A genome-wide scan for signatures of directional selection in domesticated pigs
- Sunjin Moon†1, 2, 12,
- Tae-Hun Kim†3,
- Kyung-Tai Lee3,
- Woori Kwak4, 5,
- Taeheon Lee1,
- Si-Woo Lee3,
- Myung-Jick Kim6,
- Kyuho Cho7,
- Namshin Kim8,
- Won-Hyong Chung8,
- Samsun Sung5,
- Taesung Park9,
- Seoae Cho5,
- Martien AM Groenen10,
- Rasmus Nielsen11,
- Yuseob Kim2Email author and
- Heebal Kim1, 4, 5Email author
© Moon et al.; licensee BioMed Central. 2015
Received: 1 October 2014
Accepted: 6 February 2015
Published: 25 February 2015
Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication.
Whole-genome resequencing of 30 individual pigs from domesticated breeds, Landrace and Yorkshire, and 10 Asian wild boars at ~16-fold coverage was performed resulting in over 4.3 million SNPs for 19,990 genes. We constructed a comprehensive genome map of directional selection by detecting selective sweeps using an F ST-based approach that detects directional selection in lineages leading to the domesticated breeds and using a haplotype-based test that detects ongoing selective sweeps within the breeds. We show that candidate genes under selection are significantly enriched for loci implicated in quantitative traits important to pig reproduction and production. The candidate gene with the strongest signals of directional selection belongs to group III of the metabolomics glutamate receptors, known to affect brain functions associated with eating behavior, suggesting that loci under strong selection include loci involved in behaviorial traits in domesticated pigs including tameness.
We show that a significant proportion of selection signatures coincide with loci that were previously inferred to affect phenotypic variation in pigs. We further identify functional enrichment related to behavior, such as signal transduction and neuronal activities, for those targets of selection during domestication in pigs.
KeywordsPig Domestication Selective sweep Directional selection Quantitative traits
Identification of genes under selection is a major goal in the study of domestication in animals [1-4] and plants . The process of domestication, accompanied by selection on traits related to yield, morphology, fertility and survival during captive breeding, is believed to have dramatically affected the frequency of alleles segregating among domesticated breeds [6,7]. Mutations conferring new favorable phenotypes will be subject to a ‘selective sweep’, a rapid increase in allele frequency by artificial selection. Breeds affected by such sweeps will harbor large genetic differences with other breeds and carry signatures of selection in the genomic regions involved [8-12].
Recent genome-wide scans in diverse breeds aimed to uncover the genetic basis for phenotypic variation in pigs [3,4] showed that selection mapping approaches can detect comprehensive signatures of intense artificial selection that have led to the formation of well-defined breeds, suggesting that domestic animals can serve as models for deciphering complex phenotype-genotype association through selection mapping . Previous studies suggested that European and Asian pigs were derived from multiple independent domestication events [13-15], notably from European and Asian subspecies of wild boars that are estimated to have split about ~1 million years ago , followed by the occurrence of introgression of Asian pigs into some European breeds during the Neolithic  and 18th-19th centuries [16-19]. Although the demographic history of pig domestication is highly complicated, recent studies have identified candidate genes with distinct patterns of differentiation underlying the phenotypic diversity of breeds [2,4,20], suggesting that the breed formation results in fixation of genetically differentiated gene pools within the regions under the artificial selection exercised by breeders.
To access a comprehensive analysis of genetic variations underlying domestication traits in the well-established pig breeds (i.e. Landrace and Yorkshire), we focused on investigating highly distinct patterns in genes under the artificial selection by two different approaches: an F ST-based approach that detects directional diversifying selection  and a haplotype-based test that detects very recent selective sweeps within breeds . The F ST-based statistic detects strong shifts in allele frequencies to a fixed difference between local populations. The signatures detected here are likely to capture directional selection that occurred during or shortly after the establishment of the respective breeds [22,23]. And, the haplotype-based statistic detects a rapid rise of a selected allele to an intermediate frequency during which the long-range of haplotype association is not eliminated by recombination . These signatures are likely to capture positive selection for variants that occurred after the separation between the European and Asian pigs, and where the alleles have not reached fixation in European breeds. Our previous study on the phylogenetic diversity of the Asian wild boar and European breeds showed that the Korean wild boars can serve as a distinctive outgroup to differentiate European breed-specific genetic variations during domestication . Growing evidence suggests that the sweeps and directional selection are associated with quantitative traits in domesticated animals, like pigs , chickens , cattle , and dogs .
