Genomic prediction based on data from three layer lines using non-linear regression models

Background

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods.

Methods

In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values.

Results

When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction.

Conclusions

Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0075-3) contains supplementary material, which is available to authorized users.     

Imputation of non-genotyped individuals based on genotyped relatives: assessing the imputation accuracy of a real case scenario in dairy cattle

Background

Imputation of genotypes for ungenotyped individuals could enable the use of valuable phenotypes created before the genomic era in analyses that require genotypes. The objective of this study was to investigate the accuracy of imputation of non-genotyped individuals using genotype information from relatives.

Methods

Genotypes were simulated for all individuals in the pedigree of a real (historical) dataset of phenotyped dairy cows and with part of the pedigree genotyped. The software AlphaImpute was used for imputation in its standard settings but also without phasing, i.e. using basic inheritance rules and segregation analysis only. Different scenarios were evaluated i.e.: (1) the real data scenario, (2) addition of genotypes of sires and maternal grandsires of the ungenotyped individuals, and (3) addition of one, two, or four genotyped offspring of the ungenotyped individuals to the reference population.

Results

The imputation accuracy using AlphaImpute in its standard settings was lower than without phasing. Including genotypes of sires and maternal grandsires in the reference population improved imputation accuracy, i.e. the correlation of the true genotypes with the imputed genotype dosages, corrected for mean gene content, across all animals increased from 0.47 (real situation) to 0.60. Including one, two and four genotyped offspring increased the accuracy of imputation across all animals from 0.57 (no offspring) to 0.73, 0.82, and 0.92, respectively.

Conclusions

At present, the use of basic inheritance rules and segregation analysis appears to be the best imputation method for ungenotyped individuals. Comparison of our empirical animal-specific imputation accuracies to predictions based on selection index theory suggested that not correcting for mean gene content considerably overestimates the true accuracy. Imputation of ungenotyped individuals can help to include valuable phenotypes for genome-wide association studies or for genomic prediction, especially when the ungenotyped individuals have genotyped offspring.     

Phylogenetic classification of Escherichia coli O157:H7 strains of human and bovine origin using a novel set of nucleotide polymorphisms

Background

Cattle are a reservoir of Shiga toxin-producing Escherichia coli O157:H7 (STEC O157), and are known to harbor subtypes not typically found in clinically ill humans. Consequently, nucleotide polymorphisms previously discovered via strains originating from human outbreaks may be restricted in their ability to distinguish STEC O157 genetic subtypes present in cattle. The objectives of this study were firstly to identify nucleotide polymorphisms in a diverse sampling of human and bovine STEC O157 strains, secondly to classify strains of either bovine or human origin by polymorphism-derived genotypes, and finally to compare the genotype diversity with pulsed-field gel electrophoresis (PFGE), a method currently used for assessing STEC O157 diversity.

Results

High-throughput 454 sequencing of pooled STEC O157 strain DNAs from human clinical cases (n = 91) and cattle (n = 102) identified 16,218 putative polymorphisms. From those, 178 were selected primarily within genomic regions conserved across E. coli serotypes and genotyped in 261 STEC O157 strains. Forty-two unique genotypes were observed that are tagged by a minimal set of 32 polymorphisms. Phylogenetic trees of the genotypes are divided into clades that represent strains of cattle origin, or cattle and human origin. Although PFGE diversity surpassed genotype diversity overall, ten PFGE patterns each occurred with multiple strains having different genotypes.

Conclusions

Deep sequencing of pooled STEC O157 DNAs proved highly effective in polymorphism discovery. A polymorphism set has been identified that characterizes genetic diversity within STEC O157 strains of bovine origin, and a subset observed in human strains. The set may complement current techniques used to classify strains implicated in disease outbreaks.     

Prion gene haplotypes of U.S. cattle

Background

Inguinal and scrotal hernias are of great concern to pig producers, and lead to poor animal welfare and severe economic loss. Selection against these conditions is highly preferable, but at this time no gene, Quantitative Trait Loci (QTL), or mode of inheritance has been identified in pigs or in any other species. Therefore, a complete genome scan was performed in order to identify genomic regions affecting inguinal and scrotal hernias in pigs. Records from seedstock breeding farms were collected. No clinical examinations were executed on the pigs and there was therefore no distinction between inguinal and scrotal hernias. The genome scan utilised affected sib pairs (ASP), and the data was analysed using both an ASP test based on Non-parametric Linkage (NPL) analysis, and a Transmission Disequilibrium Test (TDT).

