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. 2020 Aug 18;10:13962. doi: 10.1038/s41598-020-70894-2

Identification of strong candidate genes for backfat and intramuscular fatty acid composition in three crosses based on the Iberian pig

Daniel Crespo-Piazuelo 1,2,, Lourdes Criado-Mesas 1, Manuel Revilla 1,2, Anna Castelló 1,2, José L Noguera 3, Ana I Fernández 4, Maria Ballester 5, Josep M Folch 1,2
PMCID: PMC7435270  PMID: 32811870

Abstract

Meat quality has an important genetic component and can be modified by the fatty acid (FA) composition and the amount of fat contained in adipose tissue and muscle. The present study aimed to find genomic regions associated with the FA composition in backfat and muscle (longissimus dorsi) in 439 pigs with three different genetic backgrounds but having the Iberian breed in common. Genome-wide association studies (GWAS) were performed between 38,424 single-nucleotide polymorphisms (SNPs) covering the pig genome and 60 phenotypic traits related to backfat and muscle FA composition. Nine significant associated regions were found in backfat on the Sus scrofa chromosomes (SSC): SSC1, SSC2, SSC4, SSC6, SSC8, SSC10, SSC12, and SSC16. For the intramuscular fat, six significant associated regions were identified on SSC4, SSC13, SSC14, and SSC17. A total of 52 candidate genes were proposed to explain the variation in backfat and muscle FA composition traits. GWAS were also reanalysed including SNPs on five candidate genes (ELOVL6, ELOVL7, FADS2, FASN, and SCD). Regions and molecular markers described in our study may be useful for meat quality selection of commercial pig breeds, although several polymorphisms were breed-specific, and further analysis would be needed to evaluate possible causal mutations.

Subject terms: Animal breeding, Genome-wide association studies, Genetic markers, Quantitative trait loci

Introduction

Meat quality depends on the consumer’s perception, which is subjected to the socio-demographic backgrounds of the consumer1, and is based on factors such as the nutritional value and the organoleptic properties of meat2. These factors can be modified by the fatty acid (FA) composition and the amount of fat that is contained in adipose tissue and muscle3. In addition, the consumer is becoming more concerned about the healthfulness of meat1. Certain saturated FAs (SFAs) raise the cholesterol and low-density lipoprotein (LDL) levels in blood, which increase the risk of suffering a cardiovascular disease4,5; whereas monounsaturated FAs (MUFAs) reduce plasma total LDL-cholesterol without affecting high-density lipoprotein (HDL) levels, which have an anti-atherogenic effect4,5. Polyunsaturated FAs (PUFAs), especially long-chain omega-3 fatty acids, also reduce LDL-cholesterol levels and the risk of cardiovascular disease6, but high amount of PUFAs in meat increase its susceptibility to oxidation, producing meat with undesirable sensory properties3. In contrast, a high concentration of MUFAs improve meat flavour7.

The Iberian pig is a breed characterized by its great meat quality, due to its high intramuscular fat (IMF) content with an increased proportion of MUFAs (mostly oleic acid) and a reduced quantity of PUFAs8,9. Conversely, other commercial breeds such as Pietrain and Landrace produce lean meat with a lower proportion of fat, although Pietrain carcasses exhibit a high ratio of intermuscular to subcutaneous fat10. Duroc pigs also exhibit a high intermuscular fat weight and develop a higher proportion of intramuscular fat than the commercial breeds10, which contain more PUFAs than the Iberian breed11. Altogether, the FA composition of adipose tissue and muscle in pigs show moderate to high heritability values7,9,12, revealing the importance of the genetic component in the variability of FA composition traits. Furthermore, FA composition is an expensive trait to measure and often require the slaughter of the animals. Polymorphisms associated with these traits can be used as genetic markers to evaluate the breeding value of an animal and increase the rate of genetic gain13.

Genetic selection in pigs has been intensifying thanks to high-density genotyping platforms, such as the PorcineSNP60 BeadChip (Illumina®)14 or the Axiom™ Porcine Genotyping Array (Affymetrix, Inc.)15. These chips allow the genotyping of markers distributed along the pig genome to perform Genome-Wide Association Studies (GWAS) for production traits. Through the use of GWAS, significant Quantitative Trait Loci (QTLs) in the pig genome have been identified for the FA composition in adipose tissue and muscle in several populations of crossed and purebred pigs such as Duroc, Landrace, Large White, and Erhualian1626. In these studies, strong candidate genes related with lipid metabolism have been found for the FA composition in adipose tissue and muscle: ELOVL fatty acid elongases 6 and 7 (ELOVL617,18,21,22,24,25 and ELOVL716,19,21,22), fatty acid synthase (FASN)2123, and stearoyl-CoA desaturase (SCD)1926.

In this context, our group generated different crosses among Iberian purebred pigs and commercial breeds (IBMAP population) to identify QTLs associated with the FA composition in adipose tissue and muscle. In previous works, pigs from an IBMAP experimental backcross ((Iberian × Landrace) × Landrace) were genotyped with the PorcineSNP60 BeadChip (Illumina®) array14 and GWAS were performed for FA composition traits in adipose tissue and muscle using the Sscrofa10.2 assembly16,18. In addition, GWAS have also been used in the IBMAP population to find QTLs associated with the expression of genes involved in lipid metabolism in adipose tissue and muscle27,28.

The present study aimed to identify genomic regions associated with FA composition of backfat and muscle in pigs with three different genetic backgrounds but having the Iberian breed in common using GWAS analyses.

Material and methods

The methods reported on the present study were developed in the doctoral thesis of Crespo-Piazuelo29.

Animal material

All animals used in the present work belong to three distinct pig backcrosses: (Iberian × Landrace) × Landrace (BC1_LD, n = 158), (Iberian × Duroc) × Duroc (BC1_DU, n = 143), and (Iberian × Pietrain) × Pietrain (BC1_PI, n = 138). Pigs were raised in an intensive system and fed ad libitum with a cereal-based commercial diet until slaughtered at 187.4 ± 10.1 days of age on NOVA GENÈTICA S. A. experimental farm (Lleida, Spain). Detailed information of generation schemes, diet, growth, and housing conditions of the three backcrosses is described in Martínez-Montes et al.30.

Samples of adipose tissue (backfat), diaphragm and longissimus dorsi muscle were collected at the commercial abattoir, snap-frozen in liquid nitrogen and stored at − 80 °C. A gas chromatography of methyl esters protocol31 was used to measure the FA profile (15 backfat FAs and 17 IMF FAs) of backfat samples taken between the third and the fourth ribs and 200 g of longissimus dorsi muscle. Then, the percentage of each individual FA methyl ester was calculated out of the total amount of FAs quantified. Total percentages of SFA, MUFA, and PUFA were obtained through the sum of the individual FAs that are included on their same section in Table 1. Metabolic and FA ratios were calculated from the ratio between individual FA percentages as it is shown in Table 1.

Table 1.

Descriptive statistics including mean and SD of intramuscular fat and backfat fatty acid (FA) composition and FA indices in the merged dataset of the three backcrosses (n = 439).

Group Trait Name Backfat Intramuscular fat
Mean SD Mean SD
SFA C14:0 Myristic acid 1.14 0.11 1.17 0.24
C16:0 Palmitic acid 23.06 1.48 22.99 1.51
C17:0 Margaric acid 0.37 0.10 0.27 0.10
C18:0 Stearic acid 14.76 1.79 14.21 1.44
C20:0 Arachidic acid 0.28 0.05 0.26 0.11
SFA Saturated fatty acids 39.60 2.86 38.90 2.44
MUFA C16:1(n-7) Palmitoleic acid 1.53 0.25 2.61 0.50
C16:1(n-9) 7-Hexadecenoic acid 0.38 0.10 0.36 0.11
C17:1 Heptadecenoic acid 0.25 0.06 0.23 0.10
C18:1(n-7) Vaccenic acid 1.00 0.67 3.91 0.33
C18:1(n-9) Oleic acid 39.39 2.67 37.08 5.78
C20:1(n-9) Gondoic acid 1.02 0.16 0.82 0.20
MUFA Monounsaturated fatty acids 43.57 2.39 44.78 6.25
PUFA C18:2(n-6) Linoleic acid 14.76 2.78 11.92 5.01
C18:3(n-3) α-Linolenic acid 0.77 0.13 0.50 0.23
C20:2(n-6) Eicosadienoic acid 0.43 0.28
C20:3(n-3) Eicosatrienoic acid 2.53 2.04
C20:3(n-6) Dihomo-γ-linolenic acid 0.76 0.12 0.51 0.14
C20:4(n-6) Arachidonic acid 0.16 0.05 0.22 0.13
PUFA Polyunsaturated fatty acids 15.69 2.93 15.88 7.26
Metabolic ratios MUFA/SFA Ratio of MUFA to SFA 1.11 0.11 1.15 0.16
PUFA/SFA Ratio of PUFA to SFA 0.40 0.10 0.42 0.22
MUFA/PUFA Ratio of MUFA to PUFA 2.88 0.56 3.47 1.60
FA ratios C16:1(n-7)/C16:0 0.07 0.01 0.11 0.02
C18:1(n-7)/C16:1(n-7) 0.67 0.45 1.55 0.32
C18:1(n-9)/C18:0 2.71 0.40 2.63 0.44
C18:2(n-6)/C18:3(n-3) 19.29 1.99 26.27 11.76
C20:1(n-9)/C20:0 3.72 0.46 3.42 1.09
C20:3(n-6)/C18:2(n-6) 0.05 0.01 0.03 0.01
C20:4(n-6)/C18:2(n-6) 0.01 0.00 0.19 0.08
C20:4(n-6)/C20:3(n-6) 0.20 0.06 5.62 2.16

In total, 60 traits were analysed in backfat and IMF: 32 traits for FA percentages and 28 traits for indices of FA metabolism, including FA ratios for the activities of desaturases and elongases (Table 1).

DNA extraction and single-nucleotide polymorphism (SNP) genotyping

DNA extraction was carried out with the phenol–chloroform method32 from the diaphragm of the 439 pigs. DNA concentration and purity were measured with a Nanodrop® Spectrophotometer (ND-1000).

Two distinct systems were used for genotyping. The PorcineSNP60 BeadChip (Illumina®) array14 was employed to genotype 64,232 SNPs in the BC1_LD and BC1_PI animals using the Infinium® HD Assay Ultra protocol (Illumina®) and results were visualized through the GenomeStudio software (2011.1 version, Illumina®). The Axiom™ Porcine Genotyping Array (Affymetrix, Inc.)15 was used for genotyping 658,692 SNPs in the BC1_DU pigs and genotypes were obtained and filtered with the Axiom™ Analysis Suite 2.0. For the GWAS analysis, we only considered the 45,845 SNPs that were found in common between both platforms and mapped in the Sscrofa11.1 assembly. The SNPs on chromosome Y were removed as well as those SNPs with a minor allele frequency (MAF) < 5% or/and with missing genotypes > 5% using the PLINK software33 (1.90b5 version). Finally, a total of 38,424 SNPs remained for further analysis.

