Validation of cross-progeny variance genomic prediction using simulations and experimental data in winter elite bread wheat.

KEY MESSAGE: From simulations and experimental data, the quality of cross progeny variance genomic predictions may be high, but depends on trait architecture and necessitates sufficient number of progenies. Genomic predictions are used to select genitors and crosses in plant breeding. The usefulness criterion (UC) is a cross-selection criterion that necessitates the estimation of parental mean (PM) and progeny standard deviation (SD). This study evaluates the parameters that affect the predictive ability of UC and its two components using simulations. Predictive ability increased with heritability and progeny size and decreased with QTL number, most notably for SD. Comparing scenarios where marker effects were known or estimated using prediction models, SD was strongly impacted by the quality of marker effect estimates. We proposed a new algebraic formula for SD estimation that takes into account the uncertainty of the estimation of marker effects. It improved predictions when the number of QTL was superior to 300, especially when heritability was low. We also compared estimated and observed UC using experimental data for heading date, plant height, grain protein content and yield. PM and UC estimates were significantly correlated for all traits (PM: 0.38, 0.63, 0.51 and 0.91; UC: 0.45, 0.52, 0.54 and 0.74; for yield, grain protein content, plant height and heading date, respectively), while SD was correlated only for heading date and plant height (0.64 and 0.49, respectively). According to simulations, SD estimations in the field would necessitate large progenies. This pioneering study experimentally validates genomic prediction of UC but the predictive ability depends on trait architecture and precision of marker effect estimates. We advise the breeders to adjust progeny size to realize the SD potential of a cross.

An analytical framework to derive the expected precision of genomic selection.

BACKGROUND: Formulae to predict the precision or accuracy of genomic estimated breeding values (GEBV) are important when modelling selection schemes. Simple versions of such formulae have been proposed in the past, based on a number of simplifying hypotheses, including absence of linkage disequilibrium and linkage between loci, a population made up of unrelated individuals, and that all genetic variability of the trait is explained by the genotyped loci. These formulae were based on approximations that were not always clear. The objective of this paper is to offer a unique framework to derive equations that predict the precision of GEBV from the size of the reference population and the heritability of and number of QTL controlling the quantitative trait. RESULTS: The exact formulation of the precision of GEBV involves the expectation of the inverse of a linear function of the genomic matrix, which cannot be calculated from simple algebra but can be approximated using a Taylor polynomial expansion. First order approximations performed better than the initial prediction equations published in the literature. Second order approximations produced almost perfect estimates of precision when compared to results obtained when simulating situations that agreed with the assumptions that were required to derive the precision equations. Using this proposed framework, we present several generalizations, including multi-trait genomic evaluation. CONCLUSIONS: Although further improvements are needed to account for the complexity of practical situations, the equations proposed here can be used to derive the precision of GEBV when comparing breeding schemes a priori.

Using a very low-density SNP panel for genomic selection in a breeding program for sheep.

BACKGROUND: Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs). METHODS: A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated. RESULTS: The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from - 20 to - 34%) for the genomic design than for the classical design regardless of the genomic scenario. CONCLUSIONS: We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.

Approximated prediction of genomic selection accuracy when reference and candidate populations are related.

BACKGROUND: Genomic selection is still to be evaluated and optimized in many species. Mathematical modeling of selection schemes prior to their implementation is a classical and useful tool for that purpose. These models include formalization of a number of entities including the precision of the estimated breeding value. To model genomic selection schemes, equations that predict this reliability as a function of factors such as the size of the reference population, its diversity, its genetic distance from the group of selection candidates genotyped, number of markers and strength of linkage disequilibrium are needed. The present paper aims at exploring new approximations of this reliability. RESULTS: Two alternative approximations are proposed for the estimation of the reliability of genomic estimated breeding values (GEBV) in the case of non-independence between candidate and reference populations. Both were derived from the Taylor series heuristic approach suggested by Goddard in 2009. A numerical exploration of their properties showed that the series were not equivalent in terms of convergence to the exact reliability, that the approximations may overestimate the precision of GEBV and that they converged towards their theoretical expectations. Formulae derived for these approximations were simple to handle in the case of independent markers. A few parameters that describe the markers' genotypic variability (allele frequencies, linkage disequilibrium) can be estimated from genomic data corresponding to the population of interest or after making assumptions about their distribution. When markers are not in linkage equilibrium, replacing the real number of markers and QTL by the "effective number of independent loci", as proposed earlier is a practical solution. In this paper, we considered an alternative, i.e. an "equivalent number of independent loci" which would give a GEBV reliability for unrelated individuals by considering a sub-set of independent markers that is identical to the reliability obtained by considering the full set of markers. CONCLUSIONS: This paper is a further step towards the development of deterministic models that describe breeding plans based on the use of genomic information. Such deterministic models carry low computational burden, which allows design optimization through intensive numerical exploration.

