Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.

Marker-assisted selection holds promise for highly influencing tree breeding, especially for wood traits, by considerably reducing breeding cycles and increasing selection accuracy. In this study, we used a candidate gene approach to test for associations between 944 single-nucleotide polymorphism markers from 549 candidate genes and 25 wood quality traits in white spruce. A mixed-linear model approach, including a weak but nonsignificant population structure, was implemented for each marker-trait combination. Relatedness among individuals was controlled using a kinship matrix estimated either from the known half-sib structure or from the markers. Both additive and dominance effect models were tested. Between 8 and 21 single-nucleotide polymorphisms (SNPs) were found to be significantly associated (P # 0.01) with each of earlywood, latewood, or total wood traits. After controlling for multiple testing (Q # 0.10), 13 SNPs were still significant across as many genes belonging to different families, each accounting for between 3 and 5% of the phenotypic variance in 10 wood characters. Transcript accumulation was determined for genes containing SNPs associated with these traits. Significantly different transcript levels (P # 0.05) were found among the SNP genotypes of a 1-aminocyclopropane-1-carboxylate oxidase, a b-tonoplast intrinsic protein, and a long-chain acyl-CoA synthetase 9. These results should contribute toward the development of efficient marker-assisted selection in an economically important tree species.

BackgroundGenomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.ResultsModel fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71–0.79) and moderately high for growth (r = 0.52–0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.ConclusionsGiven the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Pedigrees reconstructed through DNA marker assigned paternities in polymix (PMX) and open pollinated (OP) progeny tests were analyzed using mixed models to test the effect of unequal male reproductive success and pedigree errors on quantitative genetic parameters. The reconstructed pedigree increased heritabilities in the larger PMX test. Increased heritability resulted from adding the paternities to the pedigree per se, not by correcting the male reproductive bias by specifying the exact pedigree. Removing hypothesized pedigree errors had no effect on quantitative parameters, either because the magnitude of the errors was too small (PMX) or the progeny test was too small to detect variance components reliably (OP). Although there was no advantage in backwards selection, the increased additive variance, heritabilities and accuracy of progeny with assigned paternities in the pedigree, should permit forward selection of offspring with greater genetic gain and complete control of coancestry for future breeding decisions. Some possible breeding population structures with the new genetic information are discussed.

proved only indirectly. Testcross methods of population improvement, including RRS, have improved GCA with Recurrent selection (RS) is a population improvement method thatgenetically narrow-based as well as broad-based testers increases the frequency of favorable alleles while maintaining genetic variation in breeding populations. Twelve University of Guelph RS (Walejko and Russell, 1977;Horner et al., 1989). Commaize (Zea mays L.) populations selected via reciprocal recurrent bining the S and RRS methods (COM) simultaneously selection (RRS), selfed-progeny recurrent selection (S ), or a method should permit the benefits of nonadditive and additive combining RRS and S (COM), were assessed for changes in the genetic genetic effects in testcross and per se evaluation, respecstructure of grain yield, grain moisture, and broken stalks, and two tively, to be realized (Goulas and Lonnquist, 1976; Dhilassociated selection indices. Partitioning of the entry sums of squares lon, 1991). Progress from COM selection is expected to from diallel matings of the original (C 0 ) and advanced (C A ) cycle be the summation of expected progress for each individpopulations using Gardner and Eberhart's Analysis II and Analysis ual method (Hallauer and Miranda Filho, 1988). III indicated genetic improvement occurred for the per se and crossThe variance of the crosses from a diallel mating performance of most populations. Accompanying the favorable scheme can be partitioned to GCA and specific combinchanges in population performance were less favorable shifts from predominantly additive genetic effects in C 0 to greater nonadditive ing ability (SCA). Griffing (1956a) demonstrated that genetic effects in C A . This shift did not substantially change the general two times the GCA variance ( 2 GCA ) contained all the combining ability estimates (g i ) of most populations. However, for additive (A) variance and a portion of the A ϫ A epigrain yield, the underlying components of g i effects were altered in static variance. Conversely, the SCA variance ( 2 SCA ) their relative importance. General combining ability (GCA) effects component contained all the dominance (D) variance in the C 0 were caused primarily by the population per se effects (v i ), and the remaining portion of the epistatic variance, inwhile in C A the GCA effects were caused predominately by parental cluding the remainder of the A ϫ A as well as the A ϫ heterotic effects (h i ).

Phenotypic stability has long been recognized as an important target in plant breeding. Stability is influenced in part by the genetic structure, i.e., level of heterogeneity and heterozygosity, of the cultivar. Yet, very little is known about the genetic components underlying stability, and how population improvement strategies influence stability. We examined 12 maize (Zea mays L.) breeding populations selected via reciprocal recurrent selection (RRS), selfed progeny recurrent selection (S), or a method combining RRS and S (COM), to examine changes in the genetic structure of the phenotypic stability of three traits (grain yield, grain moisture, and broken stalks), and two associated selection indices. Partitioning of the genotype × environment sums of squares from diallel matings of the original (C0) and advanced (CA) cycle populations into linear trends indicated that only grain yield and the unadjusted performance index (UPI) followed a predictable linear response. Grain yield and UPI linear trends were further partitioned by Gardner and Eberhart Analysis III to examine the genetic components of stability. We found that recurrent selection (RS) improved grain yield stability, and that this trait is heritable, predictable, and mostly controlled through additive gene action. Improvement in grain yield stability was observed both in cross and per se performance and was accompanied by significant improvement in the mean performance of the populations. However, the improvement in grain yield stability did not result in substantial changes in the general combining ability (gi) estimates of most populations. Our results indicate that grain yield stability can be improved through RS by selecting solely for mean performance across multiple environments.

Open-pollinated and polycross mating systems are widely used in forest genetics and breeding to quickly, simply, and inexpensively generate progenies assumed to be related as half-sibs (coefficient of relationship, r = 0.25) from a random mating population. However, nonrandom mating, such as unequal male reproductive success (RS) or selfing, can increase the genetic correlation among offspring, and thus, genetic variance and heritability are overestimated. Conversely, pedigree errors will cause additive genetic variance and heritability to be underestimated. Unequal male reproductive success and three types of potential pedigree errors (volunteers, mishandled maternal identities, and foreign pollen) were detected in operational open-pollinated and polycross red spruce ( Picea rubens Sarg.) progeny tests, through paternity testing using microsatellite (simple sequence repeat) DNA markers. The potential impact of unequal RS and pedigree errors on quantitative genetic parameters is discussed. Paternity and parentage analyses could be used to reconstruct the pedigree of any plantation consisting of sibships, where candidate parents (e.g., members of seed orchard) can be identified. This offers an alternative to traditional progeny testing for estimation of quantitative genetic parameters.

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