A genetic predictive model for canine hip dysplasia: integration of Genome Wide Association Study (GWAS) and candidate gene approaches

Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B) and case (D/E). C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85) and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors' opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds.

A genome-wide association analysis for porcine serum lipid traits reveals the existence of age-specific genetic determinants

Background

The genetic determinism of blood lipid concentrations, the main risk factor for atherosclerosis, is practically unknown in species other than human and mouse. Even in model organisms, little is known about how the genetic determinants of lipid traits are modulated by age-specific factors. To gain new insights into this issue, we have carried out a genome-wide association study (GWAS) for cholesterol (CHOL), triglyceride (TRIG) and low (LDL) and high (HDL) density lipoprotein concentrations measured in Duroc pigs at two time points (45 and 190 days).

Results

Analysis of data with mixed-model methods (EMMAX, GEMMA, GenABEL) and PLINK showed a low positional concordance between trait-associated regions (TARs) for serum lipids at 45 and 190 days. Besides, the proportion of phenotypic variance explained by SNPs at these two time points was also substantially different. The four analyses consistently detected two regions on SSC3 (124 Mb, CHOL and LDL at 190 days) and SSC6 (135 Mb, CHOL and TRIG at 190 days) with highly significant effects on the porcine blood lipid profile. Moreover, we have found that SNP variation within SSC3, SSC6, SSC10, SSC13 and SSC16 TARs is associated with the expression of several genes mapping to other chromosomes and related to lipid metabolism.

Conclusions

Our data demonstrate that the effects of genomic determinants influencing lipid concentrations in pigs, as well as the amount of phenotypic variance they explain, are influenced by age-related factors.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-758) contains supplementary material, which is available to authorized users.

Differences in muscle transcriptome among pigs phenotypically extreme for fatty acid composition

BACKGROUND

Besides having an impact on human health, the porcine muscle fatty acid profile determines meat quality and taste. The RNA-Seq technologies allowed us to explore the pig muscle transcriptome with an unprecedented detail. The aim of this study was to identify differentially-expressed genes between two groups of 6 sows belonging to an Iberian × Landrace backcross with extreme phenotypes according to FA profile.

RESULTS

We sequenced the muscle transcriptome acquiring 787.5 M of 75 bp paired-end reads. About 85.1% of reads were mapped to the reference genome. Of the total reads, 79.1% were located in exons, 6.0% in introns and 14.9% in intergenic regions, indicating expressed regions not annotated in the reference genome. We identified a 34.5% of the intergenic regions as interspersed repetitive regions. We predicted a total of 2,372 putative proteins. Pathway analysis with 131 differentially-expressed genes revealed that the most statistically-significant metabolic pathways were related with lipid metabolism. Moreover, 18 of the differentially-expressed genes were located in genomic regions associated with IMF composition in an independent GWAS study in the same genetic background. Thus, our results indicate that the lipid metabolism of FAs is differently modulated when the FA composition in muscle differs. For instance, a high content of PUFA may reduce FA and glucose uptake resulting in an inhibition of the lipogenesis. These results are consistent with previous studies of our group analysing the liver and the adipose tissue transcriptomes providing a view of each of the main organs involved in lipid metabolism.

CONCLUSIONS

The results obtained in the muscle transcriptome analysis increase the knowledge of the gene regulation of IMF deposition, FA profile and meat quality, in terms of taste and nutritional value. Besides, our results may be important in terms of human health.

