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Models in QTL mapping can be improved by considering all potential variables, i.e. we can use remaining traits other than the trait under study as potential predictors. QTL mapping is often conducted by correcting for a few fixed effects or covariates (e.g. sex, age), although many traits with potential causal relationships between them are recorded. In this work, we evaluate by simulation several procedures to identify optimum models in QTL scans: forward selection, undirected dependency graph and QTL-directed dependency graph (QDG). The latter, QDG, performed better in terms of power and false discovery rate and was applied to fatty acid (FA) composition and fat deposition traits in two pig F2 crosses from China and Spain. Compared with the typical QTL mapping, QDG approach revealed several new QTL. To the contrary, several FA QTL on chromosome 4 (e.g. Palmitic, C16:0; Stearic, C18:0) detected by typical mapping vanished after adjusting for phenotypic covariates in QDG mapping. This suggests that the QTL detected in typical mapping could be indirect. When a QTL is supported by both approaches, there is an increased confidence that the QTL have a primary effect on the corresponding trait. An example is a QTL for C16:1 on chromosome 8. In conclusion, mapping QTL based on causal phenotypic networks can increase power and help to make more biologically sound hypothesis on the genetic architecture of complex traits., (© 2011 Blackwell Verlag GmbH.)

Teat number is an important trait in sows that should accompany the increase in litter size that has been achieved in the last decades through selection. We have previously identified a genome-wide significant QTL for teat number in porcine chromosome SSC5 by means of an experimental Meishan by Iberian F(2) intercross population. In the present report, we have studied the porcine parathyroid hormone-like hormone (PTHLH) gene, which maps to SSC5, as a candidate gene for this trait, as PTHLH is involved in nipple formation during embryogenesis and nipple development during pregnancy and lactation. We have found that porcine PTHLH gene is transcribed into three mRNA species differing in the 5'UTR region. Two of these variants are reported in pigs here for the first time: one was similar to variant 1 described in humans while the other, which was generated by the retention of two small introns, has not been identified before in any other species. In addition, mRNA expression profile for two of the mRNA variants was assessed in 19 pig tissues. Porcine PTHLH showed a widespread expression as it was present in all tested tissues and relative expression of each variant was tissue dependent. Finally, we have performed an association study between a non-synonymous mutation in the coding region of this gene and sow teat number. The PTHLH polymorphism was segregating in our Meishan by Iberian F(2) population at intermediate allelic frequencies. We compared here six different statistical models to choose the one with a better fit and a lower degree of complexity. However, despite the potential negative effect of the PTHLH mutation in the signal peptide of this protein, we did not detect any association between the PTHLH genotype and the sow teat number phenotype, concluding that the causal mutation of the observed QTL is very likely not related to this gene., (© 2011 Blackwell Verlag GmbH.)

A C3469T mutation at exon 3 of the pig leptin (Lep) gene has been genotyped in diverse pig breeds yielding controversial results with regard to its association with growth, fatness and carcass traits. A similar situation has been reported for a HpaII restriction fragment length polymorphism (RFLP) in the pig leptin receptor (Lepr) gene, where associations were found depending on the statistical model employed. The main objective of our work was to investigate if leptin plasma concentrations differ in pigs with different C3469T and Lepr HpaII RFLP genotypes. With this aim, we have measured plasma leptin levels at 160 days in 68 Landrace pigs with different Lep C3469T and Lepr HpaII RFLP genotypes. Neither Lep (TT: 11.68 ng/ml, TC: 10.71 ng/ml) nor Lepr (AA: 12.6 ng/ml, AB: 10.93 ng/ml, BB: 11.74 ng/ml) genotypes influenced significantly plasma Lep concentration. Moreover, we did not find any association between Lep and Lepr genotypes and phenotypic variation at growth and fatness traits in a commercial population of 320 Landrace pigs.

