- Research article
- Open Access
A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6
https://doi.org/10.1186/1471-2156-11-42
© Duijvesteijn et al; licensee BioMed Central Ltd. 2010
- Received: 18 March 2010
- Accepted: 20 May 2010
- Published: 20 May 2010
Abstract
Background
In many countries, male piglets are castrated shortly after birth because a proportion of un-castrated male pigs produce meat with an unpleasant flavour and odour. Main compounds of boar taint are androstenone and skatole. The aim of this high-density genome-wide association study was to identify single nucleotide polymorphisms (SNPs) associated with androstenone levels in a commercial sire line of pigs. The identification of major genetic effects causing boar taint would accelerate the reduction of boar taint through breeding to finally eliminate the need for castration.
Results
The Illumina Porcine 60K+SNP Beadchip was genotyped on 987 pigs divergent for androstenone concentration from a commercial Duroc-based sire line. The association analysis with 47,897 SNPs revealed that androstenone levels in fat tissue were significantly affected by 37 SNPs on pig chromosomes SSC1 and SSC6. Among them, the 5 most significant SNPs explained together 13.7% of the genetic variance in androstenone. On SSC6, a larger region of 10 Mb was shown to be associated with androstenone covering several candidate genes potentially involved in the synthesis and metabolism of androgens. Besides known candidate genes, such as cytochrome P450 A19 (CYP2A19), sulfotransferases SULT2A1, and SULT2B1, also new members of the cytochrome P450 CYP2 gene subfamilies and of the hydroxysteroid-dehydrogenases (HSD17B14) were found. In addition, the gene encoding the ß-chain of the luteinizing hormone (LHB) which induces steroid synthesis in the Leydig cells of the testis at onset of puberty maps to this area on SSC6. Interestingly, the gene encoding the α-chain of LH is also located in one of the highly significant areas on SSC1.
Conclusions
This study reveals several areas of the genome at high resolution responsible for variation of androstenone levels in intact boars. Major genetic factors on SSC1 and SSC6 showing moderate to large effects on androstenone concentration were identified in this commercial breeding line of pigs. Known and new candidate genes cluster especially on SSC6. For one of the most significant SNP variants, the difference in the proportion of animals surpassing the threshold of consumer acceptance between the two homozygous genotypes was as much as 15.6%.
Keywords
- Significant SNPs
- Skatole
- Androstenone
- Boar Taint
- Androstenone Level
Background
In many countries, male piglets are castrated shortly after birth to prevent boar taint, which is an urine-like, unpleasant flavour and odour released at cooking or heating of pork [1]. However, recent discussions on the pain associated with castration of the piglets early in life have led to a ban on castration without anaesthesia in some countries. In addition, studies have shown that un-castrated males grow faster and have an improved feed efficiency due to reduced fat deposition [2–4]. In future, if un-castrated males will be finished, boar taint needs to be prevented. Two of the major components related to the boar taint are androstenone and skatole [5–7]. Androstenone (5α-androst-16-en-3-one) is a male sex pheromone produced by the testes and stored in adipose tissue causing a perspiration-like odour [8, 9]. Androstenone precursors are also transported to the salivary glands which are capable to produce high levels of androstenone during sexual excitement [10, 11]. Skatole possesses strong faecal odour and is produced by the bacterial breakdown of the amino-acid tryptophane in the lower gut [12]. Skatole then diffuses into fat tissue.
There is considerable variation for androstenone and skatole between and within lines of pigs. Especially androstenone has high heritability estimates ranging from 0.25 to 0.88 [13, 14]. Somewhat lower heritabilities have been reported for skatole, between 0.19 and 0.55 [15, 16]. Two linkage studies using microsatellite markers have identified several QTL regions for androstenone and skatole in experimental crosses with 485 and 187 F2 animals, respectively [17, 18] pointing towards several areas in the genome affecting these traits. Also, single candidate genes involved in androstenone synthesis and metabolism of androstenone and skatole have been analyzed at the level of RNA and protein expression and in single SNP association studies (reviewed by Robic et al., 2008 [19]). However, no conclusive results showing functional mutations affecting androstenone and skatole levels in fat tissue have been described until now. Recently, large-scale microarray expression studies have reported hundreds of differentially expressed genes which might be involved in synthesis and degradation of androstenone and skatole in testis and liver [20, 21]. Subsequent analysis of SNPs in 121 differentially expressed genes identified 10 genes associated with one of the two traits [22]. Recently, Markljung et al. (2008) [23] reported 2 QTL for androstenone in 139 animals from a cross between Hamphsire and Landrace animals. Although these studies are of limited size and resolution, they indicate that several genetic factors seem to be involved in determining the levels of these boar taint compounds.
