Christèle Robert-Granié

Performance of Genomic Selection in Mice

Selection plans in plant and animal breeding are driven by genetic evaluation. Recent developments suggest using massive genetic marker information, known as “genomic selection.” There is little evidence of its performance, though. We empirically compared three strategies for selection: (1) use of pedigree and phenotypic information, (2) use of genomewide markers and phenotypic information, and (3) the combination of both. We analyzed four traits from a heterogeneous mouse population (http://gscan.well.ox.ac.uk/), including 1884 individuals and 10,946 SNP markers. We used linear mixed models, using extensions of association analysis. Cross-validation techniques were used, providing assumption-free estimates of predictive ability. Sampling of validation and training data sets was carried out across and within families, which allows comparing across- and within-family information. Use of genomewide genetic markers increased predictive ability up to 0.22 across families and up to 0.03 within families. The latter is not statistically significant. These values are roughly comparable to increases of up to 0.57 (across family) and 0.14 (within family) in accuracy of prediction of genetic value. In this data set, within-family information was more accurate than across-family information, and populational linkage disequilibrium was not a completely accurate source of information for genetic evaluation. This fact questions some applications of genomic selection.

Sparse canonical methods for biological data integration: application to a cross-platform study

BackgroundIn the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.ResultsWe compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results.ConclusionsPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.

Improved lasso for genomic selection

Empirical experience with genomic selection in dairy cattle suggests that the distribution of the effects of single nucleotide polymorphisms (SNPs) might be far from normality for some traits. An alternative, avoiding the use of arbitrary prior information, is the Bayesian Lasso (BL). Regular BL uses a common variance parameter for residual and SNP effects (BL1Var). We propose here a BL with different residual and SNP effect variances (BL2Var), equivalent to the original Lasso formulation. The λ parameter in Lasso is related to genetic variation in the population. We also suggest precomputing individual variances of SNP effects by BL2Var, to be later used in a linear mixed model (HetVar-GBLUP). Models were tested in a cross-validation design including 1756 Holstein and 678 Montbéliarde French bulls, with 1216 and 451 bulls used as training data; 51 325 and 49 625 polymorphic SNP were used. Milk production traits were tested. Other methods tested included linear mixed models using variances inferred from pedigree estimates or integrated out from the data. Estimates of genetic variation in the population were close to pedigree estimates in BL2Var but not in BL1Var. BL1Var shrank breeding values too little because of the common variance. BL2Var was the most accurate method for prediction and accommodated well major genes, in particular for fat percentage. BL1Var was the least accurate. HetVar-GBLUP was almost as accurate as BL2Var and allows for simple computations and extensions.

Differential response of bovine mammary epithelial cells to Staphylococcus aureus or Escherichia coli agonists of the innate immune system

Mastitis caused by Escherichia coli and Staphylococcus aureus is a major pathology of dairy cows. To better understand the differential response of the mammary gland to these two pathogens, we stimulated bovine mammary epithelial cells (bMEC) with either E. coli crude lipopolysaccharide (LPS) or with S. aureus culture supernatant (SaS) to compare the transcriptomic profiles of the initial bMEC response. By using HEK 293 reporter cells for pattern recognition receptors, the LPS preparation was found to stimulate TLR2 and TLR4 but not TLR5, Nod1 or Nod2, whereas SaS stimulated TLR2. Biochemical analysis revealed that lipoteichoic acid, protein A and α-hemolysin were all present in SaS, and bMEC were found to be responsive to each of these molecules. Transcriptome profiling revealed a core innate immune response partly shared by LPS and SaS. However, LPS induced expression of a significant higher number of genes and the fold changes were of greater magnitude than those induced by SaS. Microarray data analysis suggests that the activation pathways and the early chemokine and cytokine production preceded the defense and stress responses. A major differential response was the activation of the type I IFN pathway by LPS but not by SaS. The higher upregulation of chemokines (Cxcl10, Ccl2, Ccl5 and Ccl20) that target mononuclear leucocytes by LPS than by SaS is likely to be related to the differential activation of the type I IFN pathway, and could induce a different profile of the initial recruitment of leucocytes. The MEC responses to the two stimuli were different, as LPS was associated with NF-κB and Fas signaling pathways, whereas SaS was associated with AP-1 and IL-17A signaling pathways. It is noteworthy that at the protein level secretion of TNF-α and IL-1β was not induced by either stimulus. These results suggest that the response of MEC to diffusible stimuli from E. coli and S. aureus contributes to the onset of the response with differential leucocyte recruitment and distinct inflammatory and innate immune reactions of the mammary gland to infection.

