Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes.

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations.

Use of field data in pig genomic selection schemes: a simulation study.

The aim of this study was to test how genetic gain for a trait not measured on the nucleus animals could be obtained within a genomic selection pig breeding scheme. Stochastic simulation of a pig breeding program including a breeding nucleus, a multiplier to produce and disseminate semen and a production tier where phenotypes were recorded was performed to test (1) the effect of obtaining phenotypic records from offspring of nucleus animals, (2) the effect of genotyping production animals with records for the purpose of including them in a genomic selection reference population or (3) to combine the two approaches. None of the tested strategies affected genetic gain if the trait under investigation had a low economic value of only 10% of the total breeding goal. When the relative economic weight was increased to 30%, a combination of the methods was most effective. Obtaining records from offspring of already genotyped nucleus animals had more impact on genetic gain than to genotype more distant relatives with phenotypes to update the reference population. When records cannot be obtained from offspring of nucleus animals, genotyping of production animals could be considered for traits with high economic importance.

Genomic selection for two traits in a maternal pig breeding scheme.

The objective of this study was to compare different implementations of genomic selection to a conventional maternal pig breeding scheme, when selection was based partly on production traits and partly on maternal traits. A nucleus pig breeding population with size and structure similar to Norwegian Landrace was simulated where equal weight was used for maternal and production traits. To genotype the boars at the boar station and base the final selection of boars on genomic breeding values increased total genetic gain by 13% and reduced the rate of inbreeding by 40%, without significantly affecting the relative contribution of each trait to total genetic gain. To increase the size of the reference population and thereby accuracy of selection, female sibs in the selected litters can also be genotyped to increase genetic gain for maternal traits more than for production traits, thereby resulting in an increased relative contribution of maternal traits to total genetic gain. Genotyping 2,400 females each year increased the relative contribution of maternal traits to total genetic gain from 16 to 32%. Performing preselection of males by allowing genotyping of 2 males per litter and allowing for selection across and within litters before the boar test increased genetic gain by 5 to 11%, compared with genotyping the boars at the boar station, without significant effects on the relative contribution of each trait to total genetic gain. Genotyping more animals consequently increased genetic gain. Genotyping females to build a larger reference base for maternal traits gave similar genetic gain as genotyping the same amount of additional males but with a lower rate of inbreeding and a greater contribution of maternal traits to total genetic gain. In conclusion, genotyping females should be prioritized before genotyping more males than the tested boars if the breeding goal is to increase maternal traits specifically over production traits or genomic selection is used as a tool to reduce the rate of inbreeding.

Genomic selection for maternal traits in pigs.

The aim of this study was to compare alternative designs for implementation of genomic selection to improve maternal traits in pigs, with a conventional breeding scheme and a progeny testing scheme. The comparison was done through stochastic simulation of a pig population. It was assumed that selection was performed based on a trait that could be measured on females after the first litter, with a heritability of 0.1. Genomic selection increased genetic gain and reduced the rate of inbreeding, compared with conventional selection without progeny testing. Progeny testing could also increase genetic gain and decrease the rate of inbreeding, but because of the increased generation interval, the increase in annual genetic gain was only 7%. When genomic selection was applied, genetic gain was increased by 23 to 91%, depending on which and how many animals were genotyped. Genotyping dams in addition to the male selection candidates gave increased accuracy of the genomic breeding values, increased genetic gain, and decreased rate of inbreeding. To genotype 2 or 3 males from each litter, in order to perform within-litter selection, increased genetic gain 8 to 12%, compared with schemes with the same number of genotyped females but only 1 male candidate per litter. Comparing schemes with the same total number of genotyped animals revealed that genotyping more females caused a greater increase in genetic gain than genotyping more males because greater accuracy of selection was more advantageous than increasing the number of male selection candidates. When more than 1 male per litter was genotyped, and thereby included as selection candidates, rate of inbreeding increased because of coselection of full sibs. The conclusion is that genomic selection can increase genetic gain for traits that are measured on females, which includes several traits with economic importance in maternal pig breeds. Genotyping females is essential to obtain a high accuracy of selection.

A comparison of dairy cattle breeding designs that use genomic selection.

Different dairy cattle breeding schemes were compared using stochastic simulations, in which the accuracy of the genomic breeding values was dependent on the structure of the breeding scheme, through the availability of new genotyped animals with phenotypic information. Most studies that predict the gain by implementing genomic selection apply a deterministic approach that requires assumptions about the accuracy of the genomic breeding values. The achieved genetic gain, when genomic selection was the only selection method to directly identify elite sires for widespread use and progeny testing was omitted, was compared with using genomic selection for preselection of young bulls for progeny testing and to a conventional progeny test scheme. The rate of inbreeding could be reduced by selecting more sires every year. Selecting 20 sires directly on their genomic breeding values gave a higher genetic gain than any progeny testing scheme, with the same rate of inbreeding as the schemes that used genomic selection for preselection of bulls before progeny testing. The genomic selection breeding schemes could reduce the rate of inbreeding and still increase genetic gain, compared with the conventional breeding scheme. Since progeny testing is expensive, the breeding scheme omitting the progeny test will be the cheapest one. Keeping the progeny test and use of genomic selection for preselection still has some advantages. It gives higher accuracy of breeding values and does not require a complete restructuring of the breeding program. Comparing at the same rate of inbreeding, using genomic selection for elite sire selection only gives a 13% increase in genetic gain, compared with using genomic selection for preselection. One way to reduce the costs of the scheme where genomic selection was used for preselection is to reduce the number of progeny tested bulls. This was here achieved without getting lower genetic gain or a higher rate of inbreeding.

Gene by environment interactions for production traits in Australian dairy cattle.

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.

A genome scan for quantitative trait locus by environment interactions for production traits.

Genotype by environment interactions between milk production traits and production level have often been observed. To increase the power of quantitative trait loci (QTL) detection, QTL by environment interaction was included in QTL analyses for the milk, protein, and fat yields. The aim of the study was to detect QTL with interaction effects with the production environment. The QTL effects were modeled through random regression models for within-herd production level. All autosomes except Bos taurus autosome 6 were included in the analysis. A more detailed study of chromosome 6 is planned. For milk yield, 5 QTL were observed, 2 of which had interaction effects with production level (suggestive linkage). For protein yield, 5 QTL were observed, 3 of which had interaction effects (suggestive linkage). For fat yield, 3 QTL were observed, none of which had interaction effects with the environment (suggestive linkage). Thus, some QTL with interaction effects seemingly exist for milk yield and protein yield. For such QTL, estimated correlations between slope and intercept of the effect (close to 1 or -1) indicated that only 2 alleles were segregating. The study indicates that QTL by environment interactions exist, and that random regression models that describe the environment as herd production level can detect this interaction.