Furthermore, 21 SNPs in positional candidate genes were genotyped in the 439 pigs using Taqman OpenArray™ genotyping plates custom-designed in a QuantStudio™ 12K flex Real-Time PCR System (ThermoFisher Scientific). Of these 21 SNPs, five were SNPs located on the fatty acid desaturase 2 (FADS2) gene, seven were ELOVL6-SNPs, one SNP was located on the FASN gene, three were ELOVL7-SNPs, and five were SNPs located on the SCD gene.

Genome-wide association studies (GWAS)

For the 439 pigs of the three backcrosses, GWAS were carried out between the 38,424 filtered SNPs and the FA composition and metabolic indices in backfat and IMF, described in Table 1. Thus, GEMMA software34 (0.96 version) was used to perform an univariate linear mixed model following this formula:

yijklm=Sexi+Batchj+Backcrossk+βcl+ul+λlam+eijklm,

where yijklm indicates the vector of phenotypic observations in the lth individual; sex (2 categories), batch based on slaughter day (14 categories) and backcross (3 categories) are fixed effects; β is a covariate coefficient with c being carcass weight; ul is the infinitesimal genetic effect considered as random and distributed as N(0, Kσu), where K is the numerator of the kinship matrix; λl is a − 1, 0, + 1 indicator variable depending on the lth individual genotype for the mth SNP; am represents the additive effect associated with the mth SNP; and eijklm is the residual effect. The kinship matrix was calculated with the genotypic information of the individuals using the centred relatedness matrix option of GEMMA34.

GWAS for candidate genes were performed following the previous linear mixed model between the 38,424 filtered SNPs, with the addition of the SNPs genotyped for each candidate gene, and the phenotypic traits that were associated with the region where the candidate gene was located. Five candidate gene-SNPs of the Sus scrofa chromosome (SSC) 2 region were included in the GWAS for the percentages of C16:1(n-9), C18:1(n-9) and C18:2(n-6), and the MUFA, PUFA, MUFA/PUFA and PUFA/SFA ratios in backfat. Seven candidate gene-SNPs of the SSC8 region were included in the GWAS for the percentages of C14:0, C16:0, and C16:1(n-7) in backfat. One candidate gene-SNP of the SSC12 region was included in the GWAS for the percentage of C14:0 in backfat. Three candidate gene-SNPs of the SSC16 region were included in the GWAS for the C20:1(n-9)/C20:0 ratio in backfat. Five candidate gene-SNPs of the SSC14 region were included in the GWAS for the C18:1(n-9)/C18:0 and MUFA/SFA ratios in IMF.

GWAS were also performed individually for each backcross following the previously described model without the backcross effect.

The false discovery rate (FDR) method of multiple testing described by Benjamini and Hochberg35 was used to measure the statistical significance for association studies at genome-wide level. The cut-off threshold for considering a SNP as significant was set at FDR ≤ 0.1. Quantile–quantile (Q–Q) plots and genomic inflation factors (λ) were obtained for all the GWAS with significant SNPs and are available as Supplementary Information S1.

The web based tool PhenoGram36 was used to visualize the differences obtained in the GWAS results between the merged dataset and each individual backcross.

Region analysis, gene annotation and consequence prediction

If the distance between two significant SNPs was less than 10 Mb, they were grouped inside the same QTL interval. Only QTL intervals with a minimum of two SNPs were considered for further analyses. Intervals for different traits were merged if they overlapped. In addition, QTLs were defined and annotated at 1 Mb on each side of the previously defined intervals.

The extraction of the genes contained in the QTLs was performed with the BioMart tool37 from the Ensembl project (www.ensembl.org; release 92) using the Sscrofa11.1 reference assembly. Furthermore, functional predictions of the significant SNPs were performed with the Variant Effect Predictor tool38 from the Ensembl project (release 92).

Ethics statement

All animal procedures were performed according to the Spanish Policy for Animal Protection RD1201/05, which meets the European Union Directive 86/609 about the protection of animals used in experimentation. The experimental protocol was approved by the Ethical Committee of IRTA (Institut de Recerca i Tecnologia Agroalimentàries).

Results and discussion

GWAS results

A GWAS was performed using a total of 38,424 SNPs and the 60 phenotypic traits related with backfat and IMF FA composition (percentages, indices, and ratios) in a total of 439 pigs from three different backgrounds (BC1_DU, BC1_LD, and BC1_PI). In backfat, 98 significant associated SNPs located in nine Sus scrofa chromosomal regions were significantly associated with 12 traits (FDR ≤ 0.1; Table 2). In IMF, 39 SNPs located in six genomic regions were significantly associated with six traits (FDR ≤ 0.1; Table 3). In addition, no QTLs for backfat and IMF were found in common, indicating that the regulatory mechanisms affecting the FA composition of each tissue may be different.

Table 2.

Description of the regions associated with the fatty acid composition in backfat and the candidate genes contained within them.

Region Chra Start (bp)b End (bp)b No.SNPsc Top SNP MAFd p-value FDR Trait Candidate genee
BF1 1 145,957,212 147,979,672 2 rs80899816 0.172 1.33 × 10−6 2.56 × 10−2 C16:1(n-9) GALR1
BF2 2 0 12,764,773 24 rs81306755 0.391 2.51 × 10−9 7.90 × 10−5 C16:1(n-9); C18:1(n-9); C18:2(n-6); MUFA; MUFA/PUFA*; PUFA; PUFA/SFA CPT1A; ESRRA; FADS1; FADS2; FADS3; IGF2; INS; OSBP; PLA2G16; PNPLA2; PRPF19; SIRT3; ssc-mir-192
BF3 4 80,446,371 82,565,259 4 rs80848071 0.415 5.47 × 10−6 1.40 × 10−2 MUFA/PUFA PRRX1; SLC19A2
BF4 6 15,339,713 27,053,724 3 rs81322046 0.203 3.56 × 10−7 1.37 × 10−2 C20:3(n-6) BBS2; COQ9; GOT2; SLC12A3
BF5 8 108,399,818 116,409,757 14 rs81403349 0.211 1.47 × 10−10 5.66 × 10−6 C14:0; C16:0*; C16:1(n-7) ELOVL6; HADH; PLA2G12A
BF6 10 29,495,950 31,756,975 3 rs81423282; rs81423288 0.256 1.43 × 10−5 6.03 × 10−2 C16:1(n-9) NTRK2; RMI1
BF7 10 51,874,658 53,920,353 2 rs80979357 0.095 7.51 × 10−6 4.81 × 10−2 C20:3(n-6) DNAJC1; PIP4K2A
BF8 12 0 1,910,198 12 rs81308244 0.237 1.93 × 10−10 7.41 × 10−6 C14:0 ASPSCR1; FASN; METRNL; NOTUM
BF9 16 29,669,240 48,628,216 34 rs81297480; rs81458871 0.127 2.66 × 10−8 5.12 × 10−4 C20:1(n-9)/C20:0 ELOVL7; GZMA; PIK3R1; PLPP1

aSus scrofa chromosome.

bPosition on the chromosome where the region begins (start) and finishes (end).

cNumber of significant SNPs found inside the region.

dMinor allele frequency.

eList of suggested genes for explaining part of the phenotypic variance of the trait.

*Indicates the most significant trait. If there is more than one associated trait on the region, the p-value and FDR of the top SNP refer to this trait.

Table 3.

Description of the regions associated with the fatty acid composition in intramuscular fat and the candidate genes contained within them.

Region Chra Start (bp)b End (bp)b No. SNPsc Top SNP MAFd p-value FDR Trait Candidate genee
LD1 4 19,019,566 21,057,452 2 rs80910044 0.177 2.42 × 10−6 8.92 × 10−2 C20:4(n-6)/C20:3(n-6) ENPP2; EXT1; NOV
LD2 4 122,756,546 124,979,309 3 rs81347340; rs80915252 0.437 1.18 × 10−5 7.65 × 10−2 C20:3(n-3) ABCD3; GCLM
LD3 13 175,539,436 181,652,057 3 rs81441592 0.066 1.81 × 10−6 6.96 × 10−2 C20:3(n-3) LIPI; NRIP1; ssc-let-7c
LD4 14 109,946,218 114,621,937 21 rs335655209 0.115 1.04 × 10−7 7.51 × 10−4 C18:1(n-9)/C18:0*; MUFA/SFA ELOVL3; SCD
LD5 14 140,151,934 141,755,446 6 rs318740977; rs80814938; rs80883500 0.083 3.58 × 10−6 3.27 × 10−2 C18:0 BNIP3; CYP2E1; ECHS1
LD6 17 30,061,857 32,867,849 4 rs324135473 0.148 2.65 × 10−6 9.16 × 10−2 C20:0 ABHD12; ACSS1; PANK2; ssc-mir-103–2

aSus scrofa chromosome.

bPosition on the chromosome where the region begins (start) and finishes (end).

cNumber of significant SNPs found inside the region.

dMinor allele frequency.

eList of suggested genes for explaining part of the phenotypic variance of the trait.

*Indicates the most significant trait. If there is more than one associated trait on the region, the p-value and FDR of the top SNP refer to this trait.

In the following sections, the candidate genes mapped in the genomic regions associated with the phenotypic traits of backfat and IMF are discussed in detail. The list of candidate genes is summarized in Table 2 for backfat traits and in Table 3 for IMF traits. All the significantly associated SNPs, and their predicted consequences, for the FA composition in backfat and in IMF are listed on the Supplementary Tables S1 and S2, respectively.

QTLs for fatty acid composition in backfat and candidate genes

BF1

The 145.96–147.98 Mb region of SSC1 was associated with the C16:1(n-9) content in backfat (Table 2). The rs80899816 SNP was the most significant (p-value = 1.33 × 10−6) of the two SNPs comprised in this region, but both were located inside an intergenic region. Only one candidate gene, galanin receptor 1 (GALR1), was found in this region. This gene is a member of the galanin receptor family, which bind the neuropeptide hormone galanin39. Galanin regulates a range of biological functions such as food intake, neurogenesis, memory, and gut secretion40. Remarkably, the galanin-mediated signalling cascade has been associated with an activation of adipogenesis in high-fat diet induced obese mice40,41. In zebrafish, GALR1 was up-regulated if animals were fed with a high fat fodder or with linoleic acid, participating in the accumulation of lipid droplets in cells42.

