• Home
  • E-Submission
  • Sitemap
  • Contact Us
Asian-Australas J Anim Sci. Search

CLOSE


Go to Top Go to Bottom
Asian-Australas J Anim Sci > Volume 31(12); 2018 > Article
Shin, Won, and Song: In silico approaches to discover the functional impact of non-synonymous single nucleotide polymorphisms in selective sweep regions of the Landrace genome

Abstract

Objective

The aim of this study was to discover the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) that were found in selective sweep regions of the Landrace genome

Methods

Whole-genome re-sequencing data were obtained from 40 pigs, including 14 Landrace, 16 Yorkshire, and 10 wild boars, which were generated with the Illumina HiSeq 2000 platform. The nsSNPs in the selective sweep regions of the Landrace genome were identified, and the impacts of these variations on protein function were predicted to reveal their potential association with traits of the Landrace breed, such as reproductive capacity.

Results

Total of 53,998 nsSNPs in the mapped regions of pigs were identified, and among them, 345 nsSNPs were found in the selective sweep regions of the Landrace genome which were reported previously. The genes featuring these nsSNPs fell into various functional categories, such as reproductive capacity or growth and development during the perinatal period. The impacts of amino acid sequence changes by nsSNPs on protein function were predicted using two in silico SNP prediction algorithms, i.e., sorting intolerant from tolerant and polymorphism phenotyping v2, to reveal their potential roles in biological processes that might be associated with the reproductive capacity of the Landrace breed.

Conclusion

The findings elucidated the domestication history of the Landrace breed and illustrated how Landrace domestication led to patterns of genetic variation related to superior reproductive capacity. Our novel findings will help understand the process of Landrace domestication at the genome level and provide SNPs that are informative for breeding.

INTRODUCTION

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 [36]. 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, non-synonymous 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.

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 [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://broadinstitute.github.io/picard/), 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 “SelectVariants” 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≤ 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 [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≤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.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 [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.

RESULTS

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) [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 2:62355986–62756249 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.

DISCUSSION

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].

CONCLUSION

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.

ACKNOWLEDGMENTS

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.

Notes

CONFLICT OF INTEREST

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

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.
ajas-31-12-1980f1.gif
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).
ajas-31-12-1980f2.gif
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.
ajas-31-12-1980f3.gif
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.
ajas-31-12-1980f4.gif
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).
ajas-31-12-1980f5.gif
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.
ajas-31-12-1980f6.gif
Table 1
Gene list containing non-synonymous SNPs in Landrace selective sweep regions
Gene name CHR Gene sart Gene end # ns SNP Selective sweep region Gene name CHR Gene sart Gene end # ns SNP Selective sweep region


