Dr Zabron Nziku from Tanzania Livestock Research Institute (TALIRI) visited AlphaGenes in March. We went to the SRUC Dairy Research Centre based at the Crichton Royal Farm in Dumfries. The Centre develops, implements and provides information from sustainable breeding and management systems for dairy cows and they also try to find ways to improve the health and welfare of UK dairy herds.
We finished the day in a more relaxed atmosphere at the Caerlaverock Castle.
Analysis of a large dataset reveals haplotypes carrying putatively recessive lethal and semi-lethal alleles with pleiotropic effects on economically important traits in beef cattle
In livestock, deleterious recessive alleles can result in reduced economic performance of homozygous individuals in multiple ways, e.g. early embryonic death, death soon after birth, or semi-lethality with incomplete penetrance causing reduced viability. While death is an easy phenotype to score, reduced viability is not as easy to identify. However, it can sometimes be observed as reduced conception rates, longer calving intervals, or lower survival for live born animals.
In this paper, we searched for haplotypes that carry putatively recessive lethal or semi-lethal alleles in 132,725 genotyped Irish beef cattle from five breeds: Aberdeen Angus, Charolais, Hereford, Limousin, and Simmental. We phased the genotypes in sliding windows along the genome and used five tests to identify haplotypes with absence of or reduced homozygosity. Then, we associated the identified haplotypes with 44,351 insemination records that indicated early embryonic death, and postnatal survival records. Finally, we assessed haplotype pleiotropy by estimating substitution effects on estimates of breeding value for 15 economically important traits in beef production.
We found support for one haplotype that carries a putatively recessive lethal (chromosome 16 in Simmental) and two haplotypes that carry semi-lethal alleles (chromosome 14 in Aberdeen Angus and chromosome 19 in Charolais), with population frequencies of 8.8, 15.2, and 14.4%, respectively. All of these three haplotypes showed pleiotropic effects on economically important traits for beef production. Their allele substitution effects are €2.30, €3.42, and €1.47 for the terminal index and €1.03, -€3.11, and -€0.88 for the replacement index, where the standard deviations for the terminal index are €22.52, €18.65, and €22.70 and for the replacement index they are €31.35, €29.82, and €35.79. We identified ZFAT as the candidate gene for semi-lethality in Aberdeen Angus, several candidate genes for the lethal Simmental haplotype, and no candidate genes for the semi-lethal Charolais haplotype.
We analysed genotype, reproduction, survival, and production data to detect haplotypes that carry putatively recessive lethal or semi-lethal alleles in Irish beef cattle and identified one lethal and two semi-lethal haplotypes, which have pleiotropic effects on economically important traits in beef production.
“In this paper, we review the performance of hidden Markov model-based imputation methods in animal breeding populations. Imputation is a technique that decreases the cost of genotyping animals, by allowing animal breeders to genotype most of their animals on a low-cost, low-density genotyping array, and a few of their animals on a higher-cost, higher-density genotyping array. Statistical regularities between the genotypes of the low-density and high-density genotypes can be used to “fill-in” the genotypes missing from the low-density array.
Traditionally, pedigree and heuristic based imputation methods have been used in animal breeding due to their computation efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These methods tend to be more accurate than existing heuristics in populations where pedigree information is not available (e.g. cattle, or in developing countries), but historically were too computationally expensive to be used in routine breeding applications.
To test the performance of these hidden Markov model-based imputation methods, we evaluated the accuracy and computational cost of several methods in a series of simulated populations and a real animal population. First, we tested single-step (diploid) imputation, which performs both phasing and imputation. Second, we tested pre-phasing followed by haploid imputation. Overall, we used four available diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and Minimac3).
We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, among all the methods tested, pre-phasing with Eagle2 or HAPI-UR and imputing with Minimac3 or IMPUTE2 gave the highest accuracies with both simulated and real data.
The results of this study suggest that hidden Markov model-based imputation algorithms are an accurate and computationally feasible approach for performing imputation without a pedigree when pre-phasing and haploid imputation are used. Of the algorithms tested, the combination of Eagle2 and Minimac3 gave the highest accuracy across the simulated and real datasets. These results may help decrease the costs of genotyping animals when pedigree information is not available.”