Results

Significant QTLs (p < 0.01) were detected on 8 out of 19 porcine chromosomes. The most promising QTLs, however, were detected in SSC1, SSC2, SSC5, SSC6, SSC15, SSC17 and SSCX; all of these regions showed either statistical significance with both statistical methods, or convincing significance with one of the methods. Haplotypes from these suggestive QTL regions were constructed and analysed with TDT. Of these, six different haplotypes were found to be differently transmitted (p < 0.01) to healthy and affected pigs. The most interesting result was one haplotype on SSC5 that was found to be transmitted to hernia pigs with four times higher frequency than to healthy pigs (p < 0.00005).

Conclusion

For the first time in any species, a genome scan has revealed suggestive QTLs for inguinal and scrotal hernias. While this study permitted the detection of chromosomal regions only, it is interesting to note that several promising candidate genes, including INSL3, MIS, and CGRP, are located within the highly significant QTL regions. Further studies are required in order to narrow down the suggestive QTL regions, investigate the candidate genes, and to confirm the suggestive QTLs in other populations. The haplotype associated with inguinal and scrotal hernias may help in achieving selection against the disorder.     

Ovine reference materials and assays for prion genetic testing

Background  

Genetic predisposition to scrapie in sheep is associated with several variations in the peptide sequence of the prion protein gene (PRNP). DNA-based tests for scoring PRNP codons are essential tools for eradicating scrapie and for evaluating rare alleles for increased resistance to disease. In addition to those associated with scrapie, there are dozens more PRNP polymorphisms that may occur in various flocks. If not accounted for, these sites may cause base-pair mismatching with oligonucleotides used in DNA testing. Thus, the fidelity of scrapie genetic testing is enhanced by knowing the position and frequency of PRNP polymorphisms in targeted flocks.     

Effect of a single acupuncture treatment on surgical wound healing in dogs: a randomized,single blinded,controlled pilot study

Background  

The aim of the study was to investigate the effect of acupuncture on wound healing after soft tissue or orthopaedic surgery in dogs.     

Prediction of haplotypes for ungenotyped animals and its effect on marker-assisted breeding value estimation

Background

In livestock populations, missing genotypes on a large proportion of animals are a major problem to implement the estimation of marker-assisted breeding values using haplotypes. The objective of this article is to develop a method to predict haplotypes of animals that are not genotyped using mixed model equations and to investigate the effect of using these predicted haplotypes on the accuracy of marker-assisted breeding value estimation.

Methods

For genotyped animals, haplotypes were determined and for each animal the number of haplotype copies (nhc) was counted, i.e. 0, 1 or 2 copies. In a mixed model framework, nhc for each haplotype were predicted for ungenotyped animals as well as for genotyped animals using the additive genetic relationship matrix. The heritability of nhc was assumed to be 0.99, allowing for minor genotyping and haplotyping errors. The predicted nhc were subsequently used in marker-assisted breeding value estimation by applying random regression on these covariables. To evaluate the method, a population was simulated with one additive QTL and an additive polygenic genetic effect. The QTL was located in the middle of a haplotype based on SNP-markers.

Results

The accuracy of predicted haplotype copies for ungenotyped animals ranged between 0.59 and 0.64 depending on haplotype length. Because powerful BLUP-software was used, the method was computationally very efficient. The accuracy of total EBV increased for genotyped animals when marker-assisted breeding value estimation was compared with conventional breeding value estimation, but for ungenotyped animals the increase was marginal unless the heritability was smaller than 0.1. Haplotypes based on four markers yielded the highest accuracies and when only the nearest left marker was used, it yielded the lowest accuracy. The accuracy increased with increasing marker density. Accuracy of the total EBV approached that of gene-assisted BLUP when 4-marker haplotypes were used with a distance of 0.1 cM between the markers.

Conclusions

The proposed method is computationally very efficient and suitable for marker-assisted breeding value estimation in large livestock populations including effects of a number of known QTL. Marker-assisted breeding value estimation using predicted haplotypes increases accuracy especially for traits with low heritability.     