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs.

BACKGROUND: Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations. RESULTS: We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population. CONCLUSIONS: We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance.

Genome-wide association study and biological pathway analysis of the Eimeria maxima response in broilers.

BACKGROUND: Coccidiosis is the most common and costly disease in the poultry industry and is caused by protozoans of the Eimeria genus. The current control of coccidiosis, based on the use of anticoccidial drugs and vaccination, faces serious obstacles such as drug resistance and the high costs for the development of efficient vaccines, respectively. Therefore, the current control programs must be expanded with complementary approaches such as the use of genetics to improve the host response to Eimeria infections. Recently, we have performed a large-scale challenge study on Cobb500 broilers using E. maxima for which we investigated variability among animals in response to the challenge. As a follow-up to this challenge study, we performed a genome-wide association study (GWAS) to identify genomic regions underlying variability of the measured traits in the response to Eimeria maxima in broilers. Furthermore, we conducted a post-GWAS functional analysis to increase our biological understanding of the underlying response to Eimeria maxima challenge. RESULTS: In total, we identified 22 single nucleotide polymorphisms (SNPs) with q value <0.1 distributed across five chromosomes. The highly significant SNPs were associated with body weight gain (three SNPs on GGA5, one SNP on GGA1 and one SNP on GGA3), plasma coloration measured as optical density at wavelengths in the range 465-510 nm (10 SNPs and all on GGA10) and the percentage of ß2-globulin in blood plasma (15 SNPs on GGA1 and one SNP on GGA2). Biological pathways related to metabolic processes, cell proliferation, and primary innate immune processes were among the most frequent significantly enriched biological pathways. Furthermore, the network-based analysis produced two networks of high confidence, with one centered on large tumor suppressor kinase 1 (LATS1) and 2 (LATS2) and the second involving the myosin heavy chain 6 (MYH6). CONCLUSIONS: We identified several strong candidate genes and genomic regions associated with traits measured in response to Eimeria maxima in broilers. Furthermore, the post-GWAS functional analysis indicates that biological pathways and networks involved in tissue proliferation and repair along with the primary innate immune response may play the most important role during the early stage of Eimeria maxima infection in broilers.

A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species.

BACKGROUND: With dense genotyping, many choices exist for methods to detect quantitative trait loci (QTL) in livestock populations. However, no across-species study has been conducted on the performance of different methods using real data. We compared three methods that correct for relatedness either implicitly or explicitly: linkage and linkage disequilibrium haplotype-based analysis (LDLA), efficient mixed-model association (EMMA) analysis, and Bayesian whole-genome regression (BayesC). We analyzed one chromosome in each of five datasets (dairy cattle, beef cattle, sheep, horses, and pigs) using real genotypes based on dense single nucleotide polymorphisms and phenotypes. The P values corrected for multiple testing or Bayes factors greater than 150 were considered to be significant. To complete the real data study, we also simulated quantitative trait loci (QTL) for the same datasets based on the real genotypes. Several scenarios were chosen, with different QTL effects and linkage disequilibrium patterns. A pseudo-null statistical distribution was chosen to make the significance thresholds comparable across methods. RESULTS: For the real data, the three methods generally agreed within 1 or 2 cM for the locations of QTL regions and disagreed when no signals were significant (e.g. in pigs). For certain datasets, LDLA had more significant signals than EMMA or BayesC, but they were concentrated around the same peaks. Therefore, the three methods detected approximately the same number of QTL regions. For the simulated data, LDLA was slightly less powerful and accurate than either EMMA or BayesC but this depended strongly on how thresholds were set in the simulations. CONCLUSIONS: All three methods performed similarly for real and simulated data. No method was clearly superior across all datasets or for any particular dataset. For computational efficiency and ease of interpretation, EMMA is recommended, but using more than one method is suggested.

A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep.