Genomic selection in the French Lacaune dairy sheep breed

Genomic selection aims to increase accuracy and to decrease generation intervals, thus increasing genetic gains in animal breeding. Using real data of the French Lacaune dairy sheep breed, the purpose of this study was to compare the observed accuracies of genomic estimated breeding values using different models (infinitesimal only, markers only, and joint estimation of infinitesimal and marker effects) and methods [BLUP, Bayes Cπ, partial least squares (PLS), and sparse PLS]. The training data set included results of progeny tests of 1,886 rams born from 1998 to 2006, whereas the validation set had results of 681 rams born in 2007 and 2008. The 3 lactation traits studied (milk yield, fat content, and somatic cell scores) had heritabilities varying from 0.14 to 0.41. The inclusion of molecular information, as compared with traditional schemes, increased accuracies of estimated breeding values of young males at birth from 18 up to 25%, according to the trait. Accuracies of genomic methods varied from 0.4 to 0.6, according to the traits, with minor differences among genomic approaches. In Bayes Cπ, the joint estimation of marker and infinitesimal effects had a slightly favorable effect on the accuracies of genomic estimated breeding values, and were especially beneficial for somatic cell counts, the less heritable trait. Inclusion of infinitesimal effects also improved slopes of predictive regression equations. Methods that select markers implicitly (Bayes Cπ and sparse PLS) were advantageous for some models and traits, and are of interest for further quantitative trait loci studies.

Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep

A divergent selection experiment in sheep was implemented to study the consequences of log-transformed somatic cell score (SCS)-based selection on resistance to natural intramammary infections. Using dams and progeny-tested rams selected for extreme breeding values for SCS, we created 2 groups of ewes with a strong divergence in SCS of approximately 3 genetic standard deviations. A survey of 84 first-lactation ewes of both the High and Low SCS lines indicated favorable responses to SCS-based selection on resistance to both clinical and subclinical mastitis. All clinical cases (n = 5) occurred in the High SCS line. Additionally, the frequency of chronic clinical mastitis, as detected by the presence of parenchymal abscesses, was much greater in the High SCS line (n = 21) than in the Low SCS line (n = 1). According to monthly milk bacteriological examinations of udder halves, the prevalence of infection was significantly greater (odds ratio = 3.1) in the High SCS line than in the Low SCS line, with predicted probabilities of 37 and 16%, respectively. The most frequently isolated bacteria responsible for mastitis were staphylococci: Staphylococcus auricularis (42.6% of positive samples), Staphylococcus simulans, Staphylococcus haemoliticus, Staphylococcus xylosus, Staphylococcus chromogenes, Staphylococcus lentus, Staphylococcus warneri, and Staphylococcus aureus. The incidence of positive bacteriology was greater in the High SCS line (39%) than in the Low SCS line (12%) at lambing, indicating that High SCS line ewes were especially susceptible to postpartum subclinical mastitis. Negativation of bacteriological results from one sampling time point to the next was markedly different between lines after weaning (e.g., 41 and 84% in the High and Low SCS lines, respectively). This result was consistent with differences in the duration of infection, which was much greater in the High SCS line compared with the Low SCS line. Finally, ewes from the High SCS line consistently had greater SCS in positive milk samples than did ewes from the Low SCS line (+2.04 SCS, on average), with an especially large difference between lines during the suckling period (+3.42 SCS). Altogether, the preliminary results suggest that the better resistance of Low SCS line ewes, compared with High SCS line ewes, was principally characterized by a better ability to limit infections during the peripartum period, to eliminate infections during lactation, and quantitatively to limit the inflammation process and its clinical consequences.

Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with Staphylococcus epidermidis and Staphylococcus aureus.

