Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

Genome-Wide Association Study to Identify QTL for Carcass Traits in Korean Hanwoo Cattle

This study aimed to identify genetic associations with carcass traits in Hanwoo cattle using a genome-wide association study. A total of 9302 phenotypes were analyzed, and all animals were genotyped using the Illumina Bovine 50K v.3 SNP chip. Heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) were estimated as 0.42, 0.36, 0.36, and 0.47, respectively, using the GBLUP model, and 0.47, 0.37, 0.36, and 0.42, respectively, using the Bayes B model. We identified 129 common SNPs using DGEBV and 118 common SNPs using GEBV on BTA6, BTA13, and BTA14, suggesting their potential association with the traits of interest. No common SNPs were found between the GBLUP and Bayes B methods when using residuals as a response variable in GWAS. The most promising candidate genes for CWT included SLIT2, PACRGL, KCNIP4, RP1, XKR4, LYN, RPS20, MOS, FAM110B, UBXN2B, CYP7A1, SDCBP, NSMAF, TOX, CA8, LAP3, FAM184B, and NCAPG. For EMA, the genes IBSP, LAP3, FAM184B, LCORL, NCAPG, SLC30A9, and BEND4 demonstrated significance. Similarly, CYP7B1, ARMC1, PDE7A, and CRH were associated with BF, while CTSZ, GNAS, VAPB, and RAB22A were associated with MS. This finding offers valuable insights into genomic regions and molecular mechanisms influencing Hanwoo carcass traits, aiding efficient breeding strategies.

The association of immune response and colostral immunoglobulin G in Canadian and US Holstein-Friesian dairy cows

In cattle, maternal immunoglobulins are transferred through colostrum to provide passive immunity to the neonatal calf once they are absorbed into circulation. Cows can be assessed for antibody- and cell-mediated immune responses (AMIR and CMIR, respectively), and through estimated breeding values (EBV) and genomic parent averages (GPA), cows can be classified as having high, average, or low immune response (IR). The objective of this study was to identify associations of colostral IgG concentrations with IR in dairy cows. High IR dairy cows identified by GPA or EBV were hypothesized to produce higher colostral IgG concentrations than cows with average or low IR. Colostrum was collected from Holstein dairy cows from 3 large commercial herds (n = 590) in the United States and 1 research herd at the Ontario Dairy Research Centre (n = 275) in Canada. For the US herds, IR GPA were available through genotyping. For the Canadian herd, IR EBV were available through phenotyping and pedigree information. Colostral IgG concentrations were measured by radial immunodiffusion and analyzed using general linear models in SAS. Based on a prediction equation, cows in US herds with a CMIR GPA of 1 would have colostral IgG concentrations 6.3 g/L higher on average than cows with a CMIR GPA of 0. High CMIR cows produced statistically greater colostral IgG concentrations (least squares mean ± standard error of the mean, 107.5 ± 7.7 g/L) than low CMIR cows (91.4 ± 7.1 g/L), with intermediate values for average CMIR cows (105.1 ± 5.6 g/L). No differences were found among AMIR categories in US cows. The Canadian herd showed a trend for cows with high CMIR EBV (continuous variable) to produce greater colostral IgG. No differences were observed among high, average, and low AMIR EBV classifications in Canadian cows. The findings suggest that selective breeding of Holstein cows to enhance CMIR could contribute to higher-quality colostrum in succeeding generations.

Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools

Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.

An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions

Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Genomic prediction of crossbred dairy cattle in Tanzania: A route to productivity gains in smallholder dairy systems

Selection based on genomic predictions has become the method of choice for genetic improvement in dairy cattle. This offers huge opportunity for developing countries with little or no pedigree data, and preliminary studies have shown promising results. The African Dairy Genetic Gains (ADGG) project initiated a digital system of dairy performance data collection, accompanied by genotyping in Tanzania in 2016. Currently, ADGG has the largest body of dairy performance data generated in East Africa from a smallholder dairy system. This study examines the use of genomic best linear unbiased prediction (GBLUP) and single-step (ss)GBLUP for the estimation of genetic parameters and accuracy of genomic prediction for daily milk yield and body weight in Tanzania. The estimates of heritability for daily milk yield from GBLUP and ssGBLUP were essentially the same, at 0.12 ± 0.03. The heritability estimates for daily milk yield averaged over the whole lactation from random regression model (RRM) GBLUP or ssGBLUP were 0.22 and 0.24, respectively. The heritability of body weight from GBLUP was 0.24 ± 04 but was 0.22 ± 04 from the ssGBLUP analysis. Accuracy of genomic prediction for milk yield from a forward validation was 0.57 for GBLUP based on fixed regression model or 0.55 from an RRM. Corresponding estimates from ssGBLUP were 0.59 and 0.53, respectively. Accuracy for body weight, however, was much higher at 0.83 from GBLUP and 0.77 for ssGBLUP. The moderate to high levels of accuracy of genomic prediction (0.53-0.83) obtained for milk yield and body weight indicate that selection on the basis of genomic prediction is feasible in smallholder dairy systems and most probably the only initial possible pathway to implementing sustained genetic improvement programs in such systems.

Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding

Background

Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination.

Results

We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling.

Conclusions

Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02354-7.

Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits

Genomic best linear-unbiased prediction (GBLUP) assumes equal variance for all marker effects, which is suitable for traits that conform to the infinitesimal model. For traits controlled by major genes, Bayesian methods with shrinkage priors or genome-wide association study (GWAS) methods can be used to identify causal variants effectively. The information from Bayesian/GWAS methods can be used to construct the weighted genomic relationship matrix (G). However, it remains unclear which methods perform best for traits varying in genetic architecture. Therefore, we developed several methods to optimize the performance of weighted GBLUP and compare them with other available methods using simulated and real data sets. First, two types of methods (marker effects with local shrinkage or normal prior) were used to obtain test statistics and estimates for each marker effect. Second, three weighted G matrices were constructed based on the marker information from the first step: (1) the genomic-feature-weighted G, (2) the estimated marker-variance-weighted G, and (3) the absolute value of the estimated marker-effect-weighted G. Following the above process, six different weighted GBLUP methods (local shrinkage/normal-prior GF/EV/AEWGBLUP) were proposed for genomic prediction. Analyses with both simulated and real data demonstrated that these options offer flexibility for optimizing the weighted GBLUP for traits with a broad spectrum of genetic architectures. The advantage of weighting methods over GBLUP in terms of accuracy was trait dependant, ranging from 14.8% to marginal for simulated traits and from 44% to marginal for real traits. Local-shrinkage prior EVWGBLUP is superior for traits mainly controlled by loci of a large effect. Normal-prior AEWGBLUP performs well for traits mainly controlled by loci of moderate effect. For traits controlled by some loci with large effects (explain 25-50% genetic variance) and a range of loci with small effects, GFWGBLUP has advantages. In conclusion, the optimal weighted GBLUP method for genomic selection should take both the genetic architecture and number of QTLs of traits into consideration carefully.

A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications

Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer's early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal's sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme-dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme-low EC (-3.0 and -1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28-0.56 for SC and 0.26-0.49 for HP, using RNM_H, and 0.26-0.52 for SC and 0.22-0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (-3.0) and favorable (3.0) EC levels were 0.30 for HP and -0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals' genetic merit and re-ranking of animals on different environmental conditions. SNP marker-environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1

The largest gains in accuracy in a genomic selection program come from genotyping young selection candidates who have not yet produced progeny and who might, or might not, have a phenotypic record recorded. To reduce genotyping costs and to allow for an increased amount of genomic data to be available in a population, young selection candidates may be genotyped with low-density (LD) panels and imputed to a higher density. However, to ensure that a reasonable imputation accuracy persists overtime, some parent animals originally genotyped at LD must be re-genotyped at a higher density. This study investigated the long-term impact of selectively re-genotyping parents with a medium-density (MD) SNP panel on the accuracy of imputation and on the genetic predictions using ssGBLUP in a simulated beef cattle population. Assuming a moderately heritable trait (0.25) and a population undergoing selection, the simulation generated sequence data for a founder population (100 male and 500 female individuals) and 9,000 neutral markers, considered as the MD panel. All selection candidates from generation 8 to 15 were genotyped with LD panels corresponding to a density of 0.5% (LD_0.5), 2% (LD_2), and 5% (LD_5) of the MD. Re-genotyping scenarios chose parents at random or based on EBV and ranged from 10% of male parents to re-genotyping all male and female parents with MD. Ranges in average imputation accuracy at generation 15 were 0.567 to 0.936, 0.795 to 0.985, and 0.931 to 0.995 for the LD_0.5, LD_2, and LD_5, respectively, and the average EBV accuracies ranged from 0.453 to 0.735, 0.631 to 0.784, and 0.748 to 0.807 for LD_0.5, LD_2, and LD_5, respectively. Re-genotyping parents based on their EBV resulted in higher imputation and EBV accuracies compared to selecting parents at random and these values increased with the size of LD panels. Differences between re-genotyping scenarios decreased when the density of the LD panel increased, suggesting fewer animals needed to be re-genotyped to achieve higher accuracies. In general, imputation and EBV accuracies were greater when more parents were re-genotyped, independent of the proportion of males and females. In practice, the relationship between the density of the LD panel used and the target panel must be considered to determine the number (proportion) of animals that would need to be re-genotyped to enable sufficient imputation accuracy.

Accuracy of Genomic-Polygenic and Polygenic Breeding Values for Age at First Calving and Milk Yield in Thai Multibreed Dairy Cattle

