Search

Fatty acid (FA) composition is one of the most important parameters for the assessment of meat quality in pigs. The FA composition in pork can also affect human health. Our aim was to identify quantitative trait loci (QTLs) and positional candidate genes affecting the FA profile of the longissimus dorsi muscle in a large F2 intercross between Landrace and Korean native pigs comprising 1105 F2 progeny by genome-wide association studies (GWAS) and post-GWAS high-resolution mapping analyses. We performed GWAS using the PorcineSNP60K BeadChip and a linear mixed model. Four genome-wide significant QTL regions in SSC8, SSC12, SSC14, and SSC16 were detected ( p < 2.53 × 10 -7 ). Several co-localizations of QTLs in SSC12 for oleic acid, linoleic acid, arachidonic acid, monounsaturated FAs, polyunsaturated FAs, and the polyunsaturated/saturated FA ratio were observed. To refine the QTL region in SSC12, a linkage and linkage disequilibrium analysis was applied and could narrow down the critical region to a 0.749 Mb region. Of the genes in this region, GAS7, MYH2, and MYH3 were identified as strong novel candidate genes based on further conditional association analyses. These findings provide a novel insight into the genetic basis of FA composition in pork and could contribute to the improvement of pork quality.

In livestock social interactions, social genetic effects (SGE) represent associations between phenotype of one individual and genotype of another. Such associations occur when the trait of interest is affected by transmissible phenotypes of social partners. The aim of this study was to estimate SGE and direct genetic effects (DGE, genetic effects of an individual on its own phenotype) on average daily gain (ADG) in Landrace pigs, and to conduct single-step genome-wide association study using SGE and DGE as dependent variables to identify quantitative trait loci (QTLs) and their positional candidate genes. A total of 1,041 Landrace pigs were genotyped using the Porcine SNP 60K BeadChip. Estimates of the two effects were obtained using an extended animal model. The SGE contributed 16% of the total heritable variation of ADG. The total heritability estimated by the extended animal model including both SGE and DGE was 0.52. The single-step genome-wide association study identified a total of 23 QTL windows for the SGE on ADG distributed across three chromosomes (i.e., SSC1, SSC2, and SSC6). Positional candidate genes within these QTL regions included PRDM13, MAP3K7, CNR1, HTR1E, IL4, IL5, IL13, KIF3A, EFHD2, SLC38A7, mTOR, CNOT1, PLCB2, GABRR1, and GABRR2, which have biological roles in neuropsychiatric processes. The results of biological pathway and gene network analyses also support the association of the neuropsychiatric processes with SGE on ADG in pigs. Additionally, a total of 11 QTL windows for DGE on ADG in SSC2, 3, 6, 9, 10, 12, 14, 16, and 17 were detected with positional candidate genes such as ARL15. We found a putative pleotropic QTL for both SGE and DGE on ADG on SSC6. Our results in this study provide important insights that can help facilitate a better understanding of the molecular basis of SGE for socially affected traits.

Background: We conducted a genome-wide linkage analysis to identify quantitative trait loci (QTL) that influence meat quality-related traits in a large F2 intercross between Landrace and Korean native pigs. Thirteen meat quality-related traits of the m. longissimus lumborum et thoracis were measured in more than 830 F2 progeny. All these animals were genotyped with 173 microsatellite markers located throughout the pig genome, and the GridQTL program based on the least squares regression model was used to perform the QTL analysis., Results: We identified 23 genome-wide significant QTL in eight chromosome regions (SSC1, 2, 6, 7, 9, 12, 13, and 16) (SSC for Sus Scrofa) and detected 51 suggestive QTL in the 17 chromosome regions. QTL that affect 10 meat quality traits were detected on SSC12 and were highly significant at the genome-wide level. In particular, the QTL with the largest effect affected crude fat percentage and explained 22.5% of the phenotypic variance (F-ratio = 278.0 under the additive model, nominal P = 5.5 × 10(-55)). Interestingly, the QTL on SSC12 that influenced meat quality traits showed an obvious trend for co-localization., Conclusions: Our results confirm several previously reported QTL. In addition, we identified novel QTL for meat quality traits, which together with the associated positional candidate genes improve the knowledge on the genetic structure that underlies genetic variation for meat quality traits in pigs.