BF2

On SSC2, the 0–12.76 Mb region was associated with the abundance of three FAs in backfat (Table 2), C16:1(n-9), C18:1(n-9), and C18:2(n-6), and four metabolic ratios, MUFA, PUFA, MUFA/PUFA, and PUFA/SFA. In this QTL, 24 SNPs were found significantly associated with these seven traits and 13 genes were identified as candidates to explain the variation of those traits. The most significant SNP was rs81306755 (p-value = 2.51 × 10−9) for the MUFA/PUFA ratio. This significant SNP was located inside an intron of a novel gene (ENSSSCG00000014565) that was orthologous of the IFITM (interferon-induced transmembrane) protein family. However, fatty acid desaturases 1–3 (FADS1, FADS2, FADS3) are the most promising candidate genes found in this region to explain the variations in MUFA and PUFA content in backfat, specially FADS2. The essential FAs C18:2(n-6) and C18:3(n-3) are desaturated by FADS2, which can also desaturate C16:0 and C18:1(n-9)43,44. Other candidate gene, the ssc-mir-192 gene, codifies for the miR-192 microRNA which impairs adipocyte triglyceride storage and suppresses the production of another desaturase, SCD45. The downregulation of another candidate gene, pre-mRNA processing factor 19 (PRPF19), also represses the expression of SCD and lipid droplet biogenesis in adipocytes46. In addition, three candidate genes found in this region are related with the oxidation of different FAs, carnitine palmitoyltransferase 1A (CPT1A), estrogen related receptor alpha (ESRRA), and phospholipase A2 group XVI (PLA2G16). The CPT1A gene encodes for a protein that is involved in mitochondrial β-oxidation of long-chain FAs47. ESRRA is an important regulator of the acyl-CoA dehydrogenase medium chain (ACADM) gene48, whose enzyme catalyses the initial rate limiting step in β-oxidation step of C4–C16 FAs with an optimum at C6-C849. The PLA2G16 gene encodes a major regulator of lipolysis in adipose tissue and through the regulation of FA oxidation in adipocytes may change the FA profile50. Conversely, other candidate genes found in this region have lipogenic effects, oxysterol binding protein (OSBP), insulin (INS) and insulin like growth factor 2 (IGF2). The overexpression of OSBP increased hepatic lipogenesis through insulin signalling pathways51. Thus, INS promotes FA uptake into cells and stimulates the expression of FA synthetic proteins52. In pigs, IGF2 is responsible of 10–20% of the phenotypic variation in backfat thickness53. The last two candidate genes of this region were patatin like phospholipase domain containing 2 (PNPLA2) and sirtuin 3 (SIRT3). The enzyme encoded by PNPLA2 participates in the hydrolysis of stored triglycerides in adipose tissue54, whereas the overexpression of one isoform of the SIRT3 gene altered the FA composition in mouse skeletal muscle mitochondria including the MUFA/SFA ratio55. One or more candidate genes of SSC2 may be implicated in the genetic determination of the seven traits related with the FA composition in backfat. Therefore, further analysis of fine mapping would be needed to better elucidate the associations in this region.

BF3

The 80.45–82.57 Mb region of SSC4 was associated with the MUFA/PUFA ratio in backfat (Table 2). Located in an intergenic region, rs80848071 was the most significant SNP (p-value = 5.47 × 10−6) of this QTL. Two candidate genes were found inside this region: paired related homeobox 1 (PRRX1) and solute carrier family 19 member 2 (SLC19A2). PRRX1 is a transcription factor that negatively regulates adipogenesis in adipose tissue suppressing peroxisome proliferator activated receptor gamma (PPARG)56. Therefore, PPARG suppression may increase MUFAs and decrease PUFAs in adipocytes57. The protein encoded by the other candidate gene, SLC19A2, is a thiamine transporter which has been associated with human type 2 diabetes mellitus58. In addition, free FAs in plasma were reduced in rats that were fed with a thiamine deficient diet59.

BF4

The 15.34–27.05 Mb region of SSC6 was associated with the C20:3(n-6) abundance in backfat (Table 2). The rs81322046 SNP was the most significant (p-value = 3.56 × 10−7) and was located on an intergenic region. Four candidate genes were found in this region: glutamic-oxaloacetic transaminase 2 (GOT2), coenzyme 9 (COQ9), Bardet–Biedl syndrome 2 (BBS2), and solute carrier family 12 member 3 (SLC12A3). GOT2 is secreted from adipose tissue and is found in mitochondrion and cell surface facilitating uptake of long-chain free FAs60. In addition, GOT2 negatively regulates adipocyte differentiation61. COQ9 is also found in mitochondrion and acts as a lipid-binding protein playing an essential role for cellular respiration62. Finally, BBS2 belongs to a family of genes that are involved in obesity63, while mutations in SLC12A3 affect human serum level of low-density lipoprotein cholesterol64.

BF5

The 108.40–116.41 Mb region of SSC8 was associated with the FA content of C14:0, C16:0, and C16:1(n-7) in backfat (Fig. 1 and Table 2). Of the 14 significant SNPs found in this region, the most significant (p-value = 1.47 × 10−10) for the abundance of C16:0 in backfat was the rs81403349 variant, which was located on an intron of the ankyrin 2 (ANK2) gene. In this region, the ELOVL6 gene was mapped, a promising candidate gene involved in the elongation of even C12-C16 SFAs and MUFAs65. In the BC1_LD animals, we have already reported a polymorphism in the promoter region of the ELOVL6 gene (ELOVL6:c.-533C>T), which was strongly associated with the content of C16:0 and C16:1(n-7) in backfat and IMF17. Later on, the ELOVL6:c.-394G>A polymorphism was suggested as the causal mutation for the QTL on SSC8 that affects FA composition66. Other candidate genes found in this region were phospholipase A2 group XIIA (PLA2G12A) and hydroxyacyl-CoA dehydrogenase (HADH). PLA2G12A liberates C20:4(n-6) from phospholipids67, whereas HADH catalyses the oxidation of medium- and short-chain 3-hydroxy FAs68.

Figure 1.

Figure 1

Manhattan plot representing the association analysis between the percentages of: C14:0 (A), C16:0 (B) and C16:1(n-7) (C) in backfat and SNPs distributed along the pig genome, including seven new genotyped polymorphisms for SSC8. The ELOVL6:c.-394G>A polymorphisms are included and labelled with a black circle. Red and blue lines indicate those SNPs that are below the genome-wide significance threshold (FDR ≤ 0.05 and FDR ≤ 0.1, respectively).

BF6

The 29.50–31.76 Mb region of SSC10 associated with the C16:1(n-9) abundance in backfat comprised three SNPs located in intergenic regions, being rs81423282 and rs81423288 (both p-values = 1.43 × 10−5) the most significant SNPs (Table 2). The two candidate genes found in this region, neurotrophic receptor tyrosine kinase 2 (NTRK2) and RecQ mediated genome instability 1 (RMI1), are involved in obesity69,70. However, further studies are required to discover how NTRK2 or RMI1 may be modifying the abundance in adipose tissue of a minor FA such as C16:1(n-9).

BF7

Another region (51.87–53.92 Mb) on SSC10 was associated with the abundance of C20:3(n-6) in backfat (Table 2). The most significant SNP of this QTL was rs80979357 (p-value = 7.51 × 10−6), which is located on an intron of the DNAJC1 (DnaJ heat shock protein family (Hsp 40) member C1) gene. DNAJC1 is involved in FA synthesis71 and has been found inside one genome-wide significant locus for subcutaneous adipose tissue in women72. On the other hand, another candidate gene located in this region was the PIP4K2A (phosphatidylinositol-5-phosphate 4-kinase type 2 alpha) gene. Liver of PIP4K2A-deficient mice were enriched in lipid droplets during fasting because autophagosomes failed to fuse with lysosomes at the rate needed73. Therefore, the impaired autophagy for recycling metabolites such as FAs may lead to cellular accumulation of C20:3(n-6) and other FAs in lipid droplets.

BF8

The 0–1.91 Mb region located at the beginning of SSC12 was associated with the C14:0 content in backfat (Table 2). Twelve SNPs were contained in this associated region and the most significant SNP was rs81308244 (p-value = 1.93 × 10−10), which was located in an intergenic region. Four candidate genes were found in this region, where FASN was the most promising. FASN produces predominantly C16:0 and, to a lesser extent, C14:074. It is necessary to mention that along with FASN, the ELOVL6 gene on SSC8 was also associated with the abundance of C14:0 in backfat (Fig. 1A), but FASN was not associated as ELOVL6 with the percentages of C16:0 and C16:1(n-7) (Fig. 1B,C). In addition, FASN and ELOVL6 showed a higher expression in the adipose tissue of BC1_LD pigs with low PUFA content75. Although FASN is the most promising candidate gene associated with the variation of C14:0 content, it is necessary to mention that C14:0 is mainly taken from the diet76 and it can be synthetized through other different pathways including C16:0 shortening77. In this sense, the proteins encoded by other candidate genes of this region may be affecting the C14:0 content in backfat. NOTUM (notum, palmitoleoyl-protein carboxylesterase) can bind to C14:1(n-5) and C16:1(n-7)78. In adipocytes, ASPSCR1 (ASPSCR1, UBX domain containing tether for SLC2A4) sequesters SLC2A4 (solute carrier family 2 member 4), also known as GLUT4, controlling glucose uptake79, while METRNL (meteorin like, glial cell differentiation regulator) promotes lipid metabolism and insulin sensitization80.

BF9

The 29.67–48.63 Mb region of SSC16 was associated with the differences in the C20:1(n-9)/C20:0 ratio in backfat (Fig. 2 and Table 2). A total of 34 SNPs associated with the C20:1(n-9)/C20:0 ratio in backfat were found in this region. The two most significant SNPs, rs81297480 and rs81458871 (both p-values = 2.66 × 10−8), were intergenic variants. Four candidate genes were found in this region, ELOVL7, GZMA (granzyme A), PIK3R1, and PLPP1 (phospholipid phosphatase 1). The ELOVL7 gene is a strong candidate gene to explain the variation in C20:0 and C20:1(n-9) due to its protein function, which elongates C16-C20 FAs, with a preference toward C18 FAs81. A higher expression of GZMA was observed in the mesenteric adipose tissue of beef cattle with low gain when compared with high gain animals82. The PIK3R1 gene regulates glucose import83 and in rat ovaries, the PIK3R1/AKT pathway has been involved in stearoyl-CoA desaturase 2 (SCD2) expression84. The protein encoded by PLPP1 converts lipids such as phosphatidic acid and LPA to diacylglycerols85.

Figure 2.

Figure 2

Manhattan plot representing the association analysis between the C20:1(n-9)/C20:0 ratio in backfat and SNPs distributed along the pig genome, including three new genotyped polymorphisms for SSC16. The ELOVL7:c.*1432A>G polymorphism is included and labelled with a black circle. Red and blue lines indicate those SNPs that are below the genome-wide significance threshold (FDR ≤ 0.05 and FDR ≤ 0.1, respectively).

QTLs for fatty acid composition in intramuscular fat and candidate genes

LD1

Two regions on SSC4 were associated with the FA composition of IMF. The first region (19.02–21.06 Mb) was associated with the C20:4(n-6)/C20:3(n-6) ratio (Table 3). The rs80910044 variant, located inside an intergenic region, was the most significant SNP (p-value = 2.42 × 10−6) of this QTL. Inside this region the ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene was identified, also known as autotaxin (ATX). The protein encoded by ENPP2 converts lysophosphatidylcholines to lysophosphatidic acids (LPAs) formed by different FAs such as C18:1(n-9) or C20:4(n-6)86. Furthermore, LPA is a signalling lipid involved in growth-factor like responses87 and also participates in FA esterification being a precursor of the triglycerides that are deposited in IMF88. Other candidate genes in the SSC4 region that could be modulating the C20:4(n-6)/C20:3(n-6) ratio were exostosin glycosyltransferase 1 (EXT1) and nephroblastoma overexpressed (NOV). The mutation of hepatic EXT1 increased the levels of plasma triglycerides in mice89, whereas NOV was involved in the development of obesity90 and the suppression of myogenesis91.