Single-nucleotide polymorphisms (SNPs) have been used in genomic prediction and shown to increase prediction accuracy and selection responses for economic traits in dairy cattle. The successful report in genomic prediction for improving age at first calving (AFC) and 305-d milk yield (MY) in multibreed dairy population is limited. Therefore, the objective of this research was to compare estimates of variance components, genetic parameters, and prediction accuracies for AFC and MY using a genomic-polygenic model (GPM) and a polygenic model (PM). The AFC and MY records of 9,106 first-lactating multibreed dairy cows, calved between 1991 and 2014, were collected from 1,012 Thai dairy farms. The SNP genotyped individuals were selected from cows that had completed pedigree and phenotypes information. The total genomic DNA samples of 2,661 dairy cattle were genotyped using various GeneSeek Genomic Profiler low-density bead chips (9K, 20K, and 80K). The 2-trait GPM and PM contained herd-year-season and heterosis as fixed effects, and animal additive genetic and residual as random effects. Variance components and genetic parameters were estimated using the procedure of average information-restricted maximum likelihood (AI-REML). Estimates of additive genetic variance components and heritabilities from GPM were higher than PM for AFC and MY. Correlations between AFC and MY were near zero for both models. Mean EBV accuracies were higher for GPM (32.95% for AFC and 38.24% for MY) than for PM (32.65% for AFC, and 32.99% for MY). Mean sire EBV accuracies were higher for GPM (31.35% for AFC and 36.25% for MY) than for PM (28.37% for AFC and 28.80% for MY). Thus, the GPM should be considered the model of choice to increase accuracy of genetic predictions for AFC and MY in the Thai multibreed dairy population.

Accuracy of genomic-polygenic estimated breeding value for milk yield and fat yield in the Thai multibreed dairy population with five single nucleotide polymorphism sets

Objective

The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic EBV rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population computed using five SNP sets from GeneSeek GGP80K chip.

Methods

The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information REML procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25) and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects.

Results

The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169) and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%) and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY).

Conclusion

The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable alternative to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.

High-frequency marker haplotypes in the genomic selection of dairy cattle

The aim of this study was to predict the genomic breeding value (DGV) of production, selected conformation and reproductive traits, and somatic cell score of dairy cattle in Poland using high-frequency marker haplotypes. The dataset consisted of phenotypic, genotypic, and pedigree data of 1216 Polish Holstein-Friesian bulls. The genotypic data consisted of 54,000 single-nucleotide polymorphisms (SNPs). The data were divided into two subsets: a test dataset (n = 1064) and a validation dataset (n = 152). Genotypic data were selected using three criteria: the percentage of missing genotypes, minor allele frequency, and linkage disequilibrium. The purpose of the data selection was to identify blocks of SNPs that were then used for the construction of haplotypes. Only haplotypes with a frequency higher than 25% were selected. DGV was predicted using four variants of a linear model with random haplotype effects and deregressed breeding values as the response variables. The accuracy of genomic prediction was checked by comparing DGVs with estimated breeding values (EBVs) using two methods: Pearson’s correlations and the regression of EBV on DGV. The use of high-frequency haplotypes showed a tendency to underestimate DGVs. None of the models tested was clearly superior with regard to the traits studied. DGVs of production and conformation traits as well as somatic cell score (medium or high heritability traits) were more accurate than those estimated for fertility traits (low heritability traits).

Genomic prediction of bull fertility in US Jersey dairy cattle

Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.

Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects

Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries resulting in a higher proportion of genomically proven young bulls being used in breeding. This success has been undergirded by well-established conventional genetic evaluation systems. Here, the status of GS in terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods, and imputation efficiency in breeding programs of developing countries, where smallholder systems predominate and the basic components for conventional breeding are mostly lacking is examined. Also, the application of genomic tools and identification of genome-wide signatures of selection is reviewed. The studies on genomic prediction in developing countries are mostly in dairy and beef cattle usually with small reference populations (500-3,000 animals) and are mostly cows. The input variables tended to be pre-corrected phenotypic records and the small reference populations has made implementation of various Bayesian methods feasible in addition to GBLUP. Multi-trait single-step has been used to incorporate genomic information from foreign bulls, thus GS in developing countries would benefit from collaborations with developed countries, as many dairy sires used are from developed countries where they may have been genotyped and phenotyped. Cross validation approaches have been implemented in most studies resulting in accuracies of 0.20-0.60. Genotyping animals with a mixture of HD and LD chips, followed by imputation to the HD have been implemented with imputation accuracies of 0.74-0.99 reported. This increases the prospects of reducing genotyping costs and hence the cost-effectiveness of GS. Next-generation sequencing and associated technologies have allowed the determination of breed composition, parent verification, genome diversity, and genome-wide selection sweeps. This information can be incorporated into breeding programs aiming to utilize GS. Cost-effective GS in beef cattle in developing countries may involve usage of reproductive technologies (AI and in-vitro fertilization) to efficiently propagate superior genetics from the genomics pipeline. For dairy cattle, sexed semen of genomically proven young bulls could substantially improve profitability thus increase prospects of small holder farmers buying-in into genomic breeding programs.

Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle

This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756; 3150 and 3119 records of age at first calving (AFC); weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy–Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate &lt;0.98, MAF &lt;0.05, HWE with P &lt;10−5, and r2 &gt;0.999; (2) Moderate rigor (S2): call rate &lt;0.85 and MAF &lt;0.01; (3) Low rigor (S3): only non-autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000–0.009), G2 (0.010–0.064), G3 (0.065–0.174), G4 (0.175–0.325), G5 (0.326–0.500) and G6 (0.000–0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.

Prospects and Challenges of Applied Genomic Selection-A New Paradigm in Breeding for Grain Yield in Bread Wheat

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat ( L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.

