Printer Friendly

In silico approaches to discover the functional impact of non-synonymous single nucleotide polymorphisms in selective sweep regions of the Landrace genome.


The recently developed high-throughput and cost-effective genotyping techniques allow the thorough exploration of genetic variation in domestic animals. In particular, whole-genome sequencing is a powerful approach for detecting massive amounts of single nucleotide polymorphisms (SNPs) in genome-wide sequence data. One of the strategies for studying genetic variation is to detect the selective sweep signatures based on patterns of linkage disequilibrium (LD) [1], which was proposed by Smith and Haigh [2], and other researchers have expanded and applied it [3-6]. Wang et al [7] performed a relative extended haplotype homozygosity (REHH) test to detect selective sweep regions of the Landrace genome using genotyping by genome sequencing. The genetic signature for selection of body size investigated by estimating the XP-EHH statistic in the Yucatan miniature pig [8]. Whole-genome re-sequencing of Jeju black pig (JBP) and Korean native pigs (which live on the Korean peninsula) were performed to identify signatures of positive selection in JBP, the true and pure Korean native pigs [9]. Studies of selective sweeps in pigs have revealed strong selection signatures associated with genes underlying economic traits such as the body length, disease resistance, pork yield, muscle development, and fertility [10,11].

Diverse types of variants, e.g. copy number variations, insertion/deletion (InDel) and structural variations, have been identified in the selective sweep regions of the Landrace genome [7]. Unlike many SNPs are phenotypically neutral, nonsynonymous SNPs (nsSNPs) that are located in protein-coding regions and lead to amino acid substitutions in the corresponding protein product might have functional impacts and play a role in biological processes through altering the protein structure, stability, or function, these variations are often strongly associated with several phenotypes [12]. In the case of pigs, previous studies reported the different polymorphic patterns of nsSNPs in the Toll-like receptor genes between European wild boars and domestic pigs [13].

In this study, we aimed to identify nsSNPs in the selective sweep regions of the Landrace genome that might be related to superior reproductive capacity or growth and development during the perinatal period, and gene networks that were enriched in Landrace genome. Finally, impact of amino acid changes by nsSNPs on protein function was also investigated using in silico bioinformatic tools.


Sample preparation and whole-genome re-sequencing

In this study, a whole-genome sequence data set consisting of 14 Landrace (Danish), 16 Yorkshire (Large White) pigs, and 10 wild boars, were obtained from the NCBI Sequence Read Archive database (SRP047260). FastQC software [14] were used to perform a quality check on raw sequence data. Using Trimmomatic-0.32 [15], potential adapter sequences were removed before sequence alignment. Paired-end sequence reads were mapped to the pig reference genome (Sscrofa 10.2.75) from the Ensembl database using Bowtie2 [16] with the default settings. For downstream processing and variant calling, following software packages were used: Picard tools (http://, SAMtools [17], and Genome Analysis Toolkit (GATK) [18]. "CreateSequenceDictionary" and "MarkDuplicates" Picard command-line tools were used to read reference FASTA sequences for writing bam files with only a sequence dictionary and to filter potential polymerase chain reaction duplicates, respectively. Using SAMtools, index files were created for the reference and bam files. Local realignment of sequence reads was performed to correct misalignment due to the presence of small insertions and deletions using GATK "Realigner-TargetCreator" and "IndelRealigner" arguments. In addition, base quality score recalibration was performed to obtain accurate quality scores and to correct the variation in quality with machine cycle and sequence context. For calling variants, GATK "UnifiedGenotyper" and "Select-Variants" arguments were used with the following filtering criteria. All variants with i) a Phred-scaled quality score of less than 30; ii) read depth less than 5; iii) MQ0 (total count across all samples of mapping quality zero reads) >4; or iv) a Phred-scaled p-value using Fisher's exact test of more than 200 were filtered out to reduce false-positive calls due to strand bias. "vcf-merge" tools of VCFtools [19] were used to merge all of the variants calling format files for the 40 samples. Additionally, tri-allelic SNPs were excluded, and all filtered SNPs on autosomes (a total of 26,240,429 SNPs) were annotated using an SNP annotation tool, SnpEff version 4.1a and the Ensemble Sus scrofa gene set version 75 (Sscrofa10.2.75). 53,998 nsSNPs (missense variants) were identified on autosomes from 40 sets of pig whole-genome data (Figure 1). Then, certain SNPs due to poor genotyping quality were removed; 4,174 SNPs were excluded based on Hardy-Weinberg equilibrium testing (p[less than or equal to] 0.000001). In addition, a total of 19,002 SNPs with a minor allele frequency of <0.05 were excluded. After genomic data quality control, there were 30,822 SNPs for downstream analysis.

Population structure analysis

Population structure analysis was performed to infer the population structure of the 40 pigs with whole-genome sequence data. The program STRUCTURE ( was used to evaluate the extent of substructure among the 40 individuals belonging to three pig breeds. Bayesian clustering analysis implemented in STRUCTURE (version 2.3.4) was used to estimate the population structure using 30,822 nsSNPs from the whole-genome sequencing data of the 40 pigs [20]. An initial burn-in of 10,000 iterations were followed by 10,000 iterations for parameter estimation was sufficient to ensure the convergence of parameter estimates. To estimate the number of populations (the K parameter of STRUCTURE), the dataset was analyzed by allowing for the values of K = 3 (Figure 2).

Identify nsSNPs in Landrace selective sweep regions

A previous study identified 269 selective sweep regions of the Landrace genome using the REHH test (p-value[less than or equal to]0.01), which was used to detect the recent positive selection signatures by evaluating how LD decays across the genome 7. A total of 261 of 269 selective sweep regions of the Landrace genome were on autosomes, and 345 nsSNPs belonged to 55 Landrace selective sweep regions were identified (Figure 3). Overall, 345 nsSNPs in 55 selective sweep regions of the Landrace genome belonged to 90 genes, and gene function 64 of total 90 genes were discovered. Gene ontology (GO) network analysis was performed using ClueGO [21] to infer the biological meaning of the genes related to nsSNPs in Landrace selective sweep regions.

Predicting damaging amino acid substitutions of non-synonymous SNPs specific to the Landrace breed

In this study, the functional effects of nsSNPs were predicted using the following in silico algorithms: sorting intolerant from tolerant (SIFT) [22] and polymorphism phenotyping v2 (Polyphen-2) [23]. Total 345 nsSNPs in 55 selective sweep regions of the Landrace genome were analyzed using SIFT. NsSNPs with less than 0.05 of SIFT score, which was regarded as deleterious, were used for PolyPhen-2 ver. 2.2.2 (http://genetics. analysis to predict the influence of an amino acid change on the structure and function of a protein by using specific empirical rules [23]. From the results of Polyphen-2 analysis, nsSNPs were classified into probably damaging, possibly damaging, and benign based on their scores (ranging from 0 to 1); if Polyphen-2 score for nsSNPs was more than 0.95, nsSNPs were considered to be "probably damaging", while for values between 0.5 and 0.95, they were regarded as "possibly damaging". The scores below 0.5 were classified as "benign". In this study, probably damaging and possibly damaging SNPs were judged as to have strong effects on protein function.