LD2

The second region (122.76–124.98 Mb) on SSC4 was associated with the C20:3(n-3) content in IMF (Table 3). The two most significant SNPs (both p-values = 1.18 × 10−5) of this QTL were rs81347340 and rs80915252. While the rs81347340 variant was located in an intergenic region, the rs80915252 variant was a splice region variant and a synonymous variant of the coiled-coil domain containing 18 (CCDC18) gene. Two candidate genes were found in this region: ATP binding cassette subfamily D member 3 (ABCD3) and glutamate-cysteine ligase modifier subunit (GCLM). ABCD3 encodes for a protein that imports free FAs into peroxisomes where substrates are chain-shortened by β-oxidation92. In particular, ABCD3 has been reported to preferentially transport hydrophilic unsaturated FAs such as C20:3(n-3)92. The other candidate gene, GCLM, plays a role in the metabolism of dietary lipids and mice with GCLM-deficiency were protected from weight gain and adipose deposition93.

LD3

Three candidate genes were located in the 175.54–181.65 Mb region of SSC13 associated with the abundance of C20:3(n-3) in IMF (Table 3). The most significant variant found inside this region was the rs81441592 intergenic variant (p-value = 1.81 × 10−6). The lipase I (LIPI) gene encodes for a phospholipase that breaks down phosphatidic acid into LPA. In this sense, LIPI may have a similar role as the aforementioned ENPP2 in LPA production which would affect the FAs that are deposited in IMF88. The nuclear receptor interacting protein 1 (NRIP1) gene is involved in fat accumulation94 and lipolysis95. The last candidate gene (ssc-let-7c) is transcribed into a microRNA, miR-let-7c, that regulates muscle growth in pigs, whereas other members of its family (miR-let-7a and miR-let-7e) regulate lipid deposition96.

LD4

The first region (109.95–114.62 Mb) found on SSC14 was associated with two metabolic ratios in IMF, C18:1(n-9)/C18:0 and MUFA/SFA (Table 3). This QTL comprised a total of 21 significant associated SNPs, being the rs335655209 variant the most significant SNP (p-value = 1.04 × 10−7) for the C18:1(n-9)/C18:0 ratio as well as one of the most significant SNPs (p-value = 1.11 × 10−6) for the MUFA/SFA ratio. The rs335655209 variant was located in an intron of the BTRC (beta-transducin repeat containing E3 ubiquitin protein ligase) gene. These two desaturation ratios may be modulated by the SCD gene found in this region. SCD participates in the biosynthesis of C18:1(n-9) by desaturating C18:097. Polymorphisms in SCD have been related with differences in FA composition and desaturation ratios in swine backfat and IMF98,99. However, the ELOVL3 (ELOVL fatty acid elongase 3) gene is also inside this region and may affect FA composition through the synthesis of C20-C24 SFAs and MUFAs100. Therefore, SCD and ELOVL3 are strong candidate genes to modulate the FA composition in muscle.

LD5

The second region (140.15–141.76 Mb) found on SSC14 was associated with the C18:0 abundance in IMF (Table 3). This region was comprised of six significant SNPs and three of them (rs318740977, rs80814938 and rs80883500) were the most significant variants (p-value = 3.58 × 10−6) for this QTL. The rs318740977 and rs80814938 SNPs were intronic variants of the KNDC1 (kinase non-catalytic C-lobe domain containing 1) gene, whereas the rs80883500 SNP was located inside an intron of the CALY (calcyon neuron specific vesicular protein) gene. Three candidate genes were found inside this region: enoyl-CoA hydratase, short chain 1 (ECHS1); cytochrome P450 family 2 subfamily E member 1 (CYP2E1), and BCL2 interacting protein 3 (BNIP3). ECHS1 is involved in mitochondrial FA β-oxidation, but its activity is linked to short-chain FAs101. In the same manner, CYP2E1 has preference for short SFAs and long unsaturated FAs, showing no C18:0 hydroxylase activity102. In addition, CYP2E1 activity was inhibited by PUFAs but no by C16:0 and C18:0103. Therefore, mutations in ECHS1 or CYP2E1 may increase the C18:0 abundance in IMF through the modification of short SFAs metabolism. On the other hand, BNIP3 may be responsible of the differences in C18:0 as well. The BNIP3 gene is a mitophagy regulator that, when silenced, suppressed FASN-mediated free FA synthesis104.

LD6

The 30.06–32.87 Mb region of SSC17 was associated with the C20:0 content in IMF (Table 3). The most significant SNP of this region was rs324135473 (p-value = 2.65 × 10−6), located in an intergenic region. The acyl-CoA synthase short chain family member 1 (ACSS1) gene is located within this region. ACSS1 was differentially expressed in bulls with extreme FA composition in muscle105. Therefore, in our material, ACSS1 may be increasing the SFA amount through the transformation of acetyl-CoA into FAs. Located in the SSC17 region was another relevant gene, pantothenate kinase (PANK2). Humans with mutations in PANK2 present lower levels of some FAs compared to controls106. Furthermore, inside an intron of PANK2, it is located the ssc-mir-103-2 gene, which is transcribed into the miR-103-2 microRNA. In adipocytes, miR-103 accelerates adipogenesis and increases the expression of lipid metabolism related genes such as fatty acid binding protein 4 (FABP4) and adiponectin, C1Q and collagen domain containing (ADIPOQ)107. The last candidate gene found in this SSC17 region that may be modulating the C20:0 content was abhydrolase domain containing 12 (ABHD12). ABHD12 has monoacylglycerol lipase activity and preferentially hydrolyses 2-arachidonoylglycerol, which is an ester of C20:4(n-6) and glycerol108.

Comparison between merged dataset GWAS and backcross-specific GWAS

Backcross-specific GWAS for the FA composition in backfat found 18 associated regions in BC1_LD, seven in BC1_PI, and five in BC1_DU (Fig. 3 and Supplementary Table S3), whereas the backcross-specific regions found for the FA composition in IMF were 35 in BC1_PI, 33 in BC1_LD, and 24 in BC1_DU (Fig. 4 and Supplementary Table S3). Hence, more significant associated regions were found for the FA profile in IMF than in backfat. In backfat, all the regions for FA composition found in the merged dataset overlapped with at least one backcross-specific region. There was overlapping on the first region of SSC2 (0–10.77 Mb) between BC1_LD and BC1_PI for C18:2(n-6), PUFA and MUFA/PUFA in backfat. In addition, two SSC6 regions were shared between BC1_PI and BC1_LD (12.14–18.79 Mb) and between BC1_PI and BC1_DU (25.05–27.96 Mb) for different FA composition traits in backfat (C16:0, C20:0 and C18:2(n-6)/C18:3(n-3) on the first SSC6 region, and C14:0 and SFA on the second one). Similarly, all the intramuscular FA composition regions found in the merged dataset overlapped with at least one backcross-specific region, except for the last region of SSC14 (140.15–141.76 Mb). Two regions associated with the intramuscular FA composition were overlapping in the three backcrosses, although the number of associated traits varied among backcrosses: the 85.56–101.25 Mb region on SSC2 (MUFA/SFA on BC1_PI; C20:3(n-6), C20:4(n-6), PUFA, and PUFA/SFA on BC1_DU, and C20:3(n-3) on BC1_LD), and the 10.50–45.32 Mb region on SSC4 (C18:1(n-9), C18:2(n-6), MUFA, PUFA, MUFA/SFA and PUFA/SFA on BC1_PI; C18:1(n-7)/C16:1(n-7) on BC1_DU, and C18:1(n-9), C18:2(n-6), MUFA, and MUFA/PUFA on BC1_LD). Furthermore, a total of eight regions associated with the intramuscular FA composition were overlapping between two backcrosses, on SSC2 (136.67–149.91 Mb between BC1_LD and BC1_PI), on SSC4 (54.28–95.60 Mb between BC1_DU and BC1_LD), on SSC9 (5.39–14.20 Mb between BC1_LD and BC1_PI), a second region on SSC9 (124.17–130.17 Mb between BC1_DU and BC1_PI), on SSC11 (46.32–58.27 Mb between BC1_DU and BC1_LD), a second region on SSC11 (61.64–76.84 Mb between BC1_DU and BC1_PI), on SSC13 (174.35–191.27 Mb between BC1_DU and BC1_PI), and on SSC17 (44.22–58.14 Mb between BC1_LD and BC1_PI). In summary, using different genotypic backgrounds reduced the number of significant associated regions increasing the relevance and robustness of the detected ones. However, most of the associated regions found in the merged dataset were driven by one backcross and then, mixing backcrosses resulted in the loss of loci associated to a specific backcross.

Figure 3.

Figure 3

Comparison between the associated regions along pig chromosomes for backfat FA composition in the merged dataset and in each backcross individually. The shape indicates the backcross or the merged dataset and the colour indicates the phenotypic trait as it is indicated in the legend.

Figure 4.

Figure 4

Comparison between the associated regions along pig chromosomes for intramuscular FA composition in the merged dataset and in each backcross individually. The shape indicates the backcross or the merged dataset and the colour indicates the phenotypic trait as it is indicated in the legend.

Comparison with other studies

Certain regions of the pig genome have been commonly found in association with FA composition traits in adipose tissue and muscle, where strong candidate genes are located. In accordance with previous studies, the region on SSC8 was associated with the abundance of C16:0, C16:1(n-7), and C18:1(n-9) in adipose tissue and muscle, reporting ELOVL6 as candidate gene21,22,24,25. On SSC16, the ELOVL7 gene was also proposed as a candidate gene to explain the GWAS signals associated with the C20:0 abundance and several metabolic indices, such as C20:1(n-9)/C20:0, in the abdominal fat and IMF of different pig populations19,21,22. A similar QTL signal was detected on SSC12, where the FASN gene was located, on GWAS performed for the abundance of C14:0 in adipose tissue and muscle in Erhualian and Duroc pigs21,23. The signal found on SSC14 for the percentages of C16:0, C16:1(n-7), C18:0, C18:1(n-9), SFAs, and MUFAs was also found on GWAS performed in different pig populations for FA composition in adipose tissue and IMF, which reported SCD and ELOVL3 as candidate genes1926. On a previous study in the BC1_LD animals, the FADS genes were proposed as potential candidate genes for the SSC2 QTL associated with the percentages of C16:1(n-9), C18:2(n-6), C18:3(n-3), and PUFAS in backfat109. In other studies, FADS2 was also proposed as a strong candidate gene to explain the variation of two metabolic indices (C20:3(n-6)/C18:2(n-6) and C20:4(n-6)/C20:3(n-6)) in the IMF of Erhualian pigs22, and the C20:4 to C18:2 ratio in the muscle of Duroc pigs110.