PLG 1 8,739,981 8,787,582 8 1:8670943–8797806 ENSSSCG00000015184 9 56,925,449 56,927,199 4 9:56869539–57122277
MELK 1 265,175,024 265,288,283 1 1:265063188–265212930 ENSSSCG00000026119 9 56,962,203 56,963,135 7
ZFPL1 2 6,231,271 6,235,566 1 2:6227731–6239068 ENSSSCG00000015182 9 56,971,208 56,972,140 5
ENSSSCG00000021162 2 15,576,680 15,577,609 3 2:15569156–15593980 ENSSSCG00000028463 9 56,980,334 56,981,572 5
FAM180B 2 16,204,579 16,206,256 3 2:16111708–16299440 ENSSSCG00000024117 9 57,283,042 57,284,501 5 9:57230656–57379772
ENSSSCG00000025219 2 62,507,452 62,508,408 1 2:62355986–62756249 ENSSSCG00000024455 9 57,293,941 57,296,806 1
ENSSSCG00000013821 2 62,624,616 62,625,548 14 DMTF1 9 102,893,256 102,929,921 1 9:102847568–103896296
ENSSSCG00000013822 2 62,644,870 62,645,796 14 DENND1B 10 25,096,498 25,193,569 1 10:25139986–25249094
ENSSSCG00000013819 2 62,669,703 62,670,662 8 ENSSSCG00000010907 10 26,249,079 26,284,300 17 10:26197521–26710943
MCOLN1 2 72,056,664 72,151,713 1 2:72143419–72172550 PTPRC 10 26,308,759 26,332,284 2 10:26197521–26710943
ENSSSCG00000014078 2 85,731,838 85,732,242 4 2:85467258–86506548 KIAA1462 10 45,386,450 45,428,443 4 10:45403837–45436342
ANKRD31 2 85,774,886 85,807,199 2 GJD4 10 63,677,681 63,683,060 2 10:63669866–63725092
ANKDD1B 2 86,257,325 86,321,705 3 ENSSSCG00000021829 11 11,141,413 11,236,840 1 11:10400737–11376721
SDK1 3 3,634,288 3,824,252 3 3:3730382–3773007 ENSSSCG00000020699 11 11,355,261 11,378,042 1
PLA2G6 5 6,996,414 7,059,756 1 5:6988526–7058468 CCDC168 11 78,361,372 78,368,847 22 11:78318648–78678168
BIN2 5 17,315,117 17,339,457 1 5:17248525–17487183 DNAI2 12 6,779,152 6,799,278 3 12:6771152–6805468
TAC3 5 24,048,553 24,056,427 3 5:23288996–24074802 MARCH10 12 15,897,681 15,944,341 10 12:15890650–15938045
ZBTB39 5 24,066,660 24,068,784 4 MAPT 12 17,123,471 17,172,747 2 12:16937097–17191735
NCAPD2 5 66,432,584 66,443,844 1 5:66396846–66725591 CCL23 12 41,160,877 41,165,234 3 12:41158920–41165901
VAMP1 5 66,646,135 66,647,743 1 CCL1 12 42,467,618 42,471,014 3 12:42468535–42621081
TAPBPL 5 66,647,211 66,658,624 2 ENSSSCG00000017834 12 50,542,085 50,552,985 1 12:50535159–50581774
DMBT1 6 43,728,925 43,753,137 26 6:43719388–43757067 SHPK 12 51,572,871 51,592,551 1 12:51579885–51586595
ENSSSCG00000027618 6 119,199,612 119,199,920 3 6:119198939–119344591 SPNS3 12 52,389,071 52,445,090 1 12:52401285–52444137
MCOLN2 6 119,212,826 119,273,364 1 CCDC66 13 42,284,163 42,341,496 2 13:41196871–42465605
PCNX1 7 100,745,867 100,862,081 2 7:100703442–100775415 NOC4L 14 24,724,492 24,730,021 2 14:24592939–24779049
PLD4 7 131,340,863 131,347,987 3 7:131291714–131388688 DDX51 14 24,730,045 24,732,878 2
ENSSSCG00000002551 7 131,356,311 131,359,461 5 EP400 14 24,748,336 24,847,567 2
ATP8A1 8 35,180,992 35,309,867 2 8:34998191–35275833 ENSSSCG00000010013 14 50,652,381 50,652,947 1 14:50647172–50719083
ENSSSCG00000027999 9 2,277,256 2,278,264 7 9:2223331–2577505 OSBP2 14 50,669,019 50,849,290 2
OVCH2 9 2,307,953 2,321,197 10 KIF20B 14 110,499,118 110,581,337 1 14:110280822–110542445
