Marcos Deon Vilela de Resende

Genomic selection in forest tree breeding

Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (Ne) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by Ne and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when Ne ≤ 30, while up to 20 markers/cM are necessary for larger Ne. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.

Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.)

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments

• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53-112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.

Genetic parameters and variability in physic nut accessions during early developmental stages

The objective of this work was to estimate the genetic parameters and variability among accessions (half-sib families) of physic nut (Jatropha curcas) during the early stages of development. For this study, 110 accessions in the first year of development of the physic nut germplasm bank, maintained at Embrapa Cerrados, DF, Brazil, were evaluated in situ. The experiment was established in a randomized complete block design, with two replicates and five plants per plot arranged in rows at 4x2 m spacing. Grain yield, total number of branches per plant, plant height, stem diameter, canopy projection on the row, canopy projection between rows, canopy volume, number of days until first flowering and height of the first inflorescence were evaluated. Estimates of vegetative genetic parameters showed the existence of genetic variability in the physic nut germplasm bank. Physic nut accessions of the germplasm bank were grouped into five similarity groups based on character divergence. Although preliminary, the obtained results are promising for showing potential for Jatropha improvement with selective efficiency.

Unraveling additive from nonadditive effects using genomic relationship matrices.

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

Estabilidade de cultivares e linhagens de feijoeiro em diferentes ambientes no estado de São Paulo

The present study had the objective of a genotypic evaluation of common bean lines in the state of Sao Paulo. Genotypic values of the lines for each location and for the environment mean of all locations were predicted as well. Studies on the adaptability and stability of the predicted genotypic values were realized by the BLUP procedure. The cultivar Grauna and line GEN 96A31 presented an average superiority of 10% over the general mean of the 15 environments. These values were computed including the penalization of the lines for the instability across locations and simultaneously crediting the response capacity (adaptability) to improved environments, using intrinsic properties of the MHPRVG method (Harmonic mean of the relative performance of the genetic values). The methods MHPRVG, Lin & Binns and Annicchiarico selected the same lines. Moreover, the results provided by the method MHPRVG were in the same measurement scale of the evaluated trait and were directly interpreted as genetic values simultaneously, for yield, stability and adaptability.

Selection and genetic gain in rubber tree (Hevea) populations using a mixed mating system

The components of genetic variation and genetic gain obtained with three selection methods - individual, combined and multi-effect index selection - were compared in rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] progenies. The rubber tree is a cross pollinating species with a mixed reproductive system in which the self pollination rate is 22%. Twenty-two half sib progenies were planted at experimental stations at Pindorama, Votuporanga and Jau, in Sao Paulo State, using a randomized and complete block design, with five replications and ten plants per plot. Dry rubber production was assessed when the plants were three years old. Based on the genetic variability of the populations, Pindorama was the best environment for the expression of variability. At the individual level, heritability was seriously affected when random progenies from an open pollinating population were considered as half sib progenies. Considerable overestimation of genetic gains occurred during individual, combined and multi-effect index selection when the rubber tree reproductive system was not considered as mixed. Selection based on the multi-effect index maximizes genetic progress and should be used more in rubber tree breeding programs.

Genetic improvement of sugar cane for bioenergy: the brazilian experience in network research with RIDESA

In this paper, it is presented RIDESAs model for sugar cane breeding to ethanol, and its scientific, technological and human resources training contributions. RIDESA is an inter-university network for the development of sugar cane industry in Brazil, and was formed by a technical cooperation agreement between ten public universities. The model of network management is presented in this study, which involves, among other things, the public-private partnership (Universities-Mills) for the development of cultivars. RIDESA has produced 59 cultivars since 1990 and is now responsible for 59% of the total area cultivated with this plant in Brazil. In the last five years, 286 agronomists were trained in breeding programs at universities that comprise RIDESA. In this same period, the network formed 35 professors, 24 doctors and 7 post-docs in researches with this crop. It is also presented a conceptual approach on methods of sugar cane breeding involving families and genome-wide selection.

APLICAÇÃO DA METODOLOGIA DE MODELOS MISTOS (REML/BLUP) NA ESTIMAÇÃO DE COMPONENTES DE VARIÂNCIA E PREDIÇÃO DE VALORES GENÉTICOS EM PUPUNHEIRA (Bactris gasipaes)

The peach palm is a very useful plant for feeding Brazilians as fruit or palm heart producer. The interest for the peach palm besides being a perennial culture is: growth in full sun, precocity, rusticity, capacity to shoot, flavor and non-darkening of the palm heart after the cut. Estimates of genetic parameters in peach palm are scarce and constitute the most important tool to guide the improvement programs. The objective of this work was to study the genetic variability and estimate the individual genetic value as selection criterion, using the BLUP/REML procedure (Best linear unbiased prediction/restricted maximum likelihood). Two selection strategies for the palm heart production trait were adopted: a short term (CP - selection of the 9 families with 31 individuals of bigger genetic value) and a long term (LP - selection of the 15 families with 53 individuals). The progenies were evaluated in randomized block design with three replications, the plots were composed by rows of five plants, spaced in 2.0 m x 1.0 m and with a row around the experiment in the Experimental Field of Matapi, Porto Grande municipality, Amapa State, Brazil. The evaluation was accomplished to the 26 months after planting (2nd evaluation) being collected data of plant height (AP), diameter of the plant to the lap height (DPC), palm heart size (TP), palm heart diameter (DP), residual apical weight (PRA), basal weight (PRB) and of the liquid palm heart (PP) (exportation type). The data of AP, DPC, TP and DP corresponded to the clump of roots averages that presented more than a stem. However for the characters PA, PRB and PP corresponded the sum of the stems in the clump of roots. In general, the population presented low genetic variability. The narrow sense heritability at the individuals level was: AP (18.44%), DPC (3.16%), TP (42.47%), DP (10.54%), PP (5.70%), PRB (6.15%). The genetic gain estimated in relation to average of the population for PP were of 7.18% in the LP situation and 8.40% for CP, with effective size of 30.38 and 19.00, respectively.

ESTIMATIVAS DE PARÂMETROS GENÉTICOS E PREDIÇÃO DE VALORES GENOTÍPICOS NO MELHORAMENTO DO CAFEEIRO PELO PROCEDIMENTO REML/BLUP

Objetivou-se aplicar o metodo REML/BLUP em programas de melhoramento genetico do cafeeiro, utilizando-o na estimacao de parâmetros geneticos e predicao de valores genotipicos para a especie Coffea arabica. O experimento foi instalado em julho de 1998 pela Universidade Estadual Paulista (UNESP), em Selviria (MS). As 12 cultivares selecionadas pelo Instituto Agronomico (IAC), Campinas (SP), foram avaliadas no delineamento em blocos ao acaso com quatro repeticoes e dez plantas por parcela. Os resultados revelaram baixa variabilidade genetica entre as cultivares para os caracteres altura da planta, diâmetro do caule e numero de ramos plagiotropicos, avaliados aos 26 meses. Apenas as cultivares Catuai Amarelo, Icatu Vermelho e Catuai Vermelho apresentaram valores genotipicos preditos para o diâmetro do caule superiores a media geral desse carater. A acuracia na avaliacao dos valores genotipicos das cultivares para o carater diâmetro do caule equivaleu, em media, a 76%. A adocao de uma experimentacao com duas plantas por parcela e 20 repeticoes podera elevar a 90% a acuracia seletiva para o carater diâmetro do caule. O metodo de modelos mistos (REML/BLUP) mostrou-se adequado a estimacao de parâmetros geneticos e predicao de valores genotipicos no melhoramento do cafeeiro, podendo ser empregado rotineiramente.