The impact of genetic relationship information on genomic breeding values in German Holstein cattle

Background

The impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.

Materials and methods

The data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (a_max) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs.

Results

Accuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing a_max for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size.

Conclusions

GEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.     

Estimates of genetic parameters and breeding values from western larch open-pollinated families using marker-based relationship

The availability and affordability of genetic markers made it possible to estimate quantitative genetic parameters without mating designs' structured pedigree. Here, we compared 4-year height's heritability and individuals' breeding values for a western larch common-garden population of 1,418 offspring representing 15 open-pollinated families from a 41-clone seed orchard using (a) classical pedigree models such as half- and full-sib families and (b) a molecular marker-based pedigree-free model using four pair-wise relationship estimation methods using eight informative SSR markers. The results highlighted the commonly observed inflated estimates of genetic parameters often obtained from half-sib analyses, as well as demonstrating some of the full-sib analyses' caveats. The pedigree reconstruction permitted the identification of selfed individuals, thus allowing evaluating the impact of selfing on marker-based genetic parameter estimation. The results demonstrated the utility of marker-based methods as an alternative to the classical pedigree-based approaches. Unlike the pedigree-based methods, the marker-based approach allowed better partitioning the variance components as well as separating the non-additive and additive genetic variance. The theoretical underpinning of the marker-based approach was discussed.     

Deregressing estimated breeding values and weighting information for genomic regression analyses

Background

Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes, repeated observations, records on close family members such as progeny, estimated breeding values (EBV) or their deregressed counterparts from genetic evaluations. The literature indicates that researchers are inconsistent in their approach to using EBV or deregressed data, and as to using the appropriate methods for weighting some data sources to account for heterogeneous variance.

Methods

A logical approach to using information for genomic prediction is introduced, which demonstrates the appropriate weights for analyzing observations with heterogeneous variance and explains the need for and the manner in which EBV should have parent average effects removed, be deregressed and weighted.

Results

An appropriate deregression for genomic regression analyses is EBV/r² where EBV excludes parent information and r² is the reliability of that EBV. The appropriate weights for deregressed breeding values are neither the reliability nor the prediction error variance, two alternatives that have been used in published studies, but the ratio (1 - h²)/[(c + (1 - r²)/r²)h²] where c > 0 is the fraction of genetic variance not explained by markers.

Conclusions

Phenotypic information on some individuals and deregressed data on others can be combined in genomic analyses using appropriate weighting.     

Laws restricting health insurers' use of genetic information: impact on genetic discrimination

Since 1991, 28 states have enacted laws that prohibit insurers' use of genetic information in pricing, issuing, or structuring health insurance. This article evaluates whether these laws reduce the extent of genetic discrimination by health insurers. From the data collected at multiple sites, we find that there are almost no well-documented cases of health insurers either asking for or using presymptomatic genetic test results in their underwriting decisions, either (a) before or after these laws have been enacted or (b) in states with or without these laws. By using both in-person interviews with insurers and a direct market test, we found that a person with a serious genetic condition who is presymptomatic faces little or no difficulty in obtaining health insurance. Furthermore, there are few indications that the degree of difficulty varies according to whether a state regulates the use of genetic information. Nevertheless, these laws have made it less likely that insurers will use genetic information in the future. Although insurers and agents are only vaguely aware of these laws, the laws have shaped industry norms and attitudes about the legitimacy of using this information.     

Prediction of breeding values and selection responses with genetic heterogeneity of environmental variance

There is empirical evidence that genotypes differ not only in mean, but also in environmental variance of the traits they affect. Genetic heterogeneity of environmental variance may indicate genetic differences in environmental sensitivity. The aim of this study was to develop a general framework for prediction of breeding values and selection responses in mean and environmental variance with genetic heterogeneity of environmental variance. Both means and environmental variances were treated as heritable traits. Breeding values and selection responses were predicted with little bias using linear, quadratic, and cubic regression on individual phenotype or using linear regression on the mean and within-family variance of a group of relatives. A measure of heritability was proposed for environmental variance to standardize results in the literature and to facilitate comparisons to "conventional" traits. Genetic heterogeneity of environmental variance can be considered as a trait with a low heritability. Although a large amount of information is necessary to accurately estimate breeding values for environmental variance, response in environmental variance can be substantial, even with mass selection. The methods developed allow use of the well-known selection index framework to evaluate breeding strategies and effects of natural selection that simultaneously change the mean and the variance.     

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP – Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.     

Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models

Climate change and the increasing demand for sustainable energy resources require urgent strategies to increase the accuracy of selection in tree breeding (associated with higher gain). We investigated the combined pedigree and genomic-based relationship approach and its impact on the accuracy of predicted breeding values using data from 5-year-old Eucalyptus grandis progeny trial. The number of trees that can be genotyped in a tree breeding population is limited; therefore, the combined approach can be a feasible and efficient strategy to increase the genetic gain and provide more accurate predicted breeding values. We calculated the accuracy of predicted breeding values for two growth traits, diameter at breast height and total height, using two evaluation approaches: the combined approach and the classical pedigree-based approach. We also investigated the influence of two different trait heritabilities as well as the inclusion of competition genetic effects or environmental heterogeneity in an individual-tree mixed model on the estimated variance components and accuracy of breeding values. The genomic information of genotyped trees is automatically propagated to all trees with the combined approach, including the non-genotyped mothers. This increased the accuracy of overall breeding values, except for the non-genotyped trees from the competition model. The increase in the accuracy was higher for the total height, the trait with low heritability. The combined approach is a simple, fast, and accurate genomic selection method for genetic evaluation of growth traits in E. grandis and tree species in general. It is simple to implement in a traditional individual-tree mixed model and provides an easy extension to individual-tree mixed models with competition effects and/or environmental heterogeneity.     

Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs

Due to the diversification of farming systems and climate change, farm animals are exposed to environmental disturbances to which they respond differently depending on their robustness. Disturbances such as heat stress or sanitary challenges (not always recorded, especially when they are of short duration and low intensity) have a transitory impact on animals, resulting in changes in phenotypes of production (feed intake, BW, etc.). The aim of this study was to evaluate the impact of such unknown disturbances on the estimated genetic parameters and breeding values (BV) for production traits. A population of 6 120 individuals over five generations divided into eight batches of 10 pens was generated, each individual underwent an ?100-day test period. A longitudinal phenotype mimicking piglet weight during the fattening period was simulated for each individual in two situations: disturbed and non-disturbed. The disturbed phenotype was modified according to the robustness of the animal and the intensity and duration of the disturbance that the animal was subjected to. Various sets of simulations (1 000 replicates per set) were considered depending on the type of disturbance (at the level of the batch, pen, or individual), the genetic correlation (negative, neutral, or positive) between the two components of the robustness (resistance and resilience), the genetic correlation (negative, neutral, or positive) between growth and the components of robustness, and the heritability of the components of robustness (weak or moderate). An animal model was used to estimate the genetic parameters and BV for two production traits: the BW at 100 days of age (BW₁₀₀) and average daily gain (ADG). The estimated heritability of the production traits was lower in the disturbed situation compared to the non-disturbed one (reduction of 0.08 and 0.05 points respectively for BW₁₀₀ and ADG). The correlations between estimated breeding values of the observed phenotypes (EBV) and BV for production traits in absence of disturbance were lower in the disturbed situation (reduction of 0.04 and 0.06 points for BW₁₀₀ and ADG respectively) while the partial correlation between EBV and BV for robustness was not significantly different from 0 in the two situations. These results suggest that selection in a well-controlled environment with random disturbances of low intensities does not allow to improve animal robustness while it is less effective for improving production traits than selection under no environmental disturbances.     

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.     

A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

A sampling-based method for estimating the accuracy of estimated breeding values using an animal model is presented. Empirical variances of true and estimated breeding values were estimated from a simulated n-sample. The method was validated using a small data set from the Parthenaise breed with the estimated coefficient of determination converging to the true values. It was applied to the French Salers data file used for the 2000 on-farm evaluation (IBOVAL) of muscle development score. A drawback of the method is its computational demand. Consequently, convergence can not be achieved in a reasonable time for very large data files. Two advantages of the method are that a) it is applicable to any model (animal, sire, multivariate, maternal effects...) and b) it supplies off-diagonal coefficients of the inverse of the mixed model equations and can therefore be the basis of connectedness studies.     

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

Background

The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.

Methods

Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.

Results

The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.

Conclusions

An animal''s relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.     

5. The use of genetic resources in breeding and breeding research

The dramatic increase in yields of agricultural crops over the last 40 years in developed countries has been attributed equally to improved genetic components and improved agronomic practices. The success of plant breeding is based partly on an increased understanding of the parameters involved, to a great extent on improved and more efficient methods of selection, to greater use of available genetic diversity and also to advances in a number of related disciplines including plant pathology, biochemistry, agronomy and genetics. Successes and problems associated with using various genetic resources in plant breeding are illustrated with examples from some of the world's major crops, including potatoes, barley and cotton.     

The relationship between bovine major histocompatibility complex class II polymorphism and disease studied by use of bull breeding values

The predictive value of class II DQ and DYA polymorphisms of the bovine major histocompatibility (MHC) complex (BoLA) for the incidence of disease in dairy cattle was estimated in a sample of 196 progeny-tested AI bulls of the Swedish Red and White breed. The BoLA DQ and DYA types of the bulls were determined by analysing restriction fragment length polymorphisms (RFLPs). Breeding values of bulls for clinical mastitis, all diseases including clinical mastitis and diseases other than clinical mastitis were used as measures of disease resistance or susceptibility. The relationship between MHC polymorphism and bull breeding values for disease resistance was evaluated statistically by linear regression analysis. A significant association between the haplotype DQ1A and susceptibility to clinical mastitis was revealed. No other DQ haplotype nor the DYA locus has a significant effect on any of the disease traits studied.     

The influence of breeding phenology on the genetic structure of four pond‐breeding salamanders

Understanding metapopulation dynamics requires knowledge about local population dynamics and movement in both space and time. Most genetic metapopulation studies use one or two study species across the same landscape to infer population dynamics; however, using multiple co‐occurring species allows for testing of hypotheses related to different life history strategies. We used genetic data to study dispersal, as measured by gene flow, in three ambystomatid salamanders (Ambystoma annulatum , A. maculatum , and A. opacum ) and the Central Newt (Notophthalmus viridescens louisianensis ) on the same landscape in Missouri, USA . While all four salamander species are forest dependent organisms that require fishless ponds to reproduce, they differ in breeding phenology and spatial distribution on the landscape. We use these differences in life history and distribution to address the following questions: (1) Are there species‐level differences in the observed patterns of genetic diversity and genetic structure? and (2) Is dispersal influenced by landscape resistance? We detected two genetic clusters in A. annulatum and A. opacum on our landscape; both species breed in the fall and larvae overwinter in ponds. In contrast, no structure was evident in A. maculatum and N. v. louisianensis , species that breed during the spring. Tests for isolation by distance were significant for the three ambystomatids but not for N. v. louisianensis . Landscape resistance also contributed to genetic differentiation for all four species. Our results suggest species‐level differences in dispersal ability and breeding phenology are driving observed patterns of genetic differentiation. From an evolutionary standpoint, the observed differences in dispersal distances and genetic structure between fall breeding and spring breeding species may be a result of the trade‐off between larval period length and size at metamorphosis which in turn may influence the long‐term viability of the metapopulation. Thus, it is important to consider life history differences among closely related and ecologically similar species when making management decisions.     

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.     

Predictability of heterozygosity scores and polymorphism information content values for rat genetic markers

Construction of a genetic linkage map of the laboratory rat, Rattus norvegicus, establishes the rat as a genetic model. Allele sizes were reported for 432 simple sequence length polymorphisms (SSLPs) genotyped in 12 different substrains belonging to nine different inbred strains of rats. However, these nine strains represent only a fraction of the more than 140 inbred strains available. If allele sizes are not known, alternative indices of markers' polymorphism content can be used, such as heterozygosity (H) and polymorphism information content (PIC). Here, we have determined heterozygosity scores and PIC values for all markers of the rat genetic linkage map, and we evaluate the predictability of the heterozygosity and the PIC values. Correlation analysis between the nine inbred strains reported for the rat map and ten test strains yielded r=0.42 and r=0.44 for heterozygosity and PIC values, respectively. While the correlation of the indices between the two groups of animals is low, these indices do provide a means of predicting whether a genetic marker will be informative in strains where allele sizes are not known.     

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.Subject terms: Genetic markers, Animal breeding     

The impact of PACS on the information flow

It is likely that changes in hospital operational practices made possible by the introduction of a whole hospital PACS will require the present information and data patterns to be changed significantly. Some of the factors which will affect the flow of data and information are considered, and the implications of comparing the costs of installing ‘whole hospital’ PACS systems in different institutions are discussed.