Haplotype probabilities have been implemented in association analyses performed between three candidate genes and the preweaning survival of F2 Iberian x Meishan piglets, under a categorical proportional hazard model. The additive effect of substitution of each haplotype was estimated using information of parental genotypes for non-genotyped piglets. Analysed loci were the bone morphogenetic protein type 1B receptor (BMPR1B) gene at 8q25 (three SNPs; haplotypes BMPR1B A, BMPR1B B, BMPR1B C and BMPR1B D), the dopamine beta-hydroxylase (DBH) gene at 1q2.13 (two SNPs; haplotypes DBH X, DBH Y and DBH Z), and the prolactin receptor (PRLR) gene at 16q1.4-q2.3 (six SNPs; haplotypes PRLR A, PRLR B, PRLR C, PRLR D and PRLR E). The number of analysed records were 899 (BMPR1B), 850 (DBH) and 822 (PRLR) with a censoring percentage ranged between 87 and 89%. Associations with preweaning survival were suggested for BMPR1B (p < 0.10) and associations were clearly significant for DBH haplotypes (p < 0.05), whereas the PRLR locus did not reach statistical significance (p > 0.10). The BMPR1B A haplotype could increase piglet survivability in relation to the BMPR1B B haplotype with a hazard ratio of 0.751 and a confidence interval (CI) between 0.550 and 1.025 (p < 0.10). The two remaining haplotypes did not differ significantly from the BMPR1B B haplotype, with a hazard ratio higher than 0.90 (p > 0.10). For the DBH locus, the DBH X and the DBH Z haplotypes were associated with an improved preweaning survival in relation to the DBH Y haplotype. The advantage for DBH X in terms of hazard ratio was 0.789 (CI = 0.625 to 0.994; p < 0.05) and differences between DBH Y and DBH Z reached a hazard ratio of 0.770 (CI = 0.606 to 0.977; p < 0.05). Although further researches are required, BMPR1B and DBH genes could be a useful tool to improve piglet survival under a marker assisted selection strategy.

A simulation study has been performed to evaluate the power and the rate of false positives for the detection of causal mutations under two different models of analysis. We used an F2 design generated from an F0 population of five sires of line 1 and 40 dams of line 2 to produce an F1 population of 10 sires and 80 dams. Two different locations of the causal mutation and several frequencies of the mutations in the parental populations were considered. The first model included only the genetic configuration of the mutation, while the second model also included the probability of line origin given the neutral markers. Both models performed well when the mutation at the candidate gene was the causal mutation, although a greater power was obtained using the first model, because of its relative simplicity compared to the second one. However, when the candidate gene mutation was a neutral mutation, the second model presented a lower rate of false positives than the first. Moreover, in some cases the second model allowed distinction between the neutral and the causal mutation. The F2 design has a great power to detect quantitative trait loci provided by linkage disequilibrium, but also makes it difficult to discriminate between causal and neutral mutations. Therefore a high percentage of false positives can be expected. The limitations of F2 designs for discriminating between neutral and causal mutations are discussed.

A Bayesian procedure is presented for detecting quantitative trait loci (QTL) affecting longitudinal traits. The statistical model assumes a QTL affecting the prior distribution of the parameters of a given production function, under a hierarchical Bayesian scheme. Marginal posterior distributions for the effects associated with the QTL are calculated using Markov chain Monte Carlo methods. Furthermore, the Bayesian analysis allows the use of some available relevant information that can improve the detection of the QTL substantially. To illustrate the procedure, an example of QTL detection using the Von Bertalanffy growth function is presented with a F2 pig population bred from Iberian boars and Landrace sows. Animals of the F2 population were genotyped for seven markers in chromosome 2 (SSC2). Two prior distributions for the mean effect of the parameters related with birth and adult weight were compared. On the one hand, vague prior distributions were used, and, on the other, there were assumed univariate Gaussian distributions that ensure biologically meaningful adult and birth weights on the posterior growth curves. Results from the second prior distribution supported the presence of QTL, by showing that individuals with both alleles of Iberian origin had lower rates of maturation. On the contrary, when vague priors were used, the procedure was not able to detect QTL.