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Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multistep genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. [ABSTRACT FROM AUTHOR]

Background: Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. Results: We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. Conclusions: BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species. [ABSTRACT FROM AUTHOR]

Compared with the currently widely used multi-step genomic models for genomic evaluation, single-step genomic models can provide more accurate genomic evaluation by jointly analyzing phenotypes and genotypes of all animals and can properly correct for the effect of genomic preselection on genetic evaluations. The objectives of this study were to introduce a single-step genomic model, allowing a direct estimation of single nucleotide polymorphism (SNP) effects, and to develop efficient computing algorithms for solving equations of the single-step SNP model. We proposed an alternative to the current single-step genomic model based on the genomic relationship matrix by including an additional step for estimating the effects of SNP markers. Our single-step SNP model allowed flexible modeling of SNP effects in terms of the number and variance of SNP markers. Moreover, our single-step SNP model included a residual polygenic effect with trait-specific variance for reducing inflation in genomic prediction. A kernel calculation of the SNP model involved repeated multiplications of the inverse of the pedigree relationship matrix of genotyped animals with a vector, for which numerical methods such as preconditioned conjugate gradients can be used. For estimating SNP effects, a special updating algorithm was proposed to separate residual polygenic effects from the SNP effects. We extended our single-step SNP model to general multiple-trait cases. By taking advantage of a block-diagonal (co)variance matrix of SNP effects, we showed how to estimate multivariate SNP effects in an efficient way. A general prediction formula was derived for candidates without phenotypes, which can be used for frequent, interim genomic evaluations without running the whole genomic evaluation process. We discussed various issues related to implementation of the single-step SNP model in Holstein populations with an across-country genomic reference population. [ABSTRACT FROM AUTHOR]

A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated usingasreml. The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations. [ABSTRACT FROM AUTHOR]

A number of cattle breeds have become highly specialized for milk or beef production, following strong artificial selection for these traits. In this paper, we compare allele frequencies from 9323 single nucleotide polymorphism (SNP) markers genotyped in dairy and beef cattle breeds averaged in sliding windows across the genome, with the aim of identifying divergently selected regions of the genome between the production types. The value of the method for identifying selection signatures was validated by four sources of evidence. First, differences in allele frequencies between dairy and beef cattle at individual SNPs were correlated with the effects of those SNPs on production traits. Secondly, large differences in allele frequencies generally occurred in the same location for two independent data sets (correlation 0.45) between sliding window averages. Thirdly, the largest differences in sliding window average difference in allele frequencies were found on chromosome 20 in the region of the growth hormone receptor gene, which carries a mutation known to have an effect on milk production traits in a number of dairy populations. Finally, for the chromosome tested, the location of selection signatures between dairy and beef cattle was correlated with the location of selection signatures within dairy cattle. [ABSTRACT FROM AUTHOR]

Direct genetic evaluation of profit was investigated as an alternative to a selection index. PROFk was defined as (net income)/(food requirement) until the start of the kth lactation, for k = 2 to 6. Genetic parameters such as heritabilities and genetic correlations were estimated for profit traits for Australian Holstein-Friesian and jersey dairy cattle. Heritabilities for profit until the start of a given lactation were moderate, ranging from 0·12 (for profit until the start of the second lactation in Holsteins) to 0·31 (profit until the start of the third lactation in Jerseys). Genetic correlations between profit traits were very high, and approached unity for most pairs of traits, so that profit early and late in herd life were nearly the same trait. Genetic correlations between profit traits and stayabilities until a given lactation were high, ranging from 0·71 to 0·97. Genetic correlations between profit traits and first lactation milk yield traits were approximately 0·80 for Holsteins and 0·90 for Jerseys. A single analysis urns carried out for lifetime profit using all data, including cows that were still in the herd at the time of data collection. Heritabilities were 0·13 for Holsteins and 0·19 for Jerseys. Genetic correlations between lifetime profit and first lactation yields were high. For the selection of dairy bulls, a multivariate analysis on a milk yield trait (e.g. protein yield) and profit until the last known lactation of bulls' progeny was suggested. [ABSTRACT FROM PUBLISHER]

