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.     

Genome‐wide association study identifies quantitative trait loci affecting hematological traits in an F2 intercross between Landrace and Korean native pigs

Changes affecting the status of health and robustness can bring about physiological alterations including hematological parameters in swine. To identify quantitative trait loci (QTL) associated with eight hematological traits (one leukocyte trait, six erythrocyte traits and one platelet trait), we conducted a genome‐wide association study using the PorcineSNP60K BeadChip in a resource population derived from an intercross between Landrace and Korean native pigs. A total of 36 740 SNPs from 816 F₂ progeny were analyzed for each blood‐related trait after filtering for quality control. Data were analyzed by the genome‐wide rapid association using mixed model and regression (GRAMMAR) approach. A total of 257 significant SNPs (P < 1.36 × 10^?6) on SSC3, 6, 8, 13 and 17 were identified for blood‐related traits in this study. Interestingly, the genomic region between 17.9 and 130 Mb on SSC8 was found to be significantly associated with red blood cell, mean corpuscular volume and mean corpuscular hemoglobin. Our results include the identification of five significant SNPs within five candidate genes (KIT, IL15, TXK, ARAP2 and ERG) for hematopoiesis. Further validation of these identified SNPs could give valuable information for understanding the variation of hematological traits in pigs.     

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.     

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 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.     

Whole-genome association study for the roan coat color in an intercrossed pig population between Landrace and Korean native pig

The roan coat color is characterized by white hairs intermingled with colored hairs. Candidate genes based on comparative phenotypes in horses and cattle involve the KIT and KIT ligand (MGF) genes. Here, we report the result of the whole genome scanning to detect genomic regions responsible for the roan coat color, using a three-generation pedigree of 62 pigs in an intercross between Landrace and Korean native pig. These pigs were genotyped using the PorcineSNP 60 BeadChip (Illumina, USA). The whole genome scan indicated that three genomic regions, 35～36 Mb, 38～39 Mb, and 58～59 Mb on SSC8, were commonly and highly associated/linked with the roan phenotype in the case/control, sib-pair, and linkage test, respectively. The porcine KIT was selected as a candidate gene, because it is located in one of the three significant regions and its function is related to coat color formation. SNPs and Indels within coding sequence (CDS), promoter, and 3′-UTR of KIT were surveyed. Twenty-two SNPs in the CDS reported previously, as well as nine variations in promoter (2 SNPs) and 3′-UTR (5 SNPs and 2 Indels) were detected. Although no causative mutations were identified, these results will help to elucidate the genetic mechanisms involved in the expression of the roan phenotype and will aid in identifying key mutations responsible for the roan phenotype in further studies.     

Genome-wide association analysis reveals cryptic alleles as an important factor in heterosis for fatness in chicken F2 population

Genome-wide association studies have become possible in the chicken because of the recent availability of the complete genome sequence, a polymorphism map and high-density single nucleotide polymorphism (SNP) genotyping platforms. We used these tools to study the genetic basis of a very high level of heterosis that was previously observed for fatness in two F₂ populations established by crossing one outbred broiler (meat-type) sire with dams from two unrelated, highly inbred, light-bodied lines (Fayoumi and Leghorn). In each F₂ population, selective genotyping was carried out using phenotypically extreme males for abdominal fat percentage (AF) and about 3000 SNPs. Single-point association analysis of about 500 informative SNPs per cross showed significant association ( P  < 0.01) of 15 and 24 markers with AF in the Broiler × Fayoumi and Broiler × Leghorn crosses respectively. These SNPs were on 10 chromosomes (GGA1, 2, 3, 4, 7, 8, 10, 12, 15 and 27). Interestingly, of the 39 SNPs that were significantly associated with AF, there were about twice as many homozygous genotypes associated with higher AF that traced back to the inbred lines alleles, although the broiler line had on average higher AF. These SNPs are considered to be associated with QTL with cryptic alleles. This study reveals cryptic alleles as an important factor in heterosis for fatness observed in two chicken F₂ populations, and suggests epistasis as the common underlying mechanism for heterosis and cryptic allele expression. The results of this study also demonstrate the power of high marker-density SNP association studies in discovering QTL that were not detected by previous microsatellite-based genotyping studies.     

Genome-wide association study of body weight in chicken F2 resource population

Chicken body weight is an economically important trait and great genetic progress has been accomplished in genetic selective for body weight. To identify genes and chromosome regions associated with body weight, we performed a genome-wide association study using the chicken 60 k SNP panel in a chicken F2 resource population derived from the cross between Silky Fowl and White Plymouth Rock. A total of 26 SNP effects involving 9 different SNP markers reached 5% Bonferroni genome-wide significance. A chicken chromosome 4 (GGA4) region approximately 8.6 Mb in length (71.6-80.2 Mb) had a large number of significant SNP effects for late growth during weeks 7-12. The LIM domain-binding factor 2 (LDB2) gene in this region had the strongest association with body weight for weeks 7-12 and with average daily gain for weeks 6-12. This GGA4 region was previously reported to contain body weight QTL. GGA1 and GGA18 had three SNP effects on body weight with genome-wide significance. Some of the SNP effects with the significance of "suggestive linkage" overlapped with previously reported results.     

