Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

Background  

Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.     

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.     

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0470-7) contains supplementary material, which is available to authorized users.     

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.     

Échinodermes de l'Ordovicien supérieur (Ashgill) de Sardaigne et d'Algérie

Research on the influence of sea level variations on the benthic faunas have been carried out in the Upper Ordovician of Sardinia. Study of the depositional facies and sequence analysis of the upper part (Lower Ashgill) of the Portixeddu Formation led to the identification of the sedimentary environments. Cystoids and crinoids are associated to bryozoans and brachiopods in most levels. The numerical analysis of associations and megaguilds shows that crinoids and cystoids have a higher frequency in the proximal and median facies of the upper offshore. The columnal association characterized by Conspectocrinus celticus and the coronoid Mespilocystites tregarvanicus has been discovered in the upper part of the formation. This material and complementary samples from Upper Ordovician of Sardinia and Kabylia (Algeria) bring additional data on the systematic and show the wide distribution of this fauna outside of the Ibero-armorican domain. The distribution of this echinoderm association supports a palaeogeographical position of the Ibero-armorican domain and Sardinia within the north gondwanan margin during the Lower Palaeozoic.     

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.     

Heritability of cardiovascular and personality traits in 6,148 Sardinians

In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14–102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.     

Calcitonin gene-related peptide enhances release of native brain-derived neurotrophic factor from trigeminal ganglion neurons

Activity-dependent plasticity in nociceptive pathways has been implicated in pathomechanisms of chronic pain syndromes. Calcitonin gene-related peptide (CGRP), which is expressed by trigeminal nociceptors, has recently been identified as a key player in the mechanism of migraine headaches. Here we show that CGRP is coexpressed with brain-derived neurotrophic factor (BDNF) in a large subset of adult rat trigeminal ganglion neurons in vivo. Using ELISA in situ, we show that CGRP (1-1000 nM) potently enhances BDNF release from cultured trigeminal neurons. The effect of CGRP is dose-dependent and abolished by pretreatment with CGRP receptor antagonist, CGRP(8-37). Intriguingly, CGRP-mediated BDNF release, unlike BDNF release evoked by physiological patterns of electrical stimulation, is independent of extracellular calcium. Depletion of intracellular calcium stores with thapsigargin blocks the CGRP-mediated BDNF release. Using transmission electron microscopy, our study also shows that BDNF-immunoreactivity is present in dense core vesicles of unmyelinated axons and axon terminals in the subnucleus caudalis of the spinal trigeminal nucleus, the primary central target of trigeminal nociceptors. Together, these results reveal a previously unknown role for CGRP in regulating BDNF availability, and point to BDNF as a candidate mediator of trigeminal nociceptive plasticity.     

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.