Context: Cow survival is an important trait for dairy farm profitability and animal welfare, yet it is difficult to improve because of its complexity arising, in part, from varied reasons for culling and delay in getting actual culling data, which leads to low accuracy and instability of genetic predictions. Aims: To explore the benefits of partitioning the cow survival trait into 'early survival' (survival coded as a binary trait from the first to the second lactation) and 'late survival' (survival from the second to later lactations) on genetic predictions in addition to predictors of culling decisions. Methods: The raw phenotypic survival records for 1 619 542 Holstein and 331 996 Jersey cows were used in our study. All cows within each herd were allocated to either a reference or validation set. The accuracy and stability of genetic predictions were compared across lactations in the validation set. Further, we estimated the phenotypic and genetic correlation between overall, early or late cow survival and production, type, workability, and fertility traits using bivariate sire models. Key results: The heritability of overall survival in Jerseys (0.069 ± 0.003) was higher than in Holsteins (0.044 ± 0.001). The heritability of early survival was higher than that of late survival in Holstein (0.039 ± 0.002 vs 0.036 ± 0.001) and Jersey (0.080 ± 0.006 vs 0.053 ± 0.003). The genetic correlation between early and late survival was high in both breeds (0.770 ± 0.017 in Holstein and 0.772 ± 0.028 in Jersey). Adding survival information up to the sixth lactation had a large effect on genetic predictions of overall and late survival, whereas the predictions of early survival remained the same across lactations. Milk and protein yields, somatic cell score, fertility and temperament were highly correlated with early survival in Holstein and Jersey. However, the genetic correlations between production, type or workability traits and late survival were generally weaker than those and early survival. Conclusions: Early and late survival should be considered as different traits in genetic evaluations, because they are associated with different culling decisions. Implications: Partitioning cow survival into early and late survival and analysing them as two correlated traits could improve the accuracy and the stability of estimated breeding values compared with analysing overall survival as a single trait. Survival in dairy cows is an important welfare and economic trait that is affected by many factors. The reasons for culling a cow from the first to the second lactation are often different from the ones influencing survival of cows in later lactations. To incorporate this possible difference for genetic evaluations, we explored the benefits of partitioning the cow survival trait into 'early survival' and 'late survival'. This could improve the accuracy and the stability of genetic predictions. [ABSTRACT FROM AUTHOR]

The existence of a genotype x environment interaction (G x E) for fertility traits, survival, and milk yield traits was examined by considering performance recorded in different calving systems (seasonal, split, and year round) or regions as different traits. For fertility traits and survival, G x E were also investigated by applying a random regression model using continuous environmental variables, such as level of herd milk production, temperature-humidity index, and herd size as environmental descriptors. The traits considered were calving interval, calving to first service interval (CFS), 25-d nonreturn rate at first service, pregnancy rate, survival, milk yield, fat yield, and protein yield and percentage. Data on Holstein-Friesian cows that calved between 1997 and 2005 were analyzed. The number of cows included in the analyses ranged from approximately 21,000 for pregnancy rate to approximately one-half million for survival. For all traits, heterogeneity in additive and phenotypic variances was observed. For example, for CFS the additive genetic and phenotypic variance in seasonal calving herds was only 9 and 15% of that in year-round calving herds, respectively. Genetic correlations among calving systems for milk yield traits were greater than 0.96. For calving interval, the lowest genetic correlation, of 0.83, was between split and year-round calving herds, but for CFS and pregnancy rate, genetic correlations as low as 0.37 were observed, although these estimates were associated with large standard errors. Genetic correlations between traits recorded in different Australian regions were greater than 0.89. Heritability and phenotypic variance for milk yield traits were the greatest in region 1, which consisted of Queensland, West Australia, South Australia, and New South Wales, and were least in region 3, which included Gippsland and Tasmania, in accordance with mean milk yield levels. Genetic correlations as low as 0.5 for some fertility traits between the 5th and 95th percentile of the distribution of the environmental descriptors, such as herd size and average herd milk production, were also observed. However, these estimates had large standard errors. Regardless of the environmental descriptor used, there was no evidence for the presence of a large G x E that resulted in economically significant reranking of bulls. [ABSTRACT FROM AUTHOR]

