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Hickey J.M.,University of New England of Australia | Kinghorn B.P.,University of New England of Australia | Tier B.,University of New England of Australia | Wilson J.F.,University of Edinburgh | And 3 more authors.
Genetics Selection Evolution | Year: 2011

Background: Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data. Methods. A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information. Results: The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available. Conclusions: The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets. © 2011 Hickey et al; licensee BioMed Central Ltd. Source


Hickey J.M.,University of New England of Australia | Kinghorn B.P.,University of New England of Australia | Tier B.,University of New England of Australia | Van Der Werf J.H.,University of New England of Australia | And 2 more authors.
Genetics Selection Evolution | Year: 2012

Background: Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods: An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results: Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions: The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations. © 2012 Hickey et al.; licensee BioMed Central Ltd. Source


Starkey C.P.,Cooperative Research Center for Sheep Industry Innovation | Starkey C.P.,University of New England of Australia | Geesink G.H.,University of New England of Australia | Oddy V.H.,Australian Department of Primary Industries and Fisheries | Hopkins D.L.,Australian Department of Primary Industries and Fisheries
Meat Science | Year: 2015

Meat tenderness is known to be affected by sarcomere length (SL), proteolysis and collagen content (CC). Sixty lambs were slaughtered and the Longissimus muscle was sampled. Samples for shear force (SF), SL, proteolysis indicators (desmin degradation, particle size: PS) and CC were taken after the allotted ageing periods (1, 7, and 14. days). PS explained a large part of the variation in shear force (approximately 34%) when modelled across ageing periods. Other factors (CC, SL) combined with proteolysis indicators (PS, desmin degradation) explained just under 40% of the variation in shear force. Within ageing periods SL explained a small, but significant, part of the variation in shear force after 14. days of ageing (8%) and at day 1 of ageing desmin degradation explained 17% of the variation in shear force. Methods to improve the tenderness of lamb longissimus muscle should focus on increasing the extent of post-mortem proteolysis, when processing conditions are sufficient to prevent muscle fibre shortening. © 2015 Elsevier Ltd. Source


Hayes B.J.,Australian Department of Primary Industries and Fisheries | Hayes B.J.,Cooperative Research Center for Sheep Industry Innovation | Bowman P.J.,Australian Department of Primary Industries and Fisheries | Daetwyler H.D.,Australian Department of Primary Industries and Fisheries | And 4 more authors.
Animal Genetics | Year: 2012

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed. © 2011 Stichting International Foundation for Animal Genetics. Source


Dominik S.,CSIRO | Dominik S.,Cooperative Research Center for Sheep Industry Innovation | Henshall J.M.,CSIRO | Henshall J.M.,Cooperative Research Center for Sheep Industry Innovation | And 2 more authors.
Animal Genetics | Year: 2012

The aim of this study was to fine map the genomic location of the Horns locus in the Australian Merino sheep population and to identify markers that can be used to predict the horn phenotype. A linkage disequilibrium analysis of horn data from Australian Merino sheep mapped the Horns locus to a small region on chromosome 10. A single nucleotide polymorphism in the region was found to be highly predictive for the polled phenotype in an experimental population of Merino sheep. This was owing to a dominance effect of one of the alleles when inherited maternally. It was suggested that a genetic test would provide a good predictor of the polled phenotype. Finally, an evaluation of industry data showed that the SNP is at very different frequencies in Poll Merino sheep that have been bred for polledness (based on phenotype alone) compared with the Merino sheep breed. © 2011 Stichting International Foundation for Animal Genetics. Source

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