Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip

Genome-wide identification of loci associated with growth in rainbow trout

Background

Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.

Results

A previously developed 50 K gene-transcribed SNP chip, containing ~ 21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~ 6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R² = 0.09).

Conclusion

The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

Genome-wide association study of body weight and conformation traits in neonatal sheep

Sheep, an important source of meat, dairy products and wool, play an essential part in the global agricultural economy. Body weight and body conformation are key traits in the sheep industry; however, their underlying genetic mechanisms are poorly understood. In this study, a GWAS was implemented to identify promising genes possibly linked to birth weight (BW) and body conformation traits in neonatal sheep, using a high-throughput chip (630 K). After quality control, 277 individuals and 518 203 variants were analyzed using gemma software in a mixed linear model. A total of 48 genome-wide suggestive SNPs were obtained, of which four were associated with BW, four with withers height (WH), 11 with body length (BL) and 29 with chest girth (CG). In total, 39 genes associated with BW and body conformation traits were identified by aligning to the sheep genome (Ovis aries_v4.0), and most of them were involved in the cell cycle and body development. Promising candidate genes found included the following: FOS like 2 or AP-1 transcription factor subunit (FOSL2) for BW; potassium voltage-gated channel subfamily D member 2 (KCND2) for WH; transmembrane protein 117 (TMEM117), transforming growth factor beta induced (TGFBI), and leukocyte cell-derived chemotaxin 2 (LECT2) for BL; and trafficking kinesin protein 1 (TRAK1) and LOC101102529 for CG. These results provide cues for similar studies aiming at uncovering the genetic mechanisms underlying body development, and marker-assisted selection programs focusing on BW and body conformation traits in sheep.

Genome-wide association study and genomic predictions for exterior traits in Yorkshire pigs1

The objectives of this study were to identify informative genomic regions that affect the exterior traits of purebred Korean Yorkshire pigs and to investigate and compare the accuracy of genomic prediction for response variables. Phenotypic data on body height (BH), body length (BL), and total teat number (TTN) from 2,432 Yorkshire pigs were used to obtain breeding values including as response variable the estimated breeding value (EBV) and 2 types of deregressed EBVs-one including the parent average (DEBVincPA) and the other excluding it (DEBVexcPA). A final genotype panel comprising 46,199 SNP markers was retained for analysis after quality control for common SNPs. The BayesB and BayesC methods-with various π and weighted response variables (EBV, DEBVincPA, or DEBVexcPA)-were used to estimate SNP effects, through the genome-wide association study. The significance of genomic windows (1 Mb) was obtained at 1.0% additive genetic variance and was subsequently used to identify informative genomic regions. Furthermore, SNPs with a high model frequency (≥0.90) were considered informative. The accuracy of genomic prediction was estimated using a 5-fold cross-validation with the K-means clustering method. Genomic accuracy was measured as the genomic correlation between the molecular breeding value and the individual weighted response variables (EBV, DEBVincPA, or DEBVexcPA). The number of identified informative windows (1 Mb) for BH, BL, and TTN was 4, 3, and 4, respectively. The number of significant SNPs for BH, BL, and TTN was 6, 4, and 5, respectively. Diversity π did not influence the accuracy of genomic prediction. The BayesB method showed slightly higher genomic accuracy for exterior traits than BayesC method in this study. In addition, the genomic accuracy using DEBVincPA as response variable was higher than that using other response variables. Therefore, the genomic accuracy using BayesB (π = 0.90) with DEBVinPA as a response variable was the most effective in this study. The genomic accuracy values for BH, BL, and TTN were calculated to be 0.52, 0.60, and 0.51, respectively.

