- Research article
- Open Access
Fine mapping of a QTL affecting levels of skatole on pig chromosome 7
© The Author(s). 2017
- Received: 8 February 2017
- Accepted: 11 September 2017
- Published: 11 October 2017
Previous studies in the Norwegian pig breeds Landrace and Duroc have revealed a QTL for levels of skatole located in the region 74.7–80.5 Mb on SSC7. Skatole is one of the main components causing boar taint, which gives an undesirable smell and taste to the pig meat when heated. Surgical castration of boars is a common practice to reduce the risk of boar taint, however, a selection for boars genetically predisposed for low levels of taint would help eliminating the need for castration and be advantageous for both economic and welfare reasons. In order to identify the causal mutation(s) for the QTL and/or identify genetic markers for selection purposes we performed a fine mapping of the SSC7 skatole QTL region.
A dense set of markers on SSC7 was obtained by whole genome re-sequencing of 24 Norwegian Landrace and 23 Duroc boars. Subsets of 126 and 157 SNPs were used for association analyses in Landrace and Duroc, respectively. Significant single markers associated with skatole spanned a large 4.4 Mb region from 75.9–80.3 Mb in Landrace, with the highest test scores found in a region between the genes NOVA1 and TGM1 (p < 0.001). The same QTL was obtained in Duroc and, although less significant, with associated SNPs spanning a 1.2 Mb region from 78.9–80.1 Mb (p < 0.01). The highest test scores in Duroc were found in genes of the granzyme family (GZMB and GZMH-like) and STXBP6. Haplotypes associated with levels of skatole were identified in Landrace but not in Duroc, and a haplotype block was found to explain 2.3% of the phenotypic variation for skatole. The SNPs in this region were not associated with levels of sex steroids.
Fine mapping of a QTL for skatole on SSC7 confirmed associations of this region with skatole levels in pigs. The QTL region was narrowed down to 4.4 Mb in Landrace and haplotypes explaining 2.3% of the phenotypic variance for skatole levels were identified. Results confirmed that sex steroids are not affected by this QTL region, making these markers attractive for selection against boar taint.
- Boar taint
- Fine mapping
- Whole genome re-sequencing
Boar taint is an unpleasant smell and/or taste of meat from some uncastrated male pigs. In most countries, the problem is solved by castrating piglets at a young age. Banning surgical castration would, however, be advantageous due to ethical and economic reasons, and the EU aims for alternative solutions to the boar taint issue within few years .
The two main compounds responsible for boar taint are androstenone and skatole (3-methylindole). Androstenone is a steroid hormone produced in the testicles, via the same biological pathway as testosterone and estrogens, whereas skatole is a fermentation product produced by degradation of tryptophan in the intestine [2, 3]. Both compounds are metabolized in the liver; however, deficient degradation leads to their accumulation in adipose tissue. Indole, another metabolite of tryptophan, also contributes to boar taint, but is less pronounced than the two other compounds [4, 5]. Levels of skatole and indole in adipose tissue are highly correlated [6–8], this is also true for androstenone and skatole [5, 7, 9]. Androstenone inhibits skatole metabolism [10–12], explaining why elevated levels of skatole is mainly a problem in male pigs. Heritabilities for androstenone and skatole in the range of 0.5–0.7 and ~0.4 have been reported for Norwegian Landrace and Duroc, respectively , suggesting that these boar taint compounds can be reduced by selective breeding. Our previous studies found very high genetic correlations of boar taint compounds to testosterone and estrogens (0.8–0.9 for androstenone and 0.4–0.6 for skatole, respectively) . However, some studies have suggested that selection for low levels of boar taint should be feasible as they found no negative correlations between boar taint and male fertility  or production traits [14–17]. Other studies, on the other hand, show negative correlations between boar taint and male fertility [18, 19] and boar taint and meat quality . Therefore, even though genetic selection is a promising alternative to reduce boar taint [13–16, 19, 21, 22], unfavorable correlations to steroid hormones as well as uncertainty on the cost/benefit of including boar taint in the breeding goal  have slowed down practical implementation in breeding. Thus, genetic markers breaking unfavorable correlations between boar taint compounds and sex steroids may be potent selection candidates.
Several QTL studies have been conducted to reveal genomic regions underlying boar taint [7, 23–32]. Results are quite inconsistent but with different breeds, age of the boars and definition/measurements of the traits partly explaining why results differ between studies. The region on SSC7, however, seems to consistently affect both androstenone and skatole levels in Norwegian Landrace and Duroc [7, 31] as well as in other breeds [23–25, 29]. The cytochrome P450 members CYP1A1 and CYP1A2 have been suggested as candidate genes for skatole and are located on SSC7 [31, 33]. No functional mutations have, however, been detected in any of the studies. Also, the genomic region obtained in the Norwegian populations  seems to include three different QTLs for androstenone and two for skatole.
