A genome-wide scan for quantitative trait loci affecting respiratory disease and immune capacity in Landrace pigs.

Respiratory disease is the most important health concern for the swine industry. Genetic improvement for disease resistance is challenging because of the difficulty in obtaining good phenotypes related with disease resistance; however, identification of genes or markers associated with disease resistance can help in the genetic improvement of pig health. The purpose of our study was to investigate whether quantitative trait loci (QTL) associated with disease resistance were segregated in a purebred population of Landrace pigs that had been selected for meat production traits and mycoplasmal pneumonia of swine (MPS) scores over five generations. We analysed 1395 pigs from the base to the fifth generation of this population. Two respiratory disease traits [MPS scores and atrophic rhinitis (AR) scores] and 11 immune-capacity traits were measured in 630-1332 animals at 7 weeks of age and when the animal's body weight reached 105 kg. Each of the pigs, except sires in the base population, was genotyped using 109 microsatellite markers, and then, QTL analysis of the full-sib family population with a multi-generational pedigree structure was performed. Variance component analysis was used to detect QTL associated with MPS or AR scores, and the logarithm of odds (LOD) score and genotypic heritability of the QTL were estimated. Five significant (LOD > 2.51) and 18 suggestive (LOD > 1.35) QTL for respiratory disease traits and immune-capacity traits were detected. The significant QTL for Log-MPS score, located on S. scrofa chromosome 2, could explain 87% of the genetic variance of this score in this analysis. This is the first report of QTL associated with respiratory disease lesions.

Fine mapping of quantitative trait loci for meat color on Sus scrofa chromosome 6: analysis of the swine NUDT7 gene.

In the livestock industry, meat color has become important because consumer acceptance is subject to the appearance of the product in the marketplace. Our previous analyses of a whole genome QTL scan for various meat qualities using 2 F(2) families from Japanese wild boar (known as a red meat) x Large White and from Duroc x Chinese Jinhua suggested that a meat color (heme content) QTL is located on SSC6. The objective of this study was to fine-map this SSC6 meat color QTL and subsequently investigate positional candidate genes for polymorphisms that may cause changes in meat color. Therefore, we conducted interval mapping on SSC6 using an additional 9 gene markers through combined analyses of the 2 F(2) families of Japanese wild boar x Large White (353 progeny) and Duroc x Chinese Jinhua (204 progeny). Comparative analysis with humans, mice, and cattle suggested that there were 10 functional genes in the region. Among these genes, we suggested that a novel pig gene encoding a nudix (nucleoside diphosphate linked moiety X)-type motif 7 (NUDT7, a member of the nudix hydrolases) is a strong candidate for the QTL because the mouse Nudt7 is reported to hydrolyze succinyl-CoA, a substrate of the reaction limiting the rate of heme biosynthesis. We therefore determined the pig NUDT7 gene sequence including the 5' promoter region and explored genetic polymorphisms between Japanese wild boar and Large White. We identified 116 polymorphisms within the NUDT7 CDS or in the 5' region. None of the AA substitutions were associated with the meat color QTL; however, 3 polymorphisms were found in putative transcription factor recognition sites. We then investigated the differential expression of NUDT7 in Japanese wild boar and Large White by allele-specific quantitative real-time PCR. The expression level of the Large White type allele was greater than that of the Japanese wild-boar-type allele. Consequently, we speculated that the difference in meat color between Japanese wild boar and Large White is caused partly by differential expression of this candidate gene. Upregulation of NUDT7 expression in muscle may reduce succinyl-CoA content and thus reduce the level of heme biosynthesis.

Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar x Large White intercross.

Here, we analysed quantitative trait loci (QTL) for fatty acid composition, one of the factors affecting fat quality, in a Japanese wild boar x Large White cross. We found 25 significant effects for 17 traits at 13 positions at the 5% genome-wise level, of which 16 effects for 12 traits at 10 positions were significant at the 1% level. QTL for saturated fatty acids (SFA) in back fat were mapped to swine (Sus scrofa) chromosomes (SSC) 1p, 9 and 15. QTL for unsaturated fatty acids in back fat were mapped to SSC1p, 1q, 4, 5, 9, 15 and 17. Using a regression model that fits back fat thickness as a covariate, two of the QTL for linoleic acid content on SSC4 and SSC17 were not significant, but one QTL for total SFA composition was detected on SSC5 with correction for back fat thickness. Wild boar alleles at six of seven QTL tended to increase SFAs and to decrease unsaturated fatty acids. QTL for fatty acid composition in perirenal fat were mapped on SSC2, 3, 4, 5, 6, 14, 16 and X. QTL for melting point (in back fat samples) were mapped on SSC1, 2 and 15. Wild boar alleles in QTL on SSC1 and SSC15 were associated with elevated melting points whereas those on SSC2 were associated with lower melting point measurements.