In this study, we applied both methods to whole genomes of two major domesticated breeds, Landrace and Yorkshire, using Asian wild boars as an outgroup. Distinct patterns of selection signatures were found at loci that may contribute to domestication phenotypes, including behavior. We further annotated candidates of artificial selection with our studies and those in previous QTL mapping studies. We suggest that signatures of distinct patterns of genetic variation detected here are valuable resources to integrate QTL information and genetic candidates into our understanding of the phenotypic variation in pig domestication.
Low haplotype diversity in domesticated pigs
To account for this low haplotype diversity in domestic breeds, we examined if our data set violated the assumption of random sampling of unrelated individuals, by testing potential family structure in our samples that may arise due to modern breeding practices. We used RelateAdmix  and confirmed that our sampled individuals are indeed unrelated (individuals are most likely separated for more than first-cousin relationships (4 ≥ generation); Additional file 1: Figure S1). A plausible explanation might be recent admixture that occurred during the complicated breeding history of these breeds, in which crosses are made between genetically divergent breeds that also experienced severe genetic bottlenecks. Under this perspective, a complex demographic model, incorporating multiple independent derivations of domesticated populations from wild boar followed by inbreeding and recent admixture among them, is likely needed to account for the observed low haplotype diversity [7,17,32]. Thus, instead of using a model-based approach, which involves inferring complex demographic parameters for domesticated pigs to approximate the null distribution, we followed an outlier approach to identify candidate genes under selection by taking 99th percentile of the empirical distribution. This approach has been shown to be useful in studying such samples as domesticated populations [2,4,33].
Mapping selective sweep in the domesticated pig breeds
We scanned the signatures of selection that is predicted to alter allele frequencies and haplotype structure within domesticated population. First, genome-wide PBS was calculated for a sliding window of 200 consecutive SNPs in Landrace and Yorkshire populations using wild boar as an out-group population, for detecting lineage specific reduction of allele frequencies. There is a negative relationship between PBS and the nucleotide diversity of domesticated lineages relative to wild boar (Additional file 2: Figure S2), indicating, as expected, that the signal of selection is most pronounced where nucleotide diversity is reduced in the domesticated lineage. Next, genome-wide integrated haplotype score (iHS) was calculated to detect long-range haplotype structure associated with directional selection . Because the iHS has its maximal power when selected alleles segregate at intermediate frequency, we limited eligible SNPs to those with MAF > 0.2 in each breed. High iHS values are evidence for ongoing directional selection that rapidly increase the selected allele frequency along with longer haplotype background of the selected alleles than that of the alternative allele. (Additional file 3: Figure S3).
Selective sweep mapping associated with quantitative traits loci in pig
For genes with putative signatures of directional selection, we investigated how many of them overlap with previously identified quantitative trait loci (Figure 3). We sought to annotate their potential roles in the process of domestication-related phenotypes rather than their broad functional terms in GO categories. For various trait categories, we identified QTL candidate genes as those located within the QTL-intervals on the genetic maps archived in the AnimalQTL database . The sum of QTL intervals for a given trait sub-category covers 5 ~ 8% of the reference genes. In total, 4055 (20.3%) genes were associated with one or more quantitative traits. By using PBS (Additional file 11: Figure S11A), 50 and 54 selection candidates identified in Yorkshire and Landrace, respectively, also overlap with QTL-candidate loci, which represent statistically significant overlaps (p = 0.039 and 0.0046, respectively). By using the iHS method (Additional file 11: Figure S11A), 55 and 58 candidate genes identified in Yorkshire and Landrace are also overlapping with QTL-candidate loci (p = 0.007 and 0.0001), respectively. We also observe a large proportion of overlap between selection candidates detected by both PBS and iHS methods and QTL candidates identified from previously published association studies (Additional file 11: Figure S11B): out of the total 399 selection candidate genes in Yorkshire, 104 (26.1%) genes are QTL-candidate genes (p = 3.50 × 10−7). Out of the total 398 selection candidate genes in Landrace, 111 (27.8%) genes are QTL-candidate genes (p = 1.72 × 10−6). The overlap remains significant when method-specific selection candidates and QTL candidates are examined (Additional file 11: Figure S11C; p = 0.00015 and 7.79 × 10−8 for PBS and iHS, respectively).
To detect recent selective sweeps, we used two complementary methods (PBS and iHS tests). Both methods have power primarily to detect candidates of recent domestication events but of different types of selection. Both approaches are necessary in order to map the comprehensive footprint of selection in the genome and to construct a comprehensive selection map for the pig genome.
Both selective sweep mapping and QTL mapping have potential to detect genes under artificial selection during domestication. However, each approach has its own limit: the former may be affected by directional selection not related to domestication and does not inform us about the phenotype under selection. The latter may map loci of phenotypes that are not targets of domestication selection, for example phenotypic differences caused by transient (deleterious) mutations, and is unable to study phenotypes that cannot be clearly scored in a controlled environment. This study demonstrated the advantage of combining these two approaches and reveals a list of genes with clear contribution to domestication processes.
Genetic variants underlying artificial selection during domestication
Putative signatures of selection can be considered as candidates for the development of domesticated pig breeds with well-defined traits over the past hundreds of years. A number of regions showing strong selection have been identified in previous studies [4,36,37].
Summary of overlaps between selective sweeps in European breeds and previous genomics studies on the signatures of selection
White coat color phenotype [ 4 ]
DNAJB5, RBBP4, PPRC1
ENSSSCG00000024845, ISOC1, KIAA1257, METTL13, TMTC1,
Selective sweep in domestic pigs [ 36 ]
ABLIM1, BTBD11, C14orf174, C8orf38, CD68, CILP, CXADR, DNAJB5
INSR, METTL13, PCDHAC2, RIMS1, RPL35, SYNE1
Domesticated pigs vs. wild boars [ 4 ]
BAI3, CCDC150, MPDU1, PGAP1, PKP4, ZNF638, ZNF804A
CNTFR, KBTBD12, LIMS3, PCDHAC2, PPFIA4, PRSS54, SF3B1
Asian introgression [ 4 ]
AQP3, NOLC1, SYNE1, ZNF638
Moreover, we showed that 14 genes identified in this study overlap with the ‘domestication’ genes identified in previous studies [4,37] (Table 1). Four of them, including BAI3, PKP4, PPFIA4, and PCDHAC2, are associated with cell adhesion, and, five of them, including LIMS3, BAI3, CNTFR, PKP4, and PCDHAC2, are associated with signal transduction . Of these genes, CNTFR provides an interesting evolutionary link between neuronal process and domestication. This gene encodes a member of the type 1 cytokine receptor family, which plays a critical role in neuronal cell survival, and may be associated with muscle strength and eating disorders . Along with strongest sweep signals at GRM7 on SSC13 and GRM8 on SSC18, selection on those genes would provide the molecular evidence about the underlying mechanism involved in the alteration of the behavior phenotype during pig domestication.
It is to note that the highest signal of selection at 73.06 Mb on the SSC13 was identified in a previous study , suggesting GHRL (73.47-73.48 Mb) as a putative candidate under selection. We found no window around the locus was ranked within 1% of PBS bins. Instead we found that the locus at 73.65 Mb was ranked as 44,381th, top 0.006% of 7 M SNPs, by the iHS method. This observation can explain why GHRL was not identified in our study. The whole genome resequencing technology made it possible to detect a high level of novel genetic variation at high resolution where commercial probe-based SNP array platforms have a certain bias in probing SNPs with minor allele frequency around 0.5 . Although those alleles with intermediate frequency are valuable resources for association studies and phylogenetic studies, they can have limited information of recent history of breed formation. As a result, the FST statistic averaged over all pairs of comparisons among 12 European breeds may be inappropriate to capture genetic variation that is fixed by directional positive selection. In fact, GHRL is located within the region showing strong signals of iHS. In this study, the high-resolution map of selective sweeps identified by using both PBS and iHS provides a comprehensive picture of genetic variation underlying pig domestication.
Additionally, out of 51 candidate loci involved in white coat color detected in a previous study , eight genes overlapped with this study. Five of these overlapping genes, including DNAJB5, ISOC1, METTL13, PPRC1, and RBBP4, are related with metabolic processes . But, we found no overlap between genes in the contrast of belted and non-belted pigs .
Selection on group III mGluR for tame behavior in domesticated breeds
By identifying genes harboring strong signals of directional selection, a new set of genes to be functionally validated beyond the list of QTLs were obtained. One of the most striking findings is a strong signal of artificial selection in GRM7 and GRM8. These genes are included in the mGlu group III receptors that are linked to the inhibition of cyclic AMP cascade. In dogs, GRM8 was detected to be positively selected using the method of identifying high divergence between indigenous dogs and wolves . In mice, the knockdown of GRM7 receptor mRNA levels reduced anxiety-associated behaviors, including stress levels and fear . We suggest that selection on genetic variation in the mGlu III receptors might have played a critical role in the process of domestication that converts anxiety-associated aggressive behaviors of wild population to tame behaviors for the adaptation to the community. In fact, tail biting, a stress-induced behavior, is one of the most important issues in welfare of pigs. Tail biting has been observed in ~30% of European pigs, where the Yorkshire pigs are more likely to be victims of tail biting than Landrace pigs . Further study is necessary to characterize the role of these genes in specific behavior of pigs.
Artificial selection on the formation of pig breeds
It is well known that the European breeds have been domesticated from European wild boars followed by introgression in the 18-19th century of Asian haplotypes, which were derived from Asian domesticated breeds that have their origin in the Asian wild boar [7,17-19]. The main cause of introgression was the effort to introduce Asian-specific traits, i.e., production efficiency, into European breeds. Our analysis could also detect these Asian haplotypes, which resemble those seen in Asian wild boars, segregating in the European breeds by the iHS method. Out of 18 introgressed loci identified in a previous study , six genes overlapped in this study (Table 1). ZNF638 is the most interesting candidate to note in that this gene encodes a nucleoplasimic protein associated with early regulator of adipogenesis that works as a transcription cofactor of CEBPs, controlling the expression of PPARG, and other proadipogenic genes . This gene might shed light on what sort of genes were introgressed, and selected during domestication of European pigs. As Asian wild boars were used as an out-group population in our analysis, European-specific selection signals involving introgression could be pronounced. Therefore, introgression and admixture among breeds has contributed the structure of the genomes of domesticated breeds. Thus, caution is needed for interpreting significance of selection candidates, particularly for methods using haplotype structure.
In this study the identification of putative sweeps based on high-depth whole genome NGS helps build an understanding of the effects of artificial selection during the process of animal domestication. Future studies are needed to fully characterize the process of complex admixture and introgression between pigs of different ancestry. To this end, a world-wide sampling of native breeds and wild boar genomes would be needed.
For the pig experiment, the study protocol and standard operating procedures were reviewed and approved by the National Institute of Animal Science’s Institutional Animal Care and Use Committee (No. 2009–077, C-grade).
Sample library preparation
Whole blood samples were collected from 7 males and 7 females of Landrace and 8 males and 8 females of Yorkshire (Large White) from the National institute of Animal Science, Korea and a set of muscle samples was collected from 3 males and 7 females of wild boars from the Southern part of Korea. Blood samples (10 ml) were drawn from the carotid artery and treated with heparin to prevent clotting. We randomly sheared 3 μg of genomic DNA using Covaris System to generate approximately 300-bp inserts. The fragmented DNA was end-repaired using T4 DNA polymerase and Klenow polymerase, and Illumina paired-end adaptor oligonucleotides were ligated to the ends. We analyzed the ligation mixture by electrophoresis on an agarose gel and purified fragments from specific gel slices. The purified DNA libraries were sequenced on a HiSeq2000 using recommended protocols from the manufacturer.
Genotype calling and SNP calling
We processed paired-end sequence reads (~15X coverage of Illumina’s HiSeq 2000) which provided ~15X coverage of the reference pig genome (SusSc.10.2). Reads were aligned to the reference genome with the Burrows-Wheeler Aligner (BWA; version 0.6.1) using default parameters. Then, three open-source packages were used for downstream processing and variant calling; Picard Tools, SAMtools , and Genome Analysis ToolKit . Specific options for SNP calling can be found in Additional file 18: Protocol.
Based on genotype likelihood values, we estimated the posterior probability of the minor allele frequency (p i, i = 1,2,…, 2 k) in the sample of 2 k chromosomes, where k is the sample size of breeds . The estimated values of p i , can then be used for population genetic inferences either by averaging over p i or by using a Maximum Posteriori Probability (MAP) estimate of the sample allele frequency. SNP calling can proceed in a probabilistic fashion by choosing a cut-off for p 0. And, the p 2k is so close to zero that it can be ignored because the definition of p as the minor allele frequency. We selected all sites with p 0 < 0.05 to obtain SNPs with a probability > 95%. More details on the algorithm for estimating the posterior probability can be found in . For each chromosome, we inferred haplotype phase information from all variable sites for the entire set of pig samples simultaneously using BEAGLE .
ADMIMXTURE was employed to analyse the population structure  . To mitigate the effects of LD, we pruned the markers according to the observed sample correlation using the ‘--indep-pairwise’ option of PLINK . The result of ADMIXTURE was used to address relatedness within each breed by using RelateAdmix . We further analyzed the population stratification based on the Multidimential scaling (MDS) analysis implemented in PLINK.
Construction of a neutral genetic variation
For the estimation of population demography, we collected putative neutral sites with a uniform distribution (p = 0.001) from inter-genic regions, which are defined as variable sites more than 100 kb away from the start or end of any gene in the pig reference genome, and obtained folded site frequency spectra for wild boar, Yorkshire, and Landrace. Then we built a simple demographic model of three populations with two steps of population bottleneck leading to the two current breeds - the first bottleneck at the foundation of domesticated lineage and the second at the formation of Yorkshire and Landrace. We estimated the demographic parameters using dadi . To avoid unrealistic estimations, we set the lower- and upper-boundaries of the prior distribution of the time of first domestication bottleneck, T b , to 5ky and 15ky, respectively. Using the first ten runs of converged parameters, we calculated standard deviations for the 11 parameters, and used them to set the upper- and lower-boundary of each parameter for the prior distribution of the subsequent runs. During the next 30 runs, we used the posterior of previous runs as a prior, but intentionally perturbed the starting parameters and checked to see if the parameter values had converged around the starting parameter values. We also compared this simple model and a model with another ancestral bottleneck prior to T b (total 12 parameters). The log-likelihood for the model of two bottlenecks (−log(L) ≈ −11000) was much higher than that for the three-bottleneck model (−log(L) ≈ −15000). Under the estimated values of parameters (Additional file 19: Table S6), we obtained neutral chromosomes to construct the distribution of PBS of European breeds. Additionally, we computed the number of distinct haplotypes, H (in a window of 30 SNPs), from 50,000 replicates of neutral simulations without recombination by using Hudson’s ms . Details of simulation commands can be found in the supplemental table (Additional file 19: Table S6).
Calculation of population-specific branch score (PBS)
F ST and other population differentiation indices are able to detect local selective sweeps but cannot indicate which lineage has experienced selection. The population branch statistic (PBS) has recently been proposed  to detect a significant change in allele or haplotype frequency along the lineage of one population after it diverged from other populations.
Similarly, the branch lengths for Yorkshire and wild boar, PBSY and PBSW respectively, are obtained. Namely, a population-specific PBS value represents the amount of allele frequency change at a given locus in the history of a population since its divergence from the other two populations . PBS was calculated for a sliding window of 200 SNPs with a step size of 50 SNPs, yielding 527,040 bins in total.
Calculation of absolute integrated haplotype scores |iHS|
The statistical detection of sites under incomplete selective sweep was performed by calculating iHS statistics over individual SNP sites. The iHS is derived from the extended haplotype homozygosity (EHH)  that looks for unusually long haplotypes at the selected allele compared to non-selected allele background. To investigate signatures of possible directional selection after domestication, we operationally defined the derived allele in a domesticated lineage as the minor or non-existent allele in the wild boar at the same site. The derived allele defined in this way may not be the true derived (mutant) allele at many sites. However, as we will later rank the strengths of selection signal according to the absolute values of iHS, the mis-inference of ancestral/derived state may only slightly lower the detection power. This statistic is based on the integral of the observed decay of EHH (extended haplotype homozygosity) away from a focal allele until EHH reaches 0.05 . This integrated EHH is computed for the ancestral (iHH A ) at the core SNP (iHH D ). The iHS statistic is given as the log ratio of iHH A to iHH D and its absolute value is standardized for each core-SNP frequency class to have mean of 0 and variance of 1. While iHS was calculated for all SNPs with MAF > 0.2 in each breed (7,202,005 sites in Yorkshire and 8,187,301 sites in Landrace), for the calculation of EHH all linked SNPs with any minor allele frequencies were used (i.e., the entire genomic set of 25,922,448 variable sites). The significance of the standardized iHS value was evaluated assuming that it follows normal distribution under the null model. All analysis was done by using rehh library  in R environment.
We took an outlier-approach to obtain the candidates of selection genes. First, each of the bins (PBS, Additional files 20 and 21: Table S7 and S8) or sites (|iHS|, Additional files 22 and 23: Table S9 and S10) that carries a strong selection signal is mapped to a gene (among 19,990 genes annotated in the pig genome). To have a high-resolution map, we limited the distance cutoff for gene annotations to be 1 kb: we define that a SNP belongs to a genes if it is located within the region defined by 1 kb upstream of transcription start site and 1 kb downstream of the transcription stop site. We choose the bin/site with the strongest signal if there was more than one bin/site assigned to one gene. Then, genes are ranked by the strength of the signal mapped to them. We obtained the top 1% of genes, producing 200 candidates for each breed and each method.
To assign associations with QTL, we used results of QTL mapping by previous studies that are compiled in the AnimalQTL database (www.animalgenome.org). The current release of the Pig QTLdb contains 8,402 QTLs from 356 publications. Each QTL is reported as an interval on the genetic map of the pig genome. We used mapped QTLs with sizes less than 5 cM (≈5Mbp) only. Redundant loci were excluded for further analysis. In total, 1,313 loci were obtained. After comparing the physical and genetic map of pig genome, the reported QTLs in the genome were obtained by interpolating their linkage map position via anchor markers (details in ), we assigned annotated genes to these QTLs, producing 4,055 QTL-candidate genes. These genes were further categorized into four classes, including “production”, “reproduction”, “exterior”, and “health”, according to the QTL database .
Availability of supporting data
Samples that were sequenced were archived at the Sequence Read Archive (SRA) under the accession numbers: SAMN03031146-SAMN03031158, SAMN03031171-SAMN03031195 from SRP047260 and SRP052927.
This work was supported by 2-7-10 Agenda Research (PJ00670701) from the National Institute of Animal Science; a grant (PJ008068, PJ008116) from the Next-generation BioGreen 21 Program, Rural Development Administration, Republic of Korea; a grant (2012R1A1A2004932) from the National Research Foundation of Korea and the 2013 Post-doctoral Fellowship Program of the Rural Development Administration, Republic of Korea.
We thank two anonymous reviewers for their helpful comments.
- Amaral AJ, Ferretti L, Megens H-J, Crooijmans RPMA, Nie H, Ramos-Onsins SE, et al. Genome-Wide Footprints of Pig Domestication and Selection Revealed through Massive Parallel Sequencing of Pooled DNA. PLoS One. 2011;6(4):e14782.View ArticlePubMed CentralPubMedGoogle Scholar
- Akey JM, Ruhe AL, Akey DT, Wong AK, Connelly CF, Madeoy J, et al. Tracking footprints of artificial selection in the dog genome. Proc Natl Acad Sci U S A. 2010;107(3):1160–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Rubin CJ, Megens HJ, Martinez Barrio A, Maqbool K, Sayyab S, Schwochow D, et al. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci U S A. 2012;109(48):19529–36.View ArticlePubMed CentralPubMedGoogle Scholar
- Wilkinson S, Lu ZH, Megens HJ, Archibald AL, Haley C, Jackson IJ, et al. Signatures of diversifying selection in European pig breeds. PLoS Genet. 2013;9(4):e1003453.View ArticlePubMed CentralPubMedGoogle Scholar
- Purugganan MD, Fuller DQ. The nature of selection during plant domestication. Nature. 2009;457(7231):843–8.View ArticlePubMedGoogle Scholar
- Andersson L, Georges M. Domestic-animal genomics: deciphering the genetics of complex traits. Nat Rev Genet. 2004;5(3):202–12.View ArticlePubMedGoogle Scholar
- Groenen MA, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF, et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature. 2012;491(7424):393–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZXP, Pool JE, et al. Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude. Science. 2010;329(5987):75–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Sabeti PC. Positive Natural Selection in the Human Lineage. Science. 2006;312(5780):1614–20.View ArticlePubMedGoogle Scholar
- Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 1973;74(1):175–95.PubMed CentralPubMedGoogle Scholar
- Akey JM. Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res. 2009;19(5):711–22.View ArticlePubMed CentralPubMedGoogle Scholar
- Maynard-Smith J, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 1974;23(1):23–35.View ArticleGoogle Scholar
- Giuffra E, Kijas JM, Amarger V, Carlborg O, Jeon JT, Andersson L. The origin of the domestic pig: independent domestication and subsequent introgression. Genetics. 2000;154(4):1785–91.PubMed CentralPubMedGoogle Scholar
- Larson G, Dobney K, Albarella U, Fang M, Matisoo-Smith E, Robins J, et al. Worldwide phylogeography of wild boar reveals multiple centers of pig domestication. Science. 2005;307(5715):1618–21.View ArticlePubMedGoogle Scholar
- Wu GS, Yao YG, Qu KX, Ding ZL, Li H, Palanichamy MG, et al. Population phylogenomic analysis of mitochondrial DNA in wild boars and domestic pigs revealed multiple domestication events in East Asia. Genome Biol. 2007;8(11):R245.View ArticlePubMed CentralPubMedGoogle Scholar
- Amaral AJ, Megens HJ, Crooijmans RPMA, Heuven HCM, Groenen MAM. Linkage disequilibrium decay and haplotype block structure in the pig. Genetics. 2008;179(1):569–79.View ArticlePubMed CentralPubMedGoogle Scholar
- Bosse M, Megens HJ, Frantz LAF, Madsen O, Larson G, Paudel Y, et al. Genomic analysis reveals selection for Asian genes in European pigs following human-mediated introgression. Nat Commun. 2014;5:4392.View ArticlePubMed CentralPubMedGoogle Scholar
- Jones G. Genetic aspects of domestication, common breeds and their origin. In The Genetics of the Pig. Edited by Rothschild MF, Ruvinsky A. CAB International, Wallingford, UK; 1998:17-50.Google Scholar
- Darwin C. The variation of animals and plants under domestication. John Murray, London. 1868.Google Scholar
- Wiener P, Wilkinson S. Deciphering the genetic basis of animal domestication. Proc Biol Sci Royal Soc. 2011;278(1722):3161–70.View ArticleGoogle Scholar
- Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, et al. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449(7164):913–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Innan H, Kim Y. Detecting local adaptation using the joint sampling of polymorphism data in the parental and derived populations. Genetics. 2008;179(3):1713–20.View ArticlePubMed CentralPubMedGoogle Scholar
- Kim Y, Gulisija D. Signatures of recent directional selection under different models of population expansion during colonization of new selective environments. Genetics. 2010;184(2):571–85.View ArticlePubMed CentralPubMedGoogle Scholar
- Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. PLoS Biol. 2006;4(3):e72.View ArticlePubMed CentralPubMedGoogle Scholar
- Kim TH, Kim KS, Choi BH, Yoon DH, Jang GW, Lee KT, et al. Genetic structure of pig breeds from Korea and China using microsatellite loci analysis. J Anim Sci. 2005;83(10):2255–63.PubMedGoogle Scholar
- Rubin CJ, Zody MC, Eriksson J, Meadows JR, Sherwood E, Webster MT, et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 2010;464(7288):587–91.View ArticlePubMedGoogle Scholar
- Qanbari S, Pausch H, Jansen S, Somel M, Strom TM, Fries R, et al. Classic selective sweeps revealed by massive sequencing in cattle. PLoS Genet. 2014;10(2):e1004148.View ArticlePubMed CentralPubMedGoogle Scholar
- Axelsson E, Ratnakumar A, Arendt ML, Maqbool K, Webster MT, Perloski M, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013;495(7441):360–4.View ArticlePubMedGoogle Scholar
- Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics. 2011;12:246.View ArticlePubMed CentralPubMedGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.View ArticlePubMed CentralPubMedGoogle Scholar
- Moltke I, Albrechtsen A. RelateAdmix: a software tool for estimating relatedness between admixed individuals. Bioinformatics. 2014;30(7):1027–8.View ArticlePubMedGoogle Scholar
- Ottoni C, Flink LG, Evin A, Georg C, De Cupere B, Van Neer W, et al. Pig domestication and human-mediated dispersal in western Eurasia revealed through ancient DNA and geometric morphometrics. Mol Biol Evol. 2013;30(4):824–32.View ArticlePubMed CentralPubMedGoogle Scholar
- Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biol. 2012;10(2):e1001258.View ArticlePubMed CentralPubMedGoogle Scholar
- Hu ZL, Park CA, Wu XL, Reecy JM. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res. 2013;41(Database issue):D871–9.View ArticlePubMed CentralPubMedGoogle Scholar
- Niswender CM, Conn PJ. Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annu Rev Pharmacol Toxicol. 2010;50:295–322.View ArticlePubMed CentralPubMedGoogle Scholar
- Li M, Tian S, Jin L, Zhou G, Li Y, Zhang Y, et al. Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars. Nat Genet. 2013;45(12):1431–8.View ArticlePubMedGoogle Scholar
- Knott SA, Marklund L, Haley CS, Andersson K, Davies W, Ellegren H, et al. Multiple marker mapping of quantitative trait loci in a cross between outbred wild boar and large white pigs. Genetics. 1998;149(2):1069–80.PubMed CentralPubMedGoogle Scholar
- da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.View ArticleGoogle Scholar
- Gratacos M, Escaramis G, Bustamante M, Saus E, Aguera Z, Bayes M, et al. Role of the neurotrophin network in eating disorders’ subphenotypes: body mass index and age at onset of the disease. J Psychiatr Res. 2010;44(13):834–40.View ArticlePubMedGoogle Scholar
- Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL, Beever JE, 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(8):e6524.View ArticlePubMed CentralPubMedGoogle Scholar
- Ai H, Huang L, Ren J. Genetic diversity, linkage disequilibrium and selection signatures in chinese and Western pigs revealed by genome-wide SNP markers. PLoS One. 2013;8(2):e56001.View ArticlePubMed CentralPubMedGoogle Scholar
- Wang GD, Zhai W, Yang HC, Fan RX, Cao X, Zhong L, et al. The genomics of selection in dogs and the parallel evolution between dogs and humans. Nat Commun. 2013;4:1860.View ArticlePubMedGoogle Scholar
- O’Connor RM, Thakker DR, Schmutz M, van der Putten H, Hoyer D, Flor PJ, et al. Adult siRNA-induced knockdown of mGlu7 receptors reduces anxiety in the mouse. Neuropharmacology. 2013;72:66–73.View ArticlePubMedGoogle Scholar
- Sinisalo A, Niemi JK, Heinonen M, Valros A. Tail biting and production performance in fattening pigs. Livest Sci. 2012;143(2):220–5.View ArticleGoogle Scholar
- Meruvu S, Hugendubler L, Mueller E. Regulation of adipocyte differentiation by the zinc finger protein ZNF638. J Biol Chem. 2011;286(30):26516–23.View ArticlePubMed CentralPubMedGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.View ArticlePubMed CentralPubMedGoogle Scholar
- McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.View ArticlePubMed CentralPubMedGoogle Scholar
- Nielsen R, Korneliussen T, Albrechtsen A, Li Y, Wang J. SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data. PLoS One. 2012;7(7):e37558.View ArticlePubMed CentralPubMedGoogle Scholar
- Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81(5):1084–97.View ArticlePubMed CentralPubMedGoogle Scholar
- Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009;5(10):e1000695.View ArticlePubMed CentralPubMedGoogle Scholar
- Hudson RR. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics. 2002;18(2):337–8.View ArticlePubMedGoogle Scholar
- Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics. 1992;132(2):583–9.PubMed CentralPubMedGoogle Scholar
- Cavalli-Sforza L. Human diversity. In: Proceedings of the 12th International Congress of Genetics: 1969; Tokyo. 1969. p. 405–16.Google Scholar
- Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF, et al. Detecting recent positive selection in the human genome from haplotype structure. Nature. 2002;419(6909):832–7.View ArticlePubMedGoogle Scholar
- Gautier M, Vitalis R. rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure. Bioinformatics. 2012;28(8):1176–7.View ArticlePubMedGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.