Most reproductive traits have low heritability and are greatly affected by environmental factors. Teat number and litter size are traits related to the reproduction ability of pigs. To identify quantitative trait loci (QTLs) for teat number traits, a genome-wide association study (GWAS) was conducted using an F2 intercross between Landrace and Korean native pigs. Genotype analysis was performed using the porcine SNP 60 K beadchip. The GWAS was performed using a mixed-effects model and linear regression approach. When a genome-wide threshold was determined using the Bonferroni method (P = 1.61 × 10(-6)), 38 single nucleotide polymorphism (SNP) markers in pig chromosome 7 (SSC7) were significantly associated with three teat number traits (total teat number, left teat number, and right teat number). Among these, SNPs in 5 genes (HDDC3, LOC100156276, LOC100155863, ANPEP, SCAMP2) were selected for further study based primarily on their statistical significance. A significant association was detected in SCAMP2 g.25280 G>A for total teat number (P = 2.0 × 10(-12)), HDDC3 g.1319 G>A SNP for left teat number (P = 2.3 × 10(-7)), and SCAMP2 g.14198 G>A for right teat number (P = 4.7 × 10(-12)). These results provide valuable information about the selective breeding for desirable teat numbers in pigs.

In this study, we conducted a genome-wide linkage analysis to identify the quantitative trait loci (QTL) that influence back fat thickness and carcass pH in an F(2) intercross between Landrace and Korean native pigs. Eight phenotypes related with back fat thickness and carcass pH were measured in more than 960 F(2) progeny. All experimental animals were subjected to genotypic analysis using 173 microsatellite markers located throughout the pig genome. The GridQTL program, based on the least squares regression model, was used to perform the QTL analysis. We identified 22 genome-wide significant QTL in 9 chromosomal regions (SSC1, 2, 5, 6, 7, 8, 12, 15, and 16) and 29 suggestive QTL in 16 chromosomal regions (SSC2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, and X). On SSC5, we detected a QTL affecting back fat thickness that accounted for 4.8 % of the phenotypic variance, which was the highest test statistic (F-ratio = 50.3 under the additive model, nominal P value = 2.5 × 10(-12)) observed in this study. Additionally, we showed that there were significant QTL on SSC16 affecting carcass pH traits. In conclusion, the QTL identified in this study together with associated positional candidate genes could play an important role in determining the genetic structure underlying the variation of back fat thickness and carcass pH in pigs.

Clinical-chemical traits are essential when examining the health status of individuals. The aim of this study was to identify quantitative trait loci (QTL) and the associated positional candidate genes affecting clinical-chemical traits in a reciprocal F(2) intercross between Landrace and Korean native pigs. Following an overnight fast, 25 serum phenotypes related to clinical-chemical traits (e.g., hepatic function parameters, renal function parameters, electrolyte, lipids) were measured in >970 F(2) progeny. All experimental samples were subjected to genotyping analysis using 165 microsatellite markers located across the genome. We identified eleven genome-wide significant QTL in six chromosomal regions (SSC 2, 7, 8, 13, 14, and 15) and 59 suggestive QTL in 17 chromosomal regions (SSC 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, and 18). We also observed significant effects of reciprocal crosses on some of the traits, which would seem to result from maternal effect, QTL on sex chromosomes, imprinted genes, or genetic difference in mitochondrial DNA. The role of genomic imprinting in clinical-chemical traits also was investigated. Genome-wide analysis revealed a significant evidence for an imprinted QTL in SSC4 affecting serum amylase levels. Additionally, a series of bivariate linkage analysis provided strong evidence that QTL in SSC 2, 13, 15, and 18 have a pleiotropic effect on clinical-chemical traits. In conclusion, our study detected both novel and previously reported QTL influencing clinical-chemical traits in pigs. The identified QTL together with the positional candidate genes identified here could play an important role in elucidating the genetic structure of clinical-chemical phenotype variation in humans and swine.