Transcriptional analysis of intramuscular fatty acid composition in the longissimus thoracis muscle of Iberian × Landrace back-crossed pigs

This study aimed at identifying differential gene expression conditional on the fatty acid profile of the longissimus thoracis (Lt) muscle, a prime cut of economic relevance for fresh and cured pork production. A population of 110 Iberian (25%) × Landrace (75%) back-crossed pigs was used, because these two breeds exhibit extreme profiles of intramuscular saturated fatty acid, monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) contents. Total RNA from Lt muscle was individually hybridized to GeneChip Porcine Genome arrays (Affymetrix). A principal component analysis was performed with data from the 110 animals to select 40 extreme animals based on the total fatty acid profile and the MUFA composition (MAP). Comparison of global transcription levels between extreme fatty acid profile pigs (n = 40) resulted in 219 differentially expressed probes (false discovery rate <0.10). Gene ontology, pathway and network analysis indicated that animals with higher percentages of PUFA exhibit a shift toward a more oxidative muscular metabolism state, with a raise in mitochondria function (PPARGC1A, ATF2), fatty acid uptake and oxidation (FABP5, MGLL). On the other hand, 87 probes were differentially expressed between MUFA composition groups (n = 40; false discovery rate <0.10). In particular, muscles rich in n-7 MUFA expressed higher levels of genes involved in lipid metabolism (GLUL, CRAT, PLA2G15) and lower levels of fatty acid elongation genes (ELOVL5). Moreover, the chromosomal position of FABP5, PAQR3, MGLL, PPARGC1A, GLUL and ELOVL5 co-localized with very relevant QTL for fat deposition and composition described in the same resource population. This study represents a complementary approach to identifying genes underlying these QTL effects.

Short communication: Accounting for new mutations in genomic prediction models

Genomic evaluation models so far do not allow for accounting of newly generated genetic variation due to mutation. The main target of this research was to extend current genomic BLUP models with mutational relationships (model AM), and compare them against standard genomic BLUP models (model A) by analyzing simulated data. Model performance and precision of the predicted breeding values were evaluated under different population structures and heritabilities. The deviance information criterion (DIC) clearly favored the mutational relationship model under large heritabilities or populations with moderate-to-deep pedigrees contributing phenotypic data (i.e., differences equal or larger than 10 DIC units); this model provided slightly higher correlation coefficients between simulated and predicted genomic breeding values. On the other hand, null DIC differences, or even relevant advantages for the standard genomic BLUP model, were reported under small heritabilities and shallow pedigrees, although precision of the genomic breeding values did not differ across models at a significant level. This method allows for slightly more accurate genomic predictions and handling of newly created variation; moreover, this approach does not require additional genotyping or phenotyping efforts, but a more accurate handing of available data.

Canalization analysis of birth weight in Bruna dels Pirineus beef cattle

Although heteroskedasticity has been a main topic of interest in beef cattle during recent decades, the current availability of canalization models provided new insights for animal breeding programs. Within this context, birth BW (BWT) was analyzed in the Bruna dels Pirineus beef cattle breed by implementing canalization models that accounted for heterogeneous residual variances due to systematic, permanent environmental effects and additive genetic effects. Analyses were performed on BWT data from 8,130 calves born in 12 commercial breeding herds contributing to the yield recording scheme of the Bruna dels Pirineus breed. Analytical models accounted for direct additive genetic, permanent environmental, and 4 systematic effects (i.e., age of the dam, sex of the calf, birth type, and herd-year-season), and the same effects were evaluated as potential sources of variation in the residual term. Their relevance was checked by the deviance information criterion (DIC), and only residual additive genetic, permanent environmental, birth type, and herd-year-season remained in the operational model, all of them originating relevant reductions in the DIC parameter. Bruna dels Pirineus calves showed a moderate heritability of 0.30 (95% high posterior density, 0.19 to 0.40) for BWT; additional additive genetic variability was revealed in the residual term, this being positively correlated with the direct additive genetic component (0.44; 95% high posterior density, 0.37 to 0.54). Genetic trends were evaluated on both sources of additive genetic variance, and relevant patterns were identified in several herds. Although this breed did not evidence a homogeneous genetic trend for the whole population, herd-specific positive and negative trends were revealed, suggesting the plausibility of genetic selection for canalization on BWT in beef cattle breeds. These results must be viewed as a contribution to the canalization research field, providing relevant information for the breeding scheme of the Bruna dels Pirineus breed, as well as important insights about the genetic background of BWT for the beef industry worldwide.