Selection of animals for breeding ranked on estimated breeding value maximizes genetic gain in the next generation but does not necessarily maximize long-term response. An alternative method, as practiced by plant breeders, is to build a desired genotype by selection on specific loci. Maximal long-term response in animal breeding requires selection on estimated breeding values with constraints on coancestry. In this paper, we compared long-term genetic response using either a genotype building or a genomic estimated breeding value (GEBV) strategy for the Australian Selection Index (ASI), a measure of profit. First, we used real marker effects from the Australian Dairy Herd Improvement Scheme to estimate breeding values for chromosome segments (approximately 25 cM long) for 2,650 Holstein bulls. Second, we selected 16 animals to be founders for a simulated breeding program where, between them, founders contain the best possible combination of 2 segments from 2 animals at each position in the genome. Third, we mated founder animals and their descendants over 30 generations with 2 breeding objectives: (1) to create a population with the "ideal genotype," where the best 2 segments from the founders segregate at each position, or (2) obtain the highest possible response in ASI with coancestry lower than that achieved under breeding objective 1. Results show that genotype building achieved the ideal genotype for breeding objective 1 and obtained a large gain in ASI over the current population (+A$864.99). However, selection on overall GEBV had greater short-term response and almost as much long-term gain (+A$820.42). When coancestry was lowered under breeding objective 2, selection on overall GEBV achieved a higher response in ASI than the genotype building strategy. Selection on overall GEBV seems more flexible in its selection decisions and was therefore better able to precisely control coancestry while maximizing ASI. We conclude that selection on overall GEBV while minimizing average coancestry is the more practical strategy for dairy cattle where selection is for highly polygenic traits, the reproductive rate is relatively low, and there is low tolerance of coancestry. The outcome may be different for traits controlled by few loci of relatively large effects or for different species. In contrast to other simulations, our results indicate that response to selection on overall GEBV may continue for several generations. This is because long-term genetic change in complex traits requires favorable changes to allele frequencies for many loci located throughout the genome. [ABSTRACT FROM AUTHOR]

Multiple-trait genome-wide association study (GWAS) analyses were compared with single-trait GWAS for power to discover and subsequently validate genetic markers (single nucleotide polymorphisms; SNP) associated with dairy traits. The SNP associations were discovered in 1 Holstein population and validated in both a Holstein population consisting of bulls younger than those in the discovery population and a Jersey population. The multivariate methods used were a principal component analysis and a series of bivariate analyses. The statistical power of detecting associations using multiple-trait GWAS was as good as or better than that of the best single-trait GWAS. Additional SNP associations were found with the multivariate methods that had not been discovered in the singletrait analyses; this was achieved without an increase in the false discovery rate. From the multivariate analysis, 4 common pleiotropic patterns were identified among the putative quantitative trait loci (QTL) affecting the Australian selection index. These patterns could be interpreted as a primary effect of the putative QTL on 1 or more milk components and secondary effects on other components. The multivariate analysis did not appear to increase the precision with which putative QTL were mapped. [ABSTRACT FROM AUTHOR]

Good performance in extended lactations of dairy cattle may have a beneficial effect on food costs, health, and fertility. Because data for extended lactation performance is scarce, lactation persistency has been suggested as a suitable selection criterion. Persistency phenotypes were calculated in several ways: P1 was yield relative to an approximate peak, P2 was the slope after peak production, and P3 was a measure derived to be phenotypically uncorrelated to yield and calculated as a function of linear regressions on test-day deviations of days in milk. Phenotypes P1, P2, and P3 were calculated for sires as solutions estimated from a random regression model fitted to milk yield. Because total milk yield, calculated as the sum of daily sire solutions, was correlated to P1 and P2 (r = 0.30 and 0.35 for P1 and P2, respectively), P1 and P2 were also adjusted for milk yield (P1adj and P2adj, respectively). To find genomic regions associated with the persistency phenotypes, we used a discovery population of 743 Holstein bulls proven before 2005 and 2 validation data sets of 357 Holstein bulls proven after 2005 and 294 Jersey sires. Two strategies were used to search for genomic regions associated with persistency: 1) persistency solutions were regressed on each single nucleotide polymorphism (SNP) in turn and 2) a genomic selection method (BayesA) was used where all SNP were fitted simultaneously. False discovery rates in the validation data were high (>66% in Holsteins and >77% in Jerseys). However, there were 2 genomic regions on chromosome 6 that validated in both breeds, including a cluster of 6 SNP at around 13.5 to 23.7 Mbp and another cluster of 5 SNP (70.4 to 75.6 Mbp). A third cluster validated in both breeds on chromosome 26 (0.33 to 1.46 Mbp). Validating SNP effects across 2 breeds is unlikely to happen by chance even when false discovery rates within each breed are high. However, marker-assisted selection on these selected SNP may not be the best way to improve this trait because the average variation explained by validated SNP was only 1 to 2%. Genomic selection could be a better alternative. Correlations between genomic breeding values predicted using all SNP simultaneously and estimated breeding values based on progeny test were twice as high as the equivalent correlations between estimated breeding values and parent average. Persistency is a good candidate for genomic selection because the trait is expressed late in lactation. [ABSTRACT FROM AUTHOR]