In a previous study with the BC1_LD pigs16, oleic and linoleic acid content and PUFA and PUFA/SFA QTLs were found for the 145.96–147.98 Mb region on SSC1, although this region was only associated with 7-hexadecenoic acid content in the merged dataset of the three backcrosses. The 80.45–82.57 Mb region on SSC4 has been associated in several studies with oleic and linoleic acid content111113. In the present study, this SSC4 region was associated with the MUFA/PUFA ratio, being the PRRX1 gene a clear candidate of this region due to its role in adipogenesis56. In our study, two regions were associated with dihomo-γ-linolenic acid content, although the 15.34–27.05 Mb region on SSC6 was previously associated with palmitic and eicosadienoic acid content19,114, and the 51.874–53.92 Mb region on SSC10 was previously associated with linoleic acid content114.

The 19.02–21.06 region on SSC4 was associated with C20:4(n-6)/C20:3(n-6) content in muscle, despite it has been described QTLs for linoleic acid content in the BC1_LD pigs and other population18,114. On SSC4, there was found another region (122.76–124.98 Mb) associated with eicosatrienoic acid content. However, this region has also been associated with the content of different FAs (palmitic, margaric and oleic acid)19,114,115. A strong candidate gene of this region was the ABCD3 gene, which acts as a transporter of FAs92. The 30.06–32.87 Mb region on SSC17 was previously reported in association with the margaric acid content23. Conversely, this same region was associated with the arachidic acid content in our study.

Regions 29.50–31.76 Mb on SSC10 in backfat, and 175.54–181.65 Mb on SSC13 and 140.15–141.76 Mb on SSC14 in muscle, were not found previously in association with any fat composition trait116.

GWAS for candidate genes

As some of the QTL signals found in our study were previously reported in other GWAS for FA composition in adipose tissue and muscle in several swine populations1626, we decided to genotype polymorphisms in the most recurrent candidate genes reported in these studies: FADS2, FASN, ELOVL6, ELOVL7 and SCD. A total of 21 SNPs were genotyped among these positional candidate genes. For each QTL signal, GWAS were performed again adding the polymorphisms genotyped on each candidate gene to the 38,424 SNPs already used. The results of these analyses for these five candidate genes are discussed in detail in this section.

FADS2

GWAS for the abundance of three FAs in backfat, C16:1(n-9), C18:1(n-9), and C18:2(n-6), and four metabolic ratios, MUFA, PUFA, MUFA/PUFA, and PUFA/SFA were reanalysed including five SNPs of the FADS2 gene. These SNPs were found in the genome sequences of the BC1_LD founders as described in Revilla et al.117. Two of the SNPs were located in the sixth exon (rs326796107 (FADS2:c.789T>G)) and the eighth exon (rs335011456 (FADS2:c.900C>T)) of the FADS gene, while the other three were upstream region variants (rs331050552, rs336076510 and rs344625804). These three upstream variants were also detected on a Duroc population110, where rs331050552 and rs336076510 were described to be fully linked with the rs321384923 variant, showing a strong association with the C18:2/C20:4 ratio in muscle. However, no association was found between the five FADS2-SNPs and the seven traits in the GWAS performed on the merged dataset of our population. Conversely, on the GWAS conducted in the BC1_LD backcross, FADS2:c.789T>G was significantly associated with the content of C18:2(n-6) (p-value = 5.81 × 10−6) and the PUFA (p-value = 7.30 × 10−6) and MUFA/PUFA (p-value = 1.50 × 10−6) ratios in backfat, whereas it was not the strongest association found on the SSC2 QTL of BC1_LD. The rs81355859 variant was the most significantly associated SNP for the content of C18:2(n-6) (p-value = 3.01 × 10−7), and the PUFA (p-value = 4.96 × 10−7) and MUFA/PUFA (p-value = 9.55 × 10−7) ratios in the backfat of the BC1_LD animals. Nonetheless, despite the overlap between the significantly associated regions of BC1_PI (0–10.77 Mb) and BC1_LD (2.06–10.55 Mb) on SSC2 (Fig. 3 and Supplementary Table S3), no association was found on the BC1_PI-specific GWAS between the FADS2-SNPs and the three traits (C18:2(n-6), PUFA and MUFA/PUFA) that the two SSC2 QTLs of both backcrosses have in common. Altogether, it is unlikely that any of the five genotyped SNPs of the FADS2 gene are the causal mutation for explaining the SSC2 QTL in our population. Thus, further research is needed to analyse other variants or even variants in other candidate genes that could be the causal mutation of this QTL.

ELOVL6

GWAS for the percentages of C14:0 (Fig. 1A), C16:0 (Fig. 1B), and C16:1(n-7) (Fig. 1C) in backfat were reanalysed including seven SNPs of the ELOVL6 gene. The seven SNPs located in ELOVL6 have previously been described in the BC1_LD animals17,66, four of them were upstream variants located in the proximal promoter region of ELOVL6 (rs325491325, rs345025813 (ELOVL6:c.-533C>T), rs341847499 and rs322956047 (ELOVL6:c.-394G>A)), one was located on the fourth exon (ELOVL6:c.416C>T), and two were located in the fifth exon (3′-UTR) (ELOVL6:c.1408C>T and ELOVL6:c.1922C>T). In accordance with previous works in the BC1_LD pigs66, ELOVL6:c.-394G>A was the most associated polymorphism with the percentages of C16:0 (p-value = 1.19 × 10−12) and C16:1(n-7) (p-value = 2.02 × 10−7) in backfat (Supplementary Table S4). As stated before, previous studies in our group provided genetic and functional evidence that supported the ELOVL6:c.-394G>A polymorphism as the causal mutation for the QTL on SSC8 affecting the percentages of C16:0 and C16:1(n-7) in backfat and IMF of BC1_LD animals17,66.

In addition, in our study ELOVL6:c.-394G>A SNP was also the most associated polymorphism for the abundance of C14:0 (p-value = 4.55 × 10−8) in backfat. Apart from the ELOVL6:c.-394G>A polymorphism, other three ELOVL6-SNPs (ELOVL6:c.-533C>T, ELOVL6:c.416C>T and ELOVL6:c.1922C>T) were also significantly associated with the percentages of C14:0, C16:0, and C16:1(n-7) in backfat (Supplementary Table S4). The ELOVL6:c.-394G>A was not segregating in the BC1_DU and BC1_PI backcrosses (Supplementary Table S5) and no region was found associated on SSC8 for the BC1_DU and BC1_PI pigs in the backcross-specific GWAS (Supplementary Table S3). The p-values obtained for the ELOVL6:c.-394G>A polymorphism in the backcross-specific GWAS performed in the BC1_LD were closer to the p-values obtained in the merged dataset GWAS for the percentages of C14:0 (p-value = 1.42 × 10−8), C16:0 (p-value = 5.12 × 10−15), and C16:1(n-7) (p-value = 1.56 × 10−8) in backfat. Overall, our results support the ELOVL6:c.-394G>A polymorphism as the causal mutation of SSC8 QTL for C14:0, C16:0, and C16:1(n-7) percentages in backfat in BC1_LD animals. The A allele of the ELOVL6:c.-394G>A SNP in the BC1_LD animals was probably maternally inherited from the Landrace sows. In the BC1_LD, the G allele is the predominant allele with a frequency of 0.64 and it is associated with a lower expression of the ELOVL6 gene in comparison to the A allele66. Hence, an impairment or reduction on the ELOVL6 function produces the accumulation of C16:0 and C16:1(n-7) in muscle and adipose tissue17,118, which can modify meat quality.

FASN

GWAS for the percentage of C14:0 in backfat was reanalysed including one SNP from the upstream region of the FASN gene (rs327036596), which was found in the genome sequences of the BC1_LD founders117. However, rs327036596 was not associated with the percentage of C14:0 in backfat in the merged dataset neither in the backcross-specific GWAS. Furthermore, rs327036596 was not segregating in the BC1_DU animals (Supplementary Table S5), although a genomic region (0–7.86 Mb) of SSC12 was found in association for the same trait (C14:0 in backfat) in the BC1_DU backcross (Supplementary Table S3). Therefore, other variants of FASN could be the causal mutation of this QTL. Such potential candidate may be rs324640280, which has been found in association with the C14:0 content in backfat as well as with other FAs119.

ELOVL7

GWAS for the C20:1(n-9)/C20:0 ratio in backfat was reanalysed including three SNPs of the ELOVL7 gene (Fig. 2) found in the genome sequences of the BC1_LD founders117. One of the three SNPs was an upstream variant (rs322657523) and the other two SNPs were located in the 5′-UTR (rs343494956 (ELOVL7:c.-46A>G)) and the 3′-UTR (tenth exon) (rs325490947 (ELOVL7:c.*1432A>G)). The SNP located in the tenth exon of ELOVL7 (ELOVL7:c.*1432A>G) was the only significantly associated ELOVL7-SNP (p-value = 1.48 × 10−7) (Supplementary Table S4), but other two SNPs showed a lower p-value, rs81297480 and rs81458871 (both p-values = 2.66 × 10−8). Therefore, the ELOVL7:c.*1432A>G SNP is unlikely to be the causal mutation of the SSC16 QTL for the variation of the C20:1(n-9)/C20:0 ratio in backfat. In addition, ELOVL7:c.*1432A>G was not segregating in the BC1_DU pigs and no GG individuals were observed in the BC1_PI pigs (Supplementary Table S5). The higher significance (p-value = 1.48 × 10−7) obtained in the merged dataset GWAS for the ELOVL7:c.*1432A>G SNP than the significance (p-value = 3.16 × 10−5) obtained in the BC1_LD-specific GWAS was probably due to the inclusion of the heterozygous BC1_PI individuals in the analysis of the merged dataset. Nonetheless, these findings suggest that the ELOVL7 gene is a clear candidate to explain the differences in the C20:1(n-9)/C20:0 ratio and further studies will be required to find the causal mutation.