Texel sheep are renowned for their exceptional meatiness. To identify the genes underlying this economically important feature, we performed a whole-genome scan in a Romanov x Texel F2 population. We mapped a quantitative trait locus with a major effect on muscle mass to chromosome 2 and subsequently fine-mapped it to a chromosome interval encompassing the myostatin (GDF8) gene. We herein demonstrate that the GDF8 allele of Texel sheep is characterized by a G to A transition in the 3' UTR that creates a target site for mir1 and mir206, microRNAs (miRNAs) that are highly expressed in skeletal muscle. This causes translational inhibition of the myostatin gene and hence contributes to the muscular hypertrophy of Texel sheep. Analysis of SNP databases for humans and mice demonstrates that mutations creating or destroying putative miRNA target sites are abundant and might be important effectors of phenotypic variation.

Using haplotypes for the prediction of allelic identity to fine-map QTL: characterization and properties.

BACKGROUND: Numerous methods have been developed over the last decade to predict allelic identity at unobserved loci between pairs of chromosome segments along the genome. These loci are often unobserved positions tested for the presence of quantitative trait loci (QTL). The main objective of this study was to understand from a theoretical standpoint the relation between linkage disequilibrium (LD) and allelic identity prediction when using haplotypes for fine mapping of QTL. In addition, six allelic identity predictors (AIP) were also compared in this study to determine which one performed best in theory and application. RESULTS: A criterion based on a simple measure of matrix distance was used to study the relation between LD and allelic identity prediction when using haplotypes. The consistency of this criterion with the accuracy of QTL localization, another criterion commonly used to compare AIP, was evaluated on a set of real chromosomes. For this set of chromosomes, the criterion was consistent with the mapping accuracy of a simulated QTL with either low or high effect. As measured by the matrix distance, the best AIP for QTL mapping were those that best captured LD between a tested position and a QTL. Moreover the matrix distance between a tested position and a QTL was shown to decrease for some AIP when LD increased. However, the matrix distance for AIP with continuous predictions in the [0,1] interval was algebraically proven to decrease less rapidly up to a lower bound with increasing LD in the simplest situations, than the discrete predictor based on identity by state between haplotypes (IBS hap), for which there was no lower bound. The expected LD between haplotypes at a tested position and alleles at a QTL is a quantity that increases naturally when the tested position gets closer to the QTL. This behavior was demonstrated with pig and unrelated human chromosomes. CONCLUSIONS: When the density of markers is high, and therefore LD between adjacent loci can be assumed to be high, the discrete predictor IBS hap is recommended since it predicts allele identity correctly when taking LD into account.

Genetic parameters for growth and faecal worm egg count following Haemonchus contortus experimental infestations using pedigree and molecular information.

BACKGROUND: Haemonchosis is a parasitic disease that causes severe economic losses in sheep industry. In recent years, the increasing resistance of the parasite to anthelmintics has raised the need for alternative control strategies. Genetic selection is a promising alternative but its efficacy depends on the availability of genetic variation and on the occurrence of favourable genetic correlations between the traits included in the breeding goal. The objective of this study was twofold. First, to estimate both the heritability of and the genetic correlations between growth traits and parasite resistance traits, using bivariate linear mixed animal models, from the phenotypes and genotypes of 1004 backcross lambs (considered as a single population), which underwent two subsequent experimental infestations protocols with Haemonchus contortus. Second, to compare the precision of the estimates when using two different relationship matrices: including pedigree information only or including also SNP (single nucleotide polymorphism) information. RESULTS: Heritabilities were low for average daily gain before infestation (0.10 to 0.15) and average daily gain during the first infestation (0.11 to 0.16), moderate for faecal egg counts during the first infestation (0.21 to 0.38) and faecal egg counts during the second infestation (0.48 to 0.55). Genetic correlations between both growth traits and faecal egg count during the naïve infestation were equal to zero but the genetic correlation between faecal egg count during the second infestation and growth was positive in a Haemonchus contortus free environment and negative in a contaminated environment. The standard errors of the estimates obtained by including SNP information were smaller than those obtained by including pedigree information only. CONCLUSIONS: The genetic parameters estimates suggest that growth performance can be selected for independently of selection on resistance to naïve infestation. Selection for increased growth in a non-contaminated environment could lead to more susceptible animals with long-term exposure to the infestation but it could be possible to select for increased growth in a contaminated environment while also increasing resistance to the long-term exposure to the parasite. The use of molecular information increases the precision of the estimates.

Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations.

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

Fine-mapping quantitative trait loci with a medium density marker panel: efficiency of population structures and comparison of linkage disequilibrium linkage analysis models.