The aim of this study was to fine map the most significant region affecting the level of skatole, found on SSC7 at 74.7–80.5 Mb (Sus scrofa build10.2 positions) . This QTL was identified by LDLA analysis and did not affect levels of testosterone or estrogens, making it particularly interesting for implementation in breeding. To assess the role of this QTL in levels of skatole, we performed fine mapping of the region by selecting SNPs from whole genome re-sequencing data, followed up by genotyping and association analyses.
Whole genome re-sequencing was performed on 24 Landrace and 23 Duroc pigs, and provided a total of 10.1 billion paired-end reads (PE; 2 × 100 base pairs) with a per-animal genome coverage ranging from 9-17X. Initial quality control removed approximately 15% of the reads and the remaining reads were mapped against the pig reference genome (Sscrofa 10.2) with an overall mapping percentage of 77%. SNP detection was performed in a 5.8 Mb QTL region for skatole on SSC7 (74.7–80.5 Mb). After filtering, 3836 SNPs were found in common for Landrace and Duroc for the QTL region, and 166 of these were selected for genotyping. Additionally, 22 and 23 SNPs from the Illumina 60 K BeadChip were available in the QTL region for the Landrace and Duroc boars, respectively. SNP filtering on minor allele frequency (MAF) and call rate made 126 and 157 SNPs available for association analyses in Landrace and Duroc, respectively.
Association analyses was performed to identify SNPs and haplotypes associated with skatole in the SSC7 QTL region. The SNPs were also tested for association to levels of indole, androstenone, testosterone, estradiol and estrone sulphate [See Additional files 1 and 2 for results for Landrace and Duroc, respectively]. To determine if the region contains more than one QTL we reanalyzed the data with the most significant SNP included as a fixed effect and checked if this influenced the test scores of the other markers in the region. The test revealed no other significant SNPs in the region, suggesting that the associations are caused by one QTL only.
The most significant SNPs in Landrace, located between 78.3–78.5 Mb were grouped into a haplotype block of ten SNPs (Fig. 1). Association analyses revealed that haplotypes within this block are significantly associated with levels of skatole (LRT = 24.3) and that they explain 2.3% of the phenotypic variation. A haplotype block was constructed for the most significant SNPs in Duroc at 79.8–80.1 Mb (Fig. 2). Haplotypes within this block was not significantly associated with skatole (LRT = 3.2).
Skatole is a major contributor to boar taint and selection against this compound would be advantageous for both economic and ethical reasons. The correlation between skatole and sex steroids is lower that the correlation between androstenone and sex steroids . This makes selection against high levels of skatole, as compared to androstenone, less likely to affect reproduction in the pigs.
Fine mapping in Landrace
Only two of the 21 significant SNPs are located inside genes, one in the first intron of Neuro-oncological ventral antigen 1 (NOVA1) and one in the 3’UTR region of Transglutaminase 1 (TGM1). The other 19 significant SNPs were located in a 2.5 Mb segment in the intergenic regions NOVA1 - 5S rRNA (ENSSSCG00000018509), ENSSSC00000024192 -ENSSSC00000022792 and 5S rRNA (ENSSSCG00000018509) - STXBP6.
NOVA1 is a protein that controls alternative splicing of mRNAs in different cell types [34–36], whereas TGM1 is a membrane-bound enzyme that helps to protect against infections and water loss  and is associated with skin diseases . To our current knowledge, these genes do not seem to have any relevance to skatole and we find it more likely that other mechanisms or genes in region, whose function is not yet characterized in pigs, are involved [for a list of all genes in the region see Additional file 3]. Intergenic SNPs may affect distal regulatory regions of candidate genes by changing transcription factor binding sites, interfering with chromatin signaling or by bringing chromosomes together in the nucleus . Evolutionary conserved non-coding regions are more likely to contain regulatory motifs . Some of the most significant SNPs in Landrace (rs322137732, rs344485447, rs326608782, rs431825241, rs321605443, rs344465955, rs330435414, rs334377914 with LRT scores of 19.8–34.6) showed 65–82% sequence similarity to human NOVA1 - STXBP6 intergenic regions when sequences of 200–400 bp surrounding the SNPs were blasted. However, no sequence hits were found for other mammals besides human, making it difficult to predict the degree of evolutionary conservation in this region.
