Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross

The leptin receptor gene (LEPR) is a candidate for traits related to growth and body composition, and is located on SSC6 in a region where fatness and meat composition quantitative trait loci (QTL) have previously been detected in several F2 experimental designs. The aims of this work were: (i) to fine map these QTL on a larger sample of animals and generations (F3 and backcross) of an Iberian x Landrace intercross and (ii) to examine the effects of LEPR alleles on body composition traits. Eleven single nucleotide polymorphisms (SNPs) were detected by sequencing LEPR coding regions in Iberian and Landrace pig samples. Three missense polymorphisms were genotyped by pyrosequencing in 33 F0, 70 F1, 418 F2, 86 F3 and 128 individuals coming from the backcross of four F2 males with 24 Landrace females. Thirteen microsatellites and one SNP were also genotyped. Traits analysed were: backfat thickness at different locations (BF(T)), intramuscular fat percentage (IMF(P)), eye muscle area (EM(A)), loin depth (LO(D)), weight of shoulder (SH(W)), weight of ribs (RIB(W)) and weight of belly bacon (BB(W)). Different statistical models were applied in order to evaluate the number and effects of QTL on chromosome 6 and the possible causality of the LEPR gene variants with respect to the QTL. The results support the presence of two QTL on SSC6. One, at position 60-100 cM, affects BF(T) and RIB(W). The other and more significant maps in a narrow region (130-132 cM) and affects BF(T), IMF(P), EM(A), LO(D), SH(W), RIB(W) and BB(W). Results also support the association between LEPR alleles and BF(T) traits. The possible functional implications of the analysed polymorphisms are considered.     

QTL mapping for teat number in an Iberian-by-Meishan pig intercross

The aim of this study was to investigate chromosomal regions affecting the number of teats in pigs and possible epistatic interactions between the identified quantitative trait loci (QTL). An experimental F2 cross between Iberian and Chinese Meishan lines was used for this purpose. A genomic scan was conducted with 117 markers covering the 18 porcine autosomes. Linkage analyses were performed by interval mapping using an animal model to estimate QTL and additive polygenic effects. Complementary analyses with models fitting two QTL were also carried out. The results showed three genomewide significant QTL mapping on chromosomes 5, 10 and 12, whose joint action control up to 30% of the phenotypic variance of the trait. Meishan alleles had a positive additive effect on teat number, and a positive-additive x additive-epistatic interaction was detected between QTL on chromosomes 10 and 12.     

Crossbreeding effects on pig growth and carcass traits from two Iberian strains

An experiment of a 2×2 full diallelic cross between two contemporary Iberian pig strains (Retinto: RR, and Torbiscal: TT) was conducted to estimate the crossbreeding effects for growth and carcass traits. Phenotypic records were obtained under intensive management and consisted of two different data sets. The first set comprised growth traits until weaning and was collected at two different farms (6236 and 1208 records, respectively). Specific data included individual piglet weight at birth and at weaning at 28 days and average daily gain from birth to weaning at 28 days (ADG28) for both RR and TT and their reciprocal crosses. The second set comprised growth data from birth to slaughter (~340 days and ~160 kg) and carcass traits from 349 individuals (randomly) sampled at weaning from the first dataset. Data were analyzed through a Bayesian analysis by using a reparameterization of Dickerson’s model that allowed estimation of the posterior distributions of the following crossbreeding effects: average maternal breed effect (gM), average paternal breed effect (gP) and individual heterosis (hI). Results showed that the relative magnitude of crossbreeding effects depends on the trait analyzed. Crosses where Torbiscal strain was used as mother (RT and TT) achieved the greatest performance for all growth traits at weaning, leading to remarkable gM effects. The most outstanding example is the case of ADG28 where the probability of relevance was one. In contrast, TR cross showed the greatest differences from RR cross for all growth at slaughter and carcass traits. These differences were mainly due to hI and gP crossbreeding parameters. In particular, the posterior mean of hI was more noticeable for live weight at slaughter, average daily gain at slaughter and carcass length, while gP was more relevant for hams (kg) and loins (kg) representing from 3% to10% of average performance of traits. Hence, growth traits at weaning did not reveal any notable advantage of the crossbreeding scheme because of the superiority of the Torbiscal strain with respect to its mothering ability and the small hI. However, results from growth and carcass traits at slaughter would support the implementation of a TR crossbred system. It would allow exploitation of both the gP of the Torbiscal strain and the hI between these two Iberian pig strains. Additionally, gP estimates and phenotypic differences between reciprocal crosses might suggest signs of the presence of paternal genetic imprinting in primal cuts traits.     

