Pedigree-based Model Research Articles

Autoregressive single-step model for genomic evaluation of longitudinal reproductive traits in portuguese holstein cattle.

We investigated the applicability of ssGBLUP methodology under the autoregressive model (H-AR) for genomic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle. The genotype data of 1,230 bulls and 1,645 cows were considered in our study. The reproductive traits evaluated were interval from calving to first service (ICF), calving interval (CI) and daughter pregnancy rate (DPR) measured during the first four parities. Reliability and rank correlation were used to compare the H-AR with the traditional pedigree-based autoregressive models (A-AR). In addition, a validation study was performed considering different scenarios. Higher genomic estimated breeding values (GEBV) reliabilities were obtained for genotyped bulls when evaluated under the H-AR model, with emphasis on bulls with less than 9 daughters. For this group, the averages of GEBV reliabilities corresponded to 0.62, 0.69 and 0.62 for ICF, CI and DPR, respectively, while the averages obtained by the A-AR model were 0.27, 0.15 and 0.16. The validation study was favourable to H-AR. The best results were observed in the scenario where genotyped cows were combined with contributing bulls (genotyped bulls with daughter or relationship information in the population). Overall, the results suggest that ssGBLUP methodology under the autoregressive model is a feasible and applicable approach to be used in genomic analyses of longitudinal reproductive traits in Portuguese Holstein cattle.

Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits.

With climate change, the pressure on tree breeding to provide varieties with improved resilience to biotic and abiotic stress is increasing. As such, pest resistance is of high priority but has been neglected in most tree breeding programs, given the complexity of phenotyping for these traits and delays to assess mature trees. In addition, the existing genetic variation of resistance and its relationship with productivity should be better understood for their consideration in multitrait breeding. In this study, we evaluated the prospects for genetic improvement of the levels of acetophenone aglycones (AAs) in white spruce needles, which have been shown to be tightly linked to resistance to spruce budworm. Furthermore, we estimated the accuracy of genomic selection (GS) for these traits, allowing selection at a very early stage to accelerate breeding. A total of 1,516 progeny trees established on five sites and belonging to 136 full‐sib families from a mature breeding population in New Brunswick were measured for height growth and genotyped for 4,148 high‐quality SNPs belonging to as many genes along the white spruce genome. In addition, 598 trees were assessed for levels of AAs piceol and pungenol in needles, and 578 for wood stiffness. GS models were developed with the phenotyped trees and then applied to predict the trait values of unphenotyped trees. AAs were under moderate‐to‐high genetic control (h 2: 0.43–0.57) with null or marginally negative genetic correlations with other traits. The prediction accuracy of GS models (GBLUP) for AAs was high (PAAC: 0.63–0.67) and comparable or slightly higher than pedigree‐based (ABLUP) or BayesCπ models. We show that AA traits can be improved and that GS speeds up the selection of improved trees for insect resistance and for growth and wood quality traits. Various selection strategies were tested to optimize multitrait gains.

A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.

De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation [FM]) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: (1) cancer and mutation spectra along with parental ages were similarly distributed; (2) ascertainment criteria like early-onset breast cancer (age 20–35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.

Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling.

The animal's reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator's knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termed difficult and easy from their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred the difficult and easy scores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships among difficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors of difficult and easy exhibited a large negative genetic correlation of −0.92. Moderate genetic correlation was found between DS and difficult (0.36), easy (−0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS with difficult (CVSSD: 0.35; SSD: 0.42) and easy (CVSSD: −0.35; SSD: −0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production.

Genome-wide association study and genomic prediction for resistance against Streptococcus agalactiae in hybrid red tilapia (Oreochromis spp.)