Backfat thickness and longissimus dorsi real-time ultrasound measurements in light lambs

The aim of this study was to assess the accuracy of ultrasound measurements for predicting carcass traits in 124 Spanish pascual-type lambs (13 to 16 kg carcass weight). Ultrasound images were taken transversal and longitudinal to the vertebral column and at thoracic (TV; between 12th and 13th ribs) and lumbar (LV; between first and second lumbar vertebrae) locations. Skin thickness, subcutaneous backfat thickness (BFT), and depth (DLD), width (WLD), and area (ALD) of longissimus dorsi were obtained with ImageJ 1.42q software. After slaughter, BFT (TV, 2.30 ± 0.06 mm; LV, 2.46 ± 0.06 mm), DLD (TV, 2.47 ± 0.03 cm; LV, 2.48 ± 0.03 cm), WLD (TV, 4.50 ± 0.04 cm; LV, 4.60 ± 0.04 cm), and ALD (TV, 9.96 ± 0.12 cm(2); LV, 10.19 ± 0.13 cm(2)) were directly measured on the lamb carcass. Correlations between ultrasound and direct carcass measurements were greater than 0.61 for DLD, WLD, and ALD (P < 0.05) whereas they fluctuated between 0.32 and 0.60 for BFT (P < 0.05); moreover, correlations were significantly (P < 0.05) greater for transversal than for longitudinal views. In a similar way, linear regression analyses suggested a moderate underestimation for BFT and lumbar DLD when using real-time ultrasound technologies whereas WLD, ALD, and thoracic DLD suffered from under- and overestimation for small and large values of carcass traits, respectively. After decomposing the mean square prediction error (MSPE) for the different ultrasound measurements, we found that the error due to disturbance contributed most to the MSPE followed by the error of central tendency and the error due to regression. The SE of prediction (SEP) was also calculated as an additional precision indicator, obtaining estimates less than that in previous studies with larger lambs. In conclusion, transversal ultrasound measurements at the thoracic and lumbar levels could be a useful tool for predicting DLD, WLD, and ALD in light lambs, perhaps suffering from worse prediction properties when focusing on BFT. This information could be of special relevance for light lamb producers worldwide, with a special emphasis in the Mediterranean basin where this kind of production system accounts for a large percentage of the sheep industry.

Survey of SSC12 Regions Affecting Fatty Acid Composition of Intramuscular Fat Using High-Density SNP Data

Fatty acid composition is a critical aspect of pork because it affects sensorial and technological aspects of meat quality and it is relevant for human health. Previous studies identified significant QTLs in porcine chromosome 12 for fatty acid profile of back fat (BF) and intramuscular fat (IMF). In the present study, 374 SNPs mapped in SSC12 from the 60K Porcine SNP Beadchip were used. We have combined linkage and association analyses with expression data analysis in order to identify regions of SSC12 that could affect fatty acid composition of IMF in longissimus muscle. The QTL scan showed a region around the 60-cM position that significantly affects palmitic fatty acid and two related fatty acid indexes. The Iberian QTL allele increased the palmitic content (+2.6% of mean trait). This QTL does not match any of those reported in the previous study on fatty acid composition of BF, suggesting different genetic control acting at both tissues. The SNP association analyses showed significant associations with linolenic and palmitic acids besides several indexes. Among the polymorphisms that affect palmitic fatty acid and match the QTL region at 60 cM, there were three that mapped in the Phosphatidylcholine transfer protein (PCTP) gene and one in the Acetyl-CoA Carboxylase ∝ gene (ACACA). Interestingly one of the PCTP SNPs also affected significantly unsaturated and double bound indexes and the ratio between polyunsaturated/monounsaturated fatty acids. Differential expression was assessed on longissimus muscle conditional on the genotype of the QTL and on the most significant SNPs, according to the results obtained in the former analyses. Results from the microarray expression analyses, validated by RT-qPCR, showed that PCTP expression levels significantly vary depending on the QTL as well as on the own PCTP genotype. The results obtained with the different approaches point out the PCTP gene as a powerful candidate underlying the QTL for palmitic content.

Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes

Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.

Recombination rates across porcine autosomes inferred from high-density linkage maps

Studies of the variation in recombination rate across the genome provide a better understanding of evolutionary genomics and are also an important step towards mapping and dissecting complex traits in domestic animals. With the recent completion of the porcine genome sequence and the availability of a high-density porcine single nucleotide polymorphism (SNP) array, it is now possible to construct a high-density porcine linkage map and estimate recombination rate across the genome. A total of 416 animals were genotyped with the Porcine SNP60BeadChip, and high-density chromosome linkage maps were constructed using CRI-MAP, assuming the physical order of the Sscrofa10 assembly. The total linkage map length was 2018.79 cM, using 658 meioses and 14,503 SNPs. The estimated average recombination rate across the porcine autosomes was 0.86 cM/Mb. However, a large variation in recombination rate was observed among chromosomes. The estimated average recombination rates (cM/Mb) per chromosome ranged from 0.48 in SSC1 to 1.48 in SSC10, displaying a significant negative correlation with the chromosome sizes. In addition, the analysis of the variation in the recombination rates taking 1-Mb sliding windows has allowed us to demonstrate the variation in recombination rates within chromosomes. In general, a larger recombination rate was observed in the extremes than in the centre of the chromosome. Finally, the ratio between female and male recombination rates was also inferred, obtaining a value of 1.38, with the heterogametic sex having the least recombination.

Short communication: effect of mutation age on genomic predictions

Genomic selection relies on the whole-genome evaluation of single nucleotide polymorphisms (SNP), some of them linked to quantitative trait loci (QTL). Although statistical methodology has been developed for the analysis of genomic data, little is known about the performance of SNP association studies when trying to capture variability from QTL mutations of different ages. Within this context, the influence of mutation age was analyzed under a simulation design, assuming presence or absence of selection on mutant QTL alleles. Focusing on a unique chromosome with a single QTL located in the proximal end, the performance of the genomic selection analyses was evaluated in terms of standardized mean square error (MSE). For all simulation scenarios, MSE was highest for the youngest mutations. The MSE was progressively reduced with mutation age under random mating and soft selection, and reached its maximum performance with the oldest mutations. On the other hand, moderate and strong selection caused a quick reduction of the MSE from youngest mutations to mutations arising in generations 920 to 939, thus resulting in a progressive increase for older mutations. In both cases, very young mutations escaped from genomic selection analyses, releasing a relevant amount of genetic variability that could not be captured and used in genomic selection programs. This demonstrated the need for new analytical approaches to model relevant and recent sources of variation; if captured, these young mutations could substantially contribute to current breeding schemes.

Bayesian recursive mixed linear model for gene expression analyses with continuous covariates

The analysis of microarray gene expression data has experienced a remarkable growth in scientific research over the last few years and is helping to decipher the genetic background of several productive traits. Nevertheless, most analytical approaches have relied on the comparison of 2 (or a few) well-defined groups of biological conditions where the continuous covariates have no sense (e.g., healthy vs. cancerous cells). Continuous effects could be of special interest when analyzing gene expression in animal production-oriented studies (e.g., birth weight), although very few studies address this peculiarity in the animal science framework. Within this context, we have developed a recursive linear mixed model where not only are linear covariates accounted for during gene expression analyses but also hierarchized and the effects of their genetic, environmental, and residual components on differential gene expression inferred independently. This parameterization allows a step forward in the inference of differential gene expression linked to a given quantitative trait such as birth weight. The statistical performance of this recursive model was exemplified under simulation by accounting for different sample sizes (n), heritabilities for the quantitative trait (h(2)), and magnitudes of differential gene expression (λ). It is important to highlight that statistical power increased with n, h(2), and λ, and the recursive model exceeded the standard linear mixed model with linear (nonrecursive) covariates in the majority of scenarios. This new parameterization would provide new insights about gene expression in the animal science framework, opening a new research scenario where within-covariate sources of differential gene expression could be individualized and estimated. The source code of the program accommodating these analytical developments and additional information about practical aspects on running the program are freely available by request to the corresponding author of this article.

