Using genomics to enhance selection of novel traits in North American dairy cattle.

The objectives of this paper were to briefly review progress in the genetic evaluation of novel traits in Canada and the United States, assess methods to predict selection accuracy based on cow reference populations, and illustrate how the use of indicator traits could increase genomic selection accuracy. Traits reviewed are grouped into the following categories: udder health, hoof health, other health traits, feed efficiency and methane emissions, and other novel traits. The status of activities expected to lead to national genetic evaluations is indicated for each group of traits. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach. Several deterministic methods can be used to predict the reliability of genomic evaluations based on cow reference population size, trait heritability, and other population parameters. To provide an empirical validation of those methods, predicted accuracies were compared with observed accuracies for several cow reference populations and traits. Reference populations of 2,000 to 20,000 cows were created through random sampling of genotyped Holstein cows in Canada and the United States. The effects of single nucleotide polymorphisms (SNP) were estimated from those cow records, after excluding the dams of validation bulls. Bulls that were first progeny tested in 2013 and 2014 were then used to carry out a validation and estimate the observed accuracy of genomic selection based on those SNP effects. Over the various cow population sizes and traits considered in the study, even the best prediction methods were found, on average, to either under-evaluate observed accuracy by 0.20 or over-evaluate it by 0.22, depending on the approach used to estimate the number of independently segregating chromosome segments. In some instances, differences between observed and predicted accuracies were as large as 0.47. Indicator traits can be very useful for the selection of novel traits. To illustrate this, protein yield, body weight, and mid-infrared data were used as indicator traits for feed efficiency. Using those traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50.

Male and female contributions to heterosis in lifetime performance of mice.

Six straightbred lines of mice, some of their F1 crosses and a synthetic line were used to evaluate male and female contributions to heterosis in lifetime performance measured on females. Females from each straightbred line or F1 crosses were pair-mated randomly at day 42 with either a male of the corresponding genetic group or from a synthetic line, and pairs were maintained for 155 days (lifetime). Each mother was allowed to rear all young born alive until day 18 when the young were discarded. Data were analyzed using a model in which the group mean of lifetime performance was expressed as the sum of (additive direct) genetic and environmental effects for each of the male and female genetic groups used for mating. Comparison of group means for lifetime performance revealed that estimates of F1 heterosis due to male and female averaged 10 and 9% for number of parturitions during lifetime, 7 and 28% for total number of young born alive, 6 and 31% for total body weight of young born alive, 8 and 33% for total number of young raised to day 18, 9 and 43% for total body weight of young raised to weaning, and 8 and 8% for days from first mating to last parturition. The male's contribution to heterosis in lifetime performance was smaller than female's contribution for productive traits (total number of young born alive and at day 18, and total body weight of young born alive and at day 18), and was nearly equal in reproductive traits (number of parturitions during lifetime and days from first mating to last parturition).

An efficient method of computing the numerator relationship matrix and its inverse matrix with inbreeding for large sets of animals.

The numerator relationship matrix describes the genetic relationships between individuals of a population. Its inverse is used for the prediction of breeding values, as outlined by Henderson (1975a).For large populations, the recursive method commonly used is difficult to apply because of the size of the relationship matrix. Recently Henderson (1975b) derived a method which allows computing the inverse of the numerator relationship matrix itself for a large number of animals, provided the population is non-inbred. The method presented here is an extension of Henderson's method to allow for inbreeding with large number of animals. It takes inbreeding into account and computes the numerator relationship matrix as well as its inverse. The method is particularly efficient in computer storage in that it allows handling of sets of animals larger than 5000 animals, and is almost as fast as the recursive method.