BackgroundThe existence of a genetic basis for host responses to bacterial intramammary infections has been widely documented, but the underlying mechanisms and the genes are still largely unknown. Previously, two divergent lines of sheep selected for high/low milk somatic cell scores have been shown to be respectively susceptible and resistant to intramammary infections by Staphylococcus spp. Transcriptional profiling with an 15K ovine-specific microarray of the milk somatic cells of susceptible and resistant sheep infected successively by S. epidermidis and S. aureus was performed in order to enhance our understanding of the molecular and cellular events associated with mastitis resistance.ResultsThe bacteriological titre was lower in the resistant than in the susceptible animals in the 48 hours following inoculation, although milk somatic cell concentration was similar. Gene expression was analysed in milk somatic cells, mainly represented by neutrophils, collected 12 hours post-challenge. A high number of differentially expressed genes between the two challenges indicated that more T cells are recruited upon inoculation by S. aureus than S. epidermidis. A total of 52 genes were significantly differentially expressed between the resistant and susceptible animals. Further Gene Ontology analysis indicated that differentially expressed genes were associated with immune and inflammatory responses, leukocyte adhesion, cell migration, and signal transduction. Close biological relationships could be established between most genes using gene network analysis. Furthermore, gene expression suggests that the cell turn-over, as a consequence of apoptosis/granulopoiesis, may be enhanced in the resistant line when compared to the susceptible line.ConclusionsGene profiling in resistant and susceptible lines has provided good candidates for mapping the biological pathways and genes underlying genetically determined resistance and susceptibility towards Staphylococcus infections, and opens new fields for further investigation.

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment

BackgroundThe aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.ResultsSeveral conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.ConclusionIt is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.

In vivo gene expression in granulosa cells during pig terminal follicular development

Ovarian antral follicular development is clearly dependent on pituitary gonadotrophins FSH and LH. Although the endocrine mechanism that controls ovarian folliculogenesis leading to ovulation is quite well understood, the detailed mechanisms and molecular determinants in the different follicular compartments remain to be clarified. The aim of this study was to identify the genes differentially expressed in pig granulosa cells along the terminal ovarian follicle growth, to gain a comprehensive view of these molecular mechanisms. First, we developed a specific micro-array using cDNAs from suppression subtractive hybridization libraries (345 contigs) obtained by comparison of three follicle size classes: small, medium and large antral healthy follicles. In a second step, a transcriptomic analysis using cDNA probes from these three follicle classes identified 79 differentially expressed transcripts along the terminal follicular growth and 26 predictive genes of size classes. The differential expression of 18 genes has been controlled using real-time PCR experiments validating the micro-array analysis. Finally, the integration of the data using Ingenuity Pathways Analysis identified five gene networks providing descriptive elements of the terminal follicular development. Specifically, we observed: (1) the down-expression of ribosomal protein genes, (2) the genes involved in lipid metabolism and (3) the down-expression of cell morphology and ion-binding genes. In conclusion, this study gives new insight into the gene expression during pig terminal follicular growth in vivo and suggested, in particular, a morphological change in pig granulosa cells accompanying terminal follicular growth.

A first step toward genomic selection in the multi-breed French dairy goat population.

The objectives of this study were to describe, using the goat SNP50 BeadChip (Illumina Inc., San Diego, CA), molecular data for the French dairy goat population and compare the effect of using genomic information on breeding value accuracy in different reference populations. Several multi-breed (Alpine and Saanen) reference population sizes, including or excluding female genotypes (from 67 males to 677 males, and 1,985 females), were used. Genomic evaluations were performed using genomic best linear unbiased predictor for milk production traits, somatic cell score, and some udder type traits. At a marker distance of 50kb, the average r(2) (squared correlation coefficient) value of linkage disequilibrium was 0.14, and persistence of linkage disequilibrium as correlation of r-values among Saanen and Alpine breeds was 0.56. Genomic evaluation accuracies obtained from cross validation ranged from 36 to 53%. Biases of these estimations assessed by regression coefficients (from 0.73 to 0.98) of phenotypes on genomic breeding values were higher for traits such as protein yield than for udder type traits. Using the reference population that included all males and females, accuracies of genomic breeding values derived from prediction error variances (model accuracy) obtained for young buck candidates without phenotypes ranged from 52 to 56%. This was lower than the average pedigree-derived breeding value accuracies obtained at birth for these males from the official genetic evaluation (62%). Adding females to the reference population of 677 males improved accuracy by 5 to 9% depending on the trait considered. Gains in model accuracies of genomic breeding values ranged from 1 to 7%, lower than reported in other studies. The gains in breeding value accuracy obtained using genomic information were not as good as expected because of the limited size (at most 677 males and 1,985 females) and the structure of the reference population.