QTL analysis of body weight and carcass body length traits in an F2 intercross between Landrace and Korean native pigs

Growth traits, such as body weight and carcass body length, directly affect productivity and economic efficiency in the livestock industry. We performed a genome‐wide linkage analysis to detect the quantitative trait loci (QTL) that affect body weight, growth curve parameters and carcass body length in an F₂ intercross between Landrace and Korean native pigs. Eight phenotypes related to growth were measured in approximately 1000 F₂ progeny. All experimental animals were subjected to genotypic analysis using 173 microsatellite markers located throughout the pig genome. The least squares regression approach was used to conduct the QTL analysis. For body weight traits, we mapped 16 genome‐wide significant QTL on SSC1, 3, 5, 6, 8, 9 and 12 as well as 22 suggestive QTL on SSC2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16 and 17. On SSC12, we identified a major QTL affecting body weight at 140 days of age that accounted for 4.3% of the phenotypic variance, which was the highest test statistic (F‐ratio = 45.6 under the additive model, nominal P = 2.4 × 10^?11) observed in this study. We also showed that there were significant QTL on SSC2, 5, 7, 8, 9 and 12 affecting carcass body length and growth curve parameters. Interestingly, the QTL on SSC2, 3, 5, 6, 8, 9, 10, 12 and 17 influencing the growth‐related traits showed an obvious trend for co‐localization. In conclusion, the identified QTL may play an important role in investigating the genetic structure underlying the phenotypic variation of growth in pigs.     

QTL mapping of complex binary traits in an advanced intercross line

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F(2) designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting.     

QTL, additive and epistatic effects for SCN resistance in PI 437654

PI 437654 is a unique accession because of its resistance to nearly all HG types (races) of soybean cyst nematode (Heterodera glycines Ichinohe; SCN). Objectives of this study were to confirm and refine the locations and gene action associated with SCN resistance previously discovered in PI 437654, and to identify new QTLs that may have been missed because of low coverage with genetic markers used in previous studies. Using 205 F_7:9 RILs and 276 SSR and AFLP molecular markers covering 2,406.5 cM of 20 linkage groups (LGs), we confirmed and refined the locations of major SCN resistance QTLs on LG-A2, -B1, and -G previously identified in PI 437654 or other resistant sources. We found that these major QTLs have epistatic effects among them or with other loci for SCN resistance. We also detected some new QTLs with additive or epistatic effects for SCN resistance to different HG types (races) on all LGs except LGs-B2 and -D1b. The QTL on LG-G was associated with resistance to HG types 2.5.7, 1.2.5.7, 0, and 2.7 (races 1, 2, 3, and 5), and it contributed a large proportion of the additive effects. The QTL on LG-A2 was associated with resistance to HG types 2.5.7 and 0 (races 1 and 3). The QTL on LG-B1, associated with resistance to HG types 2.5.7, 0, 2.7 (races 1, 3, and 5), was the similar QTL found in PI 90763 and PI 404198B. In addition to QTL on LGs-A2, -B1 and -G, a novel additive QTL associated with SCN resistance to HG types 0, 2.7, and 1.3.5.6.7 (race 3, 5, and 14) was identified on LG-I flanked by Sat_299 and Sat_189. Several minor QTLs on LGs-C1, D1a, H, and K were also found to be associated with SCN resistance. Confirmation of the new resistance QTL is underway by evaluating another RIL population with a different genetic background.     

QTL analysis of back fat thickness and carcass pH in an F2 intercross between Landrace and Korean native 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.     

Identification of bovine QTL for growth and carcass traits in Japanese Black cattle by replication and identical-by-descent mapping

To map quantitative trait loci (QTL) for growth and carcass traits in a purebred Japanese Black cattle population, we conducted multiple QTL analyses using 15 paternal half-sib families comprising 7860 offspring. We identified 40 QTL with significant linkages at false discovery rates of less than 0.1, which included 12 for intramuscular fat deposition called marbling and 12 for cold carcass weight or body weight. The QTL each explained 2%–13% of the phenotypic variance. These QTL included many replications and shared hypothetical identical-by-descent (IBD) alleles. The QTL for CW on BTA14 was replicated in five families with significant linkages and in two families with a 1% chromosome-wise significance level. The seven sires shared a 1.1-Mb superior Q haplotype as a hypothetical IBD allele that corresponds to the critical region previously refined by linkage disequilibrium mapping. The QTL for marbling on BTA4 was replicated in two families with significant linkages. The QTL for marbling on BTA6, 7, 9, 10, 20, and 21 and the QTL for body weight on BTA6 were replicated with 1% and/or 5% chromosome-wise significance levels. There were shared IBD Q or q haplotypes in the marbling QTL on BTA4, 6, and 10. The allele substitution effect of these haplotypes ranged from 0.7 to 1.2, and an additive effect between the marbling QTL on BTA6 and 10 was observed in the family examined. The abundant and replicated QTL information will enhance the opportunities for positional cloning of causative genes for the quantitative traits and efficient breeding using marker-assisted selection. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users.     

Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth

We used simultaneous mapping of interacting quantitative trait locus (QTL) pairs to study various growth traits in a chicken F2 intercross. The method was shown to increase the number of detected QTLs by 30 % compared with a traditional method detecting QTLs by their marginal genetic effects. Epistasis was shown to be an important contributor to the genetic variance of growth, with the largest impact on early growth (before 6 weeks of age). There is also evidence for a discrete set of interacting loci involved in early growth, supporting the previous findings of different genetic regulation of early and late growth in chicken. The genotype-phenotype relationship was evaluated for all interacting QTL pairs and 17 of the 21 evaluated QTL pairs could be assigned to one of four clusters in which the pairs in a cluster have very similar genetic effects on growth. The genetic effects of the pairs indicate commonly occurring dominance-by-dominance, heterosis and multiplicative interactions. The results from this study clearly illustrate the increase in power obtained by using this novel method for simultaneous detection of epistatic QTL, and also how visualization of genotype-phenotype relationships for epistatic QTL pairs provides new insights to biological mechanisms underlying complex traits.     

QTL detection and allelic effects for growth and fat traits in outbred pig populations

Quantitative trait loci (QTL) for growth and fatness traits have previously been identified on chromosomes 4 and 7 in several experimental pig populations. The segregation of these QTL in commercial pigs was studied in a sample of 2713 animals from five different populations. Variance component analysis (VCA) using a marker-based identity by descent (IBD) matrix was applied. The IBD coefficient was estimated with simple deterministic (SMD) and Markov chain Monte Carlo (MCMC) methods. Data for two growth traits, average daily gain on test and whole life daily gain, and back fat thickness were analysed. With both methods, seven out of 26 combinations of population, chromosome and trait, were significant. Additionally, QTL genotypic and allelic effects were estimated when the QTL effect was significant. The range of QTL genotypic effects in a population varied from 4.8% to 10.9% of the phenotypic mean for growth traits and 7.9% to 19.5% for back fat trait. Heritabilities of the QTL genotypic values ranged from 8.6% to 18.2% for growth traits, and 14.5% to 19.2% for back fat. Very similar results were obtained with both SMD and MCMC. However, the MCMC method required a large number of iterations, and hence computation time, especially when the QTL test position was close to the marker.     

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F₂ intercross between Landrace and Korean native pigs. Eight blood‐related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F₂ progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome‐wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood‐related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker‐assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.     

Genome-wide QTL analysis of meat quality-related traits in a large F2 intercross between Landrace and Korean native pigs

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0080-6) contains supplementary material, which is available to authorized users.     

Quantitative trait loci with additive effects on growth and carcass traits in a Wagyu-Limousin F2 population

A whole-genome scan for carcass traits [average daily gain during the pre-weaning, growth and finishing periods; birth weight; hot carcass weight and longissimus muscle area (LMA)] was performed on 328 F(2) progeny produced from Wagyu x Limousin-cross parents derived from eight founder Wagyu bulls. Nine significant (P 相似文献   

Genome wide QTL mapping to identify candidate genes for carcass traits in Hanwoo (Korean Cattle)

Meat quality traits are the most economically important traits affecting the beef industry in Korea. We performed a whole genome quantitative trait locus (QTL) mapping study of carcass data in Hanwoo Korean cattle. Two hundred sixty-six Hanwoo steers from 65 sires were genotyped using a 10K Affymetrix SNP chip. The average SNP interval across the bovine genome was 1.5Mb. Associations between each individual SNP and four carcass traits [carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling (MAR)] were assessed using a linear mixed model of each trait. Combined linkage and linkage disequilibrium analysis (LDLA) detected six potential QTL on BTA04, 06, 13, 16, 17, and 23 at the chromosome-wise level (P<0.05). Two MAR QTL were detected at 52.2 cM of BTA06 and 46.04 cM of BTA17. We identified three genes (ARAP2, LOC539460, and LOC511424) in the QTL region of BTA06 and seven genes (RPS14, SCARB1, LOC782103, BRI3BP, AACS, DHX37, and UBC) in the QTL region of BTA17. One significant QTL for CWT was detected at 100 cM on BTA04 and the corresponding QTL region spanned 1.7 cM from 99.7 to 101.4 cM. For EMA QTL, one significant QTL was detected at 3.9 cM of BTA23 and the most likely QTL interval was 1.4 cM, placing 15 candidate genes in the marker bracket. Finally, two QTL for BFT were identified at 68 cM on BTA13 and 24 cM on BTA16. The LPIN3 gene, which is functionally associated with lipodystrophy in humans, is located in the BFT QTL on BTA13. Thus, two potential candidate genes, acetoacetyl-CoA synthetase (AACS) and lipin (LPIN), were detected in QTL regions on BTA17 for MAR and BTA13 for BFT, respectively. In conclusion, LDLA analysis can be used to detect chromosome regions harboring QTL and candidate genes with a low density SNP panel, yielding relatively narrow confidence intervals regarding location.     

Identification of QTLs for seed and pod traits in soybean and analysis for additive effects and epistatic effects of QTLs among multiple environments

Soybean seed and pod traits are important yield components. Selection for high yield style in seed and pod along with agronomic traits is a goal of many soybean breeders. The intention of this study was to identify quantitative trait loci (QTL) underlying seed and pod traits in soybean among eleven environments in China. 147 recombinant inbred lines were advanced through single-seed-descent method. The population was derived from a cross between Charleston (an American high yield soybean cultivar) and DongNong594 (a Chinese high yield soybean cultivar). A total of 157 polymorphic simple sequence repeat markers were used to construct a genetic linkage map. The phenotypic data of seed and pod traits [number of one-seed pod, number of two-seed pod, number of three-seed pod, number of four-seed pod, number of (two plus three)-seed pod, number of (three plus four)-seed pod, seed weight per plant, number of pod per plant] were recorded in eleven environments. In the analysis of single environment, fourteen main effect QTLs were identified. In the conjoint analysis of multiple environments, twenty-four additive QTLs were identified, and additive QTLs by environments interactions (AE) were evaluated and analyzed at the same time among eleven environments; twenty-three pairs of epistatic QTLs were identified, and epistasis (additive by additive) by environments interactions (AAE) were also analyzed and evaluated among eleven environments. Comparing the results of identification between single environment mapping and multiple environments conjoint mapping, three main effect QTLs with positive additive values and another three main effect QTLs with negative additive values, had no interactions with all environments, supported that these QTLs could be used in molecular assistant breeding in the future. These different effect QTLs could supply a good foundation to the gene clone and molecular asisstant breeding of soybean seed and pod traits.     

Analysis on additive effects and additive-by-additive epistatic effects of QTLs for yield traits in a recombinant inbred line population of rice

A linkage map consisting of 158 DNA markers were constructed by using a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhenshan 97B 2 Milyang 46. Quantitative trait loci (QTLs) conditioning grain yield and five yield component traits were determined at the one-locus and two-locus levels, and genotype-by-environment (GE) interactions were analyzed. Thirty-one QTLs were detected to have significant additive effects for yield traits, of which 12 also exhibited significant epistatic effects. Sixteen significant additive-by-additive (AA) interactions were detected, of which nine occurred between QTLs with own additive effects (M_epQTLs), four occurred between QTLs showing epistatic effects only (epQTLs), and three occurred between M_epQTLs and epQTLs. Significant GE interactions were found for six QTLs with additive effects and one AA interaction. Generally, the contributions to the phenotypic variation were higher due to QTL main effects than to epistatic effects. The detection of additive effects and AA effects of a QTL interfered with each other, indicating that the detection of QTLs with main effects, as well as the magnitude and directions of the additive effects, might vary depending on their interactions with other loci.     

Characterization of the porcine FABP5 gene and its association with the FAT1 QTL in an Iberian by Landrace cross

We have characterized and mapped the porcine fatty acid binding protein 5, epidermal (FABP5) gene. According to linkage and RH mapping, this gene is located close to the FABP4 (fatty acid binding protein 4, adipocyte) gene on swine chromosome 4. We resequenced 4.7 kb of the FABP5 gene in the parental population of an Iberian x Landrace cross (IBMAP), identifying seven SNPs arranged in two distinct FABP5 haplotypes. QTL and association analyses in the IBMAP population showed that this gene is strongly associated with fat deposition. QTL and haplotype analysis revealed that both FABP4 and FABP5 (clustered in mammals) are major candidate genes for the FAT1 QTL; the most likely position for the FAT1 QTL is between these two genes. Finally, our results suggest the presence of more than one QTL affecting fatness traits on porcine chromosome 4.