Quantitative trait loci mapping for meat quality and muscle fiber traits in a Japanese wild boar x Large White intercross.

Three generations of a swine family produced by crossing a Japanese wild boar and three Large White female pigs were used to map QTL for various production traits. Here we report the results of QTL analyses for skeletal muscle fiber composition and meat quality traits based on phenotypic data of 353 F(2) animals and genotypic data of 225 markers covering almost the entire pig genome for all of the F(2) animals as well as their F(1) parents and F(0) grandparents. The results of a genome scan using least squares regression interval mapping provided evidence that QTL (<1% genome-wise error rate) affected the proportion of the number of type IIA muscle fibers on SSC2, the number of type IIB on SSC14, the relative area (RA) of type I on SSCX, the RA of type IIA on SSC6, the RA of type IIB on SSC6 and SSC14, the Minolta a* values of loin on SSC4 and SSC6, the Minolta b* value of loin on SSC15, and the hematin content of the LM on SSC6. Quantitative trait loci (<5% genome-wise error rate) were found for the number of type I on SSC1, SSC14, and SSCX, for the number of type IIA on SSC14, for the number of type IIB on SSC2, for the RA of type IIA on SSC2, for the Minolta b* value of loin on SSC3, for the pH of loin on SSC15, and for the i.m. fat content on SSC15. Twenty-four QTL were detected for 11 traits at the 5% genome-wise level. Some traits were associated with each other, so the 24 QTL were located on 11 genomic regions. In five QTL located on SSC2, SSC6, and SSC14, each wild boar allele had the effect of increasing types I and IIA muscle fibers and decreasing type IIB muscle fibers. These effects are expected to improve meat quality.

Elucidation of correspondence between swine chromosome 4 and human chromosome 1 by assigning 27 genes to the ImpRH map, and development of microsatellites in the proximity of 14 genes.

Loci affecting swine intramuscular fat content, backfat thickness, carcass weight, and daily weight gain were assigned to regions of swine chromosome (SSC) 4, which were shown to correspond to human chromosome (HSA) 1p22--> q25 by ZOO-FISH, bidirectional chromosome painting, as well as by the linkage map of genes. In order to select candidate genes responsible for the above traits from the human genome database, precise correspondence between SSC4 and HSA1 is a prerequisite. In the present study, 27 genes, PTGFR, GBP1, GBP2, GFI1, GCLM, ABCD3, EXTL2, KCNA3, ADORA3, KCND3, WNT2B, NRAS, SYCP1, PTGFRN, IGSF2, NOTCH2, S100A10, SHC1, SSR2, LMNA, CCT3, CD5L, PEA15, FCER1G, EAT2, DDR2, and LAMB3, located in the HSA1 region corresponding to SSC4 or possibly SSC4, were assigned to the IMpRH map. The alignment of genes from centromere to telomere in the SSC4 q arm is basically conserved in HSA1p22-->q25 with the direction from the q arm to the p arm, which is in good agreement with results from linkage mapping. In addition, the present study first demonstrated that WNT2B residing in the middle of the HSA1 region was assigned to SSC18 with a high lod score (> 5), and that at least three intrachromosomal rearrangements occurred in the region in the process of swine and human evolution. PTGFR, and LAMB3 localized at both ends of the HSA1 region were assigned to SSC6 and SSC9, respectively, which is consistent with regional correspondence reported earlier. In the course of the above analysis, microsatellite markers were developed in the proximity of eleven genes localized on SSC4, and three genes on other swine chromosomes.

Development of 50 gene-associated microsatellite markers using BAC clones and the construction of a linkage map of swine chromosome 4.

The development of informative polymorphic markers is essential for QTL mapping. We developed 50 microsatellite markers from BAC clones containing genes that were predicted to map swine chromosome 4 (SSC4) according to comparative analysis between human and swine chromosomes, and constructed a linkage map that consisted of 37 markers including 24 markers closely linked to genes in BAC clones. Microsatellite markers were developed by direct-sequencing of BAC clones and our results demonstrated that this method was effective for developing microsatellite markers in specific regions on chromosomes. Effective development of microsatellite markers closely linked to genes can further accelerate the comparative studies of chromosomes between different species.