Recently, a Haley-Knott-type regression method using combined linkage disequilibrium and linkage analyses (LDLA) was proposed to map quantitative trait loci (QTLs). Chromosome of 5 and 25 cM with 0·25 and 0·05 cM, respectively, between markers were simulated. The differences between the LDLA approaches with regard to QTL position accuracy were very limited, with a significantly better mean square error (MSE) with the LDLA regression (LDLA_reg) in sparse map cases; the contrary was observed, but not significantly, in dense map situations. The computing time required for the LDLA variance components (LDLA_vc) model was much higher than the LDLA_reg model. The precision of QTL position estimation was compared for four numbers of half-sib families, four different family sizes and two experimental designs (half-sibs, and full- and half-sibs). Regarding the number of families, MSE values were lowest for 15 or 50 half-sib families, differences not being significant. We observed that the greater the number of progenies per sire, the more accurate the QTL position. However, for a fixed population size, reducing the number of families (e.g. using a small number of large full-sib families) could lead to less accuracy of estimated QTL position.

Statistical properties of interval mapping methods on quantitative trait loci location: impact on QTL/eQTL analyses.

BACKGROUND: Quantitative trait loci (QTL) detection on a huge amount of phenotypes, like eQTL detection on transcriptomic data, can be dramatically impaired by the statistical properties of interval mapping methods. One of these major outcomes is the high number of QTL detected at marker locations. The present study aims at identifying and specifying the sources of this bias, in particular in the case of analysis of data issued from outbred populations. Analytical developments were carried out in a backcross situation in order to specify the bias and to propose an algorithm to control it. The outbred population context was studied through simulated data sets in a wide range of situations.The likelihood ratio test was firstly analyzed under the "one QTL" hypothesis in a backcross population. Designs of sib families were then simulated and analyzed using the QTL Map software. On the basis of the theoretical results in backcross, parameters such as the population size, the density of the genetic map, the QTL effect and the true location of the QTL, were taken into account under the "no QTL" and the "one QTL" hypotheses. A combination of two non parametric tests - the Kolmogorov-Smirnov test and the Mann-Whitney-Wilcoxon test - was used in order to identify the parameters that affected the bias and to specify how much they influenced the estimation of QTL location. RESULTS: A theoretical expression of the bias of the estimated QTL location was obtained for a backcross type population. We demonstrated a common source of bias under the "no QTL" and the "one QTL" hypotheses and qualified the possible influence of several parameters. Simulation studies confirmed that the bias exists in outbred populations under both the hypotheses of "no QTL" and "one QTL" on a linkage group. The QTL location was systematically closer to marker locations than expected, particularly in the case of low QTL effect, small population size or low density of markers, i.e. designs with low power. Practical recommendations for experimental designs for QTL detection in outbred populations are given on the basis of this bias quantification. Furthermore, an original algorithm is proposed to adjust the location of a QTL, obtained with interval mapping, which co located with a marker. CONCLUSIONS: Therefore, one should be attentive when one QTL is mapped at the location of one marker, especially under low power conditions.

Statistical distributions of test statistics used for quantitative trait association mapping in structured populations.

BACKGROUND: Spurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects. RESULTS: The expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods. CONCLUSIONS: The formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.

Genomic Prediction of Complex Traits, Principles, Overview of Factors Affecting the Reliability of Genomic Prediction, and Algebra of the Reliability.

The quality of the predictions of genetic values based on the genotyping of neutral markers (GEBVs) is a key information to decide whether or not to implement genomic selection. This quality depends on the part of the genetic variability captured by the markers and on the precision of the estimate of their effects. Selection index theory provided the framework for evaluating the accuracy of GEBVs once the information had been gathered, with the genomic relationship matrix (GRM) playing a central role. When this accuracy must be known a priori, the theory of quantitative genetics gives clues to calculate the expectation of this GRM. This chapter makes a critical inventory of the methods developed to calculate these accuracies a posteriori and a priori. The most significant factors affecting this accuracy are described (size of the reference population, number of markers, linkage disequilibrium, heritability).

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.

A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps.

BACKGROUND: In the case of an autosomal locus, four transmission events from the parents to progeny are possible, specified by the grand parental origin of the alleles inherited by this individual. Computing the probabilities of these transmission events is essential to perform QTL detection methods. RESULTS: A fast algorithm for the estimation of these probabilities conditional to parental phases has been developed. It is adapted to classical QTL detection designs applied to outbred populations, in particular to designs composed of half and/or full sib families. It assumes the absence of interference. CONCLUSION: The theory is fully developed and an example is given.

Detection of new quantitative trait Loci for susceptibility to transmissible spongiform encephalopathies in mice.