Predicting bull fertility using genomic data and biological information

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.

Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids

BACKGROUND

Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA).

RESULTS

Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions.

CONCLUSIONS

The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.

Tag SNP selection for prediction of tick resistance in Brazilian Braford and Hereford cattle breeds using Bayesian methods

BACKGROUND

Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds. Our aim was to identify genomic segments and tag single nucleotide polymorphisms (SNPs) associated with tick-resistance in Hereford and Braford cattle. The predictive performance of a very low-density tag SNP panel was estimated and compared with results obtained with a 50 K SNP dataset.

RESULTS

BayesB (π = 0.99) was initially applied in a genome-wide association study (GWAS) for this complex trait by using deregressed estimated breeding values for tick counts and 41,045 SNP genotypes from 3455 animals raised in southern Brazil. To estimate the combined effect of a genomic region that is potentially associated with quantitative trait loci (QTL), 2519 non-overlapping 1-Mb windows that varied in SNP number were defined, with the top 48 windows including 914 SNPs and explaining more than 20% of the estimated genetic variance for tick resistance. Subsequently, the most informative SNPs were selected based on Bayesian parameters (model frequency and t-like statistics), linkage disequilibrium and minor allele frequency to propose a very low-density 58-SNP panel. Some of these tag SNPs mapped close to or within genes and pseudogenes that are functionally related to tick resistance. Prediction ability of this SNP panel was investigated by cross-validation using K-means and random clustering and a BayesA model to predict direct genomic values. Accuracies from these cross-validations were 0.27 ± 0.09 and 0.30 ± 0.09 for the K-means and random clustering groups, respectively, compared to respective values of 0.37 ± 0.08 and 0.43 ± 0.08 when using all 41,045 SNPs and BayesB with π = 0.99, or of 0.28 ± 0.07 and 0.40 ± 0.08 with π = 0.999.

CONCLUSIONS

Bayesian GWAS model parameters can be used to select tag SNPs for a very low-density panel, which will include SNPs that are potentially linked to functional genes. It can be useful for cost-effective genomic selection tools, when one or a few key complex traits are of interest.

Genome-wide association study for behavior, type traits, and muscular development in Charolais beef cattle

Behavior, type traits, and muscular development are of interest for beef cattle breeding. Genome-wide association studies (GWAS) enable the identification of candidate genes, which enables gene-based selection and provides insight in the genetic architecture of these traits. The objective of the current study was to perform a GWAS for 3 behavior traits, 12 type traits, and muscular development in Charolais cattle. Behavior traits, including aggressiveness at parturition, aggressiveness during gestation period, and maternal care, were scored by farmers. Type traits, including udder conformation, teat, feet and legs, and locomotion, were scored by trained classifiers. Data used in the GWAS consisted of 3,274 cows with phenotypic records and genotyping information for 44,930 SNP. When SNP had a false discovery rate (FDR) smaller than 0.05, they were referred to as significant. When SNP had a FDR between 0.05 and 0.20, they were referred to as suggestive. Four significant and 12 suggestive regions were detected for aggressiveness during gestation, maternal care, udder balance, teat thinness, teat length, foot angle, foot depth, and locomotion. These 4 significant and 12 suggestive regions were not supported by other significant SNP in close proximity. No SNP with major effects were detected for behavior and type traits, and SNP associations for these traits were spread across the genome, suggesting that behavior and type traits were influenced by many genes, each explaining a small part of genetic variance. The GWAS identified 1 region on chromosome 2 significantly associated with muscular development, which included the myostatin gene (), which is known to affect muscularity. No other regions associated with muscular development were found. Results showed that the myostatin region associated with muscular development had pleiotropic effects on udder volume, teat thinness, rear leg, and leg angle.

Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population

Animal feeding is the most important economic component of beef production systems. Selection for feed efficiency has not been effective mainly due to difficult and high costs to obtain the phenotypes. The application of genomic selection using SNP can decrease the cost of animal evaluation as well as the generation interval. The objective of this study was to compare methods for genomic evaluation of feed efficiency traits using different cross-validation layouts in an experimental beef cattle population genotyped for a high-density SNP panel (BovineHD BeadChip assay 700k, Illumina Inc., San Diego, CA). After quality control, a total of 437,197 SNP genotypes were available for 761 Nelore animals from the Institute of Animal Science, Sertãozinho, São Paulo, Brazil. The studied traits were residual feed intake, feed conversion ratio, ADG, and DMI. Methods of analysis were traditional BLUP, single-step genomic BLUP (ssGBLUP), genomic BLUP (GBLUP), and a Bayesian regression method (BayesCπ). Direct genomic values (DGV) from the last 2 methods were compared directly or in an index that combines DGV with parent average. Three cross-validation approaches were used to validate the models: 1) YOUNG, in which the partition into training and testing sets was based on year of birth and testing animals were born after 2010; 2) UNREL, in which the data set was split into 3 less related subsets and the validation was done in each subset a time; and 3) RANDOM, in which the data set was randomly divided into 4 subsets (considering the contemporary groups) and the validation was done in each subset at a time. On average, the RANDOM design provided the most accurate predictions. Average accuracies ranged from 0.10 to 0.58 using BLUP, from 0.09 to 0.48 using GBLUP, from 0.06 to 0.49 using BayesCπ, and from 0.22 to 0.49 using ssGBLUP. The most accurate and consistent predictions were obtained using ssGBLUP for all analyzed traits. The ssGBLUP seems to be more suitable to obtain genomic predictions for feed efficiency traits on an experimental population of genotyped animals.