Characterization of the acute heat stress response in gilts: III. Genome-wide association studies of thermotolerance traits in pigs

Heat stress is one of the limiting factors negatively affecting pig production, health, and fertility. Characterizing genomic regions responsible for variation in HS tolerance would be useful in identifying important genetic factor(s) regulating physiological responses to HS. In the present study, we performed genome-wide association analyses for respiration rate (RR), rectal temperature (TR), and skin temperature (TS) during HS in 214 crossbred gilts genotyped for 68,549 single nucleotide polymorphisms (SNP) using the Porcine SNP 70K BeadChip. Considering the top 0.1% smoothed phenotypic variances explained by SNP windows, we detected 26, 26, 21, and 14 genes that reside within SNPs explaining the largest proportion of variance (top 25 SNP windows) and associated with change in RR (ΔRR) from thermoneutral (TN) conditions to HS environment, as well as the change in prepubertal TR (ΔTR), change in postpubertal ΔTR, and change in TS (ΔTS), respectively. The region between 28.85 Mb and 29.10 Mb on chromosome 16 explained about 0.05% of the observed variation for ΔRR. The growth hormone receptor (GHR) gene resides in this region and is associated with the HS response. The other important candidate genes associated with ΔRR (PAIP1, NNT, and TEAD4), ΔTR (LIMS2, TTR, and TEAD4), and ΔTS (ERBB4, FKBP1B, NFATC2, and ATP9A) have reported roles in the cellular stress response. The SNP explaining the largest proportion of variance and located within and in the vicinity of genes were related to apoptosis or cellular stress and are potential candidates that underlie the physiological response to HS in pigs.

A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds

Improvement in growth and fatness traits are the main objectives in pig all breeding programs. Tenth rib backfat thickness (10RIBBFT) and days to 100 kg (D100), which are good predictors of carcass lean content and growth rate, respectively, are economically important traits and also main breeding target traits in pigs. To investigate the genetic mechanisms of 10RIBBFT and D100 of pigs, we sampled 1,137 and 888 pigs from 2 Yorkshire populations of American and British origin, respectively, and conducted genome-wide association study (GWAS) through combined analysis and meta-analysis, to identify SNPs associated with 10RIBBFT and D100. A total of 11 and 7 significant SNPs were identified by combined analysis for 10RIBBFT and D100, respectively. And in meta-analysis, 8 and 7 significant SNPs were identified for 10RIBBFT and D100, respectively. Among them, 6 and 5 common significant SNPs in two analysis results were, respectively, identified associated with 10RIBBFT and D100, and correspondingly explained 2.09% and 0.52% of the additive genetic variance of 10RIBBFT and D100. Further bioinformatics analysis revealed 10 genes harboring or close to these common significant SNPs, 5 for 10RIBBFT and 5 for D100. In particular, Gene Ontology analysis highlighted 6 genes, PCK1, ANGPTL3, EEF1A2, TNFAIP8L3, PITX2, and PLA2G12, as promising candidate genes relevant with backfat thickness and growth. PCK1, ANGPTL3, EEF1A2, and TNFAIP8L3 could influence backfat thickness through phospholipid transport, regulation of lipid metabolic process through the glycerophospholipid biosynthesis and metabolism pathway, the metabolism of lipids and lipoproteins pathway. PITX2 has a crucial role in skeletal muscle tissue development and animal organ morphogenesis, and PLA2G12A plays a role in the lipid catabolic and phospholipid catabolic processes, which both are involved in the body weight pathway. All these candidate genes could directly or indirectly influence fat production and growth in Yorkshire pigs. Our findings provide novel insights into the genetic basis of growth and fatness traits in pigs. The candidate genes for D100 and 10RIBBFT are worthy of further investigation.

Using genome wide association studies to identify common QTL regions in three different genetic backgrounds based on Iberian pig breed

One of the major limitation for the application of QTL results in pig breeding and QTN identification has been the limited number of QTL effects validated in different animal material. The aim of the current work was to validate QTL regions through joint and specific genome wide association and haplotype analyses for growth, fatness and premier cut weights in three different genetic backgrounds, backcrosses based on Iberian pigs, which has a major role in the analysis due to its high productive relevance. The results revealed nine common QTL regions, three segregating in all three backcrosses on SSC1, 0–3 Mb, for body weight, on SSC2, 3–9 Mb, for loin bone-in weight, and on SSC7, 3 Mb, for shoulder weight, and six segregating in two of the three backcrosses, on SSC2, SSC4, SSC6 and SSC10 for backfat thickness, shoulder and ham weights. Besides, 18 QTL regions were specifically identified in one of the three backcrosses, five identified only in BC_LD, seven in BC_DU and six in BC_PI. Beyond identifying and validating QTL, candidate genes and gene variants within the most interesting regions have been explored using functional annotation, gene expression data and SNP identification from RNA-Seq data. The results allowed us to propose a promising list of candidate mutations, those identified in PDE10A, DHCR7, MFN2 and CCNY genes located within the common QTL regions and those identified near ssc-mir-103-1 considered PANK3 regulators to be further analysed.

Detecting selection signatures between Duroc and Duroc synthetic pig populations using high-density SNP chip

The development of high throughput genotyping techniques has facilitated the identification of selection signatures of pigs. The detection of genomic selection signals in a population subjected to differential selection pressures may provide insights into the genes associated with economically and biologically important traits. To identify genomic regions under selection, we genotyped 488 Duroc (D) pigs and 155 D × Korean native pigs (DKNPs) using the Porcine SNP70K BeadChip. By applying the F_ST and extended haplotype homozygosity (EHH-Rsb) methods, we detected genes under directional selection associated with growth/stature (DOCK7, PLCB4, HS2ST1, FBP2 and TG), carcass and meat quality (TG, COL14A1, FBXO5, NR3C1, SNX7, ARHGAP26 and DPYD), number of teats (LOC100153159 and LRRC1), pigmentation (MME) and ear morphology (SOX5), which are all mostly near or at fixation. These results could be a basis for investigating the underlying mutations associated with observed phenotypic variation. Validation using genome-wide association analysis would also facilitate the inclusion of some of these markers in genetic evaluation programs.

A global analysis of CNVs in swine using whole genome sequence data and association analysis with fatty acid composition and growth traits

Copy number variations (CNVs) are important genetic variants complementary to SNPs, and can be considered as biomarkers for some economically important traits in domestic animals. In the present study, a genomic analysis of porcine CNVs based on next-generation sequencing data was carried out to identify CNVs segregating in an Iberian x Landrace backcross population and study their association with fatty acid composition and growth-related traits. A total of 1,279 CNVs, including duplications and deletions, were detected, ranging from 106 to 235 CNVs across samples, with an average of 183 CNVs per sample. Moreover, we detected 540 CNV regions (CNVRs) containing 245 genes. Functional annotation suggested that these genes possess a great variety of molecular functions and may play a role in production traits in commercial breeds. Some of the identified CNVRs contained relevant functional genes (e.g., CLCA4, CYP4X1, GPAT2, MOGAT2, PLA2G2A and PRKG1, among others). The variation in copy number of four of them (CLCA4, GPAT2, MOGAT2 and PRKG1) was validated in 150 BC1_LD (25% Iberian and 75% Landrace) animals by qPCR. Additionally, their contribution regarding backfat and intramuscular fatty acid composition and growth–related traits was analyzed. Statistically significant associations were obtained for CNVR112 (GPAT2) for the C18:2(n-6)/C18:3(n-3) ratio in backfat and carcass length, among others. Notably, GPATs are enzymes that catalyze the first step in the biosynthesis of both triglycerides and glycerophospholipids, suggesting that this CNVR may contribute to genetic variation in fatty acid composition and growth traits. These findings provide useful genomic information to facilitate the further identification of trait-related CNVRs affecting economically important traits in pigs.

