An Efficient Method to Calculate Genomic Prediction Accuracy for New Individuals.

Diagonal elements of the coefficient matrix are necessary to calculate the genomic prediction accuracy. Here an improved methodology is described, to update the inverse of the coefficient matrix () for new individuals with a genotype, with and without phenotypes. Computational performance is significantly improved by re-using parts of the coefficient matrix inverse calculations that do not change from one animal to another, in combination with updated calculations for those that do change.

Estimates of genetic trend for single-step genomic evaluations.

Background: A common measure employed to evaluate the efficacy of livestock improvement schemes is the genetic trend, which is calculated as the means of predicted breeding values for animals born in successive time periods. This implies that different cohorts refer to the same base population. For genetic evaluation schemes integrating genomic information with records for all animals, genotyped or not, this is often not the case: expected means for pedigree founders are zero whereas values for genotyped animals are expected to sum to zero at the (mean) time corresponding to the frequencies that are used to center marker allele counts when calculating genomic relationships.

Study of the optimum haplotype length to build genomic relationship matrices.

Background: As genomic data becomes more abundant, genomic prediction is more routinely used to estimate breeding values. In genomic prediction, the relationship matrix ([Formula: see text]), which is traditionally used in genetic evaluations is replaced by the genomic relationship matrix ([Formula: see text]). This paper considers alternative ways of building relationship matrices either using single markers or haplotypes of different lengths.

BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data.

Background: The advent of genomic marker data has triggered the development of various Bayesian algorithms for estimation of marker effects, but software packages implementing these algorithms are not readily available, or are limited to a single algorithm, uni-variate analysis or a limited number of factors. Moreover, script based environments like R may not be able to handle large-scale genomic data or exploit model properties which save computing time or memory (RAM).

Results: BESSiE is a software designed for best linear unbiased prediction (BLUP) and Bayesian Markov chain Monte Carlo analysis of linear mixed models allowing for continuous and/or categorical multivariate, repeated and missing observations, various random and fixed factors and large-scale genomic marker data.

Technical note: Genetic principal component models for multitrait single-step genomic evaluation.

A reparameterization of the multivariate linear mixed model in genetic evaluation to principal components is described. This yields an equivalent model with a sparser coefficient matrix in the mixed model equations and, thus, reduced computational requirements to solve them. It is especially advantageous for analyses incorporating genomic relationship information with many nonzero elements in the inverse of the relationship matrix.

Managing the rate of increase in average co-ancestry in a rolling front tree breeding strategy.

In breeding forest trees, as for livestock, the goal is to capture as much genetic gain as possible for the breeding objective, while limiting long- and short-term inbreeding. The Southern Tree Breeding Association (STBA) is responsible for breeding Australia's two main commercial forest tree species and has adopted algorithms and methods commonly used in animal breeding to achieve this balance. Discrete generation breeding is the norm for most tree breeding programmes.

A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle.

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS).

Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle.

Background: The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector.

Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies.

Background: The apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.

Estimation of accuracies and expected genetic change from selection for selection indexes that use multiple-trait predictions of breeding values.

Procedures are described for estimating selection index accuracies for individual animals and expected genetic change from selection for the general case where indexes of EBVs predict an aggregate breeding objective of traits that may or may not have been measured. Index accuracies for the breeding objective are shown to take an important general form, being able to be expressed as the product of the accuracy of the index function of true breeding values and the accuracy with which that function predicts the breeding objective. When the accuracies of the individual EBVs of the index are known, prediction error variances (PEVs) and covariances (PECs) for the EBVs within animal are able to be well approximated, and index accuracies and expected genetic change from selection estimated with high accuracy.

Genomic evaluations using similarity between haplotypes.

Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values.

Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals.

Technical note: updating the inverse of the genomic relationship matrix.

A computing strategy to update the inverse of the genomic relationship matrix when new genotypes become available is described. It is shown that re-using results of previous computations can result in substantial reductions in computing time required. For instance, when the number of individuals increased by about 1% for matrices larger than 15,000, the time required for updating was less than 7% of that used for direct inversion from scratch.

Analysing quantitative parent-of-origin effects with examples from ultrasonic measures of body composition In Australian beef cattle.

Parent-of-origin effects arise when an individual's genes are modified during gametogenesis. Commonly known as imprinting, affected genes may be completely, or partially, suppressed. Individual loci in mice, human and sheep are known to be imprinted, and the quantitative effects of imprinted loci have been found for many carcass traits in cattle and pigs.

Genetic variants affecting meat and milk production traits appear to have effects on reproduction traits in cattle.

Polymorphisms located in the genes ABCG2, DGAT1, LEP, PRLR, RORC, CAPN1 and CAST previously have been associated with milk or meat production traits. In this study, these polymorphisms were examined for significant effects on reproductive traits [age at puberty (AGECL), post-partum anoestrus interval (PPAI) and the ability ovulate prior to weaning (PW)] and on a panel of correlated traits such as weight, growth and serum concentration of insulin-like growth factor I. The effects of the polymorphisms were examined in two samples of tropically adapted beef cattle: Brahman (N = 932) and Tropical Composites (N = 1097).

Fine-mapping the POLL locus in Brahman cattle yields the diagnostic marker CSAFG29.

The POLL locus has been mapped to the centromeric region of bovine chromosome 1 (BTA1) in both taurine breeds and taurine-indicine crosses in an interval of approximately 1 Mb. It has not yet been mapped in pure-bred zebu cattle. Despite several efforts, neither causative mutations in candidate genes nor a singular diagnostic DNA marker has been identified.

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation.

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.

Use of the numerator relationship matrix in genetic analysis of autopolyploid species.

Mixed models incorporating the inverse of a numerator relationship matrix (NRM) are widely used to estimate genetic parameters and to predict breeding values in animal breeding. A simple and quick method to directly calculate the inverse of the NRM has been historically developed for diploid animal species. Mixed models are less used in plant breeding partly because the existing method for diploids is not applicable to autopolyploid species.

Genome-wide association studies of female reproduction in tropically adapted beef cattle.

The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI.

"SNP Snappy": a strategy for fast genome-wide association studies fitting a full mixed model.

A strategy to reduce computational demands of genome-wide association studies fitting a mixed model is presented. Improvements are achieved by utilizing a large proportion of calculations that remain constant across the multiple analyses for individual markers involved, with estimates obtained without inverting large matrices.

A single nucleotide polymorphism in suppressor of cytokine signalling-2 is associated with growth and feed conversion efficiency in pigs.

Feed efficiency and growth are the most important traits in pig production, and very few genetic markers have been reported to be associated with feed efficiency. The suppressor of cytokine signalling-2 (encoded by SOCS2) is the main negative regulator of somatic growth, and the knockout of SOCS2 and naturally mutant mice have high-growth phenotypes. Porcine SOCS2 was selected as a primary positional candidate for feed efficiency, because it is located on chromosome 5q, in the vicinity of a Quantitative Trait Locus (QTL) region for food conversion ratio in pigs.

A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes.

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.

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.

Background: Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV.

Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens.

Two novel methods for genome wide selection (GWS) were examined for predicting the genetic merit of animals using SNP information alone. A panel of 1,546 dairy bulls with reliable EBVs was genotyped for 15,380 SNPs that spanned the whole bovine genome. Two complexity reduction methods were used, partial least squares (PLS) and regression using a genetic algorithm (GAR), to find optimal solutions of EBVs against SNP information.