Genomic prediction from observed and imputed high-density ovine genotypes

Background

Genomic prediction using high-density (HD) marker genotypes is expected to lead to higher prediction accuracy, particularly for more heterogeneous multi-breed and crossbred populations such as those in sheep and beef cattle, due to providing stronger linkage disequilibrium between single nucleotide polymorphisms and quantitative trait loci controlling a trait. The objective of this study was to evaluate a possible improvement in genomic prediction accuracy of production traits in Australian sheep breeds based on HD genotypes (600k, both observed and imputed) compared to prediction based on 50k marker genotypes. In particular, we compared improvement in prediction accuracy of animals that are more distantly related to the reference population and across sheep breeds.

Methods

Genomic best linear unbiased prediction (GBLUP) and a Bayesian approach (BayesR) were used as prediction methods using whole or subsets of a large multi-breed/crossbred sheep reference set. Empirical prediction accuracy was evaluated for purebred Merino, Border Leicester, Poll Dorset and White Suffolk sire breeds according to the Pearson correlation coefficient between genomic estimated breeding values and breeding values estimated based on a progeny test in a separate dataset.

Results

Results showed a small absolute improvement (0.0 to 8.0% and on average 2.2% across all traits) in prediction accuracy of purebred animals from HD genotypes when prediction was based on the whole dataset. Greater improvement in prediction accuracy (1.0 to 12.0% and on average 5.2%) was observed for animals that were genetically lowly related to the reference set while it ranged from 0.0 to 5.0% for across-breed prediction. On average, no significant advantage was observed with BayesR compared to GBLUP.

Genomic prediction for carcass traits in Japanese Black cattle using single nucleotide polymorphism markers of different densities

Genomic prediction (GP) of breeding values using single nucleotide polymorphism (SNP) markers can be conducted even when pedigree information is unavailable, providing phenotypes are known and marker data are provided. While use of high-density SNP markers is desirable for accurate GP, lower-density SNPs can perform well in some situations. In the present study, GP was performed for carcass weight and marbling score in Japanese Black cattle using SNP markers of varying densities. The 1791 fattened steers with phenotypic data and 189 having predicted breeding values provided by the official genetic evaluation using pedigree data were treated as the training and validation populations respectively. Genotype data on 565837 autosomal SNPs were available and SNPs were selected to provide different equally spaced SNP subsets of lower densities. Genomic estimated breeding values (GEBVs) were obtained using genomic best linear unbiased prediction incorporating one of two types of genomic relationship matrices (G matrices). The GP accuracy assessed as the correlation between the GEBVs and the corrected records divided by the square root of estimated heritability was around 0.85 for carcass weight and 0.60 for marbling score when using 565837 SNPs. The type of G matrix used gave no substantial difference in the results at a given SNP density for traits examined. Around 80% of the GP accuracy was retained when the SNP density was decreased to 1/1000 of that of all available SNPs. These results indicate that even when a SNP panel of a lower density is used, GP may be beneficial to the pre-selection for the carcass traits in Japanese Black young breeding animals.

Optimizing Training Population Data and Validation of Genomic Selection for Economic Traits in Soft Winter Wheat

Genomic selection (GS) is a breeding tool that estimates breeding values (GEBVs) of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP). The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy) increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB) resistance, softness equivalence (SE), and flour yield (FY). Four TP data sampling schemes were tested: (1) use all TP data, (2) use subsets of TP lines with low genotype-by-environment interaction, (3) use subsets of markers significantly associated with quantitative trait loci (QTL), and (4) a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB) to 0.62 (FY). On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs

The study investigated genetic architecture and predictive ability using genomic annotation of residual feed intake (RFI) and its component traits (daily feed intake [DFI], ADG, and back fat [BF]). A total of 1,272 Duroc pigs had both genotypic and phenotypic records, and the records were split into a training (968 pigs) and a validation dataset (304 pigs) by assigning records as before and after January 1, 2012, respectively. SNP were annotated by 14 different classes using Ensembl variant effect prediction. Predictive accuracy and prediction bias were calculated using Bayesian Power LASSO, Bayesian A, B, and Cπ, and genomic BLUP (GBLUP) methods. Predictive accuracy ranged from 0.508 to 0.531, 0.506 to 0.532, 0.276 to 0.357, and 0.308 to 0.362 for DFI, RFI, ADG, and BF, respectively. BayesCπ100.1 increased accuracy slightly compared to the GBLUP model and other methods. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP groups. Genomic prediction has accuracy comparable to observed phenotype, and use of genomic prediction can be cost effective by replacing feed intake measurement. Genomic annotation had less impact on predictive accuracy traits considered here but may be different for other traits. It is the first study to provide useful insights into biological classes of SNP driving the whole genomic prediction for complex traits in pigs.

Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction

Background

The objectives of this study were to investigate the accuracy of genotype imputation from low (12k) to medium (50k Illumina-Ovine) SNP (single nucleotide polymorphism) densities in purebred and crossbred Merino sheep based on a random or selected reference set and to evaluate the impact of using imputed genotypes on accuracy of genomic prediction.

Methods

Imputation validation sets were composed of random purebred or crossbred Merinos, while imputation reference sets were of variable sizes and included random purebred or crossbred Merinos or a group of animals that were selected based on high genetic relatedness to animals in the validation set. The Beagle software program was used for imputation and accuracy of imputation was assessed based on the Pearson correlation coefficient between observed and imputed genotypes. Genomic evaluation was performed based on genomic best linear unbiased prediction and its accuracy was evaluated as the Pearson correlation coefficient between genomic estimated breeding values using either observed (12k/50k) or imputed genotypes with varying levels of imputation accuracy and accurate estimated breeding values based on progeny-tests.

Results

Imputation accuracy increased as the size of the reference set increased. However, accuracy was higher for purebred Merinos that were imputed from other purebred Merinos (on average 0.90 to 0.95 based on 1000 to 3000 animals) than from crossbred Merinos (0.78 to 0.87 based on 1000 to 3000 animals) or from non-Merino purebreds (on average 0.50). The imputation accuracy for crossbred Merinos based on 1000 to 3000 other crossbred Merino ranged from 0.86 to 0.88. Considerably higher imputation accuracy was observed when a selected reference set with a high genetic relationship to target animals was used vs. a random reference set of the same size (0.96 vs. 0.88, respectively). Accuracy of genomic prediction based on 50k genotypes imputed with high accuracy (0.88 to 0.99) decreased only slightly (0.0 to 0.67 % across traits) compared to using observed 50k genotypes. Accuracy of genomic prediction based on observed 12k genotypes was higher than accuracy based on lowly accurate (0.62 to 0.86) imputed 50k genotypes.

Targeted Analysis Reveals an Important Role of JAK-STAT-SOCS Genes for Milk Production Traits in Australian Dairy Cattle

The Janus kinase and signal transducer and activator of transcription (JAK-STAT) pathway genes along with suppressors of cytokine signalling (SOCS) family genes play a crucial role in controlling cytokine signals in the mammary gland and thus mammary gland development. Mammary gene expression studies showed differential expression patterns for all the JAK-STAT pathway genes. Gene expression studies using qRT-PCR revealed differential expression of SOCS2, SOCS4, and SOCS5 genes across the lactation cycle in dairy cows. Using genotypes from 1,546 Australian Holstein-Friesian bulls, a statistical model for an association analysis based on SNPs within 500 kb of JAK-STAT pathway genes, and SOCS genes alone was constructed. The analysis suggested that these genes and pathways make a significant contribution to the Australian milk production traits. There were 24 SNPs close to SOCS1, SOCS3, SOCS5, SOCS7, and CISH genes that were significantly associated with Australian Profit Ranking (APR), Australian Selection Index (ASI), and protein yield (PY). This study supports the view that there may be some merit in choosing SNPs around functionally relevant genes for the selection and genetic improvement schemes for dairy production traits.

Genomics innovation: transforming healthcare, business, and the global economy

The genomics revolution has generated an unprecedented number of assets to propel innovation. Initial availability of genomics-based applications show a significant potential to contribute addressing global challenges, such as human health, food security, alternative sources of energies, and environmental sustainability. In the last years, most developed and emerging nations have established bioeconomy agendas where genomics plays a major role to meet their local needs. Genomic medicine is one of the most visible areas where genomics innovation is likely to contribute to a more individualized, predictive, and preventive medical practice. Examples in agriculture, dairy and beef, fishery, aquaculture, and forests industries include the effective selection of genetic variants associated to traits of economic value. Some, in addition to producing more and better foods, already represent an important increase in revenues to their respective industries. It is reasonable to predict that genomics applications will lead to a paradigm shift in our ability to ease significant health, economic, and social burdens. However, to successfully benefit from genomics innovations, it is imperative to address a number of hurdles related to generating robust scientific evidence, developing lower-cost sequencing technologies, effective bioinformatics, as well as sensitive ethical, economical, environmental, legal, and social aspects associated with the development and use of genomics innovations.

Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data

Background

Genotype imputation is an important tool for whole-genome prediction as it allows cost reduction of individual genotyping. However, benefits of genotype imputation have been evaluated mostly for linear additive genetic models. In this study we investigated the impact of employing imputed genotypes when using more elaborated models of phenotype prediction. Our hypothesis was that such models would be able to track genetic signals using the observed genotypes only, with no additional information to be gained from imputed genotypes.

Results

For the present study, an outbred mice population containing 1,904 individuals and genotypes for 1,809 pre-selected markers was used. The effect of imputation was evaluated for a linear model (the Bayesian LASSO - BL) and for semi and non-parametric models (Reproducing Kernel Hilbert spaces regressions – RKHS, and Bayesian Regularized Artificial Neural Networks – BRANN, respectively). The RKHS method had the best predictive accuracy. Genotype imputation had a similar impact on the effectiveness of BL and RKHS. BRANN predictions were, apparently, more sensitive to imputation errors. In scenarios where the masking rates were 75% and 50%, the genotype imputation was not beneficial. However, genotype imputation incorporated information about important markers and improved predictive ability, especially for body mass index (BMI), when genotype information was sparse (90% masking), and for body weight (BW) when the reference sample for imputation was weakly related to the target population.