Genetic diversity, extent of linkage disequilibrium and persistence of gametic phase in Canadian pigs

Background

Knowledge on the levels of linkage disequilibrium (LD) across the genome, persistence of gametic phase between breed pairs, genetic diversity and population structure are important parameters for the successful implementation of genomic selection. Therefore, the objectives of this study were to investigate these parameters in order to assess the feasibility of a multi-herd and multi-breed training population for genomic selection in important purebred and crossbred pig populations in Canada. A total of 3,057 animals, representative of the national populations, were genotyped with the Illumina Porcine SNP60 BeadChip (62,163 markers).

Results

The overall LD (r²) between adjacent SNPs was 0.49, 0.38, 0.40 and 0.31 for Duroc, Landrace, Yorkshire and Crossbred (Landrace x Yorkshire) populations, respectively. The highest correlation of phase (r) across breeds was observed between Crossbred animals and either Landrace or Yorkshire breeds, in which r was approximately 0.80 at 1 Mbp of distance. Landrace and Yorkshire breeds presented r ≥ 0.80 in distances up to 0.1 Mbp, while Duroc breed showed r ≥ 0.80 for distances up to 0.03 Mbp with all other populations. The persistence of phase across herds were strong for all breeds, with r ≥ 0.80 up to 1.81 Mbp for Yorkshire, 1.20 Mbp for Duroc, and 0.70 Mbp for Landrace. The first two principal components clearly discriminate all the breeds. Similar levels of genetic diversity were observed among all breed groups. The current effective population size was equal to 75 for Duroc and 92 for both Landrace and Yorkshire.

Conclusions

An overview of population structure, LD decay, demographic history and inbreeding of important pig breeds in Canada was presented. The rate of LD decay for the three Canadian pig breeds indicates that genomic selection can be successfully implemented within breeds with the current 60 K SNP panel. The use of a multi-breed training population involving Landrace and Yorkshire to estimate the genomic breeding values of crossbred animals (Landrace × Yorkshire) should be further evaluated. The lower correlation of phase at short distances between Duroc and the other breeds indicates that a denser panel may be required for the use of a multi-breed training population including Duroc.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-017-0473-y) contains supplementary material, which is available to authorized users.

Deciphering the regulation of porcine genes influencing growth, fatness and yield-related traits through genetical genomics

Genetical genomics approaches aim at identifying quantitative trait loci for molecular traits, also known as intermediate phenotypes, such as gene expression, that could link variation in genetic information to physiological traits. In the current study, an expression GWAS has been carried out on an experimental Iberian × Landrace backcross in order to identify the genomic regions regulating the gene expression of those genes whose expression is correlated with growth, fat deposition, and premium cut yield measures in pig. The analyses were conducted exploiting Porcine 60K SNP BeadChip genotypes and Porcine Expression Microarray data hybridized on mRNA from Longissimus dorsi muscle. In order to focus the analysis on productive traits and reduce the number of analyses, only those probesets whose expression showed significant correlation with at least one of the seven phenotypes of interest were selected for the eGWAS. A total of 63 eQTL regions were identified with effects on 36 different transcripts. Those eQTLs overlapping with phenotypic QTLs on SSC4, SSC9, SSC13, and SSC17 chromosomes previously detected in the same animal material were further analyzed. Moreover, candidate genes and SNPs were analyzed. Among the most promising results, a long non-coding RNA, ALDBSSCG0000001928, was identified, whose expression is correlated with premium cut yield. Association analysis and in silico sequence domain annotation support TXNRD3 polymorphisms as candidate to regulate ALDBSSCG0000001928 expression, which can be involved in the transcriptional regulation of surrounding genes, affecting productive and meat quality traits.