Sequence surrounding rs321711075 (LRT = 20.9) showed a 68% sequence similarity to the human long non-coding (lncRNA) RNA LINC00645, and the regulatory properties of lncRNA may give new meaning to GWAS associations in non-coding genomic regions . It has been shown that lncRNAs are abundant in gene deserts associated with genetic traits in human , and there are examples where specific intergenic lncRNAs are involved in trait regulation through chromatin modifications (e.g. [42, 43]). Because there are no known sequence motifs common to long non-coding RNAs they are difficult to annotate  and the search for lncRNAs in the pig genome has just begun . Whether the QTL signal for skatole in Landrace is caused by regulatory activity of a lncRNA needs to be further investigated.
None of the SNPs investigated were associated with levels of indole, androstenone, testosterone, estradiol and estrone sulphate, confirming the results of Grindflek et al. 2011 . This makes the QTL particularly interesting for breeding purposes, as it would not influence the levels of sex steroids.
Fine mapping in Duroc
The results in Duroc were less significant than those found in Landrace, which is in agreement with our earlier QTL study . The QTL peak in Duroc was in a slightly different chromosomal position than the most significant SNPs in Landrace. This may indicate that different genetic mechanisms are contributing to this QTL in the two breeds, but might also be due to differences in allele frequencies and LD structure. Furthermore, the average skatole levels are lower in Duroc (0.06 μg/g) than in Landrace (0.10 μg/g), which possibly also affects our results. The most significant SNPs associated with skatole, located in genes belonging to the granzyme family, were associated with levels of indole and androstenone in fat. The alleles associated with low levels of skatole were also associated with low levels of androstenone, indicating that selection for low boar taint is possible. No associations were found for levels of testosterone, estradiol or estrone sulphate.
The most significant results for skatole levels were found in a gene dense region on SSC7 where seven of the 18 significant SNPs are in genes of the granzyme family: GZMB and GZMH-like, while four are in the gene STXBP6 (Fig. 2). One significant SNP is non-synonymous, but predictions using SIFT  do not suggest any change in protein function. Granzymes are serine proteases found in immune related cell types such as cytotoxic T lymphocytes and natural killer cells where they play a role in eliminating diseased cells . Interestingly, tryptophan catabolism is also involved in regulation of immune responses . Specifically, granzyme B induction by interleukin has been associated with up-regulation of other immunoregulatory proteins including indoleamine 2,3-dioxygenase (IDO)  which is the rate-limiting enzyme in tryptophan metabolism . Although the connection between granzymes and skatole is not straight forward, different levels of tryptophan could reflect levels of skatole and thereby explain our results.
Two significant SNPs are located in introns of STXBP6. STXBP6 binds to the SNARE complex  and is involved in vesicle-mediated transport. The gene STX5A, encoding another syntaxin involved in SNARE interactions and vesicular transport, has been found down-regulated in rats by treatment of indole-3-carbinol, one of the metabolites of skatole . If skatole metabolites also have an effect on syntaxin genes in pigs, it might explain our results, but further studies are required to clarify any effect of this kind.
The haplotype block in Landrace showed significant association to levels of skatole. The most frequent haplotype in the block (frequency ~0.4) was associated with lower levels of skatole whereas the second most frequent haplotype (frequency ~0.22) was associated with higher levels. The difference in mean skatole levels between animals homozygous for these two haplotypes was 0.06 μg/g skatole. Considering consumer acceptance levels of 0.2 μg/g, 14.5% of Norwegian Landrace boars have been shown to have skatole levels that are too high . The SNPs within these haplotype blocks can therefore be used as genetic markers for lowering the overall skatole levels in this Landrace population.
Fine mapping of a QTL for skatole on SSC7 was conducted to narrow down the QTL region and search for genes and mechanisms underlying the QTL. The most significant results were found in the intergenic regions between 75.9–78.5 Mb and in NOVA1 and TGM1 in Landrace. In Duroc, SNPs within GZMB, GZMH-like, STXBP6 and intergenic regions at 78.8–80.1 Mb were found to be most significant. The region in Duroc was also associated with levels of indole and androstenone. Although no causal variant was detected, genetic markers for boar taint that are not associated with sex steroids have been identified and would therefore be highly relevant for selection purposes.
Animals and phenotypes
For re-sequencing purposes, 23 Norwegian Duroc and 24 Norwegian Landrace boars used in the Norsvin breeding program from 2010 to 2013 were selected. The boars were key individuals in our previous QTL studies, with either high or low levels of boar taint [7, 31], or frequently used AI boars during these three years, in order to catch as much as possible of the genetic variation present in the population.