Conclusions

In conclusion, genotype imputation is not always helpful for phenotype prediction, and so it should be considered in a case-by-case basis. In summary, factors that can affect the usefulness of genotype imputation for prediction of yet-to-be observed traits are: the imputation accuracy itself, the structure of the population, the genetic architecture of the target trait and also the model used for phenotype prediction.

Is the use of formulae a reliable way to predict the accuracy of genomic selection?

We studied four formulae used to predict the accuracy of genomic selection prior to genotyping. The objectives of our study were to investigate the impact of the parameters of each formula on the values of accuracy calculated using these formulae, and to check whether the accuracies reported in the literature are in agreement with the formulae. First, we computed the marginal distribution of accuracy (by integration) for each parameter of all four formulae: heritability h(2) , reference population size T, number of markers M and number of effective segments in the genome Me . Then, we collected 145 accuracies and corresponding parameters reported in 13 publications on genomic selection (mainly in dairy cattle), and performed analysis of variance to test the differences between observed and predicted accuracy with effects of formulae and parameters. The variation of accuracy for different values of each parameter indicated that two parameters, T and Me, had a significant impact and that considerable differences existed between the formulae (mean accuracies differed by up to 0.20 point). The results of our meta-analysis showed a big formula effect on the accuracies predicted using each formula, and also a significant effect of the value obtained for Me calculated from Ne (effective population size). Each formula can therefore be demonstrated to be optimal depending on the assumption used for Me . In conclusion, no rules can be applied to predict the reliability of genomic selection using these formulae.

SNPs selection using support vector regression and genetic algorithms in GWAS

Introduction

This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.

Results

The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.

Conclusions

The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.

Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition

BACKGROUND

The maintenance of lactation in mammals is the result of a balance between competing signals from mammary development, prolactin signalling and involution pathways. Dairy cattle are an interesting case study to investigate the effect of polymorphisms that affect the function of genes in these pathways. In dairy cattle, lactation yields and milk composition (for example protein percentage and fat percentage) are routinely recorded, and these vary greatly between individuals. In this study, we test 8058 single nucleotide polymorphisms in or close to genes in these pathways for association with milk production traits and determine the proportion of variance explained by each pathway, using data on 16 812 dairy cattle, including Holstein-Friesian and Jersey bulls and cows.

RESULTS

Single nucleotide polymorphisms close to genes in the mammary development, prolactin signalling and involution pathways were significantly associated with milk production traits. The involution pathway explained the largest proportion of genetic variation for production traits. The mammary development pathway also explained additional genetic variation for milk volume, fat percentage and protein percentage.

CONCLUSIONS

Genetic variants in the involution pathway explained considerably more genetic variation in milk production traits than expected by chance. Many of the associations for single nucleotide polymorphisms in genes in this pathway have not been detected in conventional genome-wide association studies. The pathway approach used here allowed us to identify some novel candidates for further studies that will be aimed at refining the location of associated genomic regions and identifying polymorphisms contributing to variation in lactation volume and milk composition.

Genome-wide association for growth traits in Canchim beef cattle

Studies are being conducted on the applicability of genomic data to improve the accuracy of the selection process in livestock, and genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to identify genomic regions and genes that play roles in birth weight (BW), weaning weight adjusted for 210 days of age (WW), and long-yearling weight adjusted for 420 days of age (LYW) in Canchim cattle. GWAS were performed by means of the Generalized Quasi-Likelihood Score (GQLS) method using genotypes from the BovineHD BeadChip and estimated breeding values for BW, WW, and LYW. Data consisted of 285 animals from the Canchim breed and 114 from the MA genetic group (derived from crossings between Charolais sires and ½ Canchim + ½ Zebu dams). After applying a false discovery rate correction at a 10% significance level, a total of 4, 12, and 10 SNPs were significantly associated with BW, WW, and LYW, respectively. These SNPs were surveyed to their corresponding genes or to surrounding genes within a distance of 250 kb. The genes DPP6 (dipeptidyl-peptidase 6) and CLEC3B (C-type lectin domain family 3 member B) were highlighted, considering its functions on the development of the brain and skeletal system, respectively. The GQLS method identified regions on chromosome associated with birth weight, weaning weight, and long-yearling weight in Canchim and MA animals. New candidate regions for body weight traits were detected and some of them have interesting biological functions, of which most have not been previously reported. The observation of QTL reports for body weight traits, covering areas surrounding the genes (SNPs) herein identified provides more evidence for these associations. Future studies targeting these areas could provide further knowledge to uncover the genetic architecture underlying growth traits in Canchim cattle.