Dairy farming is carried out under a wide range of production environments, including large variations in the level of feeding. Although reranking of dairy sires based on the level of feeding of their daughters has been reported, detecting the genetic mutations that cause this genotype by environment interaction has not been previously attempted. In our experiment to find genetic markers for such mutations, we selected 388 Holstein bulls from the Australian dairy bull population and genotyped them for 9,919 single nucleotide polymorphism (SNP) markers. Production data, consisting of first-lactation test-day records for milk yield, fat yield, protein yield, protein percentage, and fat percentage, from the daughters of the genotyped bulls were used to estimate the effect of each SNP, which was modeled as a regression on herd mean test-day yield, where herd mean test-day yield is a descriptor of the environment. Data were analyzed with 4 models; in 2 models, daughter records were analyzed directly, with and without taking sire relationships into account. With the other 2 models, sire reaction norms for each trait were calculated and marker effects on the sire reaction norms were estimated with and without taking sire relationships into account. The results showed that using daughter records directly and accounting for sire relationships was necessary to obtain high power and to limit the number of false positives. With this approach, SNP with significant effects were found for all traits. Log transformation of the data did not affect the power of gene detection. The significant markers were categorized according to their joint effects on production and environmental sensitivity. Potential gene candidates and application of the markers is discussed. About one-third of the significant markers affect intercept and slope in opposite directions, and some of these facilitate marker-assisted selection for robustness. [ABSTRACT FROM AUTHOR]

A 5 yr whole-system study, beginning in June 1994, compared the productivity of high [HGM; Australian Breeding Value (ABV) of 49.1 kg of fat plus protein] and low [LGM; ABV of 2.3 kg of fat plus protein] genetic merit cows. Cows from both groups were fed at 3 levels of concentrate (C): 0.34 (low C), 0.84 (medium C), and 1.71 (high C) t of DM/cow per lactation. Thus, there were 6 treatments (farmlets) composed of 18 cows each. The 30 blocks of pasture on each farmlet were matched between farmlets for pasture growth before the study (and soil characteristics and aspect). Cows were culled, and pasture and feed use were managed so as not to bias any one treatment. Genetic merit, level of feeding, and their interaction were significant effects for protein content, protein/cow, and milk and protein/ha. For fat and milk yield/cow, genetic merit and level of feeding were significant, whereas there was no significant effect of genetic merit on fat content. The difference of 46.8 kg of fat plus protein yield between the ABV of HGM and LGM cows and the actual difference in production between the 2 groups was not significantly different except for low C (27 kg) cows. This was due to a 3-fold lower protein yield difference (6 kg/cow) compared with an ABV difference for protein yield of 17.9 kg/cow. The dramatic effect of treatment on protein is in line with differences in the mean protein content (2.89% for the HGM - low C cows compared with a mean of 3.02% for the remaining groups) and mean body condition score [4.3 for HGM - low C cows compared with 4.8 for the mean of the remaining groups (scale 1 to 8)], both indicators reflecting a higher negative energy balance in the HGM - low C cows. When individual cow production was plotted against ABV for production of milk or protein yield all relationships were quadratic, but the slope was relatively flat (low response to ABV) for the low C cows, steeper for the medium C cows and steepest (but not linear) for the high C cows. The relationship between ABV for fat yield and actual fat yield was linear for all levels of concentrate. The mean milk yield/ha from pasture for the 6 farmlets over the 5 yr was 11,868 L, 11,417 L, or 7,761 L for the HGM cows fed at low C, medium C, or high C, respectively, and 10,579 L, 9,800 L, or 5,812 L for LGM cows, fed at low C, medium C, or high C, respectively. The response to concentrates fed was very high for the HGM - medium C cows at 0.115 kg fat plus protein or 1.75 L milk/kg of concentrate fed, with comparable figures of 0.083 kg and 1.0 L, 0.86 kg and 1.47 L and 0.066 and 0.92 L/kg of concentrate fed for the HGM - high C, LGM - medium C, and LGM - high C, respectively. The results show a significant genetic merit by environment (level of feeding) interaction for reproduction and most production parameters when considered in terms of the individual cow and the whole farm system. [ABSTRACT FROM AUTHOR]

A correction to the article "Invited review: Genomic Selection in Dairy Cattle: Progress and Challenges," that was published in the 2009 issue is presented.