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.     

Spiperone, identified through compound screening, activates calcium-dependent chloride secretion in the airway

Cystic fibrosis (CF) is caused by mutations in the gene producing the cystic fibrosis transmembrane conductance regulator (CFTR). CFTR functions as a Cl(-) channel. Its dysfunction limits Cl(-) secretion and enhances Na+ absorption, leading to viscous mucus in the airway. Ca2+-activated Cl(-) channels (CaCCs) are coexpressed with CFTR in the airway surface epithelia. Increases in cytosolic Ca(2+) activate the epithelial CaCCs, which provides an alternative Cl(-) secretory pathway in CF. We developed a screening assay and screened a library for compounds that could enhance cytoplasmic Ca2+, activate the CaCC, and increase Cl(-) secretion. We found that spiperone, a known antipsychotic drug, is a potent intracellular Ca2+ enhancer and demonstrated that it stimulates intracellular Ca2+, not by acting in its well-known role as an antagonist of serotonin 5-HT2 or dopamine D2 receptors, but through a protein tyrosine kinase-coupled phospholipase C-dependent pathway. Spiperone activates CaCCs, which stimulates Cl(-) secretion in polarized human non-CF and CF airway epithelial cell monolayers in vitro and in CFTR-knockout mice in vivo. In conclusion, we have identified spiperone as a new therapeutic platform for correction of defective Cl(-) secretion in CF via a pathway independent of CFTR.     

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.