A&M University peach breeding program were predicted using best linear unbiased prediction (BLUP). Data from seedlings of 108 families developed from 42 peach parents were analyzed by using a mixed linear model, with years treated as fixed and additive genotypes as random factors. The precision of the predictions was high for most parental genotypes, as indicated by the correlations (rTI) between predicted and true BVs and the standard error of the predictions (SEP). In most cases, the higher the number of progeny, the better the agreement between predicted and true BVs for that parent. Parents with observations from more than 30 seedlings had a rTI ≥ 0.90 and smaller SEPs. For all traits analyzed, the lowest precision (low rTI and high SEP) was observed for `Flordaking', whose predicted BVs was based only on pedigree information.

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Valdomiro A.B. de Souza, David H. Byrne, and Jeremy F. Taylor

Thirteen peach [Prunus persica (L.) Batsch] fruit characteristics were investigated for 3 years, 1993, 1994, and 1995, in College Station, Texas, to determine heritability, genetic and phenotypic correlations, and predicted response to selection. Seedlings of 108 families resulting from crosses among 42 peach cultivars and selections were used in the evaluations. A mixed linear model, with years treated as fixed and additive genotypes as random factors, was employed to analyze the data. Best linear unbiased prediction (BLUP) was used to estimate fixed effects. Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple trait model was used to estimate genetic and phenotypic covariances between traits. Genetic and phenotypic correlations ≥0.65 and <0.30 were considered strong or very strong and weak, respectively. Date of ripening, fruit development period (FDP) and date of full bloom had the highest heritability (h2) estimates, 0.94, 0.91, and 0.78, respectively. Fruit cheek diameter and titratable acidity (h2 = 0.31) were the traits with the lowest estimates. Fruit development period, fruit blush, and date of ripening had the highest predicted selection responses, whereas fruit suture, fruit cheek, L/W12 (ratio fruit length to average fruit diameters), and fruit tip had the lowest values. Most genetic correlations were ≥0.30 and were, in general, much higher than the corresponding phenotypic correlations. All four measures of fruit size were genetically and phenotypically very strongly correlated. Important genetic correlation estimates were also observed for date of ripening with FDP (ra = 0.93), date of ripening and FDP with fruit blush (ra = -0.77, ra = -0.72), SS (percent soluble solids) (ra = 0.63, ra = 0.62) and TA (ra = 0.55, ra = 0.64), and SS with TA (ra = -0.56). Direct selection practiced solely for early ripening and short FDP is expected to have a greater effect on correlated traits than direct selection for early bloom and large fruit mass.