Genome-wide association studies for the number of teats and teat asymmetry patterns in Large White pigs

The number of teats is a morphological trait that influences the mothering ability of the sows and thus their reproduction performances. In this study, we carried out GWASs for the total number of teats and other 12 related parameters in 821 Italian Large White heavy pigs. All pigs were genotyped with the Illumina PorcineSNP60 BeadChip array. For four investigated parameters (total number of teats, the number of teats of the left line, the number of teats of the right line and the maximum number of teats comparing the two sides), significant markers were identified on SSC7, in the region of the vertnin (VRTN) gene. Significant markers for the numbers of posterior teats and the absolute difference between anterior and posterior teat numbers were consistently identified on SSC6. The most significant SNP for these parameters was an intron variant in the TOX high mobility group box family member 3 (TOX3) gene. For the other four parameters (absolute difference between the two sides; anterior teats; the ratio between the posterior and the anterior number of teats; and the absence or the presence of extra teats) only suggestively significant markers were identified on several other chromosomes. This study further supported the role of the VRTN gene region in affecting the recorded variability of the number of teats in the Italian Large White pig population and identified a genomic region potentially affecting the biological mechanisms controlling the developmental programme of morphological features in pigs.     

Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat

The modification of flowering date is considered an important way to escape the current or future climatic constraints that affect wheat crops. A better understanding of its genetic bases would enable a more efficient and rapid modification through breeding. The objective of this study was to identify chromosomal regions associated with earliness in wheat. A 227-wheat core collection chosen to be highly contrasted for earliness was characterized for heading date. Experiments were conducted in controlled conditions and in the field for 3 years to break down earliness in the component traits: photoperiod sensitivity, vernalization requirement and narrow-sense earliness. Whole-genome association mapping was carried out using 760 molecular markers and taking into account the five ancestral group structure. We identified 62 markers individually associated to earliness components corresponding to 33 chromosomal regions. In addition, we identified 15 other significant markers and seven more regions by testing marker pair interactions. Co-localizations were observed with the Ppd-1, Vrn-1 and Rht-1 candidate genes. Using an independent set of lines to validate the model built for heading date, we were able to explain 34% of the variation using the structure and the significant markers. Results were compared with already published data using bi-parental populations giving an insight into the genetic architecture of flowering time in wheat.     

Genome-wide association study revealed a promising region and candidate genes for eggshell quality in an F2 resource population

Background

Eggshell is subject to quality loss with aging process of laying hens, and damaged eggshells result in economic losses of eggs. However, the genetic architecture underlying the dynamic eggshell quality remains elusive. Here, we measured eggshell quality traits, including eggshell weight (ESW), eggshell thickness (EST) and eggshell strength (ESS) at 11 time points from onset of laying to 72 weeks of age and conducted comprehensive genome-wide association studies (GWAS) in 1534 F₂ hens derived from reciprocal crosses between White Leghorn (WL) and Dongxiang chickens (DX).

Results

ESWs at all ages exhibited moderate SNP-based heritability estimates (0.30 ~ 0.46), while the estimates for EST (0.21 ~ 0.31) and ESS (0.20 ~ 0.27) were relatively low. Eleven independent univariate genome-wide screens for each trait totally identified 1059, 1026 and 1356 significant associations with ESW, EST and ESS, respectively. Most significant loci were in a region spanning from 57.3 to 71.4 Mb of chromosome 1 (GGA1), which together account for 8.4 ~ 16.5 % of the phenotypic variance for ESW from 32 to 72 weeks of age, 4.1 ~ 6.9 % and 2.95 ~ 16.1 % for EST and ESS from 40 to 72 weeks of age. According to linkage disequilibrium (LD) and conditional analysis, the significant SNPs in this region were in extremely strong linkage disequilibrium status. Ultimately, two missense SNPs in GGA1 and one in GGA4 were considered as promising loci on three independent genes including ITPR2, PIK3C2G, and NCAPG. The homozygotes of advantageously effective alleles on PIK3C2G and ITPR2 possessed the best eggshell quality and could partly counteract the negative effect of aging process. NCAPG had certain effect on eggshell quality for young hens.

Conclusions

Identification of the promising region as well as potential candidate genes will greatly advance our understanding of the genetic basis underlying dynamic eggshell quality and has the practical significance in breeding program for the improvement of eggshell quality, especially at the later part of laying cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1795-7) contains supplementary material, which is available to authorized users.     