SCD

GWAS for the C18:1(n-9)/C18:0 and MUFA/SFA ratios in IMF were reanalysed including five SNPs of SSC14 of the SCD gene. Of the five SNPs, rs323081995 (SCD:c.-353T>C) and rs80912566 (SCD:c.-233C>T) were described by Estany et al.120 and located in the 5′-UTR, while the other three variants (rs338494000, rs710198292 and rs331969256) located in the 3′-UTR of SCD were found in the BC1_LD founders117. On the GWAS conducted in the merged dataset, no significant signal was found for any of the five SCD-variants. Nonetheless, on the BC1_DU-specific GWAS, two SCD-SNPs (SCD:c.-353T>C and SCD:c.-233C>T) were found significantly associated with the C18:1(n-9)/C18:0 (p-values = 2.18 × 10−6 and 1.52 × 10−5, respectively) and MUFA/SFA (p-values = 1.80 × 10−5 and 5.54 × 10−5, respectively) ratios in IMF. Furthermore, in accordance with the literature120, SCD:c.-233C>T was proposed as the causal mutation for explaining part of the genetic variance for the desaturation ratios (18:1/18:0 and MUFA/SFA) in muscle. However, the most significant SNPs of the SSC14 region in BC1_DU were rs335655209 (p-value = 2.39 × 10−7) for the C18:1(n-9)/C18:0 ratio and rs80948585 (p-value = 8.71 × 10−7) for the MUFA/SFA ratio. No region was found associated on SSC14 for the BC1_LD and BC1_PI backcrosses (Supplementary Table S3). This result suggests that the causal mutation of SSC14 QTL for the C18:1(n-9)/C18:0 and MUFA/SFA ratios in IMF may be other uncharacterized genetic variants of SCD, which is in accordance with another GWAS study performed for backfat FA composition in a Duroc population24, and further studies are warranted to identify them.

Conclusions

Our results increase the knowledge of the genetic basis of FA composition and lipid metabolism. We have described fifteen regions of the pig genome that are associated with fat composition traits in adipose tissue and muscle in three different backcrosses with the Iberian breed in common. Despite some regions and candidate genes have been reported in accordance with previous association studies, we have identified new regions and candidate genes that had not been described yet. In total, 52 candidate genes were proposed. This list of candidate genes might be useful for selection of specific FA composition traits in meat of commercial pig breeds. In addition, we genotyped and analysed 21 polymorphisms of candidate genes FADS2, FASN, ELOVL6, ELOVL7 and SCD. These polymorphisms can be used as genetic markers for meat quality selection of commercial pig breeds, although some polymorphisms were breed-specific, and further analyses are warranted to find and evaluate possible causal mutations.

Supplementary information

Acknowledgements

This work was supported by the Spanish Ministerio de Economía y Competitividad (MINECO) and the Fondo Europeo de Desarrollo Regional (FEDER) with Project References: AGL2014-56369-C2 and AGL2017-82641-R. D. Crespo-Piazuelo was funded by a “Formació i Contractació de Personal Investigador Novell” (FI-DGR) Ph.D grant from the Generalitat de Catalunya (ECO/1788/2014). L. Criado-Mesas was funded with a FPI grant from the AGL2014-56369-C2 project. M. Revilla was also funded by a FI-DGR (ECO/1639/2013). M. Ballester was financially supported by a “Ramón y Cajal” Contract (RYC-2013-12573) from the Spanish Ministerio de Economía y Competitividad. We acknowledge the support of the Spanish Ministerio de Economía y Competitividad for the “Severo Ochoa Programme for Centres of Excellence in R&D” 2016–2019 (SEV-2015-0533) to the Centre for Research in Agricultural Genomics and the CERCA Programme / Generalitat de Catalunya. This work was done under the Ph.D. thesis of D. Crespo-Piazuelo co-supervised by M. Ballester and J.M. Folch.

Author contributions

J.M.F. and A.I.F. conceived and designed the experiments; J.M.F. was the principal investigator of the project; J.L.N. led and supervised the generation of the material animal used in this work; J.L.N., A.I.F. and J.M.F. collected the animal samples; D.C.P., L.C.M. and M.R. performed the pig genomic DNA extraction; A.C. genotyped the samples; D.C.P. and L.C.M. genotyped the polymorphisms in candidate genes; D.C.P. and J.M.F. performed the genome-wide association studies; D.C.P., M.B., and J.M.F. wrote the paper. All authors read and approved the final manuscript.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

is available for this paper at 10.1038/s41598-020-70894-2.