THE COMPETITIVE DEMANDS OF ELITE MALE RINK HOCKEY

The aim of this study was to simulate the activity pattern of rink hockey by designing a specific skate test (ST) to study the energy expenditure and metabolic responses to this intermittent high-intensity exercise and extrapolate the results from the test to competition. Six rink hockey players performed, in three phases, the 20-metre multi-stage shuttle roller skate test, a tournament match and the ST. Heart rate was monitored in all three phases. Blood lactate, oxygen consumption, ventilation and respiratory exchange ratio were also recorded during the ST. Peak HR was 190.7±7.2 beats · min⁻¹. There were no differences in peak HR between the three tests. Mean HR was similar between the ST and the match (86% and 87% of HR_max, respectively). Peak and mean ventilation averaged 111.0±8.8 L · min⁻¹ and 70.3±14.0 L ^· min⁻¹ (60% of V_Emax), respectively. VO_2max was 56.3±8.4 mL · kg⁻¹ · min⁻¹, and mean oxygen consumption was 40.9±7.9 mL · kg^{−1 ·} min⁻¹ (70% of VO_2max). Maximum blood lactate concentration was 7.2±1.3 mmol · L-1. ST yielded an energy expenditure of 899.1±232.9 kJ, and energy power was 59.9±15.5 kJ · min⁻¹. These findings suggest that the ST is suitable for estimating the physiological demands of competitive rink hockey, which places a heavy demand on the aerobic and anaerobic systems, and requires high energy consumption.     

Multimodal signals in male European treefrog (Hyla arborea) and the influence of population isolation on signal expression

In nocturnal treefrogs, mate choice implies the use of acoustic and visual signals. Multimodality is suspected to have evolved for either information redundancy or information complementariness. It is essential to explore multimodality in a natural context to understand the selection pressures operating on the signals. In the present study, we investigated calling and coloration in relation to male biometry and condition in four populations of European treefrog (Hyla arborea) varying in size and genetic isolation. We compared the signal intensity between core and satellite populations to estimate the impact of genetic diversity on male secondary sexual traits. The results obtained show important regional variations in both traits, likely as a result of local adaptations. Call and coloration are weakly correlated within an individual, implying that these traits likely convey different information about the signaller's identity or quality, thus supporting the hypothesis of complementariness of multiple messages. By contrast to the experimental evidence, we find that call and coloration are not related to male condition (as estimated by the residual of mass over size), suggesting that the condition‐dependence of these traits may be mediated by complex mechanisms not accurately reflected by the chosen estimator. Finally, male call and colour phenotypes present no robust pattern of variation with isolation status, probably because of variation in local selective pressures and in history of population dynamics. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 103 , 633–647.     

Identification of Mendelian inconsistencies between SNP and pedigree information of sibs

Background

Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype information are not in agreement.

Methods

Straightforward tests to detect Mendelian inconsistencies exist that count the number of opposing homozygous marker (e.g. SNP) genotypes between parent and offspring (PAR-OFF). Here, we develop two tests to identify Mendelian inconsistencies between sibs. The first test counts SNP with opposing homozygous genotypes between sib pairs (SIBCOUNT). The second test compares pedigree and SNP-based relationships (SIBREL). All tests iteratively remove animals based on decreasing numbers of inconsistent parents and offspring or sibs. The PAR-OFF test, followed by either SIB test, was applied to a dataset comprising 2,078 genotyped cows and 211 genotyped sires. Theoretical expectations for distributions of test statistics of all three tests were calculated and compared to empirically derived values. Type I and II error rates were calculated after applying the tests to the edited data, while Mendelian inconsistencies were introduced by permuting pedigree against genotype data for various proportions of animals.

Results

Both SIB tests identified animal pairs for which pedigree and genomic relationships could be considered as inconsistent by visual inspection of a scatter plot of pairwise pedigree and SNP-based relationships. After removal of 235 animals with the PAR-OFF test, SIBCOUNT (SIBREL) identified 18 (22) additional inconsistent animals.Seventeen animals were identified by both methods. The numbers of incorrectly deleted animals (Type I error), were equally low for both methods, while the numbers of incorrectly non-deleted animals (Type II error), were considerably higher for SIBREL compared to SIBCOUNT.

Conclusions

Tests to remove Mendelian inconsistencies between sibs should be preceded by a test for parent-offspring inconsistencies. This parent-offspring test should not only consider parent-offspring pairs based on pedigree data, but also those based on SNP information. Both SIB tests could identify pairs of sibs with Mendelian inconsistencies. Based on type I and II error rates, counting opposing homozygotes between sibs (SIBCOUNT) appears slightly more precise than comparing genomic and pedigree relationships (SIBREL) to detect Mendelian inconsistencies between sibs.