Patricio Munoz

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.

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.

Association genetics of oleoresin flow in loblolly pine: discovering genes and predicting phenotype for improved resistance to bark beetles and bioenergy potential

Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h(-1)) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in the southeastern United States. Additive genetic variation in oleoresin flow (h(2) ≈ 0.12-0.30) was strongly correlated between years in which precipitation varied (r(a) ≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231 SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites. SNPs in sequences similar to ethylene signaling proteins, ABC transporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5- to 2.4-fold in one generation.

Discovering candidate genes that regulate resin canal number in Pinus taeda stems by integrating genetic analysis across environments, ages, and populations.

Genetically improving constitutive resin canal development in Pinus stems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association with RCN in two growth rings and three environments in a complex pedigree of 520 Pinus taeda individuals (CCLONES). The map locations of associated genes were compared with RCN quantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottii pseudo-backcross of 345 full-sibs (BC1). Resin canal number was heritable (h(2) ˜ 0.12-0.21) and positively genetically correlated with xylem growth (rg ˜ 0.32-0.72) and oleoresin flow (rg ˜ 0.15-0.51). Sixteen well-supported candidate regulators of RCN were discovered in CCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped to RCN QTLs in BC1. Breeding is predicted to increase RCN 11% in one generation and could be accelerated with genomic selection at accuracies of 0.45-0.52 across environments. There is significant genetic variation for RCN in loblolly pine, which can be exploited in breeding for elevated terpene content.

Ridge, Lasso and Bayesian additive-dominance genomic models

BackgroundA complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes).ResultsG-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close.ConclusionsAmongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments.

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families.

The contribution of dominance to phenotype prediction in a pine breeding and simulated population

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive–dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.

Resource quality affects weapon and testis size and the ability of these traits to respond to selection in the leaf-footed cactus bug, Narnia femorata

Abstract The size of weapons and testes can be central to male reproductive success. Yet, the expression of these traits is often extremely variable. Studies are needed that take a more complete organism perspective, investigating the sources of variation in both traits simultaneously and using developmental conditions that mimic those in nature. In this study, we investigated the components of variation in weapon and testis sizes using the leaf‐footed cactus bug, Narnia femorata (Hemiptera: Coreidae) on three natural developmental diets. We show that the developmental diet has profound effects on both weapon and testis expression and scaling. Intriguingly, males in the medium‐quality diet express large weapons but have relatively tiny testes, suggesting complex allocation decisions. We also find that heritability, evolvability, and additive genetic variation are highest in the high‐quality diet for testis and body mass. This result suggests that these traits may have an enhanced ability to respond to selection during a small window of time each year when this diet is available. Taken together, these results illustrate that normal, seasonal fluctuations in the nutritional environment may play a large role in the expression of sexually selected traits and the ability of these traits to respond to selection.

A Consensus Genetic Map for Pinus taeda and Pinus elliottii and Extent of Linkage Disequilibrium in Two Genotype-Phenotype Discovery Populations of Pinus taeda

A consensus genetic map for Pinus taeda (loblolly pine) and Pinus elliottii (slash pine) was constructed by merging three previously published P. taeda maps with a map from a pseudo-backcross between P. elliottii and P. taeda. The consensus map positioned 3856 markers via genotyping of 1251 individuals from four pedigrees. It is the densest linkage map for a conifer to date. Average marker spacing was 0.6 cM and total map length was 2305 cM. Functional predictions of mapped genes were improved by aligning expressed sequence tags used for marker discovery to full-length P. taeda transcripts. Alignments to the P. taeda genome mapped 3305 scaffold sequences onto 12 linkage groups. The consensus genetic map was used to compare the genome-wide linkage disequilibrium in a population of distantly related P. taeda individuals (ADEPT2) used for association genetic studies and a multiple-family pedigree used for genomic selection (CCLONES). The prevalence and extent of LD was greater in CCLONES as compared to ADEPT2; however, extended LD with LGs or between LGs was rare in both populations. The average squared correlations, r2, between SNP alleles less than 1 cM apart were less than 0.05 in both populations and r2 did not decay substantially with genetic distance. The consensus map and analysis of linkage disequilibrium establish a foundation for comparative association mapping and genomic selection in P. taeda and P. elliottii.