ENSSSCG00000025898 9 2,361,209 2,362,147 5 FGFR1IIIC 15 55,215,592 55,269,381 1 15:55142754–55608192
ENSSSCG00000023477 9 2,370,889 2,371,830 12 LETM2 15 55,274,276 55,294,333 1
ENSSSCG00000029634 9 2,455,370 2,528,783 1 WHSC1L1 15 55,338,007 55,406,429 2
TRIM3 9 3,923,986 3,940,046 1 9:3927497–3978728 DDHD2 15 55,414,565 55,455,195 1
HPX 9 3,946,381 3,955,253 4 ASH2L 15 55,512,104 55,552,504 1
SMPD1 9 3,961,589 3,964,504 1 ENSSSCG00000029683 15 128,593,493 128,594,377 6 15:128498493–128627886
MOGAT2 9 11,119,062 11,132,962 11 9:11120076–11136889 CWC27 16 46,572,512 46,875,541 2 16:46472193–46771773
THAP12 9 11,652,415 11,669,844 2 9:11449284–11760977 CD93 17 34,381,626 34,384,902 2 17:34206246–34400408
GAB2 9 13,936,307 14,135,685 1 9:13934282–14030509 GZF1 17 34,441,517 34,447,221 3 17:34421087–34505222
ELMOD1 9 40,189,956 40,282,814 1 9:40189621–40286365 NAPB 17 34,450,368 34,485,152 1
ATM 9 40,925,895 40,945,439 3 9:40793693–41170478 CSTL1 17 34,492,910 34,496,585 2
KDELC2 9 41,043,564 41,065,077 7 CST7 17 34,906,655 34,915,135 1 17:34901568–34908632
EXPH5 9 41,073,546 41,217,329 12 DEFB119 17 39,921,302 39,931,655 2 17:39862221–40018288
ENSSSCG00000023913 9 41,145,017 41,152,176 3 DEFB116 17 39,996,662 39,999,076 1
ARHGAP20 9 43,174,648 43,222,583 1 9:43134418–43291918 ENSSSCG00000007337 17 46,357,154 46,401,936 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
GO ID GO Term Term p-value Group p-value #Genes Associated genes found
GO:0002274 Myeloid leukocyte activation 0.005 0.005 7 ATP8A1, BIN2, CD93, GAB2, MAPT, PTPRC, SHPK
GO:0006887 Exocytosis 0.001 0.001 10 ATP8A1, BIN2, CD93, EXPH5, GAB2, NAPB, PLA2G6, PLG, PTPRC, VAMP1
GO:0030667 Secretory granule membrane 0.003 0.003 5 ATP8A1, CD93, DMBT1, PTPRC, VAMP1
GO:0040017 Positive regulation of locomotion 0.016 0.017 5 ATP8A1, CCL1, KIF20B, PLG, PTPRC
GO:0051272 Positive regulation of cellular component movement 0.013 5 ATP8A1, CCL1, KIF20B, PLG, PTPRC
GO:2000147 Positive regulation of cell motility 0.012 5 ATP8A1, CCL1, KIF20B, PLG, PTPRC
GO:0030335 Positive regulation of cell migration 0.010 5 ATP8A1, CCL1, KIF20B, PLG, PTPRC
GO:0006873 Cellular ion homeostasis 0.003 0.006 7 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC
GO:0055080 Cation homeostasis 0.005 7 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC
GO:0030003 Cellular cation homeostasis 0.003 7 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC
GO:0055065 Metal ion homeostasis 0.003 7 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC
GO:0072507 Divalent inorganic cation homeostasis 0.015 5 CCL1, CCL23, MCOLN1, PLA2G6, PTPRC
GO:0006875 Cellular metal ion homeostasis 0.001 7 CCL1, CCL23, HPX, LETM2, MCOLN1, PLA2G6, PTPRC
GO:0072503 Cellular divalent inorganic cation homeostasis 0.013 5 CCL1, CCL23, MCOLN1, PLA2G6, PTPRC
GO:0055074 Calcium ion homeostasis 0.011 5 CCL1, CCL23, MCOLN1, PLA2G6, PTPRC
GO:0006874 Cellular calcium ion homeostasis 0.010 5 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 damaging Probably damaging Total
SIFT Deleterious 29 19 27 75
Tolerated 234 21 15 270
Total 263 40 42 345