Susceptibility to scrapie is largely controlled by the PRNP gene in mice and in several other species. However, individuals with identical scrapie susceptibility Prnp alleles may have very different incubation periods, suggesting the influence of other environmental and genetic factors. To detect loci influencing susceptibility to TSE, two mouse lines carrying the same PRNP genotype (C57BL and RIII) were crossed to produce an F2 population inoculated intracerebrally with a mouse-adapted scrapie strain. Linkage was studied between 72 markers and the age of death of F2 animals. Six QTL were detected, two at a genome-wide significant level (chromosomes 5 and 7) and four at a genome-wide suggestive level (chromosomes 4, 6, 8, and 17). Our results confirmed the existence of some QTL that were detected previously (chromosomes 4, 6, 7, and 8) while others were found only in the present study (chromosomes 5 and 17). Furthermore, it seems that some QTL (chromosomes 4 and 8) are involved in resistance to scrapie as well as to BSE.

Phenotyping of protein-prion (PrPsc)-accumulating cells in lymphoid and neural tissues of naturally scrapie-affected sheep by double-labeling immunohistochemistry.

Transmissible spongiform encephalopathies are fatal neurodegenerative diseases characterized by amyloid deposition of protein-prion (PrPsc), the pathogenic isoform of the host cellular protein PrPc, in the immune and central nervous systems. In the absence of definitive data on the nature of the infectious agent, PrPsc immunohistochemistry (IHC) constitutes one of the main methodologies for pathogenesis studies of these diseases. In situ PrPsc immunolabeling requires formalin fixation and paraffin embedding of tissues, followed by post-embedding antigen retrieval steps such as formic acid and hydrated autoclaving treatments. These procedures result in poor cellular antigen preservation, precluding the phenotyping of cells involved in scrapie pathogenesis. Until now, PrPsc-positive cell phenotyping relied mainly on morphological criteria. To identify these cells under the PrPsc IHC conditions, a new, rapid, and highly sensitive PrPsc double-labeling technique was developed, using a panel of screened antibodies that allow specific labeling of most of the cell subsets and structures using paraffin-embedded lymphoid and neural tissues from sheep, leading to an accurate identification of ovine PrPsc-accumulating cells. This technique constitutes a useful tool for IHC investigation of scrapie pathogenesis and may be applicable to the study of other ovine infectious diseases.

Regulation of ovarian folliculogenesis by IGF and BMP system in domestic animals.

Involvement of insulin-like growth factors (IGFs) and IGF binding proteins (IGFBPs) in ovarian folliculogenesis has been extensively studied during the last decade. In all mammalian species, IGF-I stimulates granulosa cell proliferation and steroidogenesis. The concentrations of IGF-I and -II do not vary during terminal follicular growth and atresia. In contrast, the levels of IGFBP-2 and -4, as well as IGFBP-5 in ruminants, dramatically decrease and increase during terminal follicular growth and atresia, respectively. These changes are responsible for an increase and a decrease in IGF bioavailability during follicular growth and atresia, respectively. They are partly explained by changes in ovarian expression. In particular, expression of IGFBP-2 mRNA decreases during follicular growth in ovine, bovine and porcine ovaries, and expression of IGFBP-5 mRNA dramatically increases in granulosa cells of bovine and ovine atretic follicles. Changes in IGFBP-2 and -4 levels are also due to changes in intrafollicular levels of specific proteases. Recently, we have shown that the pregnancy-associated plasma protein-A (PAPP-A) is responsible for the degradation of IGFBP-4 in preovulatory follicles of domestic animals. Expression of PAPP-A mRNA is restricted to the granulosa cell compartment, and is positively correlated to expression of aromatase and LH receptor. From recent evidence, the bone morphogenetic protein (BMP) family would also play a key role in ovarian physiology of domestic animals. In particular, we and others have recently shown that a non-conservative substitution (Q249R) in the bone morphogenetic protein-receptor type IB (BMPR-IB) coding sequence is fully associated with the hyperprolific phenotype of FecB(B)/FecB(B) Booroola ewes. BMP-4 and GDF-5, natural ligands of BMPR-IB, strongly inhibit secretion of progesterone by ovine granulosa cells in vitro, but granulosa cells from FecB(B)/FecB(B) ewes are less responsive than those from FecB(+)/FecB(+) to the action of these peptides. It is suggested that in FecB(B)/FecB(B) ewes, Q249R substitution would impair the function of BMPR-IB, leading to a precocious differentiation of granulosa cells and of follicular maturation. Interestingly, recent findings have described mutations in BMP-15 gene associated with hyperprolific phenotypes in Inverdale and Hanna ewes, suggesting that the BMP pathway plays a crucial role in the control of ovulation rate.