http://orcid.org/0000-0002-3809-6664
Figures
Abstract
Single nucleotide polymorphism (SNP) markers are used to study population structure and conservation genetics, which permits assessing similarities regarding the linkage disequilibrium and information about the relationship among individuals. To investigate the population genomic structure of 300 females and 25 males from a commercial maternal pig line we analyzed linkage disequilibrium extent, inbreeding coefficients using genomic and conventional pedigree data, and population stratification. The average linkage disequilibrium (r2) was 0.291 ± 0.312 for all adjacent SNPs, distancing less than 100 Kb (kilobase) between markers. The average inbreeding coefficients obtained from runs of homozygosity (ROH) and pedigree analyses were 0.119 and 0.0001, respectively. Low correlation was observed between the inbreeding coefficients possibly as a result of genetic recombination effect accounted for the ROH estimates or caused by pedigree identification errors. A large number of long ROHs might indicate recent inbreeding events in the studied population. A total of 36 homozygous segments were found in more than 30% of the population and these ROH harbor genes associated with reproductive traits. The population stratification analysis indicated that this population was possibly originated from two distinct populations, which is a result from crossings between the eastern and western breeds used in the formation of the line. Our findings provide support to understand the genetic structure of swine populations and may assist breeding companies to avoid a high level of inbreeding coefficients to maintain genetic diversity, showing the effectiveness of using genome-wide SNP information for quantifying inbreeding when the pedigree was incomplete or incorrect.
Citation: Joaquim LB, Chud TCS, Marchesi JAP, Savegnago RP, Buzanskas ME, Zanella R, et al. (2019) Genomic structure of a crossbred Landrace pig population. PLoS ONE 14(2): e0212266. https://doi.org/10.1371/journal.pone.0212266
Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN
Received: March 15, 2018; Accepted: January 30, 2019; Published: February 28, 2019
Copyright: © 2019 Joaquim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data necessary for replication and used to reach the conclusions are within the paper, Supporting Information files, and the Figshare Repository at https://figshare.com/articles/Genomic_structure_of_a_crossbred_Landrace_pig_population/7618739. Requests to access additional data for exclusive research purpose may be sent to the following: Embrapa Suínos e Aves contact: Dr. Monica Correa Ledur, monica.ledur@embrapa.br, telephone: 55 49 3441-0400, Rodovia BR-153, s/n, Concórdia - SC, CEP: 89700-991.
Funding: This study was funded by the Brazilian Agricultural Research Corporation – Embrapa (project number 02.09.07.006.00.01) and by the “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq) - 449564/2014-2. Letícia B Joaquim and Jorge A P Marchesi were supported by CAPES (“Coordenação de Aperfeiçoamento de Pessoal de Nível Superior”) scholarship. We would like to thank the São Paulo Research Foundation (FAPESP) for the fellowship received by Tatiane C S Chud (15/08939-0) and Rodrigo P Savegnago (2013/20091-0). Danísio P Munari was supported by a fellowship from the National Council of Technological and Scientific Development (CNPq).(2015/08939-0 and 2013/20091-0) DPM was supported by a fellowship from CNPq.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The availability of low-cost single nucleotide polymorphism (SNP) panels has become an efficient tool for evaluation of the genetic structure in livestock populations, such as cattle, poultry, and pigs. The knowledge of the linkage disequilibrium (LD) pattern can reveal the population history, as well as characterize the genetic diversity [1].
Controlling inbreeding has fundamental importance in animal production and conservation genetics because it leads to higher homozygosity, which leads to reduced genetic diversity and fitness [2]. Loss of genetic diversity has been reported in livestock breeds, for example, Vicente et al. [3] described the level of inbreeding as the major concern in native Portuguese swine breeds. Therefore, studying the loss of genetic diversity is necessary for sustainable improvement in swine populations [4]. Traditionally, the relationship among individuals are calculated from pedigree data and collateral relationships do not increase inbreeding, however, there are some disadvantages in this method, such as the inability for capturing the effect of distant relatives and the disregard of the meiotic process [5].
Inbreeding coefficients also can be estimated using genotype data, based on genomic-pedigree information [6] or calculated by the levels of homozygosity [5]. Runs of homozygosity (ROH) are contiguous homozygous stretches in an offspring genome [2]. The ROH is an alternative to estimate populations inbreeding coefficients due to more accurate detection of recessive and rare mutations compared to other methods. Furthermore, ROH detection permits describing the demographic history and domestication events in a population.
Previous studies have used SNP chip data to evaluate genetic diversity and conservation of commercial and local pigs [7–9]. Studies of the genetic structure using SNP genotyping data can provide information about the genetic ancestry of individuals and on the evolutionary history of pig’s populations that may assist breeders on mating schemes to maintain the genetic variation in swine lines. The objectives of this study were to investigate the genomic structure and population stratification in a maternal line of swine by means of the linkage disequilibrium, inbreeding coefficients, ROHs, and to determine the population stratification.
Materials and methods
Animals and DNA extraction
Tissue samples were collected from ear puncturing of the 300 females and 25 males of a maternal line of pigs. The animals were obtained from a swine breeding program located in Santa Catarina, Brazil. This line was initially developed from the crosses of eastern and western breeds and the expected proportion of the genes were 50% Landrace, 12.50% Meishan, 12.50% Xia Jing, 8.33% Hampshire, 8.33% Pietrain and 8.33% Large White. Genomic DNA was extracted from 200 mg of the harvested tissue, with PureLink Genomic DNA Mini Kit (Invitrogen, Calrsbad, USA) following the manufacturer protocol. DNA quantity, quality, and integrity were assessed using the NanoDrop 1000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA) and the samples read with 260/280 nm between 1.75–1.95 were diluted to a final concentration of 50 ng/μl.
Genotyping and quality control of samples and SNPs.
The animals were genotyped using the Illumina PorcineSNP60-v2 BeadChip, comprising 61,565 SNPs distributed throughout the porcine genome. Genotype quality control was performed using PLINK v.1.9 [10] to remove samples and SNPs with call rate lower than 90%. According to Bosse et al. [2] no additional filters, such as minor allele frequency were applied in this study.
Linkage disequilibrium.
The LD was measured using the r2 equation:
where f(A), f(a), f(B), and f(b) are the respective frequencies of alleles A, a, B and b, and D is f(AB)−f(A)f(B), as proposed by Hill and Robertson [11]. For all pairs of autosomal SNPs, the r2 measures were calculated using the parameters—ld-window 60,000—ld-window-r2 0 of PLINK [10]. The r2 was calculated for all adjacent SNPs in 60,000 Kb windows.
Inbreeding coefficient based on pedigree and genomic data
The inbreeding coefficient using pedigree data (FPED) on three generations of a swine maternal line population was estimated following the methodology proposed by Wright (1931) [12]. Inbreeding estimates were also calculated using runs of homozygosity (FROH) for autosomal SNPs by means of the PLINK v.1. [10]. SNPs located on sex chromosomes were excluded because recombination on these chromosomes is different from the autosomes, and also because the genetic map resolution for the X-chromosome differed from the autosomes in the pig genome [2]. The ROHs were defined as segments with at least 50 homozygous SNPs at a minimum distance of 1,000 Kb per animal, allowing one heterozygous SNP and one missing SNP within a 50 SNPs window [9].
From the identification of ROHs, the inbreeding coefficient for each genomic individual (FROH) was estimated using the following equation:
in which ∑LROH is the sum of all ROHs identified for each individual by length (L) of the pig genome (2,808,525 Kb, Scrofa10.2, may 2014). The ROH frequencies were calculated for all ROHs identified in the population and then compared to verify how many individuals shared the same ROHs (same start and end of the ROH positions).
The ROH regions observed in more than 30% of the samples were investigated to identify possible conserved regions and determine the genes that might be associated with adaptive traits such as survival and disease resistance. Genes located on ROH regions were identified using the UCSC genome browser (Scrofa 10.2) and Ensembl ID gene was used for the enrichment analysis. Gene ontology (GO) enrichment analysis was performed using David 6.8 database (https://david.ncifcrf.gov/home.jsp) and the results were considered statistically significant at FDR cutoff < 0.20. For visualization and reduction of redundant GO-terms, the REVIGO software [13] was used.
Population structure
Population stratification analyses considering the admixture model were carried out using STRUCTURE software v.2.3.4 [14]. The K values (number of populations or genetic groups) varying from one to eight were estimated using a Bayesian approach considering 10,000 iterations and a burn-in period of 1,000 cycles. The analyses were repeated ten times for each K while the correct number of K was estimated using the delta-k statistics, which is based on the shift rate in the likelihood logarithm between successive values of K [15]. Delta-k was calculated using the STRUCTURE HARVESTER software v.0.6.94 [16].
In addition, the level of ancestry divergence among populations (Fst) was obtained using the ADMIXTURE software [17], which the likelihood method was applied to estimate the ancestry matrix coefficients. Principal component analysis was performed for the population using PLINK v.1.9 [10]. Analyses were carried out with all SNPs and also excluding SNPs with r2 greater than 0.20 to eliminate redundancy between markers in high LD.
Results
After the genotype quality control, one animal and 3,795 SNPs were excluded due of the low call rate. Therefore, 57,770 SNPs were analyzed, including 1,498 SNPs on chromosome X (SSCX), 15 SNPs on chromosome Y (SSCY), and 4,076 SNPs with no defined position (Table 1).
Linkage disequilibrium
A total of 52,196 SNPs were used to calculate the average LD (r2) between all adjacent SNPs, with distance less than 100 Kb. The average and standard deviation of estimated r2 was 0.29 ± 0.31, indicating high variability around the mean value estimated for the LD. It was observed that as the distance between markers increased, the LD values decreased, and for distances greater than 1 Mb, the mean r2 was less than 0.20 (Fig 1). The mean r2 converged to 0.30, considered a strong LD and useful for QTL mapping, approximately at 200 to 300 Kb.
Inbreeding coefficient
ROH covered 11.89% of the genome of this population with an average of 54 ROH per animal and a maximum number of 74 ROH. The size ranged between 152,110 Kb and 652,500 Kb, with an average of 333,900 Kb. The value of FROH was 0.119, with 0.054 and 0.232 as minimum and maximum values, respectively. The pedigree analysis resulted in an average FPED of 0.00011, ranging from 0 to 0.015 and with a low correlation (0.04) between FROH and FPED (Fig 2).
Inbreeding coefficient of the commercial maternal line using genomic data (A) and pedigree records (B).
Shared ROH
No identical segments were observed to be completely shared among all the individuals. The chromosomes SSC1, SSC4, SSC7, and SSC14 were those with the largest number of shared ROHs in this population (Figs 3 and 4). The SSC1 stood out with more than 1,000 shared ROH among chromosomes SSC1, SSC4, SSC7, and SSC14.
A total 36 homozygous segments were observed in more than 30% of the population. The ROHs identified had a small size (1 to 5 Mb) and most of these segments (78%) were located on SSC17. Other ROHs observed in more than 30% of the population were on SSC1, SSC4, SSC7 and SSC14.
A total of 240 genes were found in the homozygous regions with a frequency greater than 30%. Before the GO enrichment analysis, 26 genes were identified in 17 enriched GO-terms associated with biological processes (FDR < 0.20; S1 Table), which were then reduced by redundancy to 12 GO-terms grouped in five superclusters (Fig 5). The most enriched supercluster harboring the largest number of genes was the negative regulation of hormone secretion, followed by cerebral cortex development. These biological processes may be related to the domestication process of the studied animals. The response to fungus (GO:0009620), enriched with the genes IL25 (interleukin 25) and MALT1 (MALT1 paracaspase) (S1 Table; Fig 5), may be involved with survival traits such as resistance to diseases.
Each rectangle is a single cluster representative. The representatives are joined into ‘superclusters’ of related terms, visualized with different colors.
The region between 48,283,163 and 50,313,732 bp (base pair) on SSC14 was shared among 97 individuals (30%) and harbor the gene LIF (Leukemia inhibitory factor) associated with reproductive traits. Two other genes identified on this same ROH segment, the ZNRF3 (zinc and ring finger 3) and KREMEN1 (kringle containing transmembrane protein 1), were associated with waist-hip ratio and fat distribution. The METTL3 (methyltransferase like 3) gene found on ROH located on SSC7 is involved in the adipogenesis. The most shared ROH of the population (~47%) was located on chromosome SSC17 (between 17,836,349 and 20,730,659 bp), with length of 2.89 Mb, harboring 9 genes, of which the TMX4 (thioredoxin-related transmembrane protein 4) and PLCB1 (phospholipase C beta 1) were enriched in the superclusters negative regulation of hormone secretion and cerebral cortex development, respectively (Fig 5).
Analysis of Population Structure
Two possible genetic group structures were revealed, represented by the delta-k value [14] (S1 Table). The largest portion (59%) of the population alleles were grouped in cluster 1 (Fig 6). The maximum and minimum values and mean allelic ratio for the animals sampled in cluster 1 were 0.996, 0.05 and 0.59 ± 0.23 respectively, while for cluster 2 the values were 0.95, 0.004 and 0.41 ± 0.22 (S2 Table), respectively. The results obtained from the likelihood procedure using the ADMIXTURE software were similar to those obtained in the Bayesian analysis, with the STRUCTURE software. Therefore, only the results of the STRUCTURE software are presented (S2 to S4 Figs). The average value of genomic Fst indicated that the degree of genetic divergence was 0.10 for the population studied.
The two colors represent the clusters formed according to the results: Red = Cluster 1 and Green = Cluster 2.
The results of the principal component analysis for all SNPs and for SNPs with LD less than 0.20 were similar. The population was divided into two clusters, based on the first principal component (Fig 7). The principal component can be interpreted as an index. Thereby for each individual was assigned an index value for each principal component, called an individual score of the principal component. From the visual analysis of Fig 6, an arbitrary value (0.02) for individual scores was obtained to categorize the clusters. Thus, individual scores higher or equal to 0.02 were placed in the green cluster, and animals with individual scores lower than 0.02 were placed in the blue cluster. Animals in the blue and green clusters have a higher proportion of eastern and western breeds, respectively.
Discussion
A previous study with Landrace using the Illumina Porcine 60K BeadChip reported average r2 equal to 0.32 between markers with distance 0.5 Mb, excluding the SNPs for minor allelic frequency (MAF) and Hardy-Weinberg equilibrium criteria [18]. Badke et al. [19] used the software BEAGLE, excluding the same SNPs, reported average r2 equal to 0.19 for markers spaced 0.5 Mb in Landrace populations. These values are in agreement with the average r2 obtained in this study (0.29) for a Landrace-based pig line. Furthermore, Badke et al. [19] observed a higher average r2 for distances with less than 1 Mb. The LD decreasing pattern as the distances increase is expected because as the distance between markers on the same chromosome increase and the possibility of occurrence of recombination events (breaking of the linkage between the markers) increase as well.
The low correlation between inbreeding coefficient estimates based on ROH and pedigree could be the result of genetic recombination effect accounted for by the ROH estimates, once this effect cannot be considered to estimate the inbreeding coefficients from pedigree data. Moreover, estimates based on pedigree records neglect the effects of inbreeding accumulated over generations and it does not consider pedigree errors accumulated from past generations [5].
The correlation between FROH and FPED found in this study (0.04) was lower than the 0.24 reported by Zanella et al. [9] in a Landrace population. Our results suggested that FPED has been underestimated because the pedigree analysis considered three generations and possible animal identification errors may occurred.
A high number of long ROHs (> 5 Mb) may indicate that the population has not undergone recent crossings; otherwise, the crossing between different breeds would have caused these segments to breakdown. Herrero-Medrano et al. [8] reported mean ROH size between 50 and 100 Mb, considering long ROHs those greater than 100 Mb in Iberian breeds, while Bosse et al. [2] defined long ROHs those larger than 5 Mb. These authors studied wild and commercial pigs of the Asian and European continents and found a higher proportion of average ROH (0.1 to 5 Mb) in European commercial breeds. Similar results to this study have been reported for pure lines of Landrace and Large White, in which long ROHs with an approximate average of 252 Mb and 280 Mb, respectively, were observed [9].
The high prevalence of long ROHs can also indicate recent inbreeding of the population according to our results found for FROH. Fisher [20] noted that the expected size of a DNA segment, which is identical by descent, follows the exponential distribution with mean equal to ½ g Morgans, where "g" is the number of past generations from the common ancestor. Therefore, long ROH (> 5 Mb) reflected inbreeding from a common ancestor, less than 10 past generations ago.
Inbreeding can lead to increased homozygous frequencies and risks of deleterious recessive genes to be co-expressed, causing inbreeding depression. The deleterious effects of inbreeding depression have been evaluated in several livestock species. Saura et al. [21] reported inbreeding depression in specific regions containing genes associated with litter size in Iberian pigs. Inbreeding depression was also observed in dairy cattle, where the increase of 1% of FROH resulted in a reduction of total milk yield to 205 days postpartum of 20 kg, increases in days open of 1.72 days and a decrease in some linear-type traits [22].
The high number of ROHs shared on SSC1 occurs because it is the largest chromosome of the pig genome, with a higher number of markers compared to shorter chromosomes (Table 1; Fig 3). The high number of ROH with low frequency identified at the end of the chromosomes may be due to recombination events (Fig 4). According to Tortereau et al. [23], recombination rates vary between and along chromosomes, and regions with high recombination rates tend to cluster close to the end of the chromosomes, regardless of the centromere position, as observed in this study.
The SSC17, which had the largest number of shared ROH of the population, has revealed the presence of QTLs affecting meat quality, carcass composition and signal peptide involved in stress reactions [24–25]. The ROHs identified in the present study present evidences of the selection history of this pig maternal line population. Twenty-one genes, including the PLCB1 and TMX4, located on the most frequent ROH, were enriched in the superclusters negative regulation of hormone secretion, cerebral cortex development and locomotory behavior, which indicates possible selection for behavioral traits (Fig 5). The domestication pressure on loci that control fitness traits may favor the increase in the frequency of beneficial alleles creating homozygous regions in the genome of the studied population. Frantz et al. [26] reported that the genomes of domestic pigs have strong signatures of selection at loci that affect behavior and morphology, and still, that recurrent selection for traits related to domestication likely counteracted the homogenizing effect of gene flow from wild boars and created 'islands of domestication' in the genome.
Reproductive traits have been strongly selected in the last years to increase the production, especially in maternal lines, where the fertility and morphology of the sows are fundamental for a large litter size. Important genes were identified involving reproductive traits such as LIF, which plays a role on total number of piglets born in Landrace, Large White and Duroc breeds [27], and the genes ZNRF3 and KREMEN1, which have been associated with waist-hip ratio and sexual dimorphism in the genetic basis of fat distribution [28]. Studies with pigs using other methodologies, have also identified significant portions of selection signatures that coincide with loci that control biological processes related to behavioral and reproductive traits, which may have been favored by the selection process [29–30].
The Fst value found in our study (0.10) was lower than the results reported by Herrero-Medrano et al. [8] in domestic and feral pigs in the Iberian Peninsula (0.22). The results indicated that approximately 10,000 SNPs would be sufficient to correct the effect of population stratification in potential genome-wide association studies in our population [16]. Moreover, Li et al. [31] using pair-wise Fst between Landrace and two Chinese indigenous pig breeds found Fst higher than we observed (Fst = 0.5480 for Erhualian and Fst = 0.5800 for Meishan), suggesting that Landrace had distant genetic relationship compared with the two Chinese breeds.
The results for K indicated the separation between the two genetic groups could be explained by the crossing between the Eastern and Western breeds used to form the studied population. The first cluster (population 1; Fig 6) grouped the animals with a higher proportion of Western breed alleles, while the second cluster (population 2; Fig 6) grouped the animals with a higher proportion of Eastern breed alleles. In the development of maternal lines of pigs, crossing between eastern and western breeds have been common. Maternal lines are developed to produce sows able to wean large numbers of piglets per litter per year. The western breeds Landrace and Large White and the eastern breeds, Meishan and Xia Jing, are commonly used as maternal lines due to high prolificacy and maternal ability [32].
As showed in Fig 6, the observed proportion of Eastern breed alleles was higher (~49%) than expected for this crossing (~25%), which can be attributed to genetic recombination events in the parental chromosomes that may occur during meiosis [33]. Moreover, the variation in the genetic makeup of animals regarding the allelic distribution of eastern and western breeds might be due to the selection of the sample, which sought to represent all the genetic variability of the studied population.
The results showed the importance of the SNP panels to provide knowledge on the population structure of the swine. The estimates of the inbreeding coefficient using genomic data indicated that the genetic diversity of the population studied should be frequently evaluated. Mating between less related individuals should be prioritized to ensure the efficiency of the breeding program applied to the population, to prevent inbreeding depression on traits of economic importance and to maintain genetic variation. Moreover, the study of the ROH regions of this population aids to understand of how the selective pressure and domestication can shape the genome during the animal domestication process, and it shows evidence that the ancestors of the studied population have undergone selective pressure for behavior and reproduction traits.
Supporting information
S1 Table. GO-terms associated with biological processes.
https://doi.org/10.1371/journal.pone.0212266.s001
(DOCX)
S2 Table. Structure results—Allelic ratio per animal for each cluster, obtained with the Structure software.
https://doi.org/10.1371/journal.pone.0212266.s002
(DOCX)
S1 Fig. Statistical delta-k based on the clustering method of Evanno et al.
https://doi.org/10.1371/journal.pone.0212266.s003
(TIF)
S2 Fig. Genetic makeup of animals from the studied commercial maternal line for K = 3.
Red = Cluster 1; Green = Cluster 2; Blue = Cluster 3.
https://doi.org/10.1371/journal.pone.0212266.s004
(TIF)
S3 Fig. Genetic makeup of animals from the studied commercial maternal line for K = 4.
Red = Cluster 1; Green = Cluster 2; Blue = Cluster 3; Yellow = Cluster 4.
https://doi.org/10.1371/journal.pone.0212266.s005
(TIF)
S4 Fig. Genetic makeup of animals from the studied commercial maternal line for K = 5.
Red = Cluster 1; Green = Cluster 2; Blue = Cluster 3; Yellow = Cluster 4; Pink = Cluster
https://doi.org/10.1371/journal.pone.0212266.s006
(TIF)
References
- 1. Amaral AJ, Megens HJ, Crooijmans RP, Heuven HC, Groenen MA. Linkage disequilibrium decay and haplotype block structure in the pig. Genetics 2008, 179: 569–579. pmid:18493072
- 2. Bosse M, Megens H-J, Madsen O, Paudel Y, Frantz LAF, Schook LB, et al. Regions of homozygosity in the porcine genome: consequence of demography and the recombination landscape. Plos Genet 2012, 8: e1003100. pmid:23209444
- 3. Vicente AA, Carolino MI, Sousa MCO, Ginja C, Silva FS, Martinez AM, et al. Genetic diversity in native and commercial breeds of pigs in Portugal assessed by microsatellites. J Anim Sci 2008, 86: 2496–2507. pmid:18567729
- 4. Melka MG, Schenkel F. Analysis of genetic diversity in four Canadian swine breeds using pedigree data. Can J Anim Sci 2008, 90: 331–340.
- 5. Mcquillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, et al. Runs of Homozygosity in European Populations. Am J Hum Genet 2008, 83: 359–372. pmid:18760389
- 6. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008, 91: 4414–4423. pmid:18946147
- 7. Ai H, Huang L, Ren J. Genetic Diversity, Linkage disequilibrium and delection signatures in chinese and western pigs revealed by Genome-Wide SNP markers. Plos One 2013, 8: e56001. pmid:23409110
- 8. Herrero-Medrano J, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M, et al. Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 2013, 14: 106–119. pmid:24172017
- 9. Zanella R, Peixoto JO, Cardoso LL, Biegelmeyer P, Cantao ME, Otaviano AR, et al. Genetic diversity analysis of two commercial breeds of pigs using genomic and pedigree data. Genet Selec Evol 2016, 48: 24.
- 10. Purcell S, Neale B, Todd-brown K, Thomas L, Ferreira MAR, Bender D, et al.: PLINK: a tool set for whole genome association and population-based linkage analyses. Am J Hum Genet 2007, 81: 559–575. pmid:17701901
- 11. Hill WGG, Robertson A. Linkage Disequilibrium in Finite Populations. Theor Appl Genet 1968, 38: 226–231. pmid:24442307
- 12. Wright S. Evolution in Mendelian populations. Genet 1931, 16: 97–159.
- 13. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. Plos one 2011, 6: 7.
- 14. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Eco Res 2009, 9: 1322–1332.
- 15. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Eco Res 2005, 14: 2611–2620.
- 16. Earl DA, Vonholdt BM. Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Conserv Genet Resour 2011, 4: 359–361.
- 17. Alexander DH, Novembre J, Lange K. Admixture 1.23 software manual. 2013: 11, Available in: https://www.genetics.ucla.edu/software/admixture/admixture-manual.pdf. Acesso em: 08 de janeiro de 2016
- 18. Wang L, Sorensen P, Janss L, Ostersen T, Edwards D. Genome-wide and local pattern of linkage disequilibrium and persistence of phase for 3 Danish pig breeds. BMC Genet 2013, 13: 115–126.
- 19. Badke YM, Bates RO, Ernst CW, Schwab C, Steibel JP. Estimation of linkage disequilibrium in four US pig breeds. BMC Genom 2012, 13.
- 20. Fisher RA. A fuller theory of “junctions” in inbreeding. J Hered 1954, 8: 187–197.
- 21. Saura M, Fernández A, Varona L, Fernández AI, Cara MAR, Barragán C, et al. Detecting inbreeding depression for reproductive traits in Iberian pigs using genome-wide data. Genet Selec Evol 2015, 47.
- 22. Bjelland D, Weigel K, Vukasinovic N, Nkrumah J. Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci 2013, 96: 4697–4706. pmid:23684028
- 23. Tortereau F, Servin B, Frantz L, Megens H-J, Milan D, Rohrer G, et al. A high density recombination map of the pig reveals a correlation between sex-specific recombination and GC content. BMC Genomics 2012, 13: 586–598. pmid:23152986
- 24. Pierzchala M, Cieslak D, Reiner G, Bartenschlager H, Moser G, Geldermann H. Linkage and QTL mapping for Sus scrofa chromosome 17. J Anim Breed Genet 2003, 120: 132–137.
- 25. Lahbib-Mansaisa Y, Karlskov-Mortensenb P, Momparta F, Milana D, Jørgensenb CB, Cirerab S, et al. A high-resolution comparative map between pig chromosome 17 and human chromosomes 4, 8, and 20: Identification of synteny breakpoints. Genomics 2015, 86: 405–413.
- 26. Frantz LAF, Schraiber JG, Madsen O, Megens HJ, Cagan A, Bosse M, et al. Evidence of long-term gene flow and selection during domestication from analyses of Eurasian wild and domestic pig genomes. Nat Genet 2015, 47: 1141–1148. pmid:26323058
- 27. Leonova MA, Getmantseva LV, Vasilenko VN, Klimenko A.I, Usatov AV, Bakoev SY, et al. Leukemia inhibitory factor (LIF) gene polymorphism and its impact on reproductive traits of pigs. Am J Anim Vet Sci 2015, 10(4): 212–216.
- 28. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010, 42: 949–960. pmid:20935629
- 29. Moon S, Kim TH, Lee KT, Kwak W, Lee T, Lee SW, et al. A genome-wide scan for signatures of directional selection in domesticated pigs. BMC Genomics 2015, 25: 16:130.
- 30. Wang C, Wang H, Zhang Y, Tang Z, Li K, Liu B. Genome-wide analysis reveals artificial selection on coat colour and reproductive traits in Chinese domestic pigs. Mol Ecol Resour 2015, 15(2): 414–424. pmid:25132237
- 31. Li Z, Wei S, Li H, Wu K, Cai Z, Li D, et al. Genome-wide genetic structure and differentially selected regions among Landrace, Erhualian, and Meishan pigs using specific-locus amplified fragment sequencing. Nature 2017, 7.
- 32.
Irgang R: Melhoramento Genético Aplicado à Produção de Suínos: Raças e linhagens na produção de suínos. In: Ferreira AH, Carraro B. Dallanora D, Machado G, Machado IP, Pinheiro R, Rohr S: Produção de suínos: teoria e prática, Qualitá, Brasília 2014: 51–60.
- 33. Sölkner J, Frkonja A, Raadsma HW, Jonas E, Thaller G, Gootwine E, Seroussi E, et al. Estimation of Individual Levels of Admixture in Crossbred Populations from SNP Chip Data: Examples with Sheep and Cattle Populations. Interbull Bulletin 2010, 42: 62–66.