SIFT, sorting intolerant from tolerant; Polyphen-2, polymorphism phenotyping v2.

Table 4
Forty-six non-synonymous SNPs with strong effects on protein functions based on SIFT and Polyphen-2
SNP CHR POS A1 A2 SIFT prediction SIFT score Polyphen-2 prediction Polyphen-2 score Gene Selective sweep
rs328613228 2 16,206,079 T G deleterious 0 probably damaging 0.997 FAM180B 2:16111708:16299440
2:62624837 2 62,624,837 G A deleterious 0.017 possibly damaging 0.853 ENSSSCG00000013821 2:62355986:62756249
rs340857214 2 62,625,107 G A deleterious 0.021 possibly damaging 0.539
2:62625190 2 62,625,190 A T deleterious 0.028 possibly damaging 0.934
rs335820735 2 62,644,986 A T deleterious 0.008 probably damaging 0.999 ENSSSCG00000013822
rs343007761 2 62,645,014 T G deleterious 0.018 possibly damaging 0.506
2:62645060 2 62,645,060 A G deleterious 0.012 possibly damaging 0.604
rs325197977 2 62,645,081 A G deleterious 0 possibly damaging 0.934
2:62669920 2 62,669,920 G A deleterious 0.008 possibly damaging 0.934 ENSSSCG00000013819
2:62669953 2 62,669,953 T G deleterious 0.007 possibly damaging 0.934
2:62670031 2 62,670,031 G A deleterious 0.012 probably damaging 0.999
rs342394815 2 85,732,226 T C deleterious 0.002 probably damaging 0.999 ENSSSCG00000014078 2:85467258:86506548
rs337260402 2 85,732,237 T G deleterious 0.003 probably damaging 0.97
rs326720643 2 85,775,718 A G deleterious 0.007 probably damaging 0.984 ANKRD31
rs318473425 2 86,321,677 T A deleterious 0.033 probably damaging 0.995 ANKDD1B
rs329106718 5 66,654,214 C T deleterious 0 probably damaging 0.993 TAPBPL 5:66396846:66725591
rs326638161 6 43,729,346 T C deleterious 0.007 probably damaging 0.988 DMBT1 6:43719388:43757067
rs322198139 6 43,750,820 G T deleterious 0.017 possibly damaging 0.915
rs321057648 6 43,750,963 A G deleterious 0.009 possibly damaging 0.663
6:119199835 6 119,199,835 T A deleterious 0.006 probably damaging 0.998 ENSSSCG00000027618 6:119198939:119344591
rs327779736 8 35,181,016 A T deleterious 0 possibly damaging 0.944 ATP8A1 8:34998191:35275833
rs81399633 8 35,181,037 A G deleterious 0.023 possibly damaging 0.896
rs343636299 9 2,311,094 T C deleterious 0.042 probably damaging 1 OVCH2 9:2223331:2577505
rs318298009 9 3,930,944 T A deleterious 0.006 probably damaging 0.996 TRIM3 9:3927497:3978728
9:11129485 9 11,129,485 T G deleterious 0.035 probably damaging 0.995 MOGAT2 9:11120076:11136889
rs340556206 9 11,129,936 T C deleterious 0.013 probably damaging 0.999
rs81509118 9 11,130,742 A G deleterious 0.036 probably damaging 1
rs342457070 9 11,130,778 C A deleterious 0.005 probably damaging 0.991
rs327337551 9 11,130,783 G C deleterious 0.047 possibly damaging 0.697
rs338381437 9 11,666,878 G A deleterious 0.003 probably damaging 0.983 THAP12 9:11449284:11760977
rs81214615 9 41,047,573 T A deleterious 0.024 probably damaging 0.99 KDELC2 9:40793693:41170478
rs339385194 9 41,076,701 G T deleterious 0.04 probably damaging 0.999 EXPH5
9:56962342 9 56,962,342 A G deleterious 0.028 possibly damaging 0.616 ENSSSCG00000026119 9:56869539:57122277
9:56962578 9 56,962,578 A C deleterious 0.026 probably damaging 0.994
rs328160175 9 56,971,732 G A deleterious 0.016 probably damaging 0.994 ENSSSCG00000015182
rs335643554 9 56,980,378 C T deleterious 0.032 possibly damaging 0.539 ENSSSCG00000028463
rs331490061 9 56,981,034 A G deleterious 0.004 possibly damaging 0.927
rs326014276 10 63,681,709 G C deleterious 0.037 possibly damaging 0.944 GJD4 10:63669866:63725092
rs339353031 11 78,365,823 G A deleterious 0.008 probably damaging 0.983 CCDC168 11:78318648:78678168
11:78367889 11 78,367,889 G A deleterious 0 probably damaging 0.993
rs342686832 11 78,367,955 A G deleterious 0.034 possibly damaging 0.94
rs325650226 12 15,917,860 T C deleterious 0.002 probably damaging 0.999 MARCH10 12:15890650:15938045
rs336224471 12 15,917,910 A C deleterious 0.03 possibly damaging 0.82
15:55400479 15 55,400,479 A G deleterious 0.032 probably damaging 1 WHSC1L1 15:55142754:55608192
rs339461760 16 46,612,542 C G deleterious 0.007 probably damaging 0.998 CWC27 16:46472193:46771773
rs324424231 17 46,357,195 A G deleterious 0 probably damaging 0.998 ENSSSCG00000007337 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 2:62355986–62756249
Species Match gene symbol Match ensemble gene ID Compare regions ENSSSCG00000013821 ENSSSCG00000013822 ENSSSCG00000013819