Streptococcosis is a major disease that causes severe mortality in tilapia aquaculture worldwide. Although the conventional BLUP family selection to enhance disease resistance in a commercial red tilapia stock was successful, the response was low due to the low heritability of the traits. An alternative strategy is the utilization of genomic information to identify the best performing candidates within families. In this study, we performed genome-wide association studies for red tilapia resistance to Streptococcus agalactiae using 11,480 SNPs within 110 families represented by 1020 fish. Nineteen SNP markers were found to explain ~10% of the genetic variation. We compared the accuracies of genomic prediction using the pedigree-based (PBLUP), marker-based (GBLUP), and Bayesian models. The prediction accuracy was assessed by performing ten replicates of five-fold cross-validation. In each replicate, approximately 80% of the data (n~816) were sampled for the training set and the remaining data (n~204) were used for the validation. The BayesB model yielded the highest accuracies (0.31 and 0.20) followed by GBLUP (0.25 and 0.15) and PBLUP (0.15 and 0.06) for days to death and binary trait.

Accuracy of genomic prediction of shell quality in a White Leghorn line

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring’s breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22–52% more accurate than predictions based on the maternal pedigree, and 5–7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61–0.70) and full-sib progeny tests (0.61–0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.

Single-Step Methodology for Genomic Evaluation in Turkeys (Meleagris gallopavo).

Genomic information can contribute significantly to the increase in accuracy of genetic predictions compared to using pedigree relationships alone. The main objective of this study was to compare the prediction ability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic BLUP (ssGBLUP) models. Turkey records of feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking ability were provided by Hybrid Turkeys, Kitchener, Canada. This data was analyzed using pedigree-based and single-step genomic models. The genomic relationship matrix was calculated either using observed allele frequencies, all allele frequencies equal to 0.5 or with a different scaling. To avoid potential problems with inversion, three different weighting factors were applied to combine the genomic and pedigree matrices. Across the studied traits, ssGBLUP had higher heritability estimates and significantly outperformed PBLUP in terms of accuracy. Walking ability was genetically negatively correlated to body weight and breast meat yield; however, it was not correlated to feed conversion ratio (FCR) or residual feed intake (RFI). Body weight showed a moderate positive genetic correlation to feed conversion ratio, residual feed intake and breast meat yield. Feed conversion ratio was strongly correlated to residual feed intake (0.68 ± 0.06). There was almost no genetic correlation between breast meat yield and feed efficiency traits. Larger differences in accuracy between PBLUP and ssGBLUP were observed for traits with lower heritability. Results of the three weighting factors showed only slight differences and an increase in accuracy of prediction compared to PBLUP. Slightly different levels of bias were observed across the models, but were higher among the traits; BMY was the only trait that had a regression coefficient higher than 1 (1.38 to 1.41). We show that incorporating genomic information increases the prediction accuracy for preselection of young candidate turkeys for the five traits investigated. Single-step genomic prediction showed substantially higher accuracy estimates than the pedigree-based model, and only slight differences in bias were observed across the alternate models.

Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

BackgroundAfter the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1–20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno2: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).ResultsIn general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.ConclusionsThe results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

Genomic prediction for commercial traits using univariate and multivariate approaches in Nile tilapia (Oreochromis niloticus)

BackgroundOver the past three decades, Nile tilapia industry has grown into a significant aquaculture industry spread over 120 tropical and sub-tropical countries around the world accounting for 7.4% of global aquaculture production in 2015. Across species, genomic selection has been shown to increase predictive ability and genetic gain, also extending into aquaculture. Hence, the aim of this paper is to compare the predictive abilities of pedigree- and genomic-based models in univariate and multivariate approaches, with the aim to utilise genomic selection in a Nile tilapia breeding program. A total of 1444 fish were genotyped (48,960 SNP loci) and phenotyped for body weight at harvest (BW), fillet weight (FW) and fillet yield (FY). The pedigree-based analysis utilized a deep pedigree, including 14 generations. Estimated breeding values (EBVs and GEBVs) were obtained with traditional pedigree-based (PBLUP) and genomic (GBLUP) models, using both univariate and multivariate approaches. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation with three different cross-validation approaches. Further, impact of these models and approaches on the genetic evaluation was assessed based on the ranking of the selection candidates. ResultsGBLUP univariate models were found to increase the prediction accuracy and reduce bias of prediction compared to other PBLUP and multivariate approaches. Relative to pedigree-based models, prediction accuracy increased by ~20% for FY, >75% for FW and >43% for BW. GBLUP models caused major re-ranking of the selection candidates. Within GBLUP models, there was no significant difference between rankings produced by univariate and multivariate approaches. The heritabilities using multivariate GBLUP models for BW, FW and FY were 0.19 ± 0.04, 0.17 ± 0.04 and 0.23 ± 0.04 respectively. BW showed very high genetic correlation with FW (0.96 ± 0.01) and a slightly negative genetic correlation with FY (−0.11 ± 0.15). ConclusionPredictive ability of genomic prediction models is substantially higher than for classical pedigree-based models. Genomic selection is therefore beneficial to the Nile tilapia breeding program, and it is recommended in routine genetic evaluations of commercial traits in the Nile tilapia breeding nucleus.

Increased Prediction Ability in Norway Spruce Trials Using a Marker X Environment Interaction and Non-Additive Genomic Selection Model.

A genomic selection study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in 2 Northern Swedish trials at 2 locations using exome capture as a genotyping platform. Nonadditive effects including dominance and first-order epistatic interactions (including additive-by-additive, dominance-by-dominance, and additive-by-dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. Genomic selection models partitioned additive and nonadditive genetic variances more precisely than pedigree-based models. In addition, predictive ability in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to genomic selection per year increased up to 78.9/80.8%, 86.9/82.9%, and 91.3/88.2% compared with response to phenotypic selection per year when genomic selection was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for sites 1 and 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also when they are significant may improve the genetic gain. For tree height, Pilodyn, and modulus of elasticity (MOE), response to genomic selection per year improved up to 68.9%, 91.3%, and 92.6% compared with response to phenotypic selection per year, respectively.Subject Area: Quantitative genetics and Mendelian inheritance

Handedness heritability in industrialized and nonindustrialized societies.

In modern societies, there is a decreased usage of traditional weapons to settle interpersonal or inter-group disputes compared to usage in traditional societies, possibly affecting the frequency-dependent selection on the handedness polymorphism. Another societal difference is the extensive automation of hard manual labour (including agriculture) in industrialized societies, relaxing the selection for hand specialization. Thus, selection of handedness is likely to differ between traditional and modern societies. As heritability determines the relative speed of evolutionary dynamics, handedness heritability was compared between industrialized and non-industrialized societies. First, individuals were sampled from a non-industrialized area in Indonesia, where violent conflicts are relatively frequent and tribal wars have been prevalent recently. Handedness was recorded directly or indirectly for 11,490 individuals belonging to 650 independent pedigrees, and handedness heritability was estimated using a pedigree-based animal model. Second, estimates of handedness heritability derived from published sources were collected to compare heritability estimates, accounting for various confounding variables. Non-industrialized countries displayed a significantly higher heritability value (h2 = 0.56) than that of industrialized countries (h2 = 0.20). Heritability decreased with time along the twentieth century in industrialized countries, independently of the frequency of left-handedness, and independently of the method used to measure handedness. In conclusion, the data are consistent with a decrease in handedness heritability following the industrialization process and/or the associated decrease in violence using traditional weapons. The difference in heritability between industrialized and non-industrialized countries suggests that selection of handedness is thus likely to differ between traditional and modern societies.

Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results.

The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%-56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Breeding New Cultivars for the Australian Macadamia Industry

In 2017, five new cultivars specifically selected for Australian conditions were released. These were developed from an improvement program initiated by Commonwealth Scientific and Industrial Research Organisation in the early 1990s. Progeny seeds were produced by crossing industry standard cultivars with other cultivars with elite kernel production per unit projected canopy area. Seedlings were planted at two densities (2 m and 4 m along rows) in field trials at Bundaberg in 1997 and 1998, and Northern New South Wales in late 1997, along with replicated plants of parents grafted onto seedling rootstocks. Trials were assessed for commencement of flowering, growth, yield, kernel recovery, and components of kernel quality over 8 years. Best linear unbiased predictions of clonal values were obtained for each individual progeny using a pedigree-based mixed linear model. A bio-economic model was used to estimate economic weights for a selection index of clonal values to identify elite candidates. Final approval of 20 candidates for second-stage assessment was made by an industry committee using selection index rankings and observations of tree field performance and kernel quality.

Estimating the purebred-crossbred genetic correlation of body weight in broiler chickens with pedigree or genomic relationships

BackgroundIn pig and poultry breeding programs, the breeding goal is to improve crossbred (CB) performance, whereas selection in the purebred (PB) lines is often based on PB performance. Thus, response to selection may be suboptimal, because the genetic correlation between PB and CB performance (r_{pc}) is generally lower than 1. Accurate estimates of the r_{pc} are needed, so that breeders can decide if they should collect data from CB animals. r_{pc} can be estimated either from pedigree or genomic relationships, which may produce different results. With genomic relationships, the r_{pc} estimate could be improved when relationships between purebred and crossbred animals are based only on the alleles that originate from the PB line of interest. This work presents the first comparison of estimated r_{pc} and variance components of body weight in broilers, using pedigree-based or genotype-based models, where the breed-of-origin of alleles was either ignored or considered. We used genotypes and body weight measurements of PB and CB animals that have a common sire line.ResultsOur results showed that the r_{pc} estimates depended on the relationship matrix used. Estimates were 5 to 25% larger with genotype-based models than with pedigree-based models. Moreover, r_{pc} estimates were similar (max. 7% difference) regardless of whether the model considered breed-of-origin of alleles or not. Standard errors of r_{pc} estimates were smaller with genotype-based than with pedigree-based methods, and smaller with models that ignored breed-of-origin than with models that considered breed-of-origin.ConclusionsWe conclude that genotype-based models can be useful for estimating r_{pc}, even when the PB and CB animals that have phenotypes are closely related. Considering breed-of-origin of alleles did not yield different estimates of r_{pc}, probably because the parental breeds of the CB animals were distantly related.

Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce

BackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as genotyping platform. We used 116,765 high-quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS.ResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11–14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased to more than 30% while using one site as training for GS prediction and using this model to predict the second site, indicating that G × E for tree height should be accommodated in model fitting. Using a half-sib family structure instead of full-sib structure led to a significant reduction in GS accuracy and PA. The full-sib family structure needed only 750 markers to reach similar accuracy and PA, as compared to 100,000 markers required for the half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000–8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all markers.ConclusionsThe study indicates that GS would be efficient in reducing generation time of breeding cycle in conifer tree breeding program that requires long-term progeny testing. The sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G × E is detected.

Genomic prediction for crossbred performance using metafounders.

Future genomic evaluation models to be used routinely in breeding programs for pigs and poultry need to be able to optimally use information of crossbred (CB) animals to predict breeding values for CB performance of purebred (PB) selection candidates. Important challenges in the commonly used single-step genomic best linear unbiased prediction (ssGBLUP) model are the definition of relationships between the different line compositions and the definition of the base generation per line. The use of metafounders (MFs) in ssGBLUP has been proposed to overcome these issues. When relationships between lines are known to be different from 0, the use of MFs generalizes the concept of genetic groups relying on the genotype data. Our objective was to investigate the effect of using MFs in genomic prediction for CB performance on estimated variance components, and accuracy and bias of GEBV. This was studied using stochastic simulation to generate data representing a three-way crossbreeding scheme in pigs, with the parental lines being either closely related or unrelated. Results show that using MFs, the variance components should be scaled appropriately, especially when basing them on estimates obtained with, for example a pedigree-based model. The accuracies of GEBV that were obtained using MFs were similar to accuracies without using MFs, regardless whether the lines involved in the CB were closely related or unrelated. The use of MFs resulted in a model that had similar or somewhat better convergence properties compared to other models. We recommend the use of MFs in ssGBLUP for genomic evaluations in crossbreeding schemes.

Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions

The aim of this study was to compare genetic (co)variance components and prediction accuracies of breeding values from genomic random regression models (gRRM) and pedigree-based random regression models (pRRM), both defined with or without an additional environmental gradient. The used gradient was a temperature-humidity index (THI), considered in statistical models to investigate possible genotype by environment (G×E) interactions. Data included 106,505 test-day records for milk yield (MY) and 106,274 test-day records for somatic cell score (SCS) from 12,331 genotyped Holstein Friesian daughters of 522 genotyped sires. After single nucleotide polymorphism quality control, all genotyped animals had 40,468 single nucleotide polymorphism markers. Test-day traits from recording years 2010 to 2015 were merged with temperature and humidity data from the nearest weather station. In this regard, 58 large-scale farms from the German federal states of Berlin-Brandenburg and Mecklenburg-West Pomerania were allocated to 31 weather stations. For models with a THI gradient, additive genetic variances and heritabilities for MY showed larger fluctuations in dependency of DIM and THI than for SCS. For both traits, heritabilities were smaller from the gRRM compared with estimates from the pRRM. Milk yield showed considerably larger G×E interactions than SCS. In genomic models including a THI gradient, genetic correlations between different DIM × THI combinations ranged from 0.26 to 0.94 for MY. For SCS, the lowest genetic correlation was 0.78, estimated between SCS from the last DIM class and the highest THI class. In addition, for THI × THI combinations, genetic correlations were smaller for MY compared with SCS. A 5-fold cross-validation was used to assess prediction accuracies from 4 different models. The 4 different models were gRRM and pRRM, both modeled with or without G×E interactions. Prediction accuracy was the correlation between breeding values for the prediction data set (i.e., excluding the phenotypic records from this data set) with respective breeding values considering all phenotypic information. Prediction accuracies for sires and for their daughters were largest for the gRRM considering G×E interactions. Such modeling with 2 covariates, DIM and THI, also allowed accurate predictions of genetic values at specific DIM. In comparison with a pRRM, the effect of a gRRM with G×E interactions on gain in prediction accuracies was stronger for daughters than for sires. In conclusion, we found stronger effect of THI alterations on genetic parameter estimates for MY than for SCS. Hence, gRRM considering THI especially contributed to gain in prediction accuracies for MY.

Do stronger measures of genomic connectedness enhance prediction accuracies across management units?

Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Ranking of individuals across units based on best linear unbiased prediction (BLUP) is reliable when there is a sufficient level of connectedness due to a better disentangling of genetic signal from noise. Connectedness arises from genetic relationships among individuals. Although a recent study showed that genomic relatedness strengthens the estimates of connectedness across management units compared with that of pedigree, the relationship between connectedness measures and prediction accuracies only has been explored to a limited extent. In this study, we examined whether increased measures of connectedness led to higher prediction accuracies evaluated by a cross-validation (CV) based on computer simulations. We applied prediction error variance of the difference, coefficient of determination (CD), and BLUP-type prediction models to data simulated under various scenarios. We found that a greater extent of connectedness enhanced accuracy of whole-genome prediction. The impact of genomics was more marked when large numbers of markers were used to infer connectedness and evaluate prediction accuracy. Connectedness across units increased with the proportion of connecting individuals and this increase was associated with improved accuracy of prediction. The use of genomic information resulted in increased estimates of connectedness and improved prediction accuracies compared with those of pedigree-based models when there were enough markers to capture variation due to QTL signals.