Effects of a breeding scheme combined by genomic pre-selection and progeny testing on annual genetic gain in a dairy cattle population

The effectiveness of the incorporation of genomic pre-selection into dairy cattle progeny testing (GS-PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS-PT was 13% to 43% greater when h(2)  = 0.3 and 16% to 53% greater when h(2)  = 0.1 compared with that from PT. Given h(2)  = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS-PT produced 40% to 43% greater annual genetic gain than PT. Given h(2)  = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS-PT was 48% to 53% greater than that from PT. When h(2)  = 0.3, progeny testing capacity had little effect on annual genetic gain from GS-PT. However, when h(2)  = 0.1, annual genetic gain from GS-PT increased with increasing progeny testing capacity.

Effects of single nucleotide polymorphism marker density on degree of genetic variance explained and genomic evaluation for carcass traits in Japanese Black beef cattle

Background

Japanese Black cattle are a beef breed whose meat is well known to excel in meat quality, especially in marbling, and whose effective population size is relatively low in Japan. Unlike dairy cattle, the accuracy of genomic evaluation (GE) for carcass traits in beef cattle, including this breed, has been poorly studied. For carcass weight and marbling score in the breed, as well as the extent of whole genome linkage disequilibrium (LD), the effects of equally-spaced single nucleotide polymorphisms (SNPs) density on genomic relationship matrix (G matrix), genetic variance explained and GE were investigated using the genotype data of about 40,000 SNPs and two statistical models.

Results

Using all pairs of two adjacent SNPs in the whole SNP set, the means of LD (r²) at ranges 0–0.1, 0.1–0.2, 0.2–0.5 and 0.5–1 Mb were 0.22, 0.13, 0.10 and 0.08, respectively, and 25.7, 13.9, 10.4 and 6.4% of the r² values exceeded 0.3, respectively. While about 90% of the genetic variance for carcass weight estimated using all available SNPs was explained using 4,000–6,000 SNPs, the corresponding percentage for marbling score was consistently lower. With the conventional linear model incorporating the G matrix, correlation between the genomic estimated breeding values (GEBVs) obtained using 4,000 SNPs and all available SNPs was 0.99 for carcass weight and 0.98 for marbling score, with an underestimation of the former GEBVs, especially for marbling score.

Conclusions

The Japanese Black is likely to be in a breed group with a relatively high extent of whole genome LD. The results indicated that the degree of marbling is controlled by only QTLs with relatively small effects, compared with carcass weight, and that using at least 4,000 equally-spaced SNPs, there is a possibility of ranking animals genetically for these carcass traits in this breed.

Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle

BACKGROUND

Genome wide association studies (GWAS) in most cattle breeds result in large genomic intervals of significant associations making it difficult to identify causal mutations. This is due to the extensive, low-level linkage disequilibrium within a cattle breed. As there is less linkage disequilibrium across breeds, multibreed GWAS may improve precision of causal variant mapping. Here we test this hypothesis in a Holstein and Jersey cattle data set with 17,925 individuals with records for production and functional traits and 632,003 SNP markers.

RESULTS

By using a cross validation strategy within the Holstein and Jersey data sets, we were able to identify and confirm a large number of QTL. As expected, the precision of mapping these QTL within the breeds was limited. In the multibreed analysis, we found that many loci were not segregating in both breeds. This was partly an artefact of power of the experiments, with the number of QTL shared between the breeds generally increasing with trait heritability. False discovery rates suggest that the multibreed analysis was less powerful than between breed analyses, in terms of how much genetic variance was explained by the detected QTL. However, the multibreed analysis could more accurately pinpoint the location of the well-described mutations affecting milk production such as DGAT1. Further, the significant SNP in the multibreed analysis were significantly enriched in genes regions, to a considerably greater extent than was observed in the single breed analyses. In addition, we have refined QTL on BTA5 and BTA19 to very small intervals and identified a small number of potential candidate genes in these, as well as in a number of other regions.

CONCLUSION

Where QTL are segregating across breed, multibreed GWAS can refine these to reasonably small genomic intervals. However, such QTL appear to represent only a fraction of the genetic variation. Our results suggest a significant proportion of QTL affecting milk production segregate within rather than across breeds, at least for Holstein and Jersey cattle.

Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens

The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.

Applied animal genomics: results from the field

Genomic selection (GS) is the use of statistical methods to estimate the genetic merit of a genotyped animal based on prediction equations derived from large ancestral populations with both phenotypes and genotypes. It has revolutionized the dairy cattle breeding industry and has been implemented with varying degrees of success in other animal breeding programs, including swine, poultry, and beef cattle. The findings of empirical field studies applying GS to the breeding sectors of these main animal protein industries are reviewed. Several translational considerations must be addressed before implementing GS in genetic improvement programs. These include determining and obtaining economically relevant phenotypes and determining the optimal size of the training population, cost-effective genotyping strategies, the practicality of field implementation, and the relative costs versus the benefits of the realized rate of genetic gain. GS may additionally change the optimal breeding scheme design, and studies that address this consideration are also reviewed briefly.

Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle

BACKGROUND

Identification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a "RNASE5 pathway" based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset.

RESULTS

The constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility.

CONCLUSIONS

These results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits.

Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single-nucleotide polymorphism (SNP) markers for 29 traits in Australian Holstein-Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The proportion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.