The DMU-package for Analyzing Multivariate Mixed Models has been developed over a period of more than 25 years. This paper gives an overview of new features and the recent developments around the DMU-package, including: Genomic prediction (SNPBLUP, G-BLUP and “One-Step”), Genome-wide association studies, Survival models and double hierarchical generalized linear mixed models.

Best linear unbiased prediction (BLUP) is a method for obtaining point estimates of a random effect in a mixed effect model. Over the past decade it has been used extensively in ecology and evolutionary biology to predict individual breeding values and reaction norms. These predictions have been used to infer natural selection, evolutionary change, spatial-genetic patterns, individual reaction ...

Estimated breeding values (EBVs) are traditionally obtained from pedigree information. However, EBVs from high-density genotypes can have higher accuracy than EBVs from pedigree information. At the same time, it has been shown that EBVs from genomic data lead to lower increases in inbreeding compared with traditional selection based on genealogies. Here we evaluate the performance with BLUP sel...

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could impro...

Selection progress must be carefully balanced against the conservation of genetic variation in small populations of local breeds. Well-defined breeding programs with specified selection traits are rare in local pig breeds. Given the small population size, the focus is often on the management of genetic diversity. However, in local breeds, optimum contribution selection can be applied to control...

In order to have successful application of genomic selection, reference population and marker density should be chosen properly. This study purpose was to investigate the accuracy of genomic estimated breeding values in terms of low (5K), intermediate (50K) and high (777K) densities in the simulated populations, when different scenarios were applied about the reference populations selecting. Af...

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings genetic gain. This study aimed evaluate the accuracy cow breeding value by using three methods (Pedigree index (PI), Pedigree-BLUP (PBLUP), Genomic-BLUP (GBLUP)). The reference population (n=16,971) was used estimate values for 481 females as a te...

In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression–best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR...

A total of 956 lactation records of Holstein cows kept at Kaa Albon station, Imuran Governorate, Yemen during the period from 1991 to 2003 were used to investigate the effect of some genetic and non-genetic factors (Sire, parity, season of calving, year of calving and age at first calving as covariate) on the Total Milk Yield (TMY), Lactation Length (LL), and Dry Period (DP). Components of vari...

Records from 7,200 separate closed herds with either 12 or 25 sows that were mated to either four or eight boars per year were simulated by computer. Effects of selection method, herd size, and contemporary group variability on average genetic change, genetic variance, and inbreeding over 10 yr of selection were analyzed for traits with heritabilities of .1, .3, and .6. Selection of replacement...