Selection and management of DNA markers for use in genomic evaluation

To facilitate routine genomic evaluation, a database was constructed to store genotypes for 50,972 single nucleotide polymorphisms (SNP) from the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). Multiple samples per animal are allowed. All SNP genotypes for a sample are stored in a single row. An indicator specifies whether the genotype for a sample was selected for use in genomic evaluation. Samples with low call rates or pedigree conflicts are designated as unusable. Among multiple samples that qualify for use in genomic evaluation, the one with the highest call rate is designated as usable. When multiple samples are stored for an animal, a composite is formed during extraction by using SNP genotypes from other samples to replace missing genotypes. To increase the number of SNP available, scanner output for approximately 19,000 samples was reprocessed. Any SNP with a minor allele frequency of ≥1% for Holsteins, Jerseys, or Brown Swiss was selected, which was the primary reason that the number of SNP used for USDA genomic evaluations increased. Few parent-progeny conflicts (≤1%) and a high call rate (≥90%) were additional requirements that eliminated 2,378 SNP. Because monomorphic SNP did not degrade convergence during estimation of SNP effects, a single set of 43,385 SNP was adopted for all breeds. The use of a database for genotypes, detection of conflicts as genotypes are stored, online access for problem resolution, and use of a single set of SNP for genomic evaluations have simplified tracking of genotypes and genomic evaluation as a routine and official process.     

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.     

Genotype imputation in a tropical crossbred dairy cattle population

The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ～777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORR_anim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORR_anim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORR_snp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.     

Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information

Currently, genomic evaluations use multiple-step procedures, which are prone to biases and errors. A single-step procedure may be applicable when genomic predictions can be obtained by modifying the numerator relationship matrix A to H = A + A_Δ, where A_Δ includes deviations from expected relationships. However, the traditional mixed model equations require H⁻¹, which is usually difficult to obtain for large pedigrees. The computations with H are feasible when the mixed model equations are expressed in an alternate form that also applies for singular H and when those equations are solved by the conjugate gradient techniques. Then the only computations involving H are in the form of Aq or A_Δq, where q is a vector. The alternative equations have a nonsymmetric left-hand side. Computing A_Δq is inexpensive when the number of nonzeros in A_Δ is small, and the product Aq can be calculated efficiently in linear time using an indirect algorithm. Generalizations to more complicated models are proposed. The data included 10.2 million final scores on 6.2 million Holsteins and were analyzed by a repeatability model. Comparisons involved the regular and the alternative equations. The model for the second case included simulated A_Δ. Solutions were obtained by the preconditioned conjugate gradient algorithm, which works only with symmetric matrices, and by the bi-conjugate gradient stabilized algorithm, which also works with nonsymmetric matrices. The convergence rate associated with the nonsymmetric solvers was slightly better than that with the symmetric solver for the original equations, although the time per round was twice as much for the nonsymmetric solvers. The convergence rate associated with the alternative equations ranged from 2 times lower without A_Δ to 3 times lower for the largest simulated A_Δ. When the information attributable to genomics can be expressed as modifications to the numerator relationship matrix, the proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information.     

Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2 h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.     

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits,udder type traits,and somatic cell scores in French dairy goats

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.     

Genome-wide mapping and estimation of inbreeding depression of semen quality traits in a cattle population

Inbreeding depression is known to affect quantitative traits such as male fertility and sperm quality, but the genetic basis for these associations is poorly understood. Most studies have been limited to examining how pedigree- or marker-derived genome-wide autozygosity is associated with quantitative phenotypes. In this study, we analyzed possible associations of genetic features of inbreeding depression with percentage of live spermatozoa and total number of spermatozoa in 19,720 ejaculates obtained from 554 Austrian Fleckvieh bulls during routine artificial insemination programs. Genome-wide inbreeding depression was estimated and genomic regions contributing to inbreeding depression were mapped. Inbreeding depression did affect total number of spermatozoa, and such depression was predicted by pedigree-based inbreeding levels and genome-wide inbreeding levels based on runs of homozygosity (ROH). Genome-wide inbreeding depression did not seem to affect percentage of live spermatozoa. A model incorporating genetic effects of the bull, environmental factors, and additive genetic and ROH status effects of individual single-nucleotide polymorphisms revealed genomic regions significantly associated with ROH status for total number of spermatozoa (4 regions) or percentage of live spermatozoa (5 regions). All but one region contains genes related to spermatogenesis and sperm morphology. These genomic regions contain genes affecting sperm morphogenesis and efficacy. The results highlight that next-generation sequencing may help explain some of the genetic factors contributing to inbreeding depression of sperm quality traits in Fleckvieh bulls.     

Identification of single nucleotide polymorphisms in the bovine solute carrier family 11 member 1 (SLC11A1) gene and their association with infection by Mycobacterium avium subspecies paratuberculosis

Johne's disease is a chronic enteritis caused by Mycobacterium avium ssp. paratuberculosis (MAP) that causes substantial financial losses for the cattle industry. Susceptibility to MAP infection is reported to be determined in part by genetic factors, so marker-assisted selection could help to obtain bovine populations that are increasingly resistant to MAP infection. Solute carrier family 11 member 1 (SLC11A1) was adjudged to be a potential candidate gene because of its role in innate immunity, its involvement in susceptibility to numerous intracellular infections, and its previous association with bovine MAP infection. The objectives of this study were to carry out an exhaustive process of discovery and compilation of polymorphisms in SLC11A1 gene, and to perform a population-based genetic association study to test its implication in susceptibility to MAP infection in cattle. In all, 57 single nucleotide polymorphisms (SNP) were detected, 25 of which are newly described in Bos taurus. Twenty-four SNP and two 3′-untranslated region polymorphisms, previously analyzed, were selected for a subsequent association study in 558 European Holstein-Friesian animals. The SNP c.1067C > G and c.1157-91A > T and a haplotype formed by these 2 SNP yielded significant association with susceptibility to MAP infection. The c.1067C > G is a nonsynonymous SNP that causes an amino acid change in codon 356 from proline to alanine (P356A) that could alter SLC11A1 protein function. This association study supports the involvement of SLC11A1 gene in susceptibility to MAP infection in cattle. Our results suggest that SNP c.1067C > G may be a potential causal variant, although functional studies are needed to assure this point.