dN/dS Target %id Query %id dN/dS Target %id Query %id dN/dS Target %id Query %id
Chimpanzee (Pan troglodytes) OR7A5 ENSPTRG00000010603 19:15,130,772–15,137,945 0.350 69.0 70.7 0.372 69.6 71.8 0.327 71.2 70.9
Chimpanzee (Pan troglodytes) OR7A10 ENSPTRG00000010604 19:15,143,753–15,144,682 0.377 70.6 70.1 0.333 71.8 71.8 0.338 71.8 69.4
Gibbon (Nomascus leucogenys) OR7A17 ENSNLEG00000005159 GL397382.1:231,228–275,098 0.383 71.0 70.7 0.359 70.3 70.6 0.290 73.2 70.9
Gorilla (Gorilla gorilla gorilla) OR7A10 ENSGGOG00000015049 19:15,120,105–15,121,034 - 70.6 70.1 - 70.9 70.9 - 72.2 69.7
Gorilla (Gorilla gorilla gorilla) OR7A17 ENSGGOG00000034834 19:15,160,189–15,161,115 - 72.5 72.0 - 72.8 72.8 - 73.1 70.6
Human (Homo sapiens) OR7A10 ENSG00000127515 19:14,840,948–14,841,877 0.418 70.2 69.8 0.377 70.6 70.6 0.361 71.8 69.4
Human (Homo sapiens) OR7A17 ENSG00000185385 19:14,880,426–14,881,452 0.338 72.2 71.7 0.356 72.5 72.5 0.317 72.5 70.0
Human (Homo sapiens) OR7A5 ENSG00000188269 19:14,792,490–14,835,376 0.354 69.6 71.4 0.370 70.2 72.5 0.313 71.5 71.3
Mouse (Mus musculus) Olfr1353 ENSMUSG00000042774 10:78,963,309–78,971,338 - 62.5 62.1 - 61.2 61.2 0.243 65.1 62.8
Mouse (Mus musculus) Olfr1352 ENSMUSG00000046493 10:78,981,050–78,987,903 0.238 68.6 68.2 0.224 67.3 67.3 - 68.6 66.3
Mouse (Mus musculus) Olfr19 ENSMUSG00000048101 16:16,672,228–16,676,405 0.245 68.3 67.9 0.267 66.3 66.3 0.253 67.6 65.3
Mouse (Mus musculus) Olfr57 ENSMUSG00000060205 10:79,028,741–79,036,274 0.308 66.5 68.2 0.289 64.3 66.3 0.349 65.2 65.0
Mouse (Mus musculus) Olfr1351 ENSMUSG00000063216 10:79,012,472–79,019,645 0.308 64.6 66.2 0.303 62.1 64.1 0.345 64.3 64.1
Mouse (Mus musculus) Olfr8 ENSMUSG00000094080 10:78,950,636–78,958,378 0.284 63.2 63.0 0.317 58.4 58.6 - 60.7 58.8
Mouse (Mus musculus) Olfr1354 ENSMUSG00000094673 10:78,913,171–78,920,399 0.264 63.6 63.3 - 59.0 59.2 - 62.3 60.3
Orangutan (Pongo abelii) OR7A5 ENSPPYG00000009655 19:15,004,902–15,005,858 0.373 67.9 69.5 0.395 67.3 69.3 0.351 68.9 68.4
Orangutan (Pongo abelii) OR7A10 ENSPPYG00000009656 19:15,019,264–15,020,193 0.453 69.6 69.1 0.402 70.9 70.9 0.350 71.2 68.8
Orangutan (Pongo abelii) OR7A17 ENSPPYG00000009658 19:15,062,903–15,091,843 0.344 70.9 70.4 0.339 71.5 71.5 0.342 70.9 68.4
Rat (Rattus norvegicus) Olr1073 ENSRNOG00000031688 7:13,378,338–13,379,273 - 62.1 62.1 - 61.7 62.1 0.270 65.3 63.4
Rat (Rattus norvegicus) Olr1076 ENSRNOG00000039448 7:13,424,355–13,425,311 0.263 66.0 67.5 0.248 63.8 65.7 0.285 64.8 64.4
Rat (Rattus norvegicus) Olr1075 ENSRNOG00000039449 7:13,403,899–13,404,858 0.290 67.1 68.8 0.272 66.1 68.3 0.291 67.4 67.2
Rat (Rattus norvegicus) Olr1085 ENSRNOG00000047090 7:13,673,934–13,674,866 - 63.2 63.0 0.343 58.4 58.6 0.327 62.3 60.3
Rat (Rattus norvegicus) Olr1079 ENSRNOG00000049781 7:13,488,205–13,489,137 0.276 63.6 63.3 0.395 59.4 59.6 0.336 62.6 60.6
Rat (Rattus norvegicus) Olr1077 ENSRNOG00000054107 7:13,460,476–13,461,405 0.229 69.3 68.8 0.241 67.0 67.0 0.236 68.0 65.6
Rat (Rattus norvegicus) Olr1082 ENSRNOG00000058943 7:13,553,010–13,553,963 0.279 61.8 63.0 0.348 58.0 59.6 0.342 59.6 59.1
Rat (Rattus norvegicus) Olr1083 ENSRNOG00000061480 7:13,587,479–13,588,411 0.290 63.2 63.0 0.352 60.3 60.5 0.332 62.6 60.6
Vervet-AGM (Chlorocebus sabaeus) OR7A10 ENSCSAG00000006193 6:13,469,888–13,471,167 0.347 70.2 69.8 0.330 72.2 72.2 0.348 71.8 69.4

REFERENCES

1. Ennis S. Linkage disequilibrium as a tool for detecting signatures of natural selection. Collins AR, editorLinkage 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.
crossref pmid
3. Barton NH. Linkage and the limits to natural selection. Genetics 1995; 140:821–41.
pmid pmc
4. Durrett R, Schweinsberg J. Approximating selective sweeps. Theor Popul Biol 2004; 66:129–38.
crossref pmid
5. Pennings PS, Hermisson J. Soft sweeps III: the signature of positive selection from recurrent mutation. PLoS Genet 2006; 2:e186
crossref pmid pmc
6. Przeworski M, Coop G, Wall JD. The signature of positive selection on standing genetic variation. Evolution 2005; 59:2312–23.
crossref pmid
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.
crossref pmid pmc
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
crossref pmid pmc
9. Kim J, Cho S, Caetano-Anolles K, Kim H, Ryu Y-C. Genome-wide detection and characterization of positive selection in Korean Native Black Pig from Jeju Island. BMC Genet 2015; 16:3
crossref pmid pmc
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
crossref pmid pmc
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
crossref pmid pmc pdf
12. Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 2002; 30:3894–900.
crossref pmid pmc pdf
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.
crossref pmid
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 trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–20.
crossref pmid pmc pdf
16. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–9.
crossref pmid pmc pdf
17. Li H, Handsaker B, Wysoker A, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009; 25:2078–9.
crossref pmid pmc pdf
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.
crossref pmid pmc
19. Danecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. Bioinformatics 2011; 27:2156–8.
crossref pmid pmc pdf
20. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000; 155:945–59.
pmid pmc
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.
crossref pmid pmc pdf
22. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003; 31:3812–4.
crossref pmid pmc
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.
crossref
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.
crossref pmid pmc
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.
crossref pmid
26. Teijeiro JM, Roldán 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.
crossref pmid
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.
crossref pmid
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.
crossref pmid pmc pdf
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
crossref pmid pmc


ABOUT
SPECIALTIES
BROWSE ARTICLES
FOR AUTHORS AND REVIEWERS
Editorial Office
Asian-Australasian Association of Animal Production Societies(AAAP)
Room 708 Sammo Sporex, 23, Sillim-ro 59-gil, Gwanak-gu, Seoul
08776, Korea   TEL : +82-2-888-6558    FAX : +82-2-888-6559   
E-mail : jongkha@hotmail.com               

Copyright © 2018 by Asian-Australasian Journal of Animal Sciences. All rights reserved.

Developed in M2community

Close layer
prev next