A total of 911 Duroc and 767 Landrace boars from Norsvin’s boar testing station were genotyped in this study. The boars include fathers and sons from 70 Duroc and 92 Landrace half sib families, and 60 K BeadChip genotypes (Illumina) were available for all the boars. For Landrace, another 440 sons were available with 60 K genotypes from the previous study , making imputation to the new markers genotyped in this study feasible, and these boars were also included in the association analysis. For Duroc, all the available boars were genotyped in this study. Animals were reared under similar conditions using standard commercial feed and were sacrificed over a period of 26 months. On average, the Duroc and Landrace boars reached slaughter weight (100 kg) at 156 and 143 days, respectively, and were slaughtered on average 15 days later. Blood samples for DNA extraction were collected before slaughter and subcutaneous adipose tissue samples from the neck for skatole measurements were collected at the slaughter line and stored at −40 °C until chemical analyses were performed. The boars were slaughtered in compliance with national guidelines. The pigs were stunned in an atmosphere with 90% CO2, and the carcasses were ex-sanguinated, scalded and split within 30 min post mortem.
Levels of skatole were measured in subcutaneous fat, at the Hormone laboratory, NMBU, using high performance liquid chromatography  whereas levels of androstenone in fat and plasma were analyzed by a modified time-resolved fluoroimmunoassay  and using antibody by Andresen . Plasma levels of testosterone, estradiol and estrone sulphate were analyzed at the Hormone laboratory at Oslo University Hospital. The plasma levels of testosterone were measured by a radioimmunoassay (Orion Diagnostica, Espoo, Finland) whereas plasma levels of 17β-estradiol were measured by a fluoroimmunoassay (Perkin Elmer, Turku, Finland). Levels of estrone sulphate in plasma were measured by a radioimmunoassay (Diagnostic System Laboratories, Inc., Webster, TX, USA). More information about chemical analyses and compound levels can be found in Grindflek et al. .
DNA for genotyping purposes was extracted from blood using the MagAttract DNA Blood Midi M48 protocol on the Bio-Robot M48 (Qiagen, Hilden, Germany). DNA concentration and quality were evaluated using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, DE, USA).
Re-sequencing, pre-processing and read mapping
Genomic DNA from 23 Duroc and 24 Landrace boars sequenced on an Illumina GAII (Illumina, San Diego, USA). The sequencing was performed by a commercial sequencing center (Fasteris, Switzerland) according to manufacturer’s protocols. FastQC version 0.10.1 (Babraham Bioinformatics, UK) was used for quality checking, revealing an overall per-base quality ≥30. Pre-processing of reads was done using a custom Perl script written to remove duplicated reads, trim sequencing primer sequence and remove reads shorter than 0.8 of their original length. On average, 15% of reads were filtered using this pipeline and the remaining reads were aligned to the Sus scrofa 10.2 reference genome  using the software Bowtie2 version 2.0.0 with default parameters . Mapped reads were sorted by their chromosomal coordinates using Samtools version 0.1.18 . The Picard AddOrReplaceReadGroups program (http://broadinstitute.github.io/picard/) was used to assign unique IDs to the files before SNP calling.
SNP detection, annotation and selection
SNPs were detected within each breed using Freebayes  generating a list of SNPs within the QTL region on SSC7 where each SNP was supported by a minimum of two reads across the samples. SNPs located within repeat regions (as defined by pig ensembl release 67) were removed. Moreover, to reduce the chances of false positive SNPs, the following two filtering criteria were applied: 1) The minimum total coverage of the reference allele was two, and 2) both the two homozygous and the heterozygote genotype had to be present for each SNP. The read depths for SNP positions across all sequenced samples were in the range of 12 to 882, with a mean count of 506, whereas the MAF of the SNPs ranged from 0.03 to 0.5 with a mean count of 0.22. After initial filtering a list of common SNPs for Duroc and Landrace was made, comprising 3836 SNPs within this region. The Variant Effect Predictor (VEP)  was used to annotate SNPs to gene structure elements (including exons, introns, UTRs) and to classify variants (e.g. missense, nonsense, synonymous, stop gain/loss). SNPs were selected for genotyping based on their position in order to cover the whole QTL region. Furthermore, non-synonymous SNPs were prioritized. A total of 166 SNPs were selected for genotyping. The SNPs have been submitted to NCBI dbSNP . Sequences around some of the identified SNPs were searched for similarity against human using BLAST .
Genotypes and phase inference
The boars were genotyped using matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS) assays. Assays were designed using MassARRAY Assay Design software (Agena Biosciences, Hamburg, Germany) at multiplexing levels between 8 and 27 [See Additional file 4 for assay primers], and genotyping was done using the IPLEX protocol according to the manufacturer’s instructions. Genotypes were also retrieved for porcine 60 K Illumina BeadChip SNPs in this QTL region for the same boars, as available from Grindflek et al. 2011 . SNP filtering was done for minor allele frequency (>0.05) and call rate (>0.95), reducing the number of SNPs to 157 and 126 for Duroc and Landrace, respectively, in the QTL region. The BEAGLE software version 1.0.0  was used to phase chromosomes and impute sporadic missing genotypes. The Haploview software version 4.2  was applied to calculate pair-wise LD measures for all SNP pairs and the “four gamete rule” method, as implemented in Haploview, was used to define haplotype blocks.
Single marker and haplotype association analysis
y = sire + herd-year-season + wait-station + pen + animal + sample-date + SNP/haplotype + age_25kg + days-test + days-wait + liveborn + (liveborn)2 + e
Here, y is the phenotype expressed as ln(μg/g levels of the phenotype) and the fixed effects are sire, herd-year-season, waiting in boar test station before slaughter or not, and pen. Covariates used were age at 25 kg (start of boar test), age from 25 kg to 100 kg (days in boar test), days from 100 kg to slaughter (days in waiting station) and number of live born in same litter. Animal ID, sample date for adipose tissue and SNP/haplotype were fitted as random effects. To test if the QTL region contained more than one QTL, the dataset was re-analyzed fitting the most significant SNP as a fixed effect in the model above.
Association mapping was conducted using the ASReml software v.2.00 . Log likelihood (LogL) ratios for each SNP or haplotype were estimated as the difference in LogL value between models with and without this effect. Log-likelihood ratio test (LRT) scores were calculated as two times the (LogL) and LRT was assumed approximately chi-square distributed with one degree of freedom. Multiple testing correction was done to adjust the significance threshold with the effective number of independent tests (MeffG) .
We wish to thank Ellen Dahl and Øystein Andresen for being in charge of androstenone, skatole and indole analyses done at the hormone laboratory at the Norwegian University of Life Sciences and Peter Torjesen at the hormone laboratory at Oslo University Hospital for organizing testosterone and estrogens analyses. We also highly appreciate the contribution from BioBank AS for collection and DNA extraction of all samples, Simon Taylor (CIGENE, NMBU) for data management and Kristil Kindem Sundsaasen (CIGENE, NMBU) for performing primer design.
This work was financed by the Research Council of Norway. The samples and phenotypes were provided by Norsvin.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
MvS conducted SNP selection, constructed haplotypes, performed the association studies and drafted the paper. MK was involved in planning the project, drafted the pipeline for the whole genome re-sequencing data analysis and genotyping and contributed to writing the paper. HG was involved in imputation work, QTL sequence quality work and contributed to writing the paper. RA performed the read handling, SNP detection, anngotation and contributed to writing the paper. HH was involved in sample preparations and SNP genotyping. SL was involved in planning the project, SNP selection and contributed to writing the paper. EG was involved in planning the project, provided experimental data, selected animals for sequencing and contributed to writing the paper. All authors read and approved the final manuscript.
The animals used in this study were raised at Norsvin’s boar testing station. All animals were cared for according to laws, internationally recognized guidelines and regulations controlling experiments with live animals in Norway according to the rules given by Norwegian Animal Research Authority (The Animal Protection Act of December 20th, 1974, and the Animal Protection Ordinance Concerning Experiments with Animals of January 15th, 1996). The animals used in this study were potential AI boars kept as a routine by Norsvin’s breeding program. The blood samples were standard procedure whereas tissue samples were taken after slaughter, and no ethics committee approval was needed. Veterinarians obtained all the blood samples and BioBank AS (Hamar, Norway) obtained tissue samples, following standard routine monitoring procedures and relevant guidelines. No animal experiment has been performed in the scope of this research.
Consent for publication
The authors declare that they have no competing interests.
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- Bee G, Chevillon P, Bonneau M. Entire male pig production in Europe. Anim Prod Sci. 2015;55:1347–59.Google Scholar
- Patterson RLS. 5alpha-androst-16-ene-3-one: - Compound responsible for taint in boar fat. J Sci Food Agric 1968;19(31).Google Scholar
- Vold E. Fleischproduktionseigenschaften bei ebern und kastraten. Meldinger fra Norges Landbrukshøgskole. 1970;49:1–25.Google Scholar
- Jensen MT, Cox RP, Jensen BB. 3-methylindole (skatole) and indole production by mixed populations of pig faecal bacteria. Appl Environ Microbiol. 1995;61(8):3180–4.PubMedPubMed CentralGoogle Scholar
- Hansson KE, Lundstrom K, Fjelkner-Modig S, Persson J. The importance of androstenone and skatole for boar taint. Swed J Agric Res. 1980;10:167–73.Google Scholar
- Babol J, Zamaratskaia G, Juneja RK, Lundstr”m K: the effect of age on distribution of skatole and indole levels in entire male pigs in four breeds: Yorkshire, landrace, Hampshire and Duroc. Meat Sci 2004, 67:351–358.Google Scholar
- Grindflek E, Meuwissen THE, Aasmundstad T, Hamland H, Hansen MH, Nome T, Kent M, Torjesen P, Lien S. Revealing genetic relationships between compounds affecting boar taint and reproduction in pigs. J Anim Sci. 2011;89(3):680–92.View ArticlePubMedGoogle Scholar
- Tuomola M, Vahva M, Kallio H. High-performance liquid chromatography determination of skatole and indole levels in pig serum, subcutaneous fat and submaxillary salivary glands. Journal of agricultur. Food Chem. 1996;44:1265–70.View ArticleGoogle Scholar
- Zamaratskaia G, Babol J, Andersson H, Lundstr”m K. Plasma skatole and androstenone levels in entire male pigs and relationship between boar taint compounds, sex steroids and thyroxine at various ages. Livest Prod Sci. 2004;87:91–8.Google Scholar
- Doran E, Whittington FM, Wood JD, McGivan JD. Cytochrome P450IIE1 (CYP2E1) is induced by skatole and this induction is blocked by androstenone in isolated pig hepatocytes. Chem Biol Interact. 2002;140(1):81–92.View ArticlePubMedGoogle Scholar
- Chen G, Cue RA, Lundstrom K, Wood JD, Doran O. Regulation of CYP2A6 protein expression by skatole, indole and testicular steroids in primary cultured pig hepatocytes. Drug Metab Dispos. 2008;36(1):56–60.View ArticlePubMedGoogle Scholar
- Rasmussen MK, Zamaratskaia G, Ekstrand B. Gender-related differences in cytochrome P450 in porcine liver - implication for activity, expression and inhibition by testicular steroids. Reprod Domest Anim. 2011;46(4):616–23.View ArticlePubMedGoogle Scholar
- Strathe AB, Velander I, Mark T, Ostersen T, Hansen C, Kadarmideen H. Genetic parameters for male fertility and its relationship to skatole and androstenone in Danish landrace boars. J Anim Sci. 2013;91(10):4659–68.View ArticlePubMedGoogle Scholar
- Merks JWM, Hanenberg EHAT, Bloemhof S, Knol EF. Genetic opportunities for pork production without castration. Anim Welf. 2009;18:539–44.Google Scholar
- Windig JJ, Mulder HA, Ten Napel J, Knol EF, Mathur PK, Crump RE. Genetic parameters for androstenone, skatole, indole, and human nose scores as measures of boar taint and their relationship with finishing traits. J Anim Sci. 2012;90(7):2120–9.View ArticlePubMedGoogle Scholar
- Strathe AB, Velander IH, Mark T, Kadarmideen HN. Genetic parameters for androstenone and skatole as indicators of boar taint and their relationship to production and litter size traits in Danish landrace. J Anim Sci. 2013;91(6):2587–95.View ArticlePubMedGoogle Scholar
- Rostellato R, Bonfatti V, Larzul C, Bidanel JP, Carnier P. Estimates of genetic parameters for content of boar taint compounds in adipose tissue of intact males at 160 and 220 days of age. J Anim Sci. 2015;93(9):4267–76.View ArticlePubMedGoogle Scholar
- Mercat MJ, Prunier A, Muller N, Hassenfratz C, Larzul C. Relationship between sperm production and boar taint risk of purebred or crossbred entire offspring. In: 66th Annual Meeting of the European Federation of Animal Science (EAAP). Warsaw: Wageningen Academic Publishers; 2015.Google Scholar
- Parois SP, Prunier A, Mercat MJ, Merlot E, Larzul C. Genetic relationships between measures of sexual development, boar taint, health, and aggressiveness in pigs. J Anim Sci. 2015;93(8):3749–58.View ArticlePubMedGoogle Scholar
- Haberland AM, Luther H, Hofer A, Tholen E, Simianer H, Lind B, Baes C. Efficiency of different selection strategies against boar taint in pigs. Animal. 2014;8(1):11–9.View ArticlePubMedGoogle Scholar
- Xue J, Dial GD. Raising intact male pigs for meat: detecting and preventing boar taint. Swine Health and Production. 1997;5(4):151–8.Google Scholar
- Lukic B, Pong-Wong R, Rowe SJ, De Koning DJ, Velander I, Haley CS, Archibald AL, Woolliams JA. Efficiency of genomic prediction for boar taint reduction in Danish landrace pigs. Anim Genet. 2015;46(6):607–16.View ArticlePubMedPubMed CentralGoogle Scholar
- Große-Brinkhaus C, Storck LC, Frieden L, Neuhoff C, Schellander K, Looft C, Tholen E. Genome-wide association analyses for boar taint components and testicular traits revealed regions having pleiotropic effects. BMC Genet. 2015;16:36.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee GJ, Archibald AL, Law AS, Lloyd S, Wood J, Haley CS. Detection of quantitative trait loci for androstenone, skatole and boar taint in a cross between large white and Meishan pigs. Anim Genet. 2005;36(1):14–22.View ArticlePubMedGoogle Scholar
- Quintanilla R, Demeure O, Bidanel JP, Milan D, Iannuccelli N, Amigues Y, Gruand J, Renard C, Chevalet C, Bonneau M. Detection of quantitative trait loci for fat androstenone levels in pigs. JAnim Sci. 2003;81(2):385–94.Google Scholar
- Varona L, Vidal O, Quintanilla R, Gil M, Sánchez A, Folch JM, Hortos M, Ruis MA, Amills M, Noguera JL. Bayesian analysis of quantitative trait loci for boar taint in a landrace outbred population. J Anim Sci. 2005;83:301–7.View ArticlePubMedGoogle Scholar
- Duijvesteijn N, Knol EF, Merks JW, Crooijmans RP, Groenen MA, Bovenhuis H, Harlizius B. A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6. BMC Genet. 2010;11:42.View ArticlePubMedPubMed CentralGoogle Scholar
- Duijvesteijn N, Knol EF, P. B: boar taint in entire male pigs: a GWAS for direct and indirect genetic effects on androstenone. J Anim Sci 2014.Google Scholar
- Gregersen VR, Conley LN, Sørensen KK, Guldbrandtsen B, Velander IH, Bendixen C. Genome-wide association scan and phased haplotype construction for quantitative trait loci affecting boar taint in three pig breeds. BMC Genomics. 2012;13:22.View ArticlePubMedPubMed CentralGoogle Scholar
- Rowe SJ, Karacaören B, de Koning DJ, Lukic B, Hastings-Clark N, Velander I, Haley CS, Archibald AL. Analysis of the genetics of boar taint reveals both single SNPs and regional effects. BMC Genomics. 2014;15:424.View ArticlePubMedPubMed CentralGoogle Scholar
- Grindflek E, Lien S, Hamland H, Hansen MH, Kent M, van Son M, Meuwissen TH. Large scale genome-wide association and LDLA mapping study identifies QTLs for boar taint and related sex steroids. BMC Genomics. 2011;12:362.View ArticlePubMedPubMed CentralGoogle Scholar
- Ramos AM, Duijvesteijn N, Knol EF, Merks JW, Bovenhuis H, Crooijmans RP, Groenen MA, Harlizius B. The distal end of porcine chromosome 6p is involved in the regulation of skatole levels in boars. BMC Genet. 2011;12:35.View ArticlePubMedPubMed CentralGoogle Scholar
- Lanza DL, Yost GS. Selective dehydrogenation/oxidation of 3-methylindole by cytochrome P450 enzymes. Drug Metab Dispos. 2001;29(7):950–3.PubMedGoogle Scholar
- Ratti A, Fallini C, Colombrita C, Pascale A, Laforenza U, Quattrone A, Silani V. Post-transcriptional regulation of neuro-oncological ventral antigen 1 by the neuronal RNA-binding proteins ELAV. J Biol Chem. 2008;283(12):7531–41.View ArticlePubMedGoogle Scholar
- Villate O, Turatsinze JV, Mascali LG, Grieco FA, Noqueira TC, Cunha DA, Nardelli TR, Sammeth M, Salunkhe VA, Esquerra JL, et al. Nova1 is a master regulator of alternative splicing in pancreatic beta cells. Nucleic Acids Res. 2014;42(18):11818–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Lin JC, Chi YL, Peng HY, Lu YH. RBM4-Nova1-SRSF6 splicing cascade modulates the development of brown adipocytes. Biochim Biophys Acta. 2016;1859(11):1368–79.View ArticlePubMedGoogle Scholar
- Farasat S, Wei M-H, Herman M, Liewehr DJ, Steinberg SM, Bale SJ, Fleckman P, Toro JR. Novel transglutaminase-1 mutations and genotype-phenotype investigations of 104 patients with autosomal recessive congenital ichthyosis in the USA. J Med Genet. 2009;46:103–11.View ArticlePubMedGoogle Scholar
- Herman ML, Farasat S, Steinbach PJ, Wei MH, Toure O, Fleckman P, Blake P, Bale SJ, Toro JR. Transglutaminase-1 gene mutations in autosomal recessive congenital ichthyosis: summary of mutations (including 23 novel) and modeling of TGase-1. Hum Mutat. 2009;30(4):537–47.View ArticlePubMedPubMed CentralGoogle Scholar