References

  • 1.Webb EC, O’Neill HA. The animal fat paradox and meat quality. Meat Sci. 2008;80:28–36. doi: 10.1016/j.meatsci.2008.05.029. [DOI] [PubMed] [Google Scholar]
  • 2.Wood JD, et al. Manipulating meat quality and composition. Proc. Nutr. Soc. 1999;58:363–370. doi: 10.1017/s0029665199000488. [DOI] [PubMed] [Google Scholar]
  • 3.Wood JD, et al. Fat deposition, fatty acid composition and meat quality: a review. Meat Sci. 2008;78:343–358. doi: 10.1016/j.meatsci.2007.07.019. [DOI] [PubMed] [Google Scholar]
  • 4.Mattson FH, Grundy SM. Comparison of effects of dietary saturated, monounsaturated, and polyunsaturated fatty acids on plasma lipids and lipoproteins in man. J. Lipid Res. 1985;26:194–202. [PubMed] [Google Scholar]
  • 5.Jiménez-Colmenero F, Ventanas J, Toldrá F. Nutritional composition of dry-cured ham and its role in a healthy diet. Meat Sci. 2010;84:585–593. doi: 10.1016/j.meatsci.2009.10.029. [DOI] [PubMed] [Google Scholar]
  • 6.Bucher HC, Hengstler P, Schindler C, Meier G. N-3 polyunsaturated fatty acids in coronary heart disease: a meta-analysis of randomized controlled trials. Am. J. Med. 2002;112:298–304. doi: 10.1016/s0002-9343(01)01114-7. [DOI] [PubMed] [Google Scholar]
  • 7.Cameron ND, Enser MB. Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality. Meat Sci. 1991;29:295–307. doi: 10.1016/0309-1740(91)90009-F. [DOI] [PubMed] [Google Scholar]
  • 8.Serra X, et al. A comparison of carcass, meat quality and histochemical characteristics of Iberian (Guadyerbas line) and Landrace pigs. Livest. Prod. Sci. 1998;56:215–223. [Google Scholar]
  • 9.Ibáñez-Escriche N, Magallón E, Gonzalez E, Tejeda JF, Noguera JL. Genetic parameters and crossbreeding effects of fat deposition and fatty acid profiles in Iberian pig lines. J. Anim. Sci. 2016;94:28–37. doi: 10.2527/jas.2015-9433. [DOI] [PubMed] [Google Scholar]
  • 10.Kouba M, Sellier P. A review of the factors influencing the development of intermuscular adipose tissue in the growing pig. Meat Sci. 2011;88:213–220. doi: 10.1016/j.meatsci.2011.01.003. [DOI] [PubMed] [Google Scholar]
  • 11.Benítez R, et al. Modulatory effects of breed, feeding status, and diet on adipogenic, lipogenic, and lipolytic gene expression in growing iberian and Duroc pigs. Int. J. Mol. Sci. 2017;19:22. doi: 10.3390/ijms19010022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Cameron ND. Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs. Livest. Prod. Sci. 1990;26:119–135. [Google Scholar]
  • 13.Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. doi: 10.1093/genetics/157.4.1819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ramos AM, et al. Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE. 2009;4:e6524. doi: 10.1371/journal.pone.0006524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Groenen, M. A. M. Development of a high-density Axiom® porcine genotyping array to meet research and commercial needs. In Plant and Animal Genome XXIII Conference, San Diego, CA, USA (2015).
  • 16.Ramayo-Caldas Y, et al. Genome-wide association study for intramuscular fatty acid composition in an Iberian × Landrace cross. J. Anim. Sci. 2012;90:2883–2893. doi: 10.2527/jas.2011-4900. [DOI] [PubMed] [Google Scholar]
  • 17.Corominas J, et al. Polymorphism in the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. PLoS ONE. 2013;8:e53687. doi: 10.1371/journal.pone.0053687. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Muñoz M, et al. Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genom. 2013;14:845. doi: 10.1186/1471-2164-14-845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Yang B, et al. Genome-wide association analyses for fatty acid composition in porcine muscle and abdominal fat tissues. PLoS ONE. 2013;8:e65554. doi: 10.1371/journal.pone.0065554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ros-Freixedes R, et al. Genome-Wide Association Study singles out SCD and LEPR as the two main loci influencing intramuscular fat content and fatty acid composition in Duroc pigs. PLoS ONE. 2016;11:e0152496. doi: 10.1371/journal.pone.0152496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhang W, et al. Genetic architecture of fatty acid composition in the longissimus dorsi muscle revealed by genome-wide association studies on diverse pig populations. Genet. Sel. Evol. 2016;48:5. doi: 10.1186/s12711-016-0184-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Zhang W, et al. Genome-wide association studies for fatty acid metabolic traits in five divergent pig populations. Sci. Rep. 2016;6:24718. doi: 10.1038/srep24718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sato S, et al. Genome-wide association studies reveal additional related loci for fatty acid composition in a Duroc pig multigenerational population. Anim. Sci. J. 2017;88:1482–1490. doi: 10.1111/asj.12793. [DOI] [PubMed] [Google Scholar]
  • 24.van Son M, et al. Genome-wide association study confirm major QTL for backfat fatty acid composition on SSC14 in Duroc pigs. BMC Genom. 2017;18:369. doi: 10.1186/s12864-017-3752-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Zappaterra M, Ros-Freixedes R, Estany J, Davoli R. Association study highlights the influence of ELOVL fatty acid elongase 6 gene region on backfat fatty acid composition in Large White pig breed. Animal. 2018;48:1–10. doi: 10.1017/S1751731118000484. [DOI] [PubMed] [Google Scholar]
  • 26.Viterbo VS, et al. Genome wide association study of fatty acid composition in Duroc swine. Asian-Australas. J. Anim. Sci. 2018;31:1127–1133. doi: 10.5713/ajas.17.0779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Puig-Oliveras A, et al. Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat. Sci. Rep. 2016;6:31803. doi: 10.1038/srep31803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Revilla M, et al. Expression analysis of candidate genes for fatty acid composition in adipose tissue and identification of regulatory regions. Sci. Rep. 2018;8:2045. doi: 10.1038/s41598-018-20473-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Crespo-Piazuelo D. Genomic Analysis of Fatty Acid Composition and Gut Microbiota in Pigs. Barcelona: Universitat Autònoma de Barcelona; 2018. [Google Scholar]
  • 30.Martínez-Montes ÁM, et al. Using genome wide association studies to identify common QTL regions in three different genetic backgrounds based on Iberian pig breed. PLoS ONE. 2018;13:e0190184. doi: 10.1371/journal.pone.0190184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Mach N, et al. Increasing the amount of n-3 fatty acid in meat from young Holstein bulls through nutrition. J. Anim. Sci. 2006;84:3039–3048. doi: 10.2527/jas.2005-632. [DOI] [PubMed] [Google Scholar]
  • 32.Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning: A Laboratory Manual in E3–E4. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 1989. [Google Scholar]
  • 33.Purcell S, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 2012;44:821–824. doi: 10.1038/ng.2310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 1995;57:289–300. [Google Scholar]
  • 36.Wolfe D, Dudek S, Ritchie MD, Pendergrass SA. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 2013;6:18. doi: 10.1186/1756-0381-6-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kinsella RJ, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford) 2011;2011:bar030. doi: 10.1093/database/bar030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.McLaren W, et al. The ensembl variant effect predictor. Genome Biol. 2016;17:122. doi: 10.1186/s13059-016-0974-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Jurkowski W, Yazdi S, Elofsson A. Ligand binding properties of human galanin receptors. Mol. Membr. Biol. 2013;30:206–216. doi: 10.3109/09687688.2012.750384. [DOI] [PubMed] [Google Scholar]
  • 40.Kim A, Park T. Diet-induced obesity regulates the galanin-mediated signaling cascade in the adipose tissue of mice. Mol. Nutr. Food Res. 2010;54:1361–1370. doi: 10.1002/mnfr.200900317. [DOI] [PubMed] [Google Scholar]
  • 41.Kim S, Jin Y, Choi Y, Park T. Resveratrol exerts anti-obesity effects via mechanisms involving down-regulation of adipogenic and inflammatory processes in mice. Biochem. Pharmacol. 2011;81:1343–1351. doi: 10.1016/j.bcp.2011.03.012. [DOI] [PubMed] [Google Scholar]
  • 42.Li L, et al. A novel galanin receptor 1a gene in zebrafish: tissue distribution, developmental expression roles in nutrition regulation. Comp. Biochem. Physiol. B. Biochem. Mol. Biol. 2013;164:159–167. doi: 10.1016/j.cbpb.2012.12.004. [DOI] [PubMed] [Google Scholar]
  • 43.Guillou H, D'andrea S, Rioux V, Jan S, Legrand P. The surprising diversity of Delta6-desaturase substrates. Biochem. Soc. Trans. 2004;32:86–87. doi: 10.1042/bst0320086. [DOI] [PubMed] [Google Scholar]
  • 44.Rioux V, et al. Influence of the cis-9, cis-12 and cis-15 double bond position in octadecenoic acid (18:1) isomers on the rat FADS2-catalyzed Δ6-desaturation. Chem. Phys. Lipids. 2015;187:10–19. doi: 10.1016/j.chemphyslip.2015.02.001. [DOI] [PubMed] [Google Scholar]
  • 45.Mysore R, et al. MicroRNA-192* impairs adipocyte triglyceride storage. Biochim. Biophys. Acta. 2016;1861:342–351. doi: 10.1016/j.bbalip.2015.12.019. [DOI] [PubMed] [Google Scholar]
  • 46.Cho SY, et al. Identification of mouse Prp19p as a lipid droplet-associated protein and its possible involvement in the biogenesis of lipid droplets. J. Biol. Chem. 2007;282:2456–2465. doi: 10.1074/jbc.M608042200. [DOI] [PubMed] [Google Scholar]
  • 47.Prip-Buus C, et al. Molecular and enzymatic characterization of a unique carnitine palmitoyltransferase 1A mutation in the Hutterite community. Mol. Genet. Metab. 2001;73:46–54. doi: 10.1006/mgme.2001.3176. [DOI] [PubMed] [Google Scholar]
  • 48.Sladek R, Bader JA, Giguère V. The orphan nuclear receptor estrogen-related receptor alpha is a transcriptional regulator of the human medium-chain acyl coenzyme A dehydrogenase gene. Mol. Cell. Biol. 1997;17:5400–5409. doi: 10.1128/mcb.17.9.5400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Bross P, et al. Characterization of wild-type and an active site mutant of human medium chain acyl-CoA dehydrogenase after expression in Escherichia coli. J. Biol. Chem. 1990;265:7116–7119. [PubMed] [Google Scholar]
  • 50.Ahmadian M, Duncan RE, Sul HS. The skinny on fat: lipolysis and fatty acid utilization in adipocytes. Trends Endocrinol. Metab. 2009;20:424–428. doi: 10.1016/j.tem.2009.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Yan D, et al. Oxysterol binding protein induces upregulation of SREBP-1c and enhances hepatic lipogenesis. Arterioscler. Thromb. Vasc. Biol. 2007;27:1108–1114. doi: 10.1161/ATVBAHA.106.138545. [DOI] [PubMed] [Google Scholar]
  • 52.Saltiel AR, Kahn CR. Insulin signalling and the regulation of glucose and lipid metabolism. Nature. 2001;414:799–806. doi: 10.1038/414799a. [DOI] [PubMed] [Google Scholar]
  • 53.Burgos C, et al. The effects of two alleles of IGF2 on fat content in pig carcasses and pork. Meat Sci. 2012;90:309–313. doi: 10.1016/j.meatsci.2011.07.016. [DOI] [PubMed] [Google Scholar]
  • 54.Lass A, et al. Adipose triglyceride lipase-mediated lipolysis of cellular fat stores is activated by CGI-58 and defective in Chanarin–Dorfman Syndrome. Cell Metab. 2006;3:309–319. doi: 10.1016/j.cmet.2006.03.005. [DOI] [PubMed] [Google Scholar]
  • 55.Chabi B, et al. Skeletal muscle overexpression of short isoform Sirt3 altered mitochondrial cardiolipin content and fatty acid composition. J. Bioenergy Biomembr. 2018;50:131–142. doi: 10.1007/s10863-018-9752-1. [DOI] [PubMed] [Google Scholar]
  • 56.Du B, et al. The transcription factor paired-related homeobox 1 (Prrx1) inhibits adipogenesis by activating transforming growth factor-β (TGFβ) signaling. J. Biol. Chem. 2013;288:3036–3047. doi: 10.1074/jbc.M112.440370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Sato D, et al. PPARγ activation alters fatty acid composition in adipose triglyceride, in addition to proliferation of small adipocytes, in insulin resistant high-fat fed rats. Eur. J. Pharmacol. 2016;773:71–77. doi: 10.1016/j.ejphar.2016.01.012. [DOI] [PubMed] [Google Scholar]
  • 58.Tanaka T, et al. Thiamine prevents obesity and obesity-associated metabolic disorders in OLETF rats. J. Nutr. Sci. Vitaminol. (Tokyo) 2010;56:335–346. doi: 10.3177/jnsv.56.335. [DOI] [PubMed] [Google Scholar]
  • 59.Liang CC. Metabolic changes in rats during developing thiamin deficiency. Biochem. J. 1975;146:739–740. doi: 10.1042/bj1460739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Bradbury MW, Berk PD. Mitochondrial aspartate aminotransferase: direction of a single protein with two distinct functions to two subcellular sites does not require alternative splicing of the mRNA. Biochem. J. 2000;345(Pt 3):423–427. [PMC free article] [PubMed] [Google Scholar]
  • 61.Challa TD, et al. Regulation of de novo adipocyte differentiation through cross talk between adipocytes and preadipocytes. Diabetes. 2015;64:4075–4087. doi: 10.2337/db14-1932. [DOI] [PubMed] [Google Scholar]
  • 62.Lohman DC, et al. Mitochondrial COQ9 is a lipid-binding protein that associates with COQ7 to enable coenzyme Q biosynthesis. Proc. Natl. Acad. Sci. U. S. A. 2014;111:E4697–E4705. doi: 10.1073/pnas.1413128111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Forti E, Aksanov O, Birk RZ. Temporal expression pattern of Bardet–Biedl syndrome genes in adipogenesis. Int. J. Biochem. Cell Biol. 2007;39:1055–1062. doi: 10.1016/j.biocel.2007.02.014. [DOI] [PubMed] [Google Scholar]
  • 64.An C, Zhang K, Su X. SLC12A3 variants modulate LDL cholesterol levels in the Mongolian population. Lipids Health Dis. 2017;16:29. doi: 10.1186/s12944-017-0413-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Matsuzaka T, Shimano H. Elovl6: a new player in fatty acid metabolism and insulin sensitivity. J. Mol. Med. (Berl.) 2009;87:379–384. doi: 10.1007/s00109-009-0449-0. [DOI] [PubMed] [Google Scholar]
  • 66.Corominas J, et al. Epigenetic regulation of the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. Genet. Sel. Evol. 2015;47:20. doi: 10.1186/s12711-015-0111-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Gelb MH, Valentin E, Ghomashchi F, Lazdunski M, Lambeau G. Cloning and recombinant expression of a structurally novel human secreted phospholipase A2. J. Biol. Chem. 2000;275:39823–39826. doi: 10.1074/jbc.C000671200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Jones PM, Bennett MJ. Clinical applications of 3-hydroxy fatty acid analysis by gas chromatography-mass spectrometry. Biochim. Biophys. Acta. 2011;1811:657–662. doi: 10.1016/j.bbalip.2011.06.026. [DOI] [PubMed] [Google Scholar]
  • 69.Suwa A, et al. RMI1 deficiency in mice protects from diet and genetic-induced obesity. FEBS J. 2010;277:677–686. doi: 10.1111/j.1742-4658.2009.07513.x. [DOI] [PubMed] [Google Scholar]
  • 70.Yeo GSH, et al. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat. Neurosci. 2004;7:1187–1189. doi: 10.1038/nn1336. [DOI] [PubMed] [Google Scholar]
  • 71.Nakatsuka A, et al. Vaspin is an adipokine ameliorating ER stress in obesity as a ligand for cell-surface GRP78/MTJ-1 complex. Diabetes. 2012;61:2823–2832. doi: 10.2337/db12-0232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Sung YJ, et al. Genome-wide association studies suggest sex-specific loci associated with abdominal and visceral fat. Int. J. Obes. (Lond.) 2016;40:662–674. doi: 10.1038/ijo.2015.217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Lundquist MR, et al. Phosphatidylinositol-5-phosphate 4-kinases regulate cellular lipid metabolism by facilitating autophagy. Mol. Cell. 2018;70:531–544.e9. doi: 10.1016/j.molcel.2018.03.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Christie WW, Hunter ML, Clegg RA. The effects of cerulenin on lipid metabolism in vitro in cellular preparations from the rat. Biochim. Biophys. Acta. 1981;666:284–290. doi: 10.1016/0005-2760(81)90118-1. [DOI] [PubMed] [Google Scholar]
  • 75.Corominas J, et al. Analysis of porcine adipose tissue transcriptome reveals differences in de novo fatty acid synthesis in pigs with divergent muscle fatty acid composition. BMC Genom. 2013;14:843. doi: 10.1186/1471-2164-14-843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Legrand P, Rioux V. The complex and important cellular and metabolic functions of saturated fatty acids. Lipids. 2010;45:941–946. doi: 10.1007/s11745-010-3444-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Rioux V, Catheline D, Legrand P. In rat hepatocytes, myristic acid occurs through lipogenesis, palmitic acid shortening and lauric acid elongation. Animal. 2007;1:820–826. doi: 10.1017/S1751731107000122. [DOI] [PubMed] [Google Scholar]
  • 78.Kakugawa S, et al. Notum deacylates Wnt proteins to suppress signalling activity. Nature. 2015;519:187–192. doi: 10.1038/nature14259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Bogan JS, Hendon N, McKee AE, Tsao T-S, Lodish HF. Functional cloning of TUG as a regulator of GLUT4 glucose transporter trafficking. Nature. 2003;425:727–733. doi: 10.1038/nature01989. [DOI] [PubMed] [Google Scholar]
  • 80.Li Z-Y, et al. Adipocyte metrnl antagonizes insulin resistance through PPARγ signaling. Diabetes. 2015;64:4011–4022. doi: 10.2337/db15-0274. [DOI] [PubMed] [Google Scholar]
  • 81.Naganuma T, Sato Y, Sassa T, Ohno Y, Kihara A. Biochemical characterization of the very long-chain fatty acid elongase ELOVL7. FEBS Lett. 2011;585:3337–3341. doi: 10.1016/j.febslet.2011.09.024. [DOI] [PubMed] [Google Scholar]
  • 82.Lindholm-Perry AK, et al. Relationships between the genes expressed in the mesenteric adipose tissue of beef cattle and feed intake and gain. Anim. Genet. 2017 doi: 10.1111/age.12565. [DOI] [PubMed] [Google Scholar]
  • 83.Hara K, et al. 1-Phosphatidylinositol 3-kinase activity is required for insulin-stimulated glucose transport but not for RAS activation in CHO cells. Proc. Natl. Acad. Sci. U. S. A. 1994;91:7415–7419. doi: 10.1073/pnas.91.16.7415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Moreau C, Froment P, Tosca L, Moreau V, Dupont J. Expression and regulation of the SCD2 desaturase in the rat ovary. Biol. Reprod. 2006;74:75–87. doi: 10.1095/biolreprod.105.044545. [DOI] [PubMed] [Google Scholar]
  • 85.Roberts R, Sciorra VA, Morris AJ. Human type 2 phosphatidic acid phosphohydrolases. Substrate specificity of the type 2a, 2b, and 2c enzymes and cell surface activity of the 2a isoform. J. Biol. Chem. 1998;273:22059–22067. doi: 10.1074/jbc.273.34.22059. [DOI] [PubMed] [Google Scholar]
  • 86.Tokumura A, Harada K, Fukuzawa K, Tsukatani H. Involvement of lysophospholipase D in the production of lysophosphatidic acid in rat plasma. Biochim. Biophys. Acta. 1986;875:31–38. [PubMed] [Google Scholar]
  • 87.Jalink K, Hordijk PL, Moolenaar WH. Growth factor-like effects of lysophosphatidic acid, a novel lipid mediator. Biochim. Biophys. Acta. 1994;1198:185–196. doi: 10.1016/0304-419x(94)90013-2. [DOI] [PubMed] [Google Scholar]
  • 88.Jeong J, Kwon EG, Im SK, Seo KS, Baik M. Expression of fat deposition and fat removal genes is associated with intramuscular fat content in longissimus dorsi muscle of Korean cattle steers. J. Anim. Sci. 2012;90:2044–2053. doi: 10.2527/jas.2011-4753. [DOI] [PubMed] [Google Scholar]
  • 89.Mooij HL, et al. Ext1 heterozygosity causes a modest effect on postprandial lipid clearance in humans. J. Lipid Res. 2015;56:665–673. doi: 10.1194/jlr.M053504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Pakradouni J, et al. Plasma NOV/CCN3 levels are closely associated with obesity in patients with metabolic disorders. PLoS ONE. 2013;8:e66788. doi: 10.1371/journal.pone.0066788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Sakamoto K, et al. The nephroblastoma overexpressed gene (NOV/ccn3) protein associates with Notch1 extracellular domain and inhibits myoblast differentiation via Notch signaling pathway. J. Biol. Chem. 2002;277:29399–29405. doi: 10.1074/jbc.M203727200. [DOI] [PubMed] [Google Scholar]
  • 92.van Roermund CWT, Ijlst L, Wagemans T, Wanders RJA, Waterham HR. A role for the human peroxisomal half-transporter ABCD3 in the oxidation of dicarboxylic acids. Biochim. Biophys. Acta. 2014;1841:563–568. doi: 10.1016/j.bbalip.2013.12.001. [DOI] [PubMed] [Google Scholar]
  • 93.Kendig EL, et al. Lipid metabolism and body composition in Gclm(−/−) mice. Toxicol. Appl. Pharmacol. 2011;257:338–348. doi: 10.1016/j.taap.2011.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Leonardsson G, et al. Nuclear receptor corepressor RIP140 regulates fat accumulation. Proc. Natl. Acad. Sci. U. S. A. 2004;101:8437–8442. doi: 10.1073/pnas.0401013101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Ho P-C, Chuang Y-S, Hung C-H, Wei L-N. Cytoplasmic receptor-interacting protein 140 (RIP140) interacts with perilipin to regulate lipolysis. Cell. Signal. 2011;23:1396–1403. doi: 10.1016/j.cellsig.2011.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Wang Z, et al. Identification of genes related to growth and lipid deposition from transcriptome profiles of pig muscle tissue. PLoS ONE. 2015;10:e0141138. doi: 10.1371/journal.pone.0141138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Paton CM, Ntambi JM. Biochemical and physiological function of stearoyl-CoA desaturase. Am. J. Physiol. Endocrinol. Metab. 2009;297:E28–37. doi: 10.1152/ajpendo.90897.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Uemoto Y, et al. Fine mapping of porcine SSC14 QTL and SCD gene effects on fatty acid composition and melting point of fat in a Duroc purebred population. Anim. Genet. 2012;43:225–228. doi: 10.1111/j.1365-2052.2011.02236.x. [DOI] [PubMed] [Google Scholar]
  • 99.Renaville B, et al. Candidate gene marker associations with fatty acid profiles in heavy pigs. Meat Sci. 2013;93:495–500. doi: 10.1016/j.meatsci.2012.11.019. [DOI] [PubMed] [Google Scholar]
  • 100.Zadravec D, et al. Ablation of the very-long-chain fatty acid elongase ELOVL3 in mice leads to constrained lipid storage and resistance to diet-induced obesity. FASEB J. 2010;24:4366–4377. doi: 10.1096/fj.09-152298. [DOI] [PubMed] [Google Scholar]
  • 101.Sharpe AJ, McKenzie M. Mitochondrial fatty acid oxidation disorders associated with short-chain Enoyl-CoA hydratase (ECHS1) deficiency. Cells. 2018;7:46. doi: 10.3390/cells7060046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Adas F, Berthou F, Salaün JP, Dréano Y, Amet Y. Interspecies variations in fatty acid hydroxylations involving cytochromes P450 2E1 and 4A. Toxicol. Lett. 1999;110:43–55. doi: 10.1016/s0378-4274(99)00140-x. [DOI] [PubMed] [Google Scholar]
  • 103.Yao H-T, et al. The inhibitory effect of polyunsaturated fatty acids on human CYP enzymes. Life Sci. 2006;79:2432–2440. doi: 10.1016/j.lfs.2006.08.016. [DOI] [PubMed] [Google Scholar]
  • 104.Lee HJ, et al. BNIP3 induction by hypoxia stimulates FASN-dependent free fatty acid production enhancing therapeutic potential of umbilical cord blood-derived human mesenchymal stem cells. Redox Biol. 2017;13:426–443. doi: 10.1016/j.redox.2017.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Berton MP, et al. Gene expression profile of intramuscular muscle in Nellore cattle with extreme values of fatty acid. BMC Genom. 2016;17:972. doi: 10.1186/s12864-016-3232-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Leoni V, et al. Metabolic consequences of mitochondrial coenzyme A deficiency in patients with PANK2 mutations. Mol. Genet. Metab. 2012;105:463–471. doi: 10.1016/j.ymgme.2011.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Xie H, Lim B, Lodish HF. MicroRNAs induced during adipogenesis that accelerate fat cell development are downregulated in obesity. Diabetes. 2009;58:1050–1057. doi: 10.2337/db08-1299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Navia-Paldanius D, Savinainen JR, Laitinen JT. Biochemical and pharmacological characterization of human α/β-hydrolase domain containing 6 (ABHD6) and 12 (ABHD12) J. Lipid Res. 2012;53:2413–2424. doi: 10.1194/jlr.M030411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Ballester M, et al. Analysis of the porcine APOA2 gene expression in liver, polymorphism identification and association with fatty acid composition traits. Anim. Genet. 2016;47:552–559. doi: 10.1111/age.12462. [DOI] [PubMed] [Google Scholar]
  • 110.Gol S, Pena RN, Rothschild MF, Tor M, Estany J. A polymorphism in the fatty acid desaturase-2 gene is associated with the arachidonic acid metabolism in pigs. Sci. Rep. 2018;8:14336. doi: 10.1038/s41598-018-32710-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Pérez-Enciso M, et al. A QTL on pig chromosome 4 affects fatty acid metabolism: evidence from an Iberian by Landrace intercross. J. Anim. Sci. 2000;78:2525–2531. doi: 10.2527/2000.78102525x. [DOI] [PubMed] [Google Scholar]
  • 112.Nii M, et al. Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar × Large White intercross. Anim. Genet. 2006;37:342–347. doi: 10.1111/j.1365-2052.2006.01485.x. [DOI] [PubMed] [Google Scholar]
  • 113.Kim Y, Kong M, Nam YJ, Lee C. A quantitative trait locus for oleic fatty acid content on Sus scrofa chromosome 7. J. Hered. 2006;97:535–537. doi: 10.1093/jhered/esl026. [DOI] [PubMed] [Google Scholar]
  • 114.Uemoto Y, et al. Genome-wide mapping for fatty acid composition and melting point of fat in a purebred Duroc pig population. Anim. Genet. 2012;43:27–34. doi: 10.1111/j.1365-2052.2011.02218.x. [DOI] [PubMed] [Google Scholar]
  • 115.Grindflek E, Szyda J, Liu Z, Lien S. Detection of quantitative trait loci for meat quality in a commercial slaughter pig cross. Mamm. Genome. 2001;12:299–304. doi: 10.1007/s003350010278. [DOI] [PubMed] [Google Scholar]
  • 116.Hu Z-L, Park CA, Reecy JM. Building a livestock genetic and genomic information knowledgebase through integrative developments of animal QTLdb and CorrDB. Nucleic Acids Res. 2019;47:D701–D710. doi: 10.1093/nar/gky1084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 117.Revilla M, et al. A global analysis of CNVs in swine using whole genome sequence data and association analysis with fatty acid composition and growth traits. PLoS ONE. 2017;12:e0177014. doi: 10.1371/journal.pone.0177014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Matsuzaka T, et al. Crucial role of a long-chain fatty acid elongase, Elovl6, in obesity-induced insulin resistance. Nat. Med. 2007;13:1193–1202. doi: 10.1038/nm1662. [DOI] [PubMed] [Google Scholar]
  • 119.Zappaterra M, et al. Association study between backfat fatty acid composition and SNPs in candidate genes highlights the effect of FASN polymorphism in large white pigs. Meat Sci. 2019;156:75–84. doi: 10.1016/j.meatsci.2019.05.013. [DOI] [PubMed] [Google Scholar]
  • 120.Estany J, Ros-Freixedes R, Tor M, Pena RN. A functional variant in the stearoyl-CoA desaturase gene promoter enhances fatty acid desaturation in pork. PLoS ONE. 2014;9:e86177. doi: 10.1371/journal.pone.0086177. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data Availability Statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.


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