- Research article
- Open Access
Genome sequencing and analysis of Mangalica, a fatty local pig of Hungary
© Molnár et al.; licensee BioMed Central Ltd. 2014
- Received: 2 September 2013
- Accepted: 2 September 2014
- Published: 5 September 2014
Mangalicas are fatty type local/rare pig breeds with an increasing presence in the niche pork market in Hungary and in other countries. To explore their genetic resources, we have analysed data from next-generation sequencing of an individual male from each of three Mangalica breeds along with a local male Duroc pig. Structural variations, such as SNPs, INDELs and CNVs, were identified and particular genes with SNP variations were analysed with special emphasis on functions related to fat metabolism in pigs.
More than 60 Gb of sequence data were generated for each of the sequenced individuals, resulting in 11× to 19× autosomal median coverage. After stringent filtering, around six million SNPs, of which approximately 10% are novel compared to the dbSNP138 database, were identified in each animal. Several hundred thousands of INDELs and about 1,000 CNV gains were also identified. The functional annotation of genes with exonic, non-synonymous SNPs, which are common in all three Mangalicas but are absent in either the reference genome or the sequenced Duroc of this study, highlighted 52 genes in lipid metabolism processes. Further analysis revealed that 41 of these genes are associated with lipid metabolic or regulatory pathways, 49 are in fat-metabolism and fatness-phenotype QTLs and, with the exception of ACACA, ANKRD23, GM2A, KIT, MOGAT2, MTTP, FASN, SGMS1, SLC27A6 and RETSAT, have not previously been associated with fat-related phenotypes.
Genome analysis of Mangalica breeds revealed that local/rare breeds could be a rich source of sequence variations not present in cosmopolitan/industrial breeds. The identified Mangalica variations may, therefore, be a very useful resource for future studies of agronomically important traits in pigs.
- Genome sequencing
- Fatty pig
- Breed-specific SNP
- Gene function
Due to the economic value of farm animals, their genomics, in general, and whole genome sequencing, in particular, are important issues. Results of such research have already had an impact and will continue to do so in the future in terms of production of meat, milk, fibre and other products, environmental effects of animal husbandry, breeding, animal health, feeding, and even human medical issues such as xenotransplantation and disease modelling [1, 2]. Regarding this, the genome of a number of agriculturally important animal species has been or is being completed [3–11].
Pig is one of the most important farm animals, providing about 103,000 thousand tonnes of pork for meat consumption worldwide in 2012 . Moreover, pigs can be used as a model for human diseases, such as arthritis, cardiovascular diseases, diabetes and obesity, because pigs are more similar to humans at physiological and gene level, when compared with rodent animal models . According to different sources, the predicted number of pig breeds and lines range from 350 to 730 [13, 14]. Most of these breeds are local, with only 25 found in multiple regions of a country, and a further 33 spread to more than one country . In spite of the larger number of pig breeds, only six (Large White, Duroc, Landrace, Hampshire, Berkshire and Pietrain) dominate the pork industry .
In the last decade, enormous efforts have been made to exploit the genetic and genomic resources of pigs. Genome sequencing of swine goes back to the early 2000’s, when the Sino-Danish Pig Genome Project was initiated and subsequently a 0.66× coverage genome survey, based on shotgun sequencing, was published . Deeper coverage sequencing of the pig genome was initiated by the Swine Genome Sequencing Consortium . The Sscrofa9 genome assembly was released in 2009  and the pig genome sequence was recently published . These genome resources for pig, together with specialised sequencing projects such as parallel sequencing, have had a huge impact on widening our knowledge about the pig genome, to include SNP identification and genotyping [18–20], GC variance , muscle transcriptome [22, 23], pig interactome , domestication/selection , evolution/domestication , and in a number of other recently published research topics .