- Dermitzakis ET, Reymond A, Antonarakis SE. Conserved non-genic sequences - an unexpected feature of mammalian genomes. Nat Rev Genet. 2005;6:151–7.View ArticlePubMedGoogle Scholar
- St. Laurent G, Vyatkin Y, Kapranov P. Dark matter RNA illuminates the puzzle of genome-wide association studies. BMC Med. 2014;12:97.View ArticlePubMedPubMed CentralGoogle Scholar
- Cabili MNTC, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL. Integrative annotation of human large intergenic non-coding RNAs reveals global properties and specific subclasses. Genes and Development. 2011;25:1915–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai M-C, Hung T, Argani P, Rinn JL, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010;464:1071–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Tsai M-C, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, Shi Y, Segal E, Chang HY. Long noncoding RNA as modular scaffold of histone modification complexes. Science. 2010;329(5992):689–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Gorodkin J, Hofacker IL. From structure prediction to genomic screens for novel non-coding RNAs. PLoS Comput Biol. 2011;7(8)Google Scholar
- Anthon C, Tafer H, Havgaard JH, Thomsen B, Hedegaard J, Seemann SE, Pundhir S, Kehr S, Bartschat S, Nielsen M, et al. Structured RNAs and synteny regions in the pig genome. BMC Genomics. 2014;15:459.View ArticlePubMedPubMed CentralGoogle Scholar
- Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81.View ArticlePubMedGoogle Scholar
- Cullen SP, Brunet M, Martin SJ. Granzymes in cancer and immunity. Cell Death Differ. 2010;17:616–23.View ArticlePubMedGoogle Scholar
- Mellor AL, Munn DH. Tryptophan catabolism and regulation of adaptive immunity. J Immunol. 2003;170:5809–13.View ArticlePubMedGoogle Scholar
- Lindner S, Dahlke K, Sontheimer K, Hagn M, Kaltenmeier C, Barth TFE, Beyer T, Reister F, Fabricius D, Lotfi R, et al. Interleukin 21-induced granzyme B-expressing B cells infiltrate tumors and regulate T cells. Cancer Res. 2013;73(8):2468–79.View ArticlePubMedGoogle Scholar
- Taylor MW, Feng G. Relationship between interferon-ψ, indoleamine 2,3-dioxygenase, and tryptophan catabolism. FASEB J. 1991;5:2516.PubMedGoogle Scholar
- Scales SJ, Hesser BA, Masuda ES, Scheller RH. Amisyn, a novel syntaxin-binding protein that may regulate SNARE complex assembly. J Biol Chem. 2002;277:28271–9.View ArticlePubMedGoogle Scholar
- Kang JS, Park H-J, Yoon S. Analysis of gene expression modulated by Indole-3-carbinol in Dimethylbenz[a]anthracene-induced rat mammary carcinogenesis. Molecular and Cellular Toxicology. 2009;5(3):222–9.Google Scholar
- Tuomola M, Harpio R, Knuuttila P, Mikola H, L›vgren T: time-resolved fluoroimmunoassay for the measurement of androstenone in porcine serum and fat samples. Journal of agricultural food chemistry 1997, 45:3529–3534.Google Scholar
- Andresen Ø. Development of radioimunoassay for 5alpha-adrost-16-en-3-one in pig peripheral plasma. Acta endochrinologia. 1974;76:377–87.Google Scholar
- Groenen MA, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF, Rogel-Gaillard C, Park C, Milan D, Megens HJ, et al. Analysis of pig genomes provide insight into porcine demography and evolution. Nature. 2012;491(7424):393–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Langmead BSS. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9(4):357–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Li HHB, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Garrison EMG. Haplotype-based variant detection from short-read sequencing. In: arXiv:12073907; 2012.Google Scholar
- McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. The Ensembl variant effect predictor. Genome Biol. 2016;17:122.View ArticlePubMedPubMed CentralGoogle Scholar
- Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Coordinators NR. Database resources of the National Center for biotechnology information. Nucleic Acids Res. 2017;45(D1):D12–7.View ArticleGoogle Scholar
- Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. Am J Hum Genet. 2007;81:1084–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Barrett JCFB, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–5.View ArticlePubMedGoogle Scholar
- Gilmour A, Cullis B, Welham R, Thompson R. ASREML reference manual. Sydney: New South Wales Department of Primary Industries; 2001.Google Scholar
- Gao X, Starmer J, Martin ER. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol. 2008;32(4):361–9.View ArticlePubMedGoogle Scholar