Susceptibility to osteoporotic fracture is influenced by genetic factors that can be dissected by whole-genome linkage analysis in experimental animal crosses. The aim of this study was to characterize quantitative trait loci (QTLs) for biomechanical and two-dimensional dual-energy X-ray absorptiometry (DXA) phenotypes in reciprocal F2 crosses between diabetic GK and normo-glycemic F344 rat strains and to identify possible co-localization with previously reported QTLs for bone size and structure. The biomechanical measurements of rat tibia included ultimate force, stiffness and work to failure while DXA was used to characterize tibial area, bone mineral content (BMC) and areal bone mineral density (aBMD). F2 progeny (108 males, 98 females) were genotyped with 192 genome-wide markers followed by sex- and reciprocal cross-separated whole-genome QTL analyses. Significant QTLs were identified on chromosome 8 (tibial area; logarithm of odds (LOD) = 4.7 and BMC; LOD = 4.1) in males and on chromosome 1 (stiffness; LOD = 5.5) in females. No QTLs showed significant sex-specific interactions. In contrast, significant cross-specific interactions were identified on chromosome 2 (aBMD; LOD = 4.7) and chromosome 6 (BMC; LOD = 4.8) for males carrying F344mtDNA, and on chromosome 15 (ultimate force; LOD = 3.9) for males carrying GKmtDNA, confirming the effect of reciprocal cross on osteoporosis-related phenotypes. By combining identified QTLs for biomechanical-, size- and qualitative phenotypes (pQCT and 3D CT) from the same population, overlapping regions were detected on chromosomes 1, 3, 4, 6, 8 and 10. These are strong candidate regions in the search for genetic risk factors for osteoporosis.

The F344 rat carries alleles contributing to bone fragility while the GK rat spontaneously develops type-2 diabetes. These characteristics make F344×GK crosses well suited for the identification of genes related to bone size and allow for future investigation on the association with type-2 diabetes. The aim of this study was to identify quantitative trait loci (QTLs) for bone size phenotypes measured by a new application of three-dimensional computed tomography (3DCT) and to investigate the effects of sex- and reciprocal cross. Tibia from male and female GK and F344 rats, representing the parental, F1 and F2 generations, were examined with 3DCT and analyzed for: total and cortical volumetric BMD, straight and curved length, peri- and endosteal area at mid-shaft. F2 progeny (108 male and 98 female) were genotyped with 192 genome-wide microsatellite markers (average distance 10 cM). Sex- and reciprocal cross-separated QTL analyses were performed for the identification of QTLs linked to 3DCT phenotypes and true interactions were confirmed by likelihood ratio analysis in all F2 animals. Several genome-wide significant QTLs were found in the sex- and reciprocal cross-separated progeny on chromosomes (chr) 1, 3, 4, 9, 10, 14, and 17. Overlapping QTLs for both males and females in the (GK×F344)F2 progeny were located on chr 1 (39-67 cM). This region confirms previously reported pQCT QTLs and overlaps loci for fasting glucose. Sex separated linkage analysis confirmed a male specific QTL on chr 9 (67-82 cM) for endosteal area at the fibula site. Analyses separating the F2 population both by sex and reciprocal cross identified cross specific QTLs on chr 14 (males) and chr 3 and 4 (females). Two loci, chr 4 and 6, are unique to 3DCT and separate from pQCT generated loci. The 3DCT method was highly reproducible and provided high precision measurements of bone size in the rat enabling identification of new sex- and cross-specific loci. The QTLs on chr 1 indicate potential genetic association between bone-related phenotypes and traits affecting type-2 diabetes. The results illustrate the complexity of the genetic architecture of bone size phenotypes and demonstrate the importance of complementary methods for bone analysis., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Niddm1i, a 16-Mb locus within the major diabetes QTL in the diabetic GK rat, causes impaired glucose tolerance in the congenic NIDDM1I strain. Niddm1i is homologous to both human and mouse regions linked with type 2 diabetes susceptibility. We employed multiple QTL analyses of congenic F2 progeny selected for one recombination event within Niddm1i combined with characterization of subcongenic strains. Fine mapping located one hyperglycemia locus within 700 kb (Niddm1i4, P=5x10(-6)). Two adjacent loci were also detected, and the GK allele at Niddm1i2 (500 kb) showed a glucose-raising effect, whereas it had a glucose-lowering effect at Niddm1i3 (400 kb). Most proximally, Niddm1i1 (800 kb) affecting body weight was identified. Experimental data from subcongenics supported the four loci. Sorcs1, one of the two known diabetes susceptibility genes in the region, resides within Niddm1i3, while Tcf7l2 maps outside all four loci. Multiple-marker QTL analysis incorporating the effect of cosegregating QTL as cofactors together with genetically selected progeny can remarkably enhance resolution of QTL. The data demonstrate that the species-conserved Niddm1i is a composite of at least four QTL affecting type 2 diabetes susceptibility and that two adjacent QTL (Niddm1i2GK and Niddm1i3GK) act in opposite directions.

The high- and low-growth lines of chickens have been developed from a single founder population by divergent selection for body weight at 56 days of age for more than 40 generations. The two lines show a ninefold difference in body weight at selection age and several interesting correlated selection responses such as altered body composition and metabolic differences. We have generated a reciprocal intercross comprising >800 F2 birds. In a previous study, we reported the detection of 13 quantitative trait loci (QTLs) affecting growth. Here we report QTLs for body composition (fat deposition, muscle development), weight of internal organs, and metabolic traits (plasma concentrations of glucose, insulin, cholesterol, glucagon, triglycerides, and IGF-I). Most of the QTLs with convincing statistical support mapped in the vicinity of growth QTLs. One of the most interesting observations was that the type of reciprocal cross had highly significant effects on body weight at hatch and on plasma concentrations of glucose, cholesterol, insulin, and IGF-I, but it had no significant effect on body weight at 56 days of age. The reciprocal cross explained between 15 and 35% of the phenotypic variance for weight at hatch and for plasma concentrations of glucose and insulin. The observed pattern indicated that these effects were caused by maternal effects or by genetic differences in mitochondrial DNA.

Two growth-selected lines in chickens have been developed from a single founder population by divergent selection for body weight at 56 days of age. After more than 40 generations of selection they show a nine-fold difference in body weight at selection age and large differences in growth rate, appetite, fat deposition and metabolic characteristics. We have generated a large intercross between these lines comprising more than 800 F2 birds. QTL mapping revealed 13 loci affecting growth. The most striking observation was that the allele in the high weight line in all cases was associated with enhanced growth, but each locus explained only a small proportion of the phenotypic variance using a standard QTL model (1.3-3.1%). This result is in sharp contrast to our previous study where we reported that the two-fold difference in adult body size between the red junglefowl and White Leghorn domestic chickens is explained by a small number of QTLs with large additive effects. Furthermore, no QTLs for anorexia or antibody response were detected despite large differences for these traits between the founder lines. The result is an excellent example where a large phenotypic difference between populations occurs in the apparent absence of any single locus with large phenotypic effects. The study underscores the need for powerful experimental designs in genetic studies of multifactorial traits. No QTL at all would have reached genome-wide significance using a less powerful design (e.g. approx. 200 F2 individuals) regardless of the nine-fold phenotypic difference between the founder lines for the selected trait.