Short Communication: Estimating abundance, survival and age structure of the Alberes cattle using recapture techniques

Fina, M., Casellas, J. and Piedrafita, J. 2011. Short Communication: Estimating abundance, survival and age structure of the Alberes cattle using recapture techniques. Can. J. Anim. Sci. 91: 343–347. The Alberes breed is an endangered bovine breed with an unknown population size. In this study, we estimated a total of 447.9 (435.5 to 456.6) individuals using capture-recapture methods. Overall survival and recapture estimates were 0.85±0.01 and 0.94±0.01, respectively, leading to an average longevity of 5.64 yr and with the highest death rate concentrated in the first 3 yr of life (38.94%). For breeding cows, the average length of the productive life was 8.35 yr. The current population of this local breed is too small to prevent future losses of genetic variability, and a conservation program is essential to ensure the long-term viability of the breed.

Bootstrap test for proportional hazard assumption on time-independent systematic effects of longevity data

This manuscript focuses on the development of a bootstrap test for validating the proportional hazard (PH) assumption in longevity data, avoiding parametric assumptions on baseline survival and hazard patterns, and subjective interpretations of previously developed graphical tests. Monte Carlo simulations are used to generate new data sets from the estimated Kaplan-Meier survival function, and inferences are then made on the coefficient of variation (CV) of the estimated hazard over time. One-tailed bootstrap intervals can be established, given that the CV could theoretically range between 0 (perfect PH) and +∞ (absolute loss of proportionality between hazard functions). This procedure was tested by simulation, and the obtained results suggested it as a useful statistical tool when Kaplan-Meier assumptions are satisfied. If not, this bootstrap test was robust for medium to large data sets, whereas it could suffer from statistical biases when testing small populations.

Quantitative trait loci for fatness at growing and reproductive stages in Iberian × Meishan F(2) sows

A considerable number of fatness QTL have been identified in growing pigs, but there is a lack of knowledge about the genetic architecture of this trait in gilts and sows. We have performed a genome scan, in 255 Iberian × Meishan F(2) sows, for backfat thickness (BF) at 150 (BF(150) ) and 210 (BF(210)) days of age, 30 days after conception (BF(30)) and 7-10 days before farrowing (BF(bf)). We have found one BF150 QTL in SSC6 (120 cM) that was highly significant (P < 0.001) at the chromosome-wide level and suggestive at the genome-wide level (P < 0.1). Ten additional chromosome-wide significant QTL were found for sow BF(150) (SSC1, SSC13), BF(210) (SSC6, SSC8, SSC15), BF(30) (SSC5, SSC6) and BF(bf) (SSC1, SSC6, SSC13). The location of several of the BF QTL varied depending on the growing and reproductive status of the sow, suggesting that part of these genetic effects may have a temporal pattern of phenotypic expression.

Epistasis for founder-specific inbreeding depression in rabbits

Inbreeding depression is a topic of main interest in experimental and domestic species, although previous studies simplified this genetically complex effect to the linear (or quadratic) regression coefficient linked to the inbreeding coefficient of each individual or, in more recent studies, to founder-specific inbreeding coefficients. Going beyond generalizing to these traditional scenarios, our research focused on the analysis of gene-by-gene interactions leading to epistasis for inbreeding depression effects. Under a Bayesian context, inbreeding depression effects were evaluated for weaning weight (WW) in a commercial rabbit population founded from 4 bucks and 1 doe (MARET population). Founder-specific inbreeding depression effects for the 4 bucks ranged between -81.1 and 38.3 g for each 1% inbreeding. More interestingly, 2 epistatic interactions between the partial inbreeding coefficient of 2 bucks were also significant and negative, showing a -1.9 and -1.0 g reduction on WW. These results provide the first evidence of epistatic inbreeding depression phenomena in domestic species, emphasizing the complexity of the genetic architecture in mammals.