If the SIFT score of each SNP was less than 0.05, the SNP was regarded as being deleterious, which could strongly affect protein function. Additionally, we performed PolyPhen-2 (version 2.2.2) analysis to predict the influence of an amino acid change on the structure and function of a protein by using specific empirical rules [23]. Amino acid sequences corresponding to nsSNPs of interest from the Ensembl database were obtained to perform PolyPhen-2 analysis.


DNA sequencing, data preprocessing, and genetic variant calling

A total of 26,240,429 SNPs were extracted on autosomes from the whole-genome sequences of the 40 pigs, including 14 Landrace individuals, and annotated all extracted SNPs using SnpEff version 4.1a ( [24]. Through this SNP annotation, all SNPs were divided into 31 functional classes, including nsSNPs (Figure 1). Most of the SNPs were located in intergenic or intronic regions; finally, we identified 53,998 nsSNPs (0.205% of the total SNPs). After quality control for all of the nsSNPs, there were 30,822 nsSNPs. Population structure analysis using the genotypic information on these SNPs provided the genetic relationship among breeds. The results from analyzing the population structure clearly distinguished Landrace, Yorkshire, and wild boar (Figure 2).

nsSNPs in Landrace selective sweep regions

A total of 269 selective sweep regions were obtained from a previous study on the Landrace breed to identify nsSNPs related to selective sweeps [7], and a total of 345 nsSNPs were identified from 55 Landrace selective sweep regions (Figure 3) by re-analyzing the data of previous study resequencing data of Landrace and Yorkshire [7]. Information of 345 nsSNPs in the selective sweep regions of the Landrace genome belonged to 90 genes were shown in Table 1. The average number of nsSNPs per gene was 3.83, and the gene length was not correlated to the number of nsSNPs (Figure 4). The deleted in malignant brain tumors 1 (DMBT1) gene consisted of 18 exons harboring 26 nsSNPs that were evenly distributed; this gene had the highest number of nsSNPs among the 90 genes. Moreover, there were considerable frequency differences between Landrace and other breeds (Yorkshire and wild boar) in nsSNPs of the DMBT1 gene (Figure 5). This suggests that DMBT1 is significantly affected by many nsSNPs in Landrace breed establishment. Previous studies strongly suggested an important role of DMBT1 in the process of fertilization in pigs; it was shown to be secreted in the oviduct and involved in the mechanism of fertilization in porcine species [25,26]. In particular, Ambruosi et al [25] reported that oviduct fluid containing DMBT1 protein was strongly related to the preparation of gametes for fertilization, fertilization itself, and subsequent embryonic development. Therefore, we assumed that nsSNPs of DMBT1 of Landrace might correlate with the fertilization capacity that was acquired during artificial selection, making the reproductive capacity of Landrace pigs superior to that of other breeds [27].

Among 90 genes, the functions of 64 genes were predicted, and we performed GO network analysis of these 64 genes using ClueGO [21] to draw inferences on the biological effects of nsSNPs in Landrace selective sweep regions. The information on these networks is shown in Figure 6 and Table 2. The GO network analysis revealed that 19 of the total of 64 genes were associated with five major GO terms, and these major terms were closely related to the reproductive capacity or growth and development of the Landrace breed during the perinatal period. In the GO network, seven genes (C-C motif chemokine ligand 1 [CCL1], CCL23, hemopexin, mucolipin 1, leucine zipper and EF-hand containing transmembrane protein 2, phospholipase A2 group VI [PLA2G6], and protein tyrosine phosphatase, receptor type, C [PTPRC]) were related to cellular metal ion homeostasis in seven major GO terms, and this cluster was the largest in this network. Moreover, these terms were similar to the GO results of a positively selected region identified in Wang's study of Landrace selective sweeps [7]. Metal ions are one major group of mineral; since components of follicular fluid such as Ca, Cu, and Fe significantly increase as the follicles increase in size, some minerals appear to play an important role in pig reproduction [28]. Five genes (ATPase phospholipid transporting 8A1 [ATP8A1 ], CCL1, kinesin family member 20B, plasminogen, and PTPRC) were shown to be involved in the positive regulation of locomotion, and its network consisted of four GO terms (positive regulation of locomotion, positive regulation of cellular component movement, positive regulation of cell motility, and positive regulation of cell migration). This cellular movement is a central process in the development and maintenance of multicellular organisms. In addition, tissue formation during embryonic development requires the orchestrated movement of cells in a particular direction. It is reasonable to assume that several genes of these four significant GO terms in the selective sweep regions of the Landrace genome might be related to the superior growth and development of Landrace during the perinatal period. Ten genes (ATP8A1, bridging integrator 2, CD93 molecule [CD93], exophilin 5, GRB2 associated binding protein 2, n-ethylmaleimide-sensitive factor attachment protein, beta, PLA2G6, PTPRC, and vesicle associated membrane protein 1 [VAMP1]) were associated with exocytosis, and five genes (ATP8A1, CD93, DMBT1, PTPRC, and VAMP1) were classified under the secretory granule membrane term in the GO network. The acrosome contains a single secretory granule and is located in the head of mammalian sperm; secretion from this granule is an absolute requirement for fertilization [29]. Acrosome exocytosis is a synchronized and tightly regulated all-or-nothing process, which provides a unique model for studying the multiple steps of the membrane fusion cascade [29]. Therefore, we assumed that these genes containing nsSNPs in the selective sweep region, which are related to exocytosis and the secretory granule membrane, might have been influenced by artificial selection, considering the distinctive reproductive capacity of the Landrace breed [27].

Predicting strong effects of nsSNPs on amino acid substitutions in Landrace selective sweep region

Two in silico SNP prediction algorithms, SIFT [22] and PolyPhen-2 [23], were applied to estimate the possible effects of the stabilizing residues on protein functions for 345 nsSNPs in Landrace selective sweep regions. The results of SIFT and Polyphen-2 for 345 non-synonymous SNPs are shown in Tables 3, 4.