Despite the large number of local pig breeds, only a few of them (for example Angler Satleschwein, British Saddleback, Cinta Senese, Manchado de Jabugo, Basque and Guodyerbas), were included in genome sequencing projects. In addition to the major industrial and the few local breeds, Asian and European wild boars, several Asian pig breeds and several other species of the Sus genus have also been included [9, 27–29]. However, other local breeds, of which many are endangered, should also be of great interest for genomic studies because of their importance in biodiversity, conservation, local community and even pork production issues [14, 30]. Mangalica is an example of a local/rare breed with a characteristic curly hair phenotype, which is indigenous to Hungary and was developed in the 19th century . Mangalicas are fatty-type pigs , with high intramuscular fat content . Mangalicas have three colour variants, Blond, Red and Swallow-belly, which are considered as separate breeds based on microsatellite studies . As the history of the three Mangalica breeds indicate , the Blond was bred first from old Hungarian pig races and pigs of Mediterranean origin, and then it contributed to the two newer breeds, Red and Swallow-belly Mangalicas. Reproduction studies are quite numerous in Mangalica [34–38], but genetic studies are rare . Previously we have described that the mtDNA D-loop sequences of Mangalicas display low diversity, but the maternal lineages that they represent are genetically distant from cosmopolitan breeds kept in Hungary  and very likely originate from one particular European ancient line .
In order to explore how the genomes of Mangalicas differ from the reference pig genome, we have sequenced a male individual of each of the three Mangalica breeds along with a male Duroc individual of Hungarian origin. The genome sequence of Mangalicas can serve as a basis for future conservation of the breeds and for an extended Mangalica pork industry.
Three Mangalica male pigs with a Mangalica-specific mitochondrial D-loop haplotype were selected  for genome sequencing. These animals were kept at Emőd, Hungary, registered at the Hungarian Mangalica gene-bank as pedigree sires. They were previously assessed as Blond, Red and Swallow-belly Mangalicas, respectively, under the Hungarian Mangalica Standard and by microsatellite analysis. A Duroc male of Hungarian origin was also sequenced, because we have found previously that Duroc pigs of international or Hungarian origin belong to different maternal lineages  and Mangalica × Duroc F1 hybrids are processed at industrial scale in Hungary for pork products.
Mapped reads (%)
Autosomal median coverage
Identification of genetic variants
Categories of sequence variations
The detection of large INDELs was not the scope of the current study, and so only INDELs shorter than 52 bp were identified. For the genomes of the Blond, Red, Swallow-belly Mangalicas and the Duroc pig, approximately 6.9 × 105, 6.2 × 105, 6.1 × 105 and 4.5 × 105 such INDELs were identified, respectively. Of these, 99.9% were novel compared to the dbSNP138 database. With respect to the size distribution, of the INDELs among the four genomes, single base-pair INDELs were the most abundant (Additional file 5). Exonic INDELs were sorted into eight categories: frame-shift deletions, frame-shift insertions, frame-shift block substitutions, non-frame-shift deletions, non-frame-shift insertions, non-frame-shift block substitutions, stop-gains and stop-losses (Additional file 6). In exonic INDELs, apart from the relatively large number of one base-pair variations that cause ORF shifts, +/− 3 base-pair changes, which do not effect the ORF, were identified in higher numbers than two or four base-pair variations (Additional file 7). An elevated number of one base-pair INDELs when compared with other sizes has also been reported by others [42, 43]. Our comparison with the platinum human exonic data obtained from Illumina’s BaseSpace (https://basespace.illumina.com/datacentral) provided the same result (data not shown) suggesting that our analysis with the pig genome is reliable.
Analysis of genes with exonic, non-synonymous SNPs
Functional, QTL and pathway annotation of the genes
To study the possible relationship between the 52 genes in the lipid metabolic process GO category and QTLs, the chromosomal position of each genes was compared to the positions of the “Fatness” and “Fat composition” QTLs downloaded from the QTLdb, Release 19, . Forty-nine genes are in one or more fat-related QTLs with 14 genes on chromosome 14, overlapped by 15 fat-associated QTLs (Additional file 11). Because of this large proportion (~28%) of genes on chromosome 14, we performed an enrichment analysis for the 14-gene set and a control set of 1282 genes, both are in the same region of chromosome 14 determined by the 15 QTLs. The corrected P-value for lipid metabolic genes in the control and in our set was 4.80 × 10−3 and 2.95 × 10−19, respectively, indicating that the enrichment of the 14 genes in these QTLs deviate significantly from random.
Fatty acid composition of meats is an important dietetic and health issue for pork consumers. We, therefore, compared those genes, which are in saturated and unsaturated fatty acid QTLs and found that nine genes were in common across both fatty acid categories, while the saturated and unsaturated QTL groups each contained two unique genes, NKX2-3 and EPHX2, and OMA1 and FAM135B, respectively (Additional file 12).
Genotyping SNPs in other breeds
The 90 SNPs in the above described 52 genes were present in all three sequenced Mangalicas, but absent from the sequenced Duroc and the reference genome. To learn about their wider occurrence, we have “e-genotyped” 55 animals whose genome was sequenced  for these SNPs. The results indicate that the frequencies of these SNPs vary amongst the 55 individuals (Additional file 13). Clustering of the average frequencies revealed four clusters among the individuals, where Mangalica represents a separate cluster and European, international/Hungarian Duroc, and non-European pigs and/or wild boars comprise the three other related groups (Additional file 14). The clear separation of Mangalicas from other breeds by those 90 SNPs might have the potential in practical applications, such as whole genome selection in breeding.
It was found that four SNPs are present only in Mangalicas, but not in the genotyped individuals (Additonal file 13). All of these SNPs are in one gene, MOGAT2 (ENSSSCG00000014861), which encodes a monoacylglycerol O-acyltransferase 2 enzyme, and is in several back- and belly-fat QTLs and in the “Fat digestion and absorption” (KEGG: 04975) pathway (Additional file 11). It is possible, therefore, that this gene has a particular role of the development of the fatty-pig phenotype of Mangalicas.
Genotyping the FASN gene
The genome of one individual each of the three Mangalica breeds (Blond, Red and Swallow-belly), and a Duroc animal from a Hungarian herd was sequenced and analysed. More than 100 million reads were obtained from the genome of each animal. On average for the four genomes sequenced, 81% of the reads were mapped to the reference genome, resulting in 14.5× median autosomal coverage. Millions of SNP and hundred-thousands of INDEL variations were identified in the three Mangalicas and the one Duroc genome, respectively, when compared to the reference pig genome assembly Sscrofa 10.2. By filtering the SNPs, about five to six million variations were obtained, and about one-tenth of these were novel SNPs compared to the dbSNP138 database (Additional file 3).
For functional analysis, we selected 2,328 exonic non-synonymous SNPs present in each sequenced Mangalica individual, but absent from either the reference genome or the Hungarian Duroc animal. These SNPs were mapped to 1,389 pig genes present in the Ensembl database. Since Mangalicas are fatty-type pigs, and the SNPs were identified in comparison with Duroc, a lean-type pig, we were particularly interested in fat-related genes in this set. Fifty-two genes were found belonging to lipid-related metabolic process categories and were further analysed using QTL and pathway data-mining. Of the 52 genes, 49 and 41 are associated with fat-related QTL regions and KEGG pathways, respectively (Additional file 11).
Some of the 52 genes, for example ACACA, ANKRD23, GM2A, KIT, MOGAT2, MTTP, FASN, SGMS1, SLC27A6 and RETSAT, which we have highlighted here, have been previously described in the context of fat-related characteristics in pigs [50–54]. Of these genes, FASN, a gene encoding a fatty acid synthase, has been shown to be associated with a cis-11-Eicosenoic acid (C20:1) percentage QTL in a Guadyerbas × Landrace cross, although none of the identified SNPs had any putative effect on the protein structure . The FASN protein is a homodimeric, multifunctional enzyme with six catalytic domains, which are required for the cyclic elongation of fatty acids  and catalyses 32 reactions in the fatty acid biosynthesis [KEGG:ssc00061] pathway. Targeted mutagenesis of the FASN gene and inhibition of the FASN protein in mice resulted in reduced total body fat  and body weight , respectively. We have identified two SNPs in this gene in Mangalicas that result in a R443Q (SNP1) and a T1088I (SNP2) amino acid change. The amino acid in position 443 is part of the α-helix in the protein’s inter-domain linker. Since glutamine is more hydrophilic than arginine, the amino acid substitution may affect the relative position of the two functional domains by modulating the flexibility of the linker connecting them . The amino acid in position 1,088 is part of the dehydratase domain of the FASN protein. This domain catalyses the conversion of β-hydroxyacyl-ACP to β-enoyl-ACP in the cyclic elongation of fatty acids . T1088 is in close vicinity to the active site of the dehydratase domain containing an open-ended hydrophobic tunnel . Predicting hydrophobicity of amino acids along the FASN polypeptide revealed that the substituting I1088 is strongly hydrophobic, while T1088 is hydrophilic (data not shown). It is possible, therefore, that in the FASNT1088I protein the substrate-binding nature of the active site is altered, which may influence the dehydration step of the fatty acid cyclic elongation. This might be particularly important in Mangalicas, where no BB homozygotes were found. Thus the active site in the catalytic domain of their FASN protein is expected to be hydrophobic, although allele-specific expression of the FASN gene in heterozygotes might influence this.
It is known that feeding regimes influence fatty acid composition and meat’s marbling in Mangalicas [31, 58], similar to other pig breeds and farm animals. In lipid metabolism, the “Fat digestion and absorption” and “Bile secretion” pathways are involved in the metabolism of dietary fats. These two pathways are connected to the “Glycerolipid metabolism”, “Fatty acid metabolism” and “Fatty acid biosynthesis” pathways. Our study highlighted a number of genes in these metabolic pathways and in the PPAR signalling pathway (Figure 6). We have identified one gene, MOGAT2 (ENSSSCG00000014861), with seven SNPs, of which four are present in Mangalicas, but not in other 56 sequenced pig individuals (see Results). The MOGAT2 protein catalyses the conversion of 1-acylglycerol obtained from dietary fat into diacylglycerol in the smooth endoplasmatic reticulum of the small intestinal epithelial cells, and thus participates in the production of chylomicron (“Fat digestion and absorption” pathway, KEGG:04975). Chylomicron affects the PPAR signalling pathway, which in turn regulates a number of lipid metabolic processes (Figure 6). It is possible, therefore, that polymorphisms that affect genes in this complex networks of pathways, which are also part of relevant QTLs, may be responsible for the differences in fattening, fat composition and any related phenotypes that were observed between breeds in response to different feeding regimes. For example, the MOGAT2 gene was found to be part of the lipid concentration biological function, modulated in backfat .
The discovery of genes behind agriculturally important traits is a difficult task in farm animals, in particular when the intermediate- or end-phenotypes are determined by QTLs. In this study, we described the genome sequencing and analysis of three Hungarian Mangalica individuals representing each of the three Mangalica breeds, which are local, fatty type pigs with a niche role in the pork market. After filtering, millions of SNPs were identified in each animal compared to the reference genome, and about 10% of them are novel compared to the porcine SNP entries of the dbSNP138 database. This finding highlights that sequencing genomes of individuals of rare/local breeds can provide large amounts of data identifying genomic variations relative to the reference genome of the same species. These variations can be the basis for gene discoveries. With special emphasis on pig fatness, by annotating and comparing exonic, non-synonymous Mangalica-specific SNPs to QTLs and pathways, we identified a number of candidate genes, which can serve for future genotyping, expression, structure-function, and biological network studies and in applications, such as molecular breeding and meat identification or tracing in both Mangalica and other breeds.
Pig blood samples were obtained from the MANGFOOD consortium’s Biobank at the Agricultural Biotechnology Center, Gödöllő, Hungary. Total DNA was extracted using the Duplicα® Prep Automatic Extraction System and the Duplicα® Blood DNA kit (EuroClone, Milan, Italy). DNA concentration was measured using the Quant-iT™ PicoGreen dsDNA® Assay (Life Technologies, Budapest, Hungary). Preparation of 500 bp fragment libraries and 2 × 100 bp Illumina paired-end genome sequencing was performed by Aros Applied Biotechnology (Aarhus, Denmark) as a custom service, using Illumina’s HiSeq2000 platform.
The Sus scrofa reference genome sequence 10.2 was indexed using the “bwtsw” algorithm option of BWA 0.5.9rc1  followed by mapping the short sequence reads to the indexed genome using the default settings and the paired-end method of the same software. The obtained BAM files were sorted and indexed for further analyses.
To detect small genetic variants (SNPs and INDELs), the SAMtools  and GATK (version: 2.3-9-ge5ebf34)  variant calling pipelines were employed. In SAMtools, base-calling was performed using the “mpileup” command and the “-E -D -S -u” parameters of SAMtools 0.1.18. The “view” command of BCFtools was used to call the variants using the “-bvcg” parameters. VCF files were then generated by the “vcfutils.pl” script using the “varFilter” option and SNPs and INDELs were extracted. Finally, SNPs, which had a Phred score higher than 30 (i.e. their base-calling accuracy is larger then 99.9%), and a high-quality read coverage of minimum three, were filtered using a custom script. INDELs were used in downstream analyses without filtering. For GATK, the dbSNP138 data were used as a training set. Other settings were used according to the GATK best practice online documentation. Results obtained by the two pipelines were compared using the BEDTools’  “intersectBed” module for SNPs and using our custom script for INDELS; only concordant variations were processed further.
Copy number variations (CNVs) were detected as described by Paudel and coworkers  using the mrCaNaVar (version 0.51) software . The window size was set to 1,000 bp. We selected windows where the copy number and the standard deviation were bigger than three and 0.7, respectively, for the three Mangalicas. After that step the regions were chained.
To determine novel variants in our sequence data, we compared the identified SNPs and INDELs with the dbSNP138 data using BEDTools  and annotated the detected genetic variants using ANNOVAR . Following the ANNOVAR analysis, non-synonymous exonic SNPs, which were present only in Mangalicas, were determined by BEDTools’ “multiIntersectBed” module. Genes carrying these variants were identified using a custom script. Comparison of SNPs in the lipid metabolism genes amongst genome sequenced animals (this study and literature 44) were also performed using the “multiIntersectBed” module of BEDTools.
Gene ontology analysis was performed by the web-based software PANTHER . For overrepresentation analyses, Biomart’s  enrichment analysis option with 0.05 cut off P-value was employed using the Sscrofa 10.2 reference genome as background. Random sets of genes was generated by a custom Python script. Fat-related pig QTLs and their positions were downloaded from the QTLdb (Release 19) database , and their extension was compared with the position of the SNPs of selected genes manually. Genes were annotated into pathways using the KEGG database.
Data from Ensembl were retrieved using BioMart ; Venn diagrams were generated using the software Venny ; clustering was performed using CIMminer  with Manhattan distance and complete linkage clustering settings.
To genotype the two Mangalica-specific SNPs in the FASN gene, High Resolution Melting (HRM) analysis was performed with a Rotor-Gene Q 5plex HRM Platform using a saturating dye (EvaGreen) technology (Qiagen, Hilden, Germany). PCR reactions were performed in 25 μl reaction volumes using 60 ng total DNA as template and the Type-it HRM PCR kit (Qiagen, Hilden, Germany), according to the instruction of the manufacturer. The primers for FASN SNP1 and SNP2 were FASN1_F: 5′ CGCGATCTCGTTGAGCAT 3′, FASN1_R: 5′ GTGCAGACCCTGCTGGAG 3′ and FASN2_F: 5′ GGATAGGCTTGAGATGCTCTT 3′, FASN2_R 5′ GTGGTGGTGGACAGGAATCT 3′, respectively. Reactions were carried out with an initial denaturation step at 95°C for 5 min, followed by 35 cycles of 95°C for 15 sec, 60°C for 30 sec and 72°C for 10 sec and then HRM curves were generated by acquiring florescence data between 80 and 91°C. Individuals with homozygous and heterozygous genotypes were assigned according to their HRM curve determined by the Rotor-Gene software and visual inspection.
Availability of supporting data
The data sets supporting the results of this article are included within the article and its additional files. Sequence data are deposited to the NCBI Sequence Read Archive under identifier SRP039012.
The authors are grateful to Drs László Hiripi and Andrew Griffiths for critical reading and Professor Michael McManus for editing the manuscript. The authors thank the Center of Clinical Genomics and Personalised Medicine at the University of Debrecen and The Hungarian National Information Infrastructure Development Institute for providing supercomputing facilities, supported by grant TÁMOP-4.2.2.C-11/1/KONV-2012-0010, for data analysis. Pig blood samples were collected by the MANGFOOD consortium and we are grateful for obtaining them for this study. This work was supported by the Hungarian National Development Agency grants TECH_08-A3/2-2008-0405 and BAROSS_KM07_KM_ESZK_07-2008-0033, and by the Hungarian Ministry of Agriculture.
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