Using RNA-Seq SNP data to reveal potential causal mutations related to pig production traits and RNA editing

RNA-Seq technology is widely used in quantitative gene expression studies and identification of non-annotated transcripts. However this technology also can be used for polymorphism detection and RNA editing in transcribed regions in an efficient and cost-effective way. This study used SNP data from an RNA-Seq assay to identify genes and mutations underlying production trait variations in an experimental pig population. The hypothalamic and hepatic transcriptomes of nine extreme animals for growth and fatness from an (Iberian × Landrace) × Landrace backcross were analyzed by RNA-Seq methodology, and SNP calling was conducted. More than 125 000 single nucleotide variants (SNVs) were identified in each tissue, and 78% were considered to be potential SNPs, those SNVs segregating in the context of this study. Potential informative SNPs were detected by considering those showing a homozygous or heterozygous genotype in one extreme group and the alternative genotype in the other group. In this way, 4396 and 1862 informative SNPs were detected in hypothalamus and liver respectively. Out of the 32 SNPs selected for validation, 25 (80%) were confirmed as actual SNPs. Association analyses for growth, fatness and premium cut yields with 19 selected SNPs were carried out, and four potential causal genes (RETSAT, COPA, RNMT and PALMD) were identified. Interestingly, new RNA editing modifications were detected and validated for the NR3C1:g.102797 (ss1985401074) and ACSM2B:g.13374 (ss1985401075) positions and for the COG3:g3.4525 (ss1985401087) modification previously identified across vertebrates, which could lead to phenotypic variation and should be further investigated.

Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat

The aim of this work is to better understand the genetic mechanisms determining two complex traits affecting porcine meat quality: intramuscular fat (IMF) content and its fatty acid (FA) composition. With this purpose, expression Genome-Wide Association Study (eGWAS) of 45 lipid-related genes associated with meat quality traits in swine muscle (Longissimus dorsi) of 114 Iberian × Landrace backcross animals was performed. The eGWAS identified 241 SNPs associated with 11 genes: ACSM5, CROT, FABP3, FOS, HIF1AN, IGF2, MGLL, NCOA1, PIK3R1, PLA2G12A and PPARA. Three expression Quantitative Trait Loci (eQTLs) for IGF2, ACSM5 and MGLL were identified, showing cis-acting effects, whereas 16 eQTLs had trans regulatory effects. A polymorphism in the ACSM5 promoter region associated with its expression was identified. In addition, strong candidate genes regulating ACSM5, FOS, PPARA, PIK3R1, PLA2G12A and HIF1AN gene expression were also seen. Notably, the analysis highlighted the NR3C1 transcription factor as a strong candidate gene involved in the regulation of the 45 genes analysed. Finally, the IGF2, MGLL, MC2R, ARHGAP6, and NR3C1 genes were identified as potential regulators co-localizing within QTLs for fatness and growth traits in the IBMAP population. The results obtained increase our knowledge in the functional regulatory mechanisms involved in these complex traits.

Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection

The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized.

Genome-wide association analyses reveal significant loci and strong candidate genes for growth and fatness traits in two pig populations

BACKGROUND

Recently, genome-wide association studies (GWAS) have been reported on various pig traits. We performed a GWAS to analyze 22 traits related to growth and fatness on two pig populations: a White Duroc × Erhualian F2 intercross population and a Chinese Sutai half-sib population.

RESULTS

We identified 14 and 39 loci that displayed significant associations with growth and fatness traits at the genome-wide level and chromosome-wide level, respectively. The strongest association was between a 750 kb region on SSC7 (SSC for Sus scrofa) and backfat thickness at the first rib. This region had pleiotropic effects on both fatness and growth traits in F2 animals and contained a promising candidate gene HMGA1 (high mobility group AT-hook 1). Unexpectedly, population genetic analysis revealed that the allele at this locus that reduces fatness and increases growth is derived from Chinese indigenous pigs and segregates in multiple Chinese breeds. The second strongest association was between the region around 82.85 Mb on SSC4 and average backfat thickness. PLAG1 (pleiomorphic adenoma gene 1), a gene under strong selection in European domestic pigs, is proximal to the top SNP and stands out as a strong candidate gene. On SSC2, a locus that significantly affects fatness traits mapped to the region around the IGF2 (insulin-like growth factor 2) gene but its non-imprinting inheritance excluded IGF2 as a candidate gene. A significant locus was also detected within a recombination cold spot that spans more than 30 Mb on SSCX, which hampered the identification of plausible candidate genes. Notably, no genome-wide significant locus was shared by the two experimental populations; different loci were observed that had both constant and time-specific effects on growth traits at different stages, which illustrates the complex genetic architecture of these traits.

CONCLUSIONS

We confirm several previously reported QTL and provide a list of novel loci for porcine growth and fatness traits in two experimental populations with Chinese Taihu and Western pigs as common founders. We showed that distinct loci exist for these traits in the two populations and identified HMGA1 and PLAG1 as strong candidate genes on SSC7 and SSC4, respectively.

A co-association network analysis of the genetic determination of pig conformation, growth and fatness

BACKGROUND

Several QTLs have been identified for major economically relevant traits in livestock, such as growth and meat quality, revealing the complex genetic architecture of these traits. The use of network approaches considering the interactions of multiple molecules and traits provides useful insights into the molecular underpinnings of complex traits. Here, a network based methodology, named Association Weight Matrix, was applied to study gene interactions and pathways affecting pig conformation, growth and fatness traits.

RESULTS

The co-association network analysis underpinned three transcription factors, PPARγ, ELF1, and PRDM16 involved in mesoderm tissue differentiation. Fifty-four genes in the network belonged to growth-related ontologies and 46 of them were common with a similar study for growth in cattle supporting our results. The functional analysis uncovered the lipid metabolism and the corticotrophin and gonadotrophin release hormone pathways among the most important pathways influencing these traits. Our results suggest that the genes and pathways here identified are important determining either the total body weight of the animal and the fat content. For instance, a switch in the mesoderm tissue differentiation may determinate the age-related preferred pathways being in the puberty stage those related with the miogenic and osteogenic lineages; on the contrary, in the maturity stage cells may be more prone to the adipocyte fate. Hence, our results demonstrate that an integrative genomic co-association analysis is a powerful approach for identifying new connections and interactions among genes.

CONCLUSIONS

This work provides insights about pathways and key regulators which may be important determining the animal growth, conformation and body proportions and fatness traits. Molecular information concerning genes and pathways here described may be crucial for the improvement of genetic breeding programs applied to pork meat production.

Discovery of candidate genes for muscle traits based on GWAS supported by eQTL-analysis

Biochemical and biophysical processes that take place in muscle under relaxed and stressed conditions depend on the abundance and activity of gene products of metabolic and structural pathways. In livestock at post-mortem, these muscle properties determine aspects of meat quality and are measurable. The conversion of muscle to meat mimics pathological processes associated with muscle ischemia, injury or damage in humans and it is an economic factor in pork production. Linkage, association, and expression analyses independently contributed to the identification of trait-associated molecular pathways and genes. We aim at providing multiple evidences for the role of specific genes in meat quality by integrating a genome-wide association study (GWAS) for meat quality traits and the detection of eQTL based on trait-correlated expressed genes and trait-associated markers. The GWAS revealed 51 and 200 SNPs significantly associated with meat quality in a crossbred Pietrain×(German Landrace×Large White) (Pi×(GL×LW)) and a purebred German Landrace (GL) population, respectively. Most significant SNPs in Pi×(GL×LW) were located on chromosomes (SSC) 4 and 6. The data of 47,836 eQTLs at a significance level of p<10^-5 were used to scale down the number candidate genes located in these regions. These SNPs on SSC4 showed association with expression levels of ZNF704, IMPA1, and OXSR1; SSC6 SNPs were associated with expression of SIGLEC10 and PIH1D1. Most significant SNPs in GL were located on SSC6 and associated with expression levels of PIH1D1, SIGLEC10, TBCB, LOC100518735, KIF1B, LOC100514845, and two unknown genes. The abundance of transcripts of these genes in muscle, in turn, is significantly correlated with meat quality traits. We identified several genes with evidence for their candidacy for meat quality arising from the integrative approach of a genome-wide association study and eQTL analysis.

Copy number variants in a highly inbred Iberian porcine strain

We carried out a comprehensive genomic analysis of porcine copy number variants (CNVs) based on whole-genome SNP genotyping data and provided new measures of genomic diversity (number, length and distribution of CNV events) for a highly inbred strain (the Guadyerbas strain). This strain represents one of the most ancient surviving populations of the Iberian breed, and it is currently in serious danger of extinction. CNV detection was conducted on the complete Guadyerbas population, adjusted for genomic waves, and used strict quality criteria, pedigree information and the latest porcine genome annotation. The analysis led to the detection of 65 CNV regions (CNVRs). These regions cover 0.33% of the autosomal genome of this particular strain. Twenty-nine of these CNVRs were identified here for the first time. The relatively low number of detected CNVRs is in line with the low variability and high inbreeding estimated previously for this Iberian strain using pedigree, microsatellite or SNP data. A comparison across different porcine studies has revealed that more than half of these regions overlap with previously identified CNVRs or multicopy regions. Also, a preliminary analysis of CNV detection using whole-genome sequence data for four Guadyerbas pigs showed overlapping for 16 of the CNVRs, supporting their reliability. Some of the identified CNVRs contain relevant functional genes (e.g., the SCD and USP15 genes), which are worth being further investigated because of their importance in determining the quality of Iberian pig products. The CNVR data generated could be useful for improving the porcine genome annotation.

Genome wide screening of candidate genes for improving piglet birth weight using high and low estimated breeding value populations

Birth weight is an economically important trait in pig production because it directly impacts piglet growth and survival rate. In the present study, we performed a genome wide survey of candidate genes and pathways associated with individual birth weight (IBW) using the Illumina PorcineSNP60 BeadChip on 24 high (HEBV) and 24 low estimated breeding value (LEBV) animals. These animals were selected from a reference population of 522 individuals produced by three sires and six dam lines, which were crossbreds with multiple breeds. After quality-control, 43,257 SNPs (single nucleotide polymorphisms), including 42,243 autosomal SNPs and 1,014 SNPs on chromosome X, were used in the data analysis. A total of 27 differentially selected regions (DSRs), including 1 on Sus scrofa chromosome 1 (SSC1), 1 on SSC4, 2 on SSC5, 4 on SSC6, 2 on SSC7, 5 on SSC8, 3 on SSC9, 1 on SSC14, 3 on SSC18, and 5 on SSCX, were identified to show the genome wide separations between the HEBV and LEBV groups for IBW in piglets. A DSR with the most number of significant SNPs (including 7 top 0.1% and 31 top 5% SNPs) was located on SSC6, while another DSR with the largest genetic differences in F_ST was found on SSC18. These regions harbor known functionally important genes involved in growth and development, such as TNFRSF9 (tumor necrosis factor receptor superfamily member 9), CA6 (carbonic anhydrase VI) and MDFIC (MyoD family inhibitor domain containing). A DSR rich in imprinting genes appeared on SSC9, which included PEG10 (paternally expressed 10), SGCE (sarcoglycan, epsilon), PPP1R9A (protein phosphatase 1, regulatory subunit 9A) and ASB4 (ankyrin repeat and SOCS box containing 4). More importantly, our present study provided evidence to support six quantitative trait loci (QTL) regions for pig birth weight, six QTL regions for average birth weight (ABW) and three QTL regions for litter birth weight (LBW) reported previously by other groups. Furthermore, gene ontology analysis with 183 genes harbored in these 27 DSRs suggested that protein, metal, ion and ATP binding, viral process and innate immune response present important pathways for deciphering their roles in fetal growth or development. Overall, our study provides useful information on candidate genes and pathways for regulating birth weight in piglets, thus improving our understanding of the genetic mechanisms involved in porcine embryonic or fetal development.

Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data

Background

Porcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach.

Results

The single model analyses, linkage and GWAS, revealed over 30 and 20 chromosomal regions, 24 of them identified here for the first time, specifically associated to the content of diverse fatty acids in BF and IMF, respectively. The analyses with multitrait models allowed identifying for the first time with a formal statistical approach seven different regions with pleiotropic effects on particular fatty acids in both fat deposits. The most relevant were found on SSC8 for C16:0 and C16:1(n-7) fatty acids, detected by both linkage and GWAS approaches. Other detected pleiotropic regions included one on SSC1 for C16:0, two on SSC4 for C16:0 and C18:2, one on SSC11 for C20:3 and the last one on SSC17 for C16:0. Finally, a targeted eQTL scan focused on regions showing tissue-consistent effects was conducted with Longissimus and fat gene expression data. Some powerful candidate genes and regions were identified such as the PBX1, RGS4, TRIB3 and a transcription regulatory element close to ELOVL6 gene to be further studied.

Conclusions

Complementary genome scans have confirmed several chromosome regions previously associated to fatty acid composition in backfat and intramuscular fat, but even more, to identify new ones. Although most of the detected regions were tissue-specific, supporting the hypothesis that the major part of genes affecting fatty acid composition differs among tissues, seven chromosomal regions showed tissue-consistent effects. Additional gene expression analyses have revealed powerful target regions to carry the mutation responsible for the pleiotropic effects.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-845) contains supplementary material, which is available to authorized users.

Identification of genes regulating growth and fatness traits in pig through hypothalamic transcriptome analysis

Previous studies on Iberian × Landrace (IBMAP) pig intercrosses have enabled the identification of several quantitative trait locus (QTL) regions related to growth and fatness traits; however, the genetic variation underlying those QTLs are still unknown. These traits are not only relevant because of their impact on economically important production traits, but also because pig constitutes a widely studied animal model for human obesity and obesity-related diseases. The hypothalamus is the main gland regulating growth, food intake, and fat accumulation. Therefore, the aim of this work was to identify genes and/or gene transcripts involved in the determination of growth and fatness in pig by a comparison of the whole hypothalamic transcriptome (RNA-Seq) in two groups of phenotypically divergent IBMAP pigs. Around 16,000 of the ∼25.010 annotated genes were expressed in these hypothalamic samples, with most of them showing intermediate expression levels. Functional analyses supported the key role of the hypothalamus in the regulation of growth, fat accumulation, and energy expenditure. Moreover, 58,927 potentially new isoforms were detected. More than 250 differentially expressed genes and novel transcript isoforms were identified between the two groups of pigs. Twenty-one DE genes/transcripts that colocalized in previously identified QTL regions and/or whose biological functions are related to the traits of interest were explored in more detail. Additionally, the transcription factors potentially regulating these genes and the subjacent networks and pathways were also analyzed. This study allows us to propose strong candidate genes for growth and fatness based on expression patterns, genomic location, and network interactions.

Association mapping of quantitative trait loci for carcass and meat quality traits at the central part of chromosome 2 in Italian Large White pigs

Association mapping of the central part of porcine chromosome 2 harboring QTLs for carcass and meat quality traits was performed with 17 gene-tagged SNPs located between 44.0 and 77.5 Mb on a physical map (Sscrofa10.2) in Italian Large White pigs. For the analyzed animals records of estimated breeding values for average daily gain, back fat thickness, lean cuts, ham weight, feed conversion ratio, pH1, pHu, CIE L*, CIE a*, CIE b* and drip loss were available. A significant QTL for fat deposition (adjusted P=0.0081) and pH1 (adjusted P=0.0972) to MYOD1 at position 44.4 Mb and a QTL for growth and meatiness (adjusted P=0.0238-0.0601) to UBL5 at position 68.9 Mb were mapped. These results from association mapping are much more accurate than those from linkage mapping and facilitate further search for position candidate genes and causative mutations needed for application of markers through marker assisted selection.