Genetic analysis of an F2 intercross between two strains of Japanese quail provided evidence for quantitative trait loci affecting carcass composition and internal organs

The purpose of this study was to identify genomic regions, quantitative trait loci (QTL), affecting carcass traits on chromosome 1 in an F2 population of Japanese quail. For this purpose, two white and wild strains of Japanese quail (16 birds) were crossed reciprocally and F1 generation (34 birds) was created. The F2 generation was produced by intercrossing of the F1 birds. Phenotypic data including carcass weight, internal organs and carcass parts were collected on F2 animals (422 birds). The total mapping population (472 birds) was genotyped for 8 microsatellite markers on chromosome 1. QTL analysis was performed with interval mapping method applying the line-cross model. Significant QTL were identified for breast weight at 0 (P < 0.01), 172 (P < 0.05) and 206 (P < 0.01), carcass weight at 91 (P < 0.05), carcass fatness at 0 (P < 0.01), pre-stomach weight at 206 (P < 0.01) and uropygial weight gland at 197 (P < 0.01) cM on chromosome 1. There was also evidence for imprinted QTL affecting breast weight (P < 0.01) on chromosome 1. The proportion of the F2 phenotypic variation explained by the significant additive, dominance and imprinted QTL effects ranged from 1.0 to 7.3 %, 1.2 to 3.3 % and 1.4 to 2.2 %, respectively.     

A new strategy for estimating recombination fractions between dominant markers from an F2 population

Although most high-density linkage maps have been constructed from codominant markers such as single-nucleotide polymorphisms (SNPs) and microsatellites due to their high linkage information, dominant markers can be expected to be even more significant as proteomic technique becomes widely applicable to generate protein polymorphism data from large samples. However, for dominant markers, two possible linkage phases between a pair of markers complicate the estimation of recombination fractions between markers and consequently the construction of linkage maps. The low linkage information of the repulsion phase and high linkage information of coupling phase have led geneticists to construct two separate but related linkage maps. To circumvent this problem, we proposed a new method for estimating the recombination fraction between markers, which greatly improves the accuracy of estimation through distinction between the coupling phase and the repulsion phase of the linked loci. The results obtained from both real and simulated F2 dominant marker data indicate that the recombination fractions estimated by the new method contain a large amount of linkage information for constructing a complete linkage map. In addition, the new method is also applicable to data with mixed types of markers (dominant and codominant) with unknown linkage phase.     

Genome-wide association study of shank length and diameter at different developmental stages in chicken F2 resource population

In order to find SNPs and genes affecting shank traits, we performed a GWAS in a chicken F2 population of eight half-sib families from five hatches derived from reciprocal crosses between an Arian fast-growing line and an Urmia indigenous slow-growing chicken. A total of 308 birds were genotyped using a 60K chicken SNP chip. Shank traits including shank length and diameter were measured weekly from birth to 12 weeks of age. A generalized linear model and a compressed mixed linear model (CMLM) were applied to achieve the significant regions. The value of the average genomic inflation factor (λ statistic) of the CMLM model (0.99) indicated that the CMLM was more effective than the generalized linear model in controlling the population structure. The genes surrounding significant SNPs and their biological functions were identified from NCBI, Ensembl and UniProt databases. The results indicated that 12 SNPs at 12 different ages passed the LD-adjusted 5% Bonferroni significant threshold. Two SNPs were significant for shank length and nine SNPs were significant for shank diameter. The significant SNPs were located near to or inside 11 candidate genes. The results showed that a number of significant SNPs in the middle ages were higher than the rest. The MXRA8 gene was related to the significant SNP at week 1 that promotes proliferation of growth plate chondrocytes. A unique SNP of Gga_rs16689511 located on chicken Z chromosome within the LOC101747628 gene was related to shank length at three different ages of birds (weeks 8, 9 and 11). The significant SNPs for shank diameter were found at weeks 4 and 7 (four and five SNPs respectively). The identifications of SNPs and genes here could contribute to a better understanding of the genetic control of shank traits in chicken.     

Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population

Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F₂ resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1–6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.Subject terms: Genome-wide association studies, Genetic linkage study, Development, DNA sequencing, Animal breeding     

Quantitative gene expression analysis on chromosome 6 between Korean native pigs and Yorkshire breeds for fat deposition

Pig chromosome 6 (SSC6) has been reported to have QTL affecting backfat thickness (BFT) and intramuscular fat (IMF). A human-pig comparative map covering 18 autosomes with the highest resolution has been constructed and based on this map SSC6 has conserved syntenicgroups with human chromosome (HSA) 16, 19, 1, and 18. In this study, the pig Affy elements mapped to the SSC6 were analyzed, and the differentially expressed genes in three tissues (liver, backfat and loin muscle) between Yorkshire and Korean Native Pigs (KNP) were collected, in particular those genes located in the internal between markers SW1355 and SW1823 where a quantitative trait loci (QTL) affecting the intramuscular fat content (IMF) have been detected in multiple pig populations. The genes listed here may offer information for further study the candidate genes affecting these QTL on the expression level.     

Genome-wide interval mapping using SNPs identifies new QTL for growth,body composition and several physiological variables in an F2 intercross between fat and lean chicken lines

Background

For decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F₂ intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome.

Results

Sixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter.

Conclusions

The medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken.