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) , which was proposed by Smith and Haigh , and other researchers have expanded and applied it [3-6]. Wang et al  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 . 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 . 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 . 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 . 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 .
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.
MATERIALS AND METHODS
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  were used to perform a quality check on raw sequence data. Using Trimmomatic-0.32 , 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  with the default settings. For downstream processing and variant calling, following software packages were used: Picard tools (http:// broadinstitute.github.io/picard/), SAMtools , and Genome Analysis Toolkit (GATK) . "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  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 (https://web.stanford.edu/group/pritchardlab/structure.html) 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 . 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  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)  and polymorphism phenotyping v2 (Polyphen-2) . 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. bwh.harvard.edu/pph2/) analysis to predict the influence of an amino acid change on the structure and function of a protein by using specific empirical rules . 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 . 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 (http://snpeff.sourceforge.net/SnpSift.html) . 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 , 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 . 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  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 .
Among 90 genes, the functions of 64 genes were predicted, and we performed GO network analysis of these 64 genes using ClueGO  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 . 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 . 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 . 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 . 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 .
Predicting strong effects of nsSNPs on amino acid substitutions in Landrace selective sweep region
Two in silico SNP prediction algorithms, SIFT  and PolyPhen-2 , 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 . Martien et al  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 . 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 . 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 . 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 .
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.
CONFLICT OF INTEREST
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. ac.uk/projects/fastqc/
[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: firstname.lastname@example.org
(1) Department of Animal Biotechnology, Chonbuk National University, Jeonju 54896, Korea
(2) The Molecular Genetics and Breeding Center, Chonbuk National University, Jeonju 54896, Korea
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 SMPD1 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 locomotion GO:0051272 Positive regulation of 0.013 5 cellular component movement GO:2000147 Positive regulation of cell 0.012 5 motility GO:0030335 Positive regulation of cell 0.010 5 migration 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 homeostasis GO:0006875 Cellular metal ion 0.001 7 homeostasis 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 homeostasis GO ID Associated genes found GO:0002274 ATP8A1, BIN2, CD93, GAB2, MAPT, PTPRC, SHPK 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, PTPRC GO:0055080 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC GO:0030003 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC GO:0055065 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC GO:0072507 CCL1, CCL23, MCOLN1, PLA2G6, PTPRC GO:0006875 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC 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 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 rs340857214 2:62625190 rs335820735 rs343007761 2:62645060 rs325197977 2:62669920 2:62669953 2:62670031 rs342394815 2:85467258:86506548 rs337260402 rs326720643 rs318473425 rs329106718 5:66396846:66725591 rs326638161 6:43719388:43757067 rs322198139 rs321057648 6:119199835 6:119198939:119344591 rs327779736 8:34998191:35275833 rs81399633 rs343636299 9:2223331:2577505 rs318298009 9:3927497:3978728 9:11129485 9:1 1120076:1 1136889 rs340556206 rs81509118 rs342457070 rs327337551 rs338381437 9:11449284:11760977 rs81214615 9:40793693:41170478 rs339385194 9:56962342 9:56869539:57122277 9:56962578 rs328160175 rs335643554 rs331490061 rs326014276 10:63669866:63725092 rs339353031 11:78318648:78678168 11:78367889 rs342686832 rs325650226 12:15890650:15938045 rs336224471 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 PolyPhen-2. 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 ENSSSCG00000013821 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 ENSSSCG00000013822 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 ENSSSCG00000013819 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
|Printer friendly Cite/link Email Feedback|
|Author:||Shin, Donghyun; Won, Kyung-Hye; Song, Ki-Duk|
|Publication:||Asian - Australasian Journal of Animal Sciences|
|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.|