Recently, the first high-density 60K porcine SNP array has been developed [24] that offers a much higher resolution. A genome-wide association study (GWAS) was initiated using the SNP array to identify the chromosomal regions and specific SNPs influencing boar taint levels in a commercial breeding population. However, mean skatole levels (75 ng/g fat) in this population were far below the threshold accepted by consumers of 250 ng/g fat [25]. To reduce genotyping costs, a selective genotyping strategy for androstenone was applied. In this study, we present the results of a GWAS in pigs by genotyping 987 un-castrated male pigs from a commercial breeding population with large phenotypic variability for androstenone levels in fat, using the 60K (64,232) SNP array. The GWA resulted in an increased resolution compared to previous linkage studies. A large cluster of candidate genes within a 10 Mb region on SSC6 was identified. In addition, three new areas on SSC1 were detected that affect androstenone levels in this breeding line.
Methods
Animals and phenotypes
This experiment was conducted strictly in line with the regulations of the Dutch law on the protection of animals. Phenotypic measurements on androstenone were obtained from 1,663 boars slaughtered at a mean hot carcass weight of 95.71 kg. All the boars were purebred animals from a composite Duroc sire-line. Boar taint compounds were measured using fat samples from the neck collected from the left carcass side. The samples were stored under vacuum at -20°C. For androstenone, a fat extraction was done on the fat samples as described by Tuomola et al., 1997 [26]. Thereafter, androstenone concentrations in liquid fat were estimated by time-resolved fluoro-immunoassay [26] at the Hormone laboratory, Oslo.
Selective genotyping
A simulation study was performed in order to select about 1000 animals from 1663 candidates for genotyping in an optimal way using the existing pedigree [28]. Ten markers and 1 QTL were simulated on 1 chromosome and also 1 chromosome was simulated without a QTL for determining the false-positive rate for a given threshold. Four alternatives for selecting 1000 individuals to be genotyped out of 1663 candidates were compared: 1. random, 2. selecting large half-sib families, 3. selecting high and low phenotypes, 4. selecting high and low phenotypes within full sib families. ANOVA was used to analyze each marker and determine the F-statistic. Selection of high and low phenotypes within full sibs showed the highest power (results not shown). Applying the selection of high and low phenotypes (within full sib families) to our data set consisting of 1663 pigs resulted in 987 pigs selected for genotyping. These pigs originated from 57 sires and 212 dams. Among them, 45 sires and 11 dams were available for genotyping.
Genotyping and quality control
Genotyping was performed using the PorcineSNP60 Beadchip of Illumina (San Diego, CA, USA) [24]. A total of 1043 samples (including sires and dams) were genotyped for 64,232 SNPs at Service XS (Leiden, The Netherlands) and data quality was evaluated. The average call rate for all samples was 98.4% ± 3.4. A total of 63 animals were removed due to pedigree errors (<99% correct genotypes). After quality control, 943 animals were available for the GWAS with 106 singletons and 313 divergent full sib pairs (2 or more full sibs). For the SNPs, a threshold of 30 pedigree errors or more was applied and 190 SNPs were removed. In addition, 10,210 SNPs were removed because of low quality score (GenCall score <0.7). A minor allele frequency of 0.01 was applied removing another 4,925 SNPs of which 980 were monomorphic. In total, 47,897 SNPs remained for the GWAS.
Genome-wide association analysis
Corrected log-transformed androstenone was analyzed as a quantitative trait under an additive model using the QFAM module of PLINK [29]. The more stringent within-sib-ship test within QFAM was performed which is robust for population stratification compared to the total-sib-ship test. Nominal scores were permuted to obtain an empirical p-value while maintaining familial correlation between genotype and phenotype. The permutation procedure employed by QFAM corrects for relatedness within families and was performed 1,000,000 times. Genomic control was used to correct for score inflation introduced by relatedness between family units (sib ships) [30]. False-discovery rate (FDR) was applied to correct for multiple-testing. The R package q-value [31] was used to calculate a FDR-based q value to measure the statistical significance at the genome-wide level for association studies. The cut-off of significant association at the whole genome level was set at q-value ≤ 0.05. The total variance explained by a SNP was calculated using ASReml version 2.0, [27]. For ASReml the full model (as described earlier and including the polygenic effect) was used for the animals genotyped including the SNP as a random effect.
The fraction of the phenotypic variance explained by the
.
Linkage disequilibrium (LD) between SNPs was quantified as r² on all the animals of the GWA study using Haploview (V4.2; [32]) and the LD block was defined by the criteria of Gabriel et al. (2002) [33].
Identification of candidate genes
Porcine transcripts and annotation were downloaded from the porcine Ensembl data base (build9) and aligned with the human RefSeq mRNA sequences using BLAT [34]. The human-porcine comparative map was calculated based on the orthologous human-porcine transcripts and for the syntenic regions annotations were downloaded from the NCBI database (build37). Additional candidate genes present in human but not identified in the BLAT search against the human transcriptome were mapped to SSC6 performing a BLAST alignment with the porcine cDNA (SULT2A1) or the human homolog (SULT2B1, HSD17B14) against the porcine genome sequence (build9).
Results
Descriptive statistics for traits measured.
Trait | N | mean | SD | min | max |
---|---|---|---|---|---|
Boar taint compounds | |||||
Androstenone (μg/g) | 943 | 1.88 | 1.67 | 0.07 | 10.10 |
Skatole (ng/g) | 942 | 91.11 | 97.48 | 6.00 | 928.00 |
Indole (ng/g) | 942 | 54.15 | 64.79 | 8.00 | 678.00 |
Ln-androstenone | 943 | 0.25 | 0.91 | -2.66 | 2.31 |
Finishing traits | |||||
Hot carcass weight | 943 | 95.71 | 10.95 | 67.60 | 136.20 |
Fat depth at slaughter | 943 | 14.96 | 2.93 | 7.60 | 27.60 |
Age at slaughter | 943 | 179.80 | 9.26 | 152.00 | 247.00 |
Distribution of androstenone for the full dataset (N = 1663) and after selective genotyping was applied (N = 987).
Association between ln-androstenone and 40,525 mapped SNPs across 18 autosomes using an additive model. Each dot represents one SNP. On the y-axis are -log10 (p-values), and on the x-axis are the physical positions of the SNPs by chromosome. Cut-off value is 4.35 which equals a FDR q-value ≤ 0.05.
Box plots of the distribution of the untransformed androstenone concentrations for the SNP MARC0049189 (nr 15). The mean is given in bold.
Linkage disequilibrium plot for the region between 36.9 Mb and 39.7 Mb on SSC6. All 31 significant SNPs (p ≤ 0.05 after FDR) and intervening SNPs for all animals (N = 943) are shown (A). The values in the boxes are pair wise SNP correlations (r²) and the box colour reflects the degree of correlation. B Haplotypes with all SNPs from the LD block are shown. Each line represents a haplotype and the frequency of the haplotype in this population is given at the end of the line. Haplotypes with a frequency below 2% are not included. Two SNPs are tagged and the SNP names are given in C.
Discussion
Filtering of SNP data and statistical analyses
Quality control of the SNPs was based on the GenCall score, MAF and pedigree errors. Hardy-Weinberg equilibrium (HWE) was not considered relevant as a quality control tool as HWE is underpowered to detect genotyping errors [35] and only extreme sib pairs have been genotyped. GWA studies are particularly prone to spurious associations because ten thousands of associations are tested inflating the rate of false positives [36, 37]. In this study, FDR was used to control for false-positive associations due to multiple testing. The genomic control approach was used to account for spurious association due to population stratification [30] and because the breeding line is a composite line derived from three different breeds. Correction for the inflation by division reduces the unadjusted p-value to adjusted levels and accounts for relatedness between the sib ships and possible population stratification. However, in this study the deviation from the chi-square distribution under the null-hypothesis (no association) was very low (λGC = 1.06).
QTL areas
Location of the QTL from PigQTLdb for boar taint traits on the physical map of Sus scrofa build9 SSC6. The references and traits of the QTLs are given in Table 3. Positions in Mb were deduced from a BLAST alignment with the microsatellite markers. The green bar indicates the region found in this GWA study between 33 Mb and 45 Mb.
Candidate Genes
Candidate genes derived from porcine Ensembl build9.
Chromosome | Start position | End position | Porcine transcript | Gene |
---|---|---|---|---|
SSC1 | 58190063 | 58192283 | ENSSSCT00000004751 | Glycoprotein hormones, α chain |
SSC6 | 33615478 | 33622941 | ENSSSCT00000003325 | CYP2A19 |
SSC6 | 33821587 | 33821766 | ENSSSCG00000003001 | CYP2A6 |
SSC6 | 37189463 | 37189682 | ENSSSCT00000003463 | Sulfotransferase |
SSC6 | 37567155 | 37586075 | ENSSSCT00000003479 | HSD17B14 |
SSC6 | 37754569 | 37755346 | ENSSSCT00000003498 | LHB |
Overview of the identified QTL and flanking microsatellites on SSC6 for traits related to boar taint.
Nr. | Trait | Flanking markers | Reference |
---|---|---|---|
1 | Subjective pork odor | SW1353 - SW1057 | Lee et al., 2005 [17] |
2 | Subjective pork flavor in lean | SWR1130 (SW492) - SW782 | Lee et al., 2005 [17] |
3 | Smell intensity | S0087 - S0003 | Szyda et al., 2003 [40] |
4 | Androstenone, laboratory | SW782 - SW1823 (SW316) | Lee et al., 2005 [17] |
5 | Subjective boar flavor in lean | SW782 - SW322 | Lee et al., 2005 [17] |
6 | Skatole, laboratory | S0059 (SW1473) - S0121 (S0299) | Varona et al., 2005 [42] |
7 | Smell intensity | S0003 - SW322 | Grindflek et al., 2001 [41] |
8 | Skatole, sensory panel | S0121 (S0299) - SW322 | Lee et al., 2005 [17] |
9 | Skatole, laboratory | S0121 (S0299) - SW322 | Lee et al., 2005 [17] |
Taken together, there is overwhelming evidence from previous QTL studies, candidate genes and differential expression that the region on SSC6 contains genetic elements affecting androstenone levels in boars. In order to disentangle the effects of the regions containing the CYP450 genes and the area around 37 Mb, a mixed-model analysis combining the effects of two SNPs (H3GA0052956 at 33.5 and MARC0049189 at 38.3 Mb) was performed. In this model the fraction of the phenotypic variance explained by both SNPs is 2.1% and 3.6% and together 5.7%. This means that both regions explain a part of the effect of the whole region but due to the high LD between the SNPs they capture the same variation individually (5.76%, additional file 1). Therefore, both areas remain relevant for the determination of androstenone levels in this population. This breed is a composite line which could explain this large extent of LD. More data from other unrelated lines or crossbred animals showing the same effect are needed to further reduce the region of interest.
Effect size and application for breeding
Due to the skewed distribution of androstenone levels, even the use of a single marker would reduce the proportion of animals surpassing the threshold for consumer acceptance of 2 μg/g fat considerably. The difference between the two homozygous genotypes amounts to 15.6% (Figure 3). Sorting all offspring by the estimated androstenone effect of marker 50 and comparing the haplotypes of the 10 highest animals shows that all individuals are homozygous for the first haplotype shown in Figure 4. Furthermore, this haplotype is completely absent in the group of 10 animals with the lowest effects (data not shown). The 5 major SNPs (SNP nr. 1, 5, 6 on SSC1 and SNP nr. 15, 124 on SSC6) on SSC1 and 6 together explain 8.8% of the phenotypic variance, and considering a heritability of 64% [47] they account for 13.7% of the additive genetic variance.
A sustainable breeding scheme takes also into account the correlated effects on other production and reproduction traits. In general, the genetic correlations with growth, fatness and muscle depth are very low and favourable and therefore no serious negative effects on genetic progress due to selection against androstenone are to be expected [47]. Also, the positive genetic correlation with skatole would reduce skatole levels indirectly. However, the genetic correlation with fertility traits needs special attention. Male fertility data are not available on the animals in this study because they were slaughtered as commercial fatteners. Female fertility observations are only available on related animals and therefore estimates of genetic parameters have large standard errors (data not shown). A more extended study is underway to monitor the effects of selection against androstenone on male and female fertility. Furthermore, the effects in other lines that form part of the crossbreeding scheme to produce fattening pigs will be investigated.
Conclusion
This study clearly shows the large increase in resolution of high-density SNP panels compared to earlier linkage studies using microsatellite markers. Several regions in the genome affect androstenone levels in fat in this commercial breeding line of pigs. The genome-wide significant SNPs detected on SSC1 and SSC6 show moderate to large effects explaining a fraction of the phenotypic variance of 2-6%. The candidate genes identified in these areas in the pig genome or via the comparative map in human include genes investigated in earlier reports. In addition, new genes from the pathways of the synthesis and metabolism of androstenone such as LHA, LHB, and HSD17B14 are detected. The rather large LD block seen in this population around 33-45 Mb on SSC6 prevents to disentangle the combined effects of these genes and to pinpoint more specifically the responsible genetic elements. Nevertheless, the most significant SNPs can already be used to accelerate genetic progress in breeding against androstenone in this sire line. However, genetic correlations with production traits and especially possible negative effects on fertility traits will deserve special attention.
Declarations
Acknowledgements
We are grateful to Marcos Ramos, Wageningen University, for quality control of the SNP data and TOPIGS for providing the data of non-castrated boars and tissue sampling.
This research project has been co-financed by the European Commission, within the 6th Framework Programme, contract No. FOOD-CT-2006-016250 ("SABRE"). The text represents the authors' views and does not necessarily represent a position of the Commission who will not be liable for the use made of such information.
Authors’ Affiliations
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