According to the SIFT analysis, 75 of 345 nsSNPs were classified as being deleterious (for some SNPs, there was low confidence in the findings regarding deleteriousness). PolyPhen-2 calculates the true-positive rate as a fraction of predicted mutations; its results showed that 82 amino acid variants involving nsSNPs in the selective sweep regions of the Landrace genome were likely to exert deleterious functional effects. In addition, 46 of these nsSNPs overlapped with the SIFT results. From the results of the two bioinformatics tools, we reasoned that 46 of the 345 nsSNPs might have strong effects on biological mechanisms during the process of Landrace domestication (Table 4). Forty-six nsSNPs that had strong effects on protein function were distributed among 26 genes and 19 selective sweep regions. In addition, 2:62355986-62756249 among the 55 selective sweep regions containing nsSNPs had the most nsSNPs (37 SNPs), and the results of the two tools for predicting the nsSNP effects showed that 10 of 37 SNPs in 2:6235598662756249 had strong effects on protein function. This was the largest number of nsSNPs with a strong effect among the total of 55 selective sweep regions of the Landrace genome containing an nsSNP. In addition, three genes belonged to this selective sweep region: ENSSSCG00000013821, ENSSSCG00000013822, and ENSSSCG00000013819. Because the selective region (2: 62355986-62756249) where this gene is located has not been annotated, we estimated the approximate functions of these three genes by analyzing their orthologs. We searched for orthologous genes of these three genes for which the detailed function had been discovered in placental mammals; there were no one-to-one orthologous genes and only many-to-many orthologous genes (Table 5). Because the lists of orthologs of the three genes were the same, we guessed that the functions of the three genes would be very similar. Because the orthologous genes consisted of 18 genes from 8 species from placental mammals and all 18 genes were related to olfactory receptors, we assumed that ENSSSCG00000013821, ENSSSCG 00000013822, and ENSSSCG00000013819 were inferred as olfactory receptors. In a previous study of pig evolution, one of the several significant features of porcine genome expansion involved the olfactory receptor gene family [30]. Martien et al [26] reported that there are 1,301 porcine olfactory receptor genes and 343 partial olfactory receptor genes. This large number of functional olfactory receptor genes most probably reflects the strong reliance of pigs on their sense of smell while scavenging for food. The presence of greater number of nsSNPs in genes related to olfactory receptors suggested important roles of these genes during selection. Additionally, the monoacylglycerol O-acyltransferase 2 (MOGAT2) gene was shown to have the greatest number of nsSNPs with a strong effect among the 90 genes. Five SNPs of the total of 11 nsSNPs in the MOGAT2 gene had strong effects on protein function in this study. Although our GO network analysis did not reveal any particularly important network of MOGAT2, this gene has been reported to be important in porcine backfat adipose tissue, which is related to the concentration of lipid and lipid synthesis, as revealed by a transcriptome analysis comparing Landrace and other breeds [31]. In addition, 3 of 26 nsSNPs in the DMBT1 gene were considered to have strong effects on protein function, as revealed by the SIFT and Polyphen-2 results.


Given the interest of the meat production industry in improving the meat quality or piglet number, a genetic investigation focusing on the selective sweep regions of the Landrace genome was previously performed [7]. This study provided vital information for domestic pig breeding. In most selective sweep studies using whole-genome sequencing data, all SNPs, including nsSNPs, were used to detect selective sweep regions. As nsSNPs are mutations that alter the amino acid sequences of encoded proteins, their presence results in a phenotypic change in the organism. Such changes are usually subjected to natural selection. In the case of Landrace, the domestication process had a shorter generation interval than natural selection. Therefore, we believe that nsSNPs had a diverse evolutionary history during the domestication and artificial selection processes, and advanced studies are required to achieve an accurate interpretation of the Landrace genome using nsSNP information after exploring Landrace positive selection based on whole-genome sequence data. In this study, we performed several analyses of nsSNPs of the Landrace genome to obtain a better understanding of the whole genome. We assumed that the information on these nsSNPs might be associated with novel important biological mechanisms related to particular traits of the Landrace breed. For the precise analysis of the characteristics of the Landrace breed from a genomic perspective, we investigated the biological meaning of nsSNPs in the selective sweep regions of the Landrace genome used in a previous study [7]. As a result, there was no correlation between the number of nsSNPs and gene length per 90 genes containing an nsSNP within the selective sweep regions of the Landrace genome (Figure 5), which was contrary to our expectations. Considering that 22 of 90 genes overlapped with multiple selective sweep regions while the others belonged to a single selective sweep region, we assumed that genes containing many nsSNPs in the selective sweep regions of the Landrace genome were more meaningful than our expectation. Subsequently, based on GO network analysis using genes containing 345 nsSNPs in the selective sweep regions of the Landrace genome, a large proportion of selective sweep regions of the Landrace genome where strong amino acid sequence changes had occurred, were involved in the superior reproductive capacity or growth and development of the Landrace breed during the perinatal period. Some of the GO network results overlapped with the GO analysis of all the selective sweep regions in a previous study, while others involved novel interpretations of the Landrace genome [7].


Our results strongly suggested that Landrace genetic variants, which could give rise to changes in amino acid sequences, might be important factors for the superior reproductive capacity of this breed. We aimed to perform analyses of the Landrace genome using nsSNPs in selective sweep regions. Our results showed that most of the genes affected by nsSNPs in the selective sweep regions may be closely related to the superior reproductive capacity or growth and development of the Landrace breed during the perinatal period. Furthermore, there were indications that nsSNPs in selection had impacted in Landrace breed establishment. This study will provide insights into the impact of the process of domestication on the Landrace genome.


We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.


This study was supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ01110901, PJ01315101), Rural Development Administration, Republic of Korea. The authors

are grateful to this organization.


[1.] Ennis S. Linkage disequilibrium as a tool for detecting signatures of natural selection. In: Collins AR, editor. Linkage disequilibrium and association mapping. Totowa, NJ, USA: Humana Press; 2007. p. 59-70.

[2.] Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res 1974;23:23-35.

[3.] Barton NH. Linkage and the limits to natural selection. Genetics 1995;140:821-41.

[4.] Durrett R, Schweinsberg J. Approximating selective sweeps. Theor Popul Biol 2004;66:129-38.

[5.] Pennings PS, Hermisson J. Soft sweeps III: the signature of positive selection from recurrent mutation. PLoS Genet 2006; 2:e186.

[6.] Przeworski M, Coop G, Wall JD. The signature of positive selection on standing genetic variation. Evolution 2005;59:2312-23.

[7.] Wang Z, Chen Q Yang Y, et al. A genome-wide scan for selection signatures in Yorkshire and Landrace pigs based on sequencing data. Anim Genet 2014;45:808-16.

[8.] Kim H, Song KD, Kim HJ, et al. Exploring the genetic signature of body size in Yucatan miniature pig. PloS One 2015;10: e0121732.

[9.] Kim J, Cho S, Caetano-Anolles K, Kim H, Ryu Y-C. Genomewide detection and characterization of positive selection in Korean Native Black Pig from Jeju Island. BMC Genet 2015; 16:3.

[10.] Amaral AJ, Ferretti L, Megens H-J, et al. Genome-wide footprints of pig domestication and selection revealed through massive parallel sequencing of pooled DNA. PloS One 2011; 6:e14782.

[11.] Li M, Tian S, Yeung CK, et al. Whole-genome sequencing of Berkshire (European native pig) provides insights into its origin and domestication. Sci Rep 2014;4:4678.

[12.] Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 2002;30:3894-900.

[13.] Bergman I-M, Rosengren JK, Edman K, Edfors I. European wild boars and domestic pigs display different polymorphic patterns in the Toll-like receptor (TLR) 1, TLR2, and TLR6 genes. Immunogenetics 2010;62:49-58.

[14.] Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. https://www.bioinformatics.babraham.

[15.] Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trim mer for Illumina sequence data. Bioinformatics 2014;30:2114-20.

[16.] Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9:357-9.

[17.] Li H, Handsaker B, Wysoker A, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078-9.

[18.] McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303.

[19.] Danecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. Bioinformatics 2011;27:2156-8.

[20.] Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000;155: 945-59.

[21.] Bindea G, Mlecnik B, Hackl H, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009;25:1091-3.

[22.] Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812-4.

[23.] Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013;76:7.20.1-41.

[24.] Cingolani P, Platts A, Wang LL, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 2012;6:80-92.

[25.] Ambruosi B, Accogli G, Douet C, et al. Deleted in malignant brain tumor 1 is secreted in the oviduct and involved in the mechanism of fertilization in equine and porcine species. Reproduction 2013;146:119-33.

[26.] Teijeiro JM, Roldan ML, Marini PE. Molecular identification of the sperm selection involved porcine sperm binding glycoprotein (SBG) as deleted in malignant brain tumors 1 (DMBT1). Biochimie 2012;94:263-7.

[27.] Chen P. Genetic improvement of lean growth rate and reproductive traits in pigs [Retrospective Theses and Dissertations]. Ames, IA, USA: Iowa State University; 2002.

[28.] Subha G. Role of biochemical factors and mineral supplementation in livestock ration for maintenance of their fertility and healthy reproductive status: a review. Res J Chem Sci ISSN 2013;3:102-6.

[29.] Mayorga LS, Tomes CN, Belmonte SA. Acrosomal exocytosis, a special type of regulated secretion. IUBMB life 2007;59:286-92.

[30.] Groenen MA, Archibald AL, Uenishi H, et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature 2012;491:393-8.

[31.] Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, et al. Analysis of porcine adipose tissue transcriptome reveals differences in de novo fatty acid synthesis in pigs with divergent muscle fatty acid composition. BMC Genomics 2013;14:843.

Donghyun Shin (1), Kyung-Hye Won (1), and Ki-Duk Song (1,2) *

* Corresponding Author: Ki-Duk Song Tel: +82-63-219-5523, Fax: +82-63-270-5937; E-mail:

(1) Department of Animal Biotechnology, Chonbuk National University, Jeonju 54896, Korea

(2) The Molecular Genetics and Breeding Center, Chonbuk National University, Jeonju 54896, Korea


Donghyun Shin

Kyung-Hye Won

Ki-Duk Song

Submitted Feb 8, 2018; Revised Apr 13, 2018; Accepted May 29, 2018

Caption: Figure 1. Functional classification of total single nucleotide polymorphisms (SNPs) from 40 pig whole-genome sequences (16 Yorkshire, 14 Landrace, and 10 wild boar). After SNP calling, all filtered SNPs (a total of 26,240,429 SNPs) were annotated using an SNP annotation tool, SnpEff version 4.1a (reference), and the Ensembl Sus scrofa gene set version 75 (Sscrofa10.2.75). Through SnpEff, we divided all SNPs into 31 functional classes containing non- synonymous SNPs (missense variants), as shown in this figure. The dotted line box in this figure indicates non-synonymous SNPs.

Caption: Figure 2. Population structure analysis using STRUCTURE. Each individual is represented by a vertical bar, and the length of each colored segment in each of the vertical bars represents the proportion contributed by ancestral populations (K = 3).

Caption: Figure 3. Genotypes of 345 non-synonymous single nucleotide polymorphisms (SNPs) in Landrace selective sweep regions. The genotype patterns of 345 non-synonymous SNPs in the selective sweep regions of the Landrace genome are represented by a heat map. The colors of the boxes represent the genotypes of each of the 40 individuals from the whole-genome sequencing data. Dark blue indicates that the genotypes of both the alleles were the same as that of the minor allele. Blue boxes indicate that one of the two alleles was the same as the minor allele and the other was the same as the major allele. Sky blue means that the genotypes of both alleles were the same as that of the major allele. The left side of the figure shows a list of each SNP name, which consists of the chromosome, position, and minor allele type. The gray box at the bottom of the figure indicates the three breeds.

Caption: Figure 4. Correlation between length and number of single nucleotide polymorphisms (SNPs) in genes related to non-synonymous SNPs (nsSNPs) in Landrace selective sweep regions.

Caption: Figure 5. Frequency difference of non-synonymous single nucleotide polymorphisms (nsSNPs) in deleted in malignant brain tumors 1 genes between Landrace and other breeds (Yorkshire and wild boar).

Caption: Figure 6. Gene ontology (GO) network analysis of genes related to non-synonymous single nucleotide polymorphisms (SNPs) in Landrace selective sweep regions. Significant results of GO analysis using genes related to non-synonymous SNPs in the selective sweep regions of the Landrace genome with our criteria in ClueGO packages of Cytoscape (number of genes = 4, sharing group percentage = 40.0). These results are largely divided into eight clusters as follows.
Table 1. Gene list containing non-synonymous SNPs in Landrace
selective sweep regions

Gene name                   CHR    Gene sart       Gene end

PLG                          1     8,739,981      8,787,582
MELK                         1    265,175,024    265,288,283
ZFPL1                        2     6,231,271      6,235,566
ENSSSCG00000021162           2     15,576,680     15,577,609
FAM180B                      2     16,204,579     16,206,256
ENSSSCG00000025219           2     62,507,452     62,508,408
ENSSSCG00000013821           2     62,624,616     62,625,548
ENSSSCG00000013822           2     62,644,870     62,645,796
ENSSSCG00000013819           2     62,669,703     62,670,662
MCOLN1                       2     72,056,664     72,151,713
ENSSSCG00000014078           2     85,731,838     85,732,242
ANKRD31                      2     85,774,886     85,807,199
ANKDD1B                      2     86,257,325     86,321,705
SDKI                         3     3,634,288      3,824,252
PLA2G6                       5     6,996,414      7,059,756
BIN2                         5     17,315,117     17,339,457
TAC3                         5     24,048,553     24,056,427
ZBTB39                       5     24,066,660     24,068,784
NCAPD2                       5     66,432,584     66,443,844
VAMP1                        5     66,646,135     66,647,743
TAPBPL                       5     66,647,211     66,658,624
DMBT1                        6     43,728,925     43,753,137
ENSSSCG0000002761S           6    119,199,612    119,199,920
MCOLN2                       6    119,212,826    119,273,364
PCNX1                        7    100,745,867    100,862,081
PLD4                         7    131,340,863    131,347,987
ENSSSCG0000000255I           7    131,356,311    131,359,461
ATP8A1                             35,180,992     35,309,867
ENSSSCG00000027999           9     2,277,256      2,278,264
OVCH2                        9     2,307,953      2,321,197
ENSSSCG00000025898           9     2,361,209      2,362,147
ENSSSCG00000023477           9     2,370,889      2,371,830
ENSSSCG00000029634           9     2,455,370      2,528,783
TRIM3                        9     3,923,986      3,940,046
HPX                          9     3,946,381      3,955,253
SMPD1                        9     3,961,589      3,964,504
MOGAT2                       9     11,119,062     11,132,962
THAP12                       9     11,652,415     11,669,844
GAB2                         9     13,936,307     14,135,685
ELMOD1                       9     40,189,956     40,282,814
ATM                          9     40,925,895     40,945,439
KDELC2                       9     41,043,564     41,065,077
EXPH5                        9     41,073,546     41,217,329
ENSSSCG00000023913           9     41,145,017     41,152,176
ARHGAP2J                     9    43,1 74,648     43,222,583
ENSSSCG00000015184           9     56,925,449     56,927,199
ENSSSCG00000026119           9     56,962,203     56,963,135
ENSSSCG00000015182           9     56,971,208     56,972,140
ENSSSCG00000028463           9     56,980,334     56,981,572
ENSSSCG00000024117           9     57,283,042     57,284,501
ENSSSCG0J0J0J0J0J0J24455     9     57,293,941     57,296,806
DMTF1                        9    102,893,256    102,929,921
DENND1B                     10     25,096,498     25,193,569
ENSSSCG00000010907          10     26,249,079     26,284,300
PTPRC                       10     26,308,759     26,332,284
KIAA1462                    10     45,386,450     45,428,443
GJD4                        10     63,677,681     63,683,060
ENSSSCG00000021829          11     11,141,413     11,236,840
ENSSSCG00000020699          11    1 1,355,261     11,378,042
CCDC168                     11     78,361,372     78,368,847
DNAI2                       12     6,779,152      6,799,278
MARCH10                     12    1 5,897,681     15,944,341
MAPT                        12    1 7,1 23,471   1 7,1 72,747
CCL23                       12     41,160,877     41,165,234
CCL1                        12     42,467,618     42,471,014
ENSSSCG00000017834          12     50,542,085     50,552,985
SHPK                        12     51,572,871     51,592,551
SPNS3                       12     52,389,071     52,445,090
CCDC66                      13     42,284,163     42,341,496
NOC4L                       14     24,724,492     24,730,021
DDX51                       14     24,730,045     24,732,878
EP40J                       14     24,748,336     24,847,567
ENSSSCG00000010013          14     50,652,381     50,652,947
OSBP2                       14     50,669,019     50,849,290
KIF20B                      14    110,499,1 18   110,581,337
FGFR1IIIC                   15     55,215,592     55,269,381
LETM2                       15     55,274,276     55,294,333
WHSC1L1                     15     55,338,007     55,406,429
DDHD2                       15     55,414,565     55,455,195
ASH2L                       15     55,512,104     55,552,504
ENSSSCG00000029683          15    1 28,593,493   128,594,377
CWC27                       16     46,572,512     46,875,541
CD93                        17     34,381,626     34,384,902
GZF1                        17    34,441,51 7     34,447,221
NAPB                        17     34,450,368    34,485,1 52
CSTL1                       17     34,492,910     34,496,585
CST7                        17     34,906,655     34,915,135
DEFB119                     17     39,921,302     39,931,655
DEFB116                     17     39,996,662     39,999,076
ENSSSCG00000007337          17     46,357,154     46,401,936

Gene name                   # ns SNP   Selective sweep region

PLG                            8       1:8670943-8797806
MELK                           1       1:265063188-265212930
ZFPL1                          1       2:6227731-6239068
ENSSSCG00000021162             3       2:15569156-15593980
FAM180B                        3       2:16111708-16299440
ENSSSCG00000025219             1       2:62355986-62756249
ENSSSCG00000013821             14
ENSSSCG00000013822             14
ENSSSCG00000013819             8
MCOLN1                         1       2:72143419-72172550
ENSSSCG00000014078             4       2:85467258-86506548
ANKRD31                        2
ANKDD1B                        3
SDKI                           3       3:3730382-3773007
PLA2G6                         1       5:6988526-7058468
BIN2                           1       5:17248525-17487183
TAC3                           3       5:23288996-24074802
ZBTB39                         4
NCAPD2                         1       5:66396846-66725591
VAMP1                          1
TAPBPL                         2
DMBT1                          26      6:43719388-43757067
ENSSSCG0000002761S             3       6:119198939-119344591
MCOLN2                         1
PCNX1                          2       7:100703442-100775415
PLD4                           3       7:131291714-131388688
ENSSSCG0000000255I             5
ATP8A1                         2       8:34998191-35275833
ENSSSCG00000027999             7       9:2223331-2577505
OVCH2                          10
ENSSSCG00000025898             5
ENSSSCG00000023477             12
ENSSSCG00000029634             1
TRIM3                          1       9:3927497-3978728
HPX                            4
MOGAT2                         r       9:11120076-11136889
THAP12                         2       9:11449284-11760977
GAB2                           1       9:13934282-14030509
ELMOD1                         1       9:40189621-40286365
ATM                            3       9:40793693-41170478
KDELC2                         7
EXPH5                          12
ENSSSCG00000023913             3
ARHGAP2J                       1       9:43134418-43291918
ENSSSCG00000015184             4       9:56869539-57122277
ENSSSCG00000026119             7
ENSSSCG00000015182             5
ENSSSCG00000028463             5
ENSSSCG00000024117             5       9:57230656-57379772
ENSSSCG0J0J0J0J0J0J24455       1
DMTF1                          1       9:102847568-103896296
DENND1B                        1       10:25139986-25249094
ENSSSCG00000010907             17      10:26197521-26710943
PTPRC                          2       10:26197521-26710943
KIAA1462                       4       10:45403837-45436342
GJD4                           2       10:63669866-63725092
ENSSSCG00000021829             1       11:10400737-11376721
ENSSSCG00000020699             1
CCDC168                        22      11:78318648-78678168
DNAI2                          3       12:6771152-6805468
MARCH10                        10      12:1589J650-15938045
MAPT                           2       12:16937097-17191735
CCL23                          3       12:41158920-41165901
CCL1                           3       12:42468535-42621081
ENSSSCG00000017834             1       12:50535159-50581774
SHPK                           1       12:51579885-51586595
SPNS3                          1       12:52401285-52444137
CCDC66                         2       13:41196871-42465605
NOC4L                          2       14:24592939-24779049
DDX51                          2
EP40J                          2
ENSSSCG00000010013             1       14:50647172-50719083
OSBP2                          2
KIF20B                         1       14:110280822-110542445
FGFR1IIIC                      1       15:55142754-55608192
LETM2                          1
WHSC1L1                        2
DDHD2                          1
ASH2L                          1
ENSSSCG00000029683             6       15:128498493-128627886
CWC27                          2       16:46472193-46771773
CD93                           2       17:34206246-34400408
GZF1                           3       17:34421087-34505222
NAPB                           1
CSTL1                          2
CST7                           1       17:34901568-34908632
DEFB119                        2       17:39862221-40018288
DEFB116                        1
ENSSSCG00000007337             2       17:46275105:46424519

SNPs, single nucleotide polymorphisms; nsSNPs, non-synonymous SNPs.

We show the information of genes containing non-synonymous SNPs. In
this table, the fifth column indicates the number of non-synonymous
SNPs in each gene and the seventh column presents information on the
selective sweep regions of the Landrace genome and selective sweep
name, consisting of chromosome, start position, and end position.

Table 2. Information of gene ontology (GO) network analysis of genes
related to non-synonymous SNPs in Landrace selective sweep regions

                                             Term      Group
GO ID                  GO Term              p-value   p-value   #Genes

GO:0002274   Myeloid leukocyte activation    0.005     0.005      7

GO:0006887   Exocytosis                      0.001     0.001      10

GO:0030667   Secretory granule membrane      0.003     0.003      5

GO:0040017   Positive regulation of          0.016     0.017      5

GO:0051272   Positive regulation of          0.013                5
             cellular component movement

GO:2000147   Positive regulation of cell     0.012                5

GO:0030335   Positive regulation of cell     0.010                5

GO:0006873   Cellular ion homeostasis        0.003     0.006      7

GO:0055080   Cation homeostasis              0.005                7

GO:0030003   Cellular cation homeostasis     0.003                7

GO:0055065   Metal ion homeostasis           0.003                7

GO:0072507   Divalent inorganic cation       0.015                5

GO:0006875   Cellular metal ion              0.001                7

GO:0072503   Cellular divalent inorganic     0.013                5
             cation homeostasis

GO:0055074   Calcium ion homeostasis         0.011                5

GO:0006874   Cellular calcium ion            0.010                5

GO ID                 Associated genes found

GO:0002274   ATP8A1, BIN2, CD93, GAB2, MAPT, PTPRC,

GO:0006887   ATP8A1, BIN2, CD93, EXPH5, GAB2, NAPB,
             PLA2G6, PLG, PTPRC, VAMP1

GO:0030667   ATP8A1, CD93, DMBT1, PTPRC, VAMP1

GO:0040017   ATP8A1, CCL1, KIF20B, PLG, PTPRC

GO:0051272   ATP8A1, CCL1, KIF20B, PLG, PTPRC

GO:2000147   ATP8A1, CCL1, KIF20B, PLG, PTPRC

GO:0030335   ATP8A1, CCL1, KIF20B, PLG, PTPRC

GO:0006873   CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6,

GO:0055080   CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6,

GO:0030003   CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6,

GO:0055065   CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6,

GO:0072507   CCL1, CCL23, MCOLN1, PLA2G6, PTPRC

GO:0006875   CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6,

GO:0072503   CCL1, CCL23, MCOLN1, PLA2G6, PTPRC

GO:0055074   CCL1, CCL23, MCOLN1, PLA2G6, PTPRC

GO:0006874   CCL1, CCL23, MCOLN1, PLA2G6, PTPRC

SNPs, single nucleotide polymorphisms.

Significant results of GO analysis using genes related to
non-synonymous SNPs in the selective sweep regions of the
Landrace genome with our criteria in ClueGO packages of Cytoscape
(number of genes = 4, sharing group percentage = 40.0). These
results are largely divided into eight clusters as follows.

Table 3. Summary of non-synonymous single amino acid variation in
genes of Landrace selective sweep using SIFT and Polyphen-2


                     Benign   Possibly    Probably   Total
                              damaging    damaging

SIFT   Deleterious     29        19          27       75
       Tolerated      234        21          15       270
       Total          263        40          42       345

Table 4. Forty-six non-synonymous SNPs with strong effects on protein
functions based on SIFT and Polyphen-2

                                               SIFT       SIFT
SNP           CHR       POS       A1   A2   prediction    score

rs328613228    2    16,206,079    T    G    deleterious     0
2:62624837     2    62,624,837    G    A    deleterious   0.017
rs340857214    2    62,625,107    G    A    deleterious   0.021
2:62625190     2    62,625,190    A    T    deleterious   0.028
rs335820735    2    62,644,986    A    T    deleterious   0.008
rs343007761    2    62,645,014    T    G    deleterious   0.018
2:62645060     2    62,645,060    A    G    deleterious   0.012
rs325197977    2    62,645,081    A    G    deleterious     0
2:62669920     2    62,669,920    G    A    deleterious   0.008
2:62669953     2    62,669,953    T    G    deleterious   0.007
2:62670031     2    62,670,031    G    A    deleterious   0.012
rs342394815    2    85,732,226    T    C    deleterious   0.002
rs337260402    2    85,732,237    T    G    deleterious   0.003
rs326720643    2    85,775,718    A    G    deleterious   0.007
rs318473425    2    86,321,677    T    A    deleterious   0.033
rs329106718    5    66,654,214    C    T    deleterious     0
rs326638161    6    43,729,346    T    C    deleterious   0.007
rs322198139    6    43,750,820    G    T    deleterious   0.017
rs321057648    6    43,750,963    A    G    deleterious   0.009
6:119199835    6    119,199,835   T    A    deleterious   0.006
rs327779736    8    35,181,016    A    T    deleterious     0
rs81399633     8    35,181,037    A    G    deleterious   0.023
rs343636299    9     2,311,094    T    C    deleterious   0.042
rs318298009    9     3,930,944    T    A    deleterious   0.006
9:11129485     9    11,129,485    T    G    deleterious   0.035
rs340556206    9    1 1,129,936   T    C    deleterious   0.013
rs81509118     9    1 1,130,742   A    G    deleterious   0.036
rs342457070    9    1 1,130,778   C    A    deleterious   0.005
rs327337551    9    1 1,130,783   G    C    deleterious   0.047
rs338381437    9    11,666,878    G    A    deleterious   0.003
rs81214615     9    41,047,573    T    A    deleterious   0.024
rs339385194    9    41,076,701    G    T    deleterious   0.04
9:56962342     9    56,962,342    A    G    deleterious   0.028
9:56962578     9    56,962,578    A    C    deleterious   0.026
rs328160175    9    56,971,732    G    A    deleterious   0.016
rs335643554    9    56,980,378    C    T    deleterious   0.032
rs331490061    9    56,981,034    A    G    deleterious   0.004
rs326014276   10    63,681,709    G    C    deleterious   0.037
rs339353031   11    78,365,823    G    A    deleterious   0.008
11:78367889   11    78,367,889    G    A    deleterious     0
rs342686832   11    78,367,955    A    G    deleterious   0.034
rs325650226   12    15,917,860    T    C    deleterious   0.002
rs336224471   12    15,917,910    A    C    deleterious   0.03
15:55400479   15    55,400,479    A    G    deleterious   0.032
rs339461760   16    46,612,542    C    G    deleterious   0.007
rs324424231   17    46,357,195    A    G    deleterious     0

                 Polyphen-2       Polyphen-
SNP              prediction        2 score           Gene

rs328613228   probably damaging     0.997           FAM180B
2:62624837    possibly damaging     0.853     ENSSSCG00000013821
rs340857214   possibly damaging     0.539
2:62625190    possibly damaging     0.934
rs335820735   probably damaging     0.999     ENSSSCG00000013822
rs343007761   possibly damaging     0.506
2:62645060    possibly damaging     0.604
rs325197977   possibly damaging     0.934
2:62669920    possibly damaging     0.934     ENSSSCG00000013819
2:62669953    possibly damaging     0.934
2:62670031    probably damaging     0.999
rs342394815   probably damaging     0.999     ENSSSCG00000014078
rs337260402   probably damaging     0.97
rs326720643   probably damaging     0.984           ANKRD31
rs318473425   probably damaging     0.995           ANKDD1B
rs329106718   probably damaging     0.993           TAPBPL
rs326638161   probably damaging     0.988            DMBT1
rs322198139   possibly damaging     0.915
rs321057648   possibly damaging     0.663
6:119199835   probably damaging     0.998     ENSSSCG00000027618
rs327779736   possibly damaging     0.944           ATP8A1
rs81399633    possibly damaging     0.896
rs343636299   probably damaging       1              OVCH2
rs318298009   probably damaging     0.996            TRIM3
9:11129485    probably damaging     0.995           MOGAT2
rs340556206   probably damaging     0.999
rs81509118    probably damaging       1
rs342457070   probably damaging     0.991
rs327337551   possibly damaging     0.697
rs338381437   probably damaging     0.983           THAP12
rs81214615    probably damaging     0.99            KDELC2
rs339385194   probably damaging     0.999            EXPH5
9:56962342    possibly damaging     0.616     ENSSSCG00000026119
9:56962578    probably damaging     0.994
rs328160175   probably damaging     0.994     ENSSSCG00000015182
rs335643554   possibly damaging     0.539     ENSSSCG00000028463
rs331490061   possibly damaging     0.927
rs326014276   possibly damaging     0.944            GJD4
rs339353031   probably damaging     0.983           CCDC168
11:78367889   probably damaging     0.993
rs342686832   possibly damaging     0.94
rs325650226   probably damaging     0.999           MARCH10
rs336224471   possibly damaging     0.82
15:55400479   probably damaging       1             WHSC1L1
rs339461760   probably damaging     0.998            CWC27
rs324424231   probably damaging     0.998     ENSSSCG00000007337

SNP              Selective sweep

rs328613228    2:16111708:16299440
2:62624837     2:62355986:62756249
rs342394815    2:85467258:86506548
rs329106718    5:66396846:66725591
rs326638161    6:43719388:43757067
6:119199835   6:119198939:119344591
rs327779736    8:34998191:35275833
rs343636299     9:2223331:2577505
rs318298009     9:3927497:3978728
9:11129485    9:1 1120076:1 1136889
rs338381437    9:11449284:11760977
rs81214615     9:40793693:41170478
9:56962342     9:56869539:57122277
rs326014276   10:63669866:63725092
rs339353031   11:78318648:78678168
rs325650226   12:15890650:15938045
15:55400479   15:55142754:55608192
rs339461760   16:46472193:46771773
rs324424231   17:46275105:46424519

SNPs, single nucleotide polymorphisms; SIFT, sorting intolerant
from tolerant; Polyphen-2, polymorphism phenotyping v2.

We identified that 46 of 345 non-synonymous SNPs in the selective
sweep regions of the Landrace genome had strong effects on protein
function as determined with both in silico tools: SIFT and

Table 5. Information on the orthologs of three genes
(ENSSSCG00000013821, ENSSSCG00000013822, and ENSSSCG000000138149)
in selective sweep 2:6235598662756249

                                    Match gene      Match ensemble
Species                               symbol           gene ID

Chimpanzee (Pan troglodytes)          OR7A5       ENSPTRG00000010603
Chimpanzee (Pan troglodytes)          OR7A1C      ENSPTRG00000010604
Gibbon (Nomascus leucogenys)          OR7A17      ENSNLEG00000005159
Gorilla (Gorilla gorilla gorilla)     OR7A10      ENSGGOG00000015049
Gorilla (Gorilla gorilla gorilla)     OR7A17      ENSGGOG00000034834
Human (Homo sapiens)                  OR7A1J       ENSG00000127515
Human (Homo sapiens)                  OR7A17       ENSG00000185385
Human (Homo sapiens)                  OR7A5        ENSG00000188269
Mouse (Mus musculus)                 Olfr1353     ENSMUSG00000042774
Mouse (Mus musculus)                 Olfr1352     ENSMUSG00000046493
Mouse (Mus musculus)                  Olfr19      ENSMUSG00000048101
Mouse (Mus musculus)                  Olfr57     ENSMUSG00000060205
Mouse (Mus musculus)                  OlfrW       ENSMUSG00000063216
Mouse (Mus musculus)                  Olfr8       ENSMUSG00000094080
Mouse (Mus musculus)                 Olfr1354     ENSMUSG00000094673
Orangutan (Pongo abelii)              OR7A5       ENSPPYG00000009655
Orangutan (Pongo abelii)              OR7A10      ENSPPYG00000009656
Orangutan (Pongo abelii)              OR7A17      ENSPPYG00000009658
Rat (Rattus norvegicus)              Olr1073      ENSRNOG00000031688
Rat (Rattus norvegicus)              Olr1076      ENSRNOG00000039448
Rat (Rattus norvegicus)              Olr1075      ENSRNOG00000039449
Rat (Rattus norvegicus)              Olr1085      ENSRNOG00000047090
Rat (Rattus norvegicus)              Olr1079      ENSRNOG00000049781
Rat (Rattus norvegicus)              Olr1077      ENSRNOG00000054107
Rat (Rattus norvegicus)              Olr1082      ENSRNOG00000058943
Rat (Rattus norvegicus)              Olr1083      ENSRNOG00000061480
Vervet-AGM (Chlorocebus sabaeus)      OR7A10      ENSCSAG00000006193

Species                                  Compare regions

Chimpanzee (Pan troglodytes)         19:15,130,772-15,137,945
Chimpanzee (Pan troglodytes)         19:15,143,753-15,144,682
Gibbon (Nomascus leucogenys)        GL397382.1:231,228-275,098
Gorilla (Gorilla gorilla gorilla)    19:15,120,105-15,121,034
Gorilla (Gorilla gorilla gorilla)    19:15,160,189-15,161,115
Human (Homo sapiens)                 19:14,840,948-14,841,877
Human (Homo sapiens)                 19:14,880,426-14,881,452
Human (Homo sapiens)                 19:14,792,490-14,835,376
Mouse (Mus musculus)                 10:78,963,309-78,971,338
Mouse (Mus musculus)                 10:78,981,050-78,987,903
Mouse (Mus musculus)                 16:16,672,228-16,676,405
Mouse (Mus musculus)                 10:79,028,741-79,036,274
Mouse (Mus musculus)                 10:79,012,472-79,019,645
Mouse (Mus musculus)                 10:78,950,636-78,958,378
Mouse (Mus musculus)                 10:78,913,171-78,920,399
Orangutan (Pongo abelii)             19:15,004,902-15,005,858
Orangutan (Pongo abelii)             19:15,019,264-15,020,193
Orangutan (Pongo abelii)             19:15,062,903-15,091,843
Rat (Rattus norvegicus)              7:13,378,338-1 3,379,273
Rat (Rattus norvegicus)              7:13,424,355-13,425,311
Rat (Rattus norvegicus)              7:13,403,899-13,404,858
Rat (Rattus norvegicus)              7:13,673,934-13,674,866
Rat (Rattus norvegicus)              7:13,488,205-13,489,137
Rat (Rattus norvegicus)              7:13,460,476-13,461,405
Rat (Rattus norvegicus)              7:1 3,553,010-13,553,963
Rat (Rattus norvegicus)              7:13,587,479-13,588,411
Vervet-AGM (Chlorocebus sabaeus)     6:13,469,888-13,471,167


                                            Target   Query
Species                             dN/dS    %id      %id

Chimpanzee (Pan troglodytes)        0.350    69.0    70.7
Chimpanzee (Pan troglodytes)        0.377    70.6    70.1
Gibbon (Nomascus leucogenys)        0.383    71.0    70.7
Gorilla (Gorilla gorilla gorilla)    --      70.6    70.1
Gorilla (Gorilla gorilla gorilla)    --      72.5    72.0
Human (Homo sapiens)                0.418    70.2    69.8
Human (Homo sapiens)                0.338    72.2    71.7
Human (Homo sapiens)                0.354    69.6    71.4
Mouse (Mus musculus)                 --      62.5    62.1
Mouse (Mus musculus)                0.238    68.6    68.2
Mouse (Mus musculus)                0.245    68.3    67.9
Mouse (Mus musculus)                0.308    66.5    68.2
Mouse (Mus musculus)                0.308    64.6    66.2
Mouse (Mus musculus)                0.284    63.2    63.0
Mouse (Mus musculus)                0.264    63.6    63.3
Orangutan (Pongo abelii)            0.373    67.9    69.5
Orangutan (Pongo abelii)            0.453    69.6    69.1
Orangutan (Pongo abelii)            0.344    70.9    70.4
Rat (Rattus norvegicus)              --      62.1    62.1
Rat (Rattus norvegicus)             0.263    66.0    67.5
Rat (Rattus norvegicus)             0.290    67.1    68.8
Rat (Rattus norvegicus)              --      63.2    63.0
Rat (Rattus norvegicus)             0.276    63.6    63.3
Rat (Rattus norvegicus)             0.229    69.3    68.8
Rat (Rattus norvegicus)             0.279    61.8    63.0
Rat (Rattus norvegicus)             0.290    63.2    63.0
Vervet-AGM (Chlorocebus sabaeus)    0.347    70.2    69.8


                                            Target   Query
Species                             dN/dS    %id      %id

Chimpanzee (Pan troglodytes)        0.372    69.6    71.8
Chimpanzee (Pan troglodytes)        0.333    71.8    71.8
Gibbon (Nomascus leucogenys)        0.359    70.3    70.6
Gorilla (Gorilla gorilla gorilla)    --      70.9    70.9
Gorilla (Gorilla gorilla gorilla)    --      72.8    72.8
Human (Homo sapiens)                0.377    70.6    70.6
Human (Homo sapiens)                0.356    72.5    72.5
Human (Homo sapiens)                0.370    70.2    72.5
Mouse (Mus musculus)                 --      61.2    61.2
Mouse (Mus musculus)                0.224    67.3    67.3
Mouse (Mus musculus)                0.267    66.3    66.3
Mouse (Mus musculus)                0.289    64.3    66.3
Mouse (Mus musculus)                0.303    62.1    64.1
Mouse (Mus musculus)                0.317    58.4    58.6
Mouse (Mus musculus)                 --      59.0    59.2
Orangutan (Pongo abelii)            0.395    67.3    69.3
Orangutan (Pongo abelii)            0.402    70.9    70.9
Orangutan (Pongo abelii)            0.339    71.5    71.5
Rat (Rattus norvegicus)              --      61.7    62.1
Rat (Rattus norvegicus)             0.248    63.8    65.7
Rat (Rattus norvegicus)             0.272    66.1    68.3
Rat (Rattus norvegicus)             0.343    58.4    58.6
Rat (Rattus norvegicus)             0.395    59.4    59.6
Rat (Rattus norvegicus)             0.241    67.0    67.0
Rat (Rattus norvegicus)             0.348    58.0    59.6
Rat (Rattus norvegicus)             0.352    60.3    60.5
Vervet-AGM (Chlorocebus sabaeus)    0.330    72.2    72.2


                                            Target   Query
Species                             dN/dS    %id      %id

Chimpanzee (Pan troglodytes)        0.327    71.2    70.9
Chimpanzee (Pan troglodytes)        0.338    71.8    69.4
Gibbon (Nomascus leucogenys)        0.290    73.2    70.9
Gorilla (Gorilla gorilla gorilla)    --      72.2    69.7
Gorilla (Gorilla gorilla gorilla)    --      73.1    70.6
Human (Homo sapiens)                0.361    71.8    69.4
Human (Homo sapiens)                0.317    72.5    70.0
Human (Homo sapiens)                0.313    71.5    71.3
Mouse (Mus musculus)                0.243    65.1    62.8
Mouse (Mus musculus)                 --      68.6    66.3
Mouse (Mus musculus)                0.253    67.6    65.3
Mouse (Mus musculus)                0.349    65.2    65.0
Mouse (Mus musculus)                0.345    64.3    64.1
Mouse (Mus musculus)                 --      60.7    58.8
Mouse (Mus musculus)                 --      62.3    60.3
Orangutan (Pongo abelii)            0.351    68.9    68.4
Orangutan (Pongo abelii)            0.350    71.2    68.8
Orangutan (Pongo abelii)            0.342    70.9    68.4
Rat (Rattus norvegicus)             0.270    65.3    63.4
Rat (Rattus norvegicus)             0.285    64.8    64.4
Rat (Rattus norvegicus)             0.291    67.4    67.2
Rat (Rattus norvegicus)             0.327    62.3    60.3
Rat (Rattus norvegicus)             0.336    62.6    60.6
Rat (Rattus norvegicus)             0.236    68.0    65.6
Rat (Rattus norvegicus)             0.342    59.6    59.1
Rat (Rattus norvegicus)             0.332    62.6    60.6
Vervet-AGM (Chlorocebus sabaeus)    0.348    71.8    69.4
COPYRIGHT 2018 Asian - Australasian Association of Animal Production Societies
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Shin, Donghyun; Won, Kyung-Hye; Song, Ki-Duk
Publication:Asian - Australasian Journal of Animal Sciences
Article Type:Report
Date:Dec 1, 2018
Previous Article:Fatty acid compositions, free radical scavenging activities, and antioxidative enzyme activities of high-preference and low-preference beef cuts of...
Next Article:The effect of nutrition and body condition of triplet-bearing ewes during late pregnancy on the behaviour of ewes and lambs.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |