| Home | Subscription | E-Submission | Sitemap | Contact Us |
Asian-Australas J Anim Sci > Volume 30(8); 2017 > Article
Ji, Zhou, Guo, Huang, and Ma: Genome-wide association study identifies 22 new loci for body dimension and body weight traits in a White Duroc×Erhualian F2 intercross population



Growth-related traits are important economic traits in the swine industry. However, the genetic mechanism of growth-related traits is little known. The aim of this study was to screen the candidate genes and molecular markers associated with body dimension and body weight traits in pigs.


A genome-wide association study (GWAS) on body dimension and body weight traits was performed in a White Duroc×Erhualian F2 intercross by the illumina PorcineSNP60K Beadchip. A mixed linear model was used to assess the association between single nucleotide polymorphisms (SNPs) and the phenotypes.


In total, 611 and 79 SNPs were identified significantly associated with body dimension traits and body weight respectively. All SNPs but 62 were located into 23 genomic regions (quantitative trait loci, QTLs) on 14 autosomal and X chromosomes in Sus scrofa Build 10.2 assembly. Out of the 23 QTLs with the suggestive significance level (5×10−4), three QTLs exceeded the genome-wide significance threshold (1.15×10−6). Except the one on Sus scrofa chromosome (SSC) 7 which was reported previously all the QTLs are novel. In addition, we identified 5 promising candidate genes, including cell division cycle 7 for abdominal circumference, pleiomorphic adenoma gene 1 and neuropeptides B/W receptor 1 for both body weight and cannon bone circumference on SSC4, phosphoenolpyruvate carboxykinase 1, and bone morphogenetic protein 7 for hip circumference on SSC17.


The results have not only demonstrated a number of potential genes/loci associated with the growth-related traits in pigs, but also laid a foundation for studying the genes’ role and further identifying causative variants underlying these loci.


Pork is the largest source of meat production in the world [1]. Generally, improving economically important traits, e.g. production-related traits in pigs have greatly concerned both producers and breeders. Body dimension (e.g. body height, length and width) and body weight are important production traits. They are complex quantitative traits and show low to moderate heritability [2], so that the traditional selection for them may not be very efficient. In contrast, the use of genomics and molecular techniques can speed genetic improvement and increase levels of production quickly [3].
Thousands of quantitative trait loci (QTLs) for different traits have been identified since the first QTL study for growth and body composition in pigs was reported [4]. To date, 1,424 QTLs for production traits have been deposited in the PigQTLdb (http://www.genome.iastate.edu/cgi-bin/QTLdb/SS/index). However, most of QTLs span a very large chromosomal region identified by linkage analysis with low density markers. As a result, only a handful of quantitative trait nucleotides for complexed traits have been identified in agricultural animals [5,6]. Fortunately, the emergence of high-throughput genotyping platform and single nucleotide polymorphism (SNP) arrays have enabled
The Chinese Erhualian is one of the most prolific pig breeds in the world while its productive efficiency is much lower than that of White Duroc cultivated by PIC company [7]. Therefore, the genetic architecture of production traits must be distinct between the two breeds. We have constructed a three-generation resource population by crossing White Duroc boars and Chinese Erhualian sows, and a diverse set of phenotype traits including body dimension and body weight traits at 210 d have been recorded. The objective of this study was to identify QTL and positional candidate genes for body dimension and body weight traits at 210 d by genome-wide association studies (GWAS).


All samples were collected according to the guidelines for the care and use of experimental animals approved by the State Council of the People’s Republic of China. The ethics committee of Jiangxi Agricultural University specifically approved this study.

Animals and phenotypic measurement

A White Duroc×Erhualian F2 resource population was used in this study. It was developed and managed as described previously [8]. Briefly, 2 White Duroc sires and 17 Erhualian dams were mated to produce F1 animals in 2001, from which 9 F1 boars and 59 F1 sows were intercrossed to produce 983 F2 males and 929 F2 females in 6 batches from 2003 to 2006. All F2 animals were raised at the experimental farm in Jiangxi Agricultural University (Nanchang, China). They were fed with similar diet under a standardized feeding and management regimen and given free access to water. All piglets were weaned at 46 days and the males were castrated at 90 days. At 210±6 days of age, a total of 124 castrated males from 1st batch were measured for body dimension traits and a total of 741 progeny including 340 females and 401 males in 6 batches were measured for body weight (BW). Body dimension traits consist of abdominal circumference (AC), body height (BH), body length (BL), cannon bone circumference (CBC), chest circumference (CC), chest depth (CD), chest width (CW), hip circumference (HC). The details of the measurement methods were described by Ma et al [9].

Genotyping and quality control

Genomic DNA was isolated from ear tissues using a routine phenol/chloroform extraction method. The DNA concentration of the samples was adjusted to 50 ng/μL using the Nanodrop ND-1000 (Peqlab Biotechnology, Erlangen, Germany) and DNA quality was assessed by gel electrophoresis using 1% agarose gels. Samples were genotyped with the Illumina PorcineSNP60 BeadChip, using the Infinium HD Assay Ultra protocol (Illumina, Inc., San Diego, CA, USA). Quality control was carried out using PLINK v1.07 [10]. Briefly, SNPs were removed if they had genotype-missing rates >0.1 or minor allele frequencies<0.05 or Hardy-Weinberg p≤10−5. Samples were removed on low (<95%) call rate. After that, all 125 individuals passed the filter and a final set of 43,517 SNPs were selected for subsequent analysis.

Statistical analysis

The association analyses were conducted using GenABEL in the R software [11]. A mixed linear model was used to test for the association between SNPs and the recorded traits by a two-stage approach. For body weight measured in 340 females and 401 males from 6 batches, sex and batch were fitted as fixed effects in the mixed model. Firstly, the phenotypic residual vector e* was calculated as follows:
Where y is the vector of phenotypes; b is the vector of fixed effects including sex and batch, and X is the incidence matrix for b; u is the vector of random polygenic additive effects with N(0,Gσu2), G is the genomic relationship matrix, σu2 is the polygenic additive variance, Z is the incidence matrix for u.
Then, a family-based score test was explored to detect associations between SNPs and traits by the following regression model, each time one SNP:
Where a is the estimator of the SNP allele substitution effect, S is the incidence vector of a; e is the vector of residual errors following N(0,Iσe2), I is an identity matrix, σe2 is the variance of residual error.
As for body dimension traits which were only recorded in males in the 1st batch, sex and batch effects were excluded from the above model.
The association analysis of chromosome X was based on the assumption of complete and uniform X-inactivation in females and a similar effect size between males and females. Thus, females are considered to have 0, 1, or 2 copies of an allele as in an autosomal analysis; males are considered to have 0 or 2 copies of the same allele, i.e. male hemizygotes are considered equivalent to female homozygous states.
In this study, the genome-wide significance threshold was determined by the Bonferroni method [12], which was equal to 1.15× 10−6 (0.05/43517). The suggestive significance level was set to 5×10−4 [13]. GWA peaks with p<5×10−4 at a distance of more than 15 Mb were considered as different QTLs.
The influence of population stratification was assessed by examining the distribution of test statistics and assessing their deviation from the null distribution (that expected under the null hypothesis of no SNP associated with the trait) in a quantile-quantile (Q-Q) plot [14]. The Q-Q plot was constructed using R software.
Haplotype block analysis or linkage disequilibrium was performed in the region which contained multiple significant SNPs clusters around the peak SNP. The HAPLOVIEW V4.2 software with default settings was used to determine the haplotype blocks [15].

Bioinformatics analyses

SNP positions on chromosomes and the closest genes to tag (significant and suggestive) SNPs associated with traits were obtained by using Sscrofa 10.2 genome assembly from Ensembl website (http://www.ensembl.org/Sus_scrofa/Info/Index). The overlap between our GWAS data and previously mapped QTL data were assessed using the PigQTLdb (http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). To identify the candidate genes, we queried the information about the associations between all candidate genes within 1 Mb bin size on either side of GWAS lead SNPs.


Phenotype and correlations between traits

The description of traits, number of records, means, standard deviation, minimum and maximum are summarized in Table 1. Figure 1 shows that the correlation coefficients between BW and body dimension traits, which were positive and highly significant (0.55≤r≤0.86, p<0.01).

Population stratification assessment

The Q-Q plots of the test statistics in GWAS are shown in Figure 2. The lambda values for all traits are close to 1 except CBC (λ = 1.33). The Q-Q plots and the λ value indicated that there are no very strong stratification existed.

GWAS signals for traits

We identified a total of 611 tag SNPs for the body dimension traits and 79 tag SNPs for body weight in this study (Table 2). Sixty-two SNPs were not assigned to any chromosome. There were 100 mapped SNPs reaching genome-wide significance level, including 95 SNPs for CBC on SSC7, 2 SNPs for BW on SSC7 and SSCX respectively, 1 SNP for HC on SSC17 (Figure 3). All the significant SNPs except for unmapped markers represented 23 QTLs, of which all but one on SSC7 have not been reported previously.
As the lambda value of CBC (1.33) is a little inflated, we applied the GRAMMAR-GC method to re-evaluate the association between the three tag SNPs (i.e., ASGA0095875, H3GA0013212, MARC0058766) and CBC. The resulting p-values differed slightly from the previously reported p-values, indicating that the three SNPs represent reliable association signals.
Previous studies have shown that the SSC7 region from 35.0 Mb to 43.9 Mb contains multiple significant SNPs associated with CBC [16,17]. Recently, a GWA study performed by Wang et al [18] in a Large White×Minzhu pig population identified 138 SNPs significantly associated with BH, BL, CBC, and HC within a 36.9 Mb region from 20.8 Mb to 57.7 Mb. Coincidently, we also detected 134 SNPs for BH, 54 SNPs for BL, 308 SNPs for CBC and 51 SNPs for BW in the SSC7 region spanning from 13.0 Mb to 55.4 Mb. The most significant SNPs ASGA0100868 (at 31.91 Mb) for BH, ASGA0032302 (at 32.95 Mb) for BW and MARC0058766 (at 34.80 Mb) for CBC were in strong linkage disequilibrium (r2>0.8). We selected the top SNP ASGA0100868 for BH to estimate allelic substitution effects of the SSC7 QTL (Table 3). Pigs with AA genotype had statistically higher phenotypic values than pigs carrying the AG and GG genotypes. And the allelic substitution effect accounted for 2.13 cm of BH, 4.06 kg of BW and 1.18 cm of CBC (Table 3). This is consistent with our previous genome-wide linkage study that identified a QTL within the SSC7 region with large effect on growth traits in this F2 population [8].
This is the first study to detect QTL for HC in pigs. There were 31 SNPs significantly associated with HC, of which 27 fall in the region from 61.6 Mb to 68.0 Mb on SSC17. The remaining two SNPs were detected on SSC7 and one on SSC3. Only one SNP was not assigned to pig chromosomes. The most significant SNP associated with HC was the SNP MARC0037499 at 65.44 Mb with the p value of 1.12×10−6 on SSC17 and it is near to the gene C20orf85. At this locus, the GG genotype had greater HC (79.45 ±4.58 cm) compared to the AG (76.05±4.35 cm) and the AA (71.50±4.66 cm) genotypes. The four highly significant SNPs MARC0037499, ASGA0078226, ALGA0096393, and M1GA 0022553 for HC on SSC17 were just in a constructed haplotype block that spans 79 Kb (Figure 4). No annotated genes are present in the haplotype block, indicating that the QTL effect may be caused by a regulatory mutation. Candidate genes phosphoenolpyruvate carboxykinase 1 (PCK1) and bone morphogenetic protein 7 (BMP7) are located adjacent to the block.
Nine SNPs associated with AC at the suggestive significance level were found at five chromosome regions. The SNP INRA 0039280 on SSC13 was positioned within the gene ENSSSCG 00000023343. In addition we identified 22 SNPs associated with CC, CD, and CW. Eighteen out of the 22 SNPs indicated 8 QTLs on SSC2, 3, 4, 7, 11, 13, and 15. One SNP ASGA0085473 was mapped in the intron of gene ENSSSCG00000027422. The positions of the other 17 SNPs were close to the locations of 7 genes including U6, SLIT and NTRK like family member 5 (SLITRK5), solute carrier organic anion transporter family member 3A1 (SLCO3A1), protocadherin 17 (PCDH17), glutamate decarboxylase like 1 (GADL1), ENSSSCG00000029321 and ENSSSCG 00000029992.

Candidate genes for major QTLs


Three QTLs were detected on SSC4: one for AC, one for BW and CBC and another for CD. The peak SNP MARC0100434 for AC (at 137.6 Mb) was located 650 Kb away from cell division cycle 7 (CDC7) gene. This gene can regulate the expression of the DNA unwinding element-binding protein in replication initiation and hypomorphic mutation in CDC7 reduced mouse growth rate [19]. Therefore, we regard CDC7 as a candidate for AC. The QTL effect on both BW and CBC may be due to the candidate gene pleiomorphic adenoma gene 1 (PLAG1) and/or neuropeptides B/W receptor 1 (GPR7). PLAG1 has been found to be associated with cattle height [20]. Moreover, PLAG1 variants were also associated with growth and fatness traits in an European Wild boar× Large White F2 intercross population [21] and the present F2 population, as well as our Chinese Sutai half-sib population [7]. In addition, GPR7 is important for maintaining long-term energy homeostasis and has been implicated to play a role in human obesity development [22].


Chromosome 7 harbors one QTL for BH, BL, BW, and CBC. The top SNP MARC0058766 for CBC with the genome-wide significance level was close to the gene high mobility group AT-Hook 1 (HMGA1). HMGA1 is one member of the high mobility group A family. HMGA1/HMGA2 double knock-out mice is smaller than only the HMGA1 knock-out mice, implicating that HMGA1 has a determination on body size [23]. A study showed that HMGA1 can serve as a mediator of glucose disposal by regulating the activity of insulin-like growth factor 1 [24]. HMGA1 has been considered as a candidate gene for limb bone length in a Large White×Minzhu intercross population [25].


On SSC17, we detected 2 and 27 SNPs in the region from 65 Mb to 70 Mb that were significantly associated with BL and HC respectively. The GWA top SNP MARC0037499 for HC resides at 65.44 Mb, nearby the PCK1 gene. PCK1 was found to be associated with diabetes and obesity [26]. It plays an important role in maintaining the lipid metabolism and glucose homeostasis and disease prevention [27]. Additionally, BMP7 is located 601Kb away from the peak SNP. This gene is a member of the transforming growth factor-β (TGF-β) family, which stimulates the differentiation of osteoblasts from mesenchymal stem cells both in vitro and in vivo [28]. It is also involved in the formation and development of numerous organs [29,30]. So we propose PCK1 and BMP7 as promising candidate genes for BL and HC.


This GWA study identified a total of 690 SNPs significantly associated with 8 body dimension traits and body weight. We confirmed the QTL for CBC on SSC7, for which HMGA1 has been considered as candidate gene. Another 22 QTLs for these traits have been reported for the first time. There were three QTLs reaching genome-wide significance including one QTL for both BW and CBC, one for BW and one for HC. In addition, a set of candidate genes adjacent to the GWA signals were proposed here due to their functional relationships with the corresponding traits. These results will advance our understanding of the genetic basis of body dimension and body weight traits in pigs.


The study was supported by the Development Programs for Basic Research of China (973 Programs, No. 2014CB160311) and the Key Project of National Nature Science Foundation of China (No. 31230069).



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

Figure 1
Phenotypic correlation coefficients between body dimension and body weight traits. AC, abdominal circumference; BH, body height; BL, body length; BW, body weight, CBC, cannon bone circumference; CC, chest circumference; CD, chest depth; CW, chest width; HC, hip circumference. The dots indicate the significant (p<0.01) correlation coefficients between each pair of traits.Round shadow size and color depth represent the degree of the correlation coefficient.
Figure 2
The quantile-quantile (Q-Q) plots for (A) abdominal circumference, (B) body height, (C) body length, (D) body weight, (E) cannon bone circumference, (F) chest circumference, (G) chest depth, (H) chest width, (I) hip circumference. The horizontal axis indicates the expected −log10 (p-values) and the vertical axis indicates the observed −log10 (p-values). The diagonal line represents y = x, which corresponds to the null hypothesis.
Figure 3
Manhattan plot of the genome-wide association study (GWAS) result. Chromosomes 1–18 and 19 (X) are shown in different colors. The X-axis represents the chromosomes, and the Y- axis shows the −log10 (p-value). The horizontal, dashed line represents the chromosome-wide significance thresholds and the real line reflects the genome-wide significance thresholds.
Figure 4
Haplotype block at linkage disequilibrium (LD) on a 508 Kb region on SSC17 associated with hip circumference. The block of 79 Kb, which contained the most significant single nucleotide polymorphism (SNP) being MARC0037499 (noted by a star).
Table 1
Descriptive statistics of 210 d body dimension and body weight traits from the White Duroc×Erhualian F2 intercross pig population.
Traits N Mean SD Minimum Maximum
AC (cm) 124 116.33 6.96 100.00 136.00
BH (cm) 124 59.60 3.49 51.70 69.90
BL (cm) 124 126.69 7.74 104.00 145.00
BW (kg) 741 83.77 15.01 31.00 132.00
CBC (cm) 124 16.30 1.33 13.30 19.50
CC (cm) 124 107.22 6.07 90.50 125.00
CD (cm) 124 36.35 2.45 31.40 42.70
CW (cm) 124 31.53 2.46 22.40 37.50
HC (cm) 124 77.84 5.03 62.00 88.00

SD, standard deviation; AC, abdominal circumference; BH, body height; BL, body length; BW, body weight; CBC, cannon bone circumference; CC, chest circumference; CD, chest depth; CW, chest width; HC, hip circumference.

Table 2
Description of SNPs significantly associated with the 210 d body dimension and body weight traits
Traits Peak SNP No1) Chr2) Pos (bp)3) p value4) Nearest genes5) Candidate genes6)
AC MARC0100434 4 4 137588537 4.66×10−5 ENSSSCG00000022534 CDC7
ASGA0094517 2 10 77141165 3.7×10−4 ARL5B
INRA0039280 1 13 402143 2.2×10−4 ENSSSCG00000023343
ASGA0091668 1 15 41967209 3.71×10−4 U6
DRGA0015878 1 16 15519203 2.47×10−4 ENSSSCG00000018139
BH ASGA0100868 134 7 31914593 7.17×10−6 KHDRBS2 HMGA1
BL ASGA0012617 1 2 150575241 2.28×10−4 ARHGAP26
M1GA0027226 54 7 30592663 5.64×10−5 FAM83B HMGA1
ASGA0078573 2 17 69246232 3.08×10−4 GATA5
ALGA0100018 1 X 116418847 3.31×10−4 STAG2
BW ASGA0007965 2 1 303920214 2.81×10−4 USP20
ALGA0026044 8 4 82850635 1.56×10−5 ENSSSCG00000006251 PLAG1, GPR7
ALGA0103680 2 5 84405613 3.26×10−4 ENSSSCG00000000854
ASGA0032302 51 7 32957768 2.45×10−7 ENSSSCG00000001493 HMGA1
MARC0015961 1 11 75714526 4.69×10−4 TM9SF2
H3GA0033954 1 12 25879920 4.99×10−4 ENSSSCG00000017550
ASGA0065790 1 14 113554994 8.29×10−5 IDE
ASGA0074521 1 16 80649589 4.52×10−4 ADCY2
M1GA0023914 2 X 142291232 4.35×10−7 IRAK1
CBC ASGA0095875 1 3 92014512 4.22×10−5 ADGRF4
H3GA0013212 1 4 84441903 6.77×10−5 KOR PLAG1, GPR7
MARC0058766 308 7 34803564 7.89×10−10 GRM4 HMGA1
CC ASGA0085473 1 3 97789312 2.32×10−4 ENSSSCG00000027422
ASGA0091668 2 15 41967209 2.81×10−4 U6
CD DBWU0000455 1 4 93802039 3.79×10−4 ENSSSCG00000029321
ALGA0062565 5 11 63767066 3.22×10−4 SLITRK5
CW ALGA0105132 3 2 42589487 4.63×10−4 ENSSSCG00000029992
ALGA0043224 3 7 93251213 1.28×10−4 SLCO3A1
DRGA0011077 2 11 32515591 1.46×10−4 PCDH17
ASGA0056435 1 13 19066947 3.34×10−4 GADL1
HC MARC0064460 1 3 9157999 1.99×10−4 ENSSSCG00000007681
DRGA0007893 2 7 84299964 3.32×10−4 ENSSSCG00000002175
MARC0037499 27 17 65441894 1.12×10−6 C20orf85 PCK1, BMP7

SNP, single nucleotide polymorphism; AC, abdominal circumference; BH, body height; BL, body length; BW, body weight; CBC, cannon bone circumference; CC, chest circumference; CD, chest depth; CW, chest width; HC, hip circumference.

1) The number of significant SNPs within the QTL regions.

2),3) The positions of the associated SNPs on the Sus Scrofa Build 10.2 assembly.

4) The p value in bold surpassed the genome-wise significance threshold.

5) Gene names starting with ENSSSCG represent Ensemble nomenclature while other gene symbols represent HUGO nomenclature.

6) Genes within 1 Mb upstream and downstream of the SNP were related to body dimensions in other GWA studies and/or functional tests.

Table 3
The allelic substitution effect of the top SNP ASGA0100868 on BH, BW, and CBC
Genotype BH BW CBC
AA 61.99 (n = 33) 86.26 (n = 168) 17.33 (n = 33)
GA 59.15 (n = 64) 85.29 (n = 393) 16.33 (n = 64)
GG 57.73 (n = 27) 78.15 (n = 180) 14.98 (n = 27)
F-value 14.90** 17.68** 36.71**

SNP, single nucleotide polymorphism; BH, body height; BW, body weight; CBC, cannon bone circumference.

Mean values are shown for each genotype class. The difference in mean values among the three genotypes were obtained by F-test.

* p<0.05;

** p<0.01.


1. McGlone J. The future of pork production in the world: towards sustainable, welfare-positive systems. Animals. 2013; 3:401–15.
crossref pmid pmc
2. Johnson ZB., & Nugent RA. Heritability of body length and measures of body density and their relationship to backfat thickness and loin muscle area in swine. J Anim Sci. 2003; 81:1943–9.
crossref pmid
3. Rothschild MF., & Plastow GS. Impact of genomics on animal agriculture and opportunities for animal health. Trends Biotechnol. 2008; 26:21–5.
crossref pmid
4. Andersson L, Haley CS., & Ellegren H. , et alGenetic mapping of quantitative trait loci for growth and fatness in pigs. Science. 1994; 263:1771–4.
crossref pmid
5. Freking BA, Murphy SK., & Wylie AA. , et alIdentification of the single base change causing the callipyge muscle hypertrophy phenotype, the only known example of polar overdominance in mammals. Genome Res. 2002; 12:1496–506.
crossref pmid pmc
6. Van Laere AS, Nguyen M., & Braunschweig M. , et alA regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature. 2003; 425:832–6.
crossref pmid
7. Qiao R, Gao J., & Zhang Z. , et alGenome-wide association analyses reveal significant loci and strong candidate genes for growth and fatness traits in two pig populations. Genet Sel Evol. 2015; 47:17
crossref pmid pmc
8. Ai H, Ren J., & Zhang Z. , et alDetection of quantitative trait loci for growth- and fatness-related traits in a large-scale White Duroc x Erhualian intercross pig population. Anim Genet. 2012; 43:383–91.
crossref pmid
9. Ma J, Ren J., & Guo Y. , et alGenome-wide identification of quantitative trait loci for carcass composition and meat quality in a large-scale White Duroc×Chinese Erhualian resource population. Anim Genet. 2009; 40:637–47.
crossref pmid
10. Purcell S, Neale B., & Todd-Brown K. , et alPLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81:559–75.
crossref pmid pmc
11. Aulchenko YS, Ripke S, Isaacs A., & van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007; 23:1294–6.
crossref pmid
12. Yang Q, Cui J, Chazaro I, Cupples LA., & Demissie S. Power and type I error rate of false discovery rate approaches in genome-wide association studies. BMC Genet. 2005; 6: Suppl 1S134
crossref pmid pmc
13. Sanchez MP, Tribout T., & Iannuccelli N. , et alA genome-wide association study of production traits in a commercial population of Large White pigs: evidence of haplotypes affecting meat quality. Genet Sel Evol. 2014; 46:12
crossref pmid pmc
14. Pearson TA., & Manolio TA. How to interpret a genome-wide association study. JAMA. 2008; 299:1335–44.
crossref pmid
15. Barrett JC, Fry B, Maller J., & Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005; 21:263–5.
crossref pmid
16. Okumura N, Matsumoto T., & Hayashi T. , et alGenomic regions affecting backfat thickness and cannon bone circumference identified by genome-wide association study in a Duroc pig population. Anim Genet. 2013; 44:454–7.
crossref pmid
17. Uemoto Y, Nagamine Y., & Kobayashi E. , et alQuantitative trait loci analysis on Sus scrofa chromosome 7 for meat production, meat quality, and carcass traits within a Duroc purebred population. J Anim Sci. 2008; 86:2833–9.
crossref pmid
18. Wang L, Zhang L., & Yan H. , et alGenome-wide association studies identify the loci for 5 exterior traits in a Large White x Minzhu pig population. PLoS One. 2014; 9:e103766
crossref pmid pmc
19. Kim JM, Takemoto N, Arai K., & Masai H. Hypomorphic mutation in an essential cell-cycle kinase causes growth retardation and impaired spermatogenesis. EMBO J. 2003; 22:5260–72.
crossref pmid pmc
20. Karim L, Takeda H., & Lin L. , et alVariants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nat Genet. 2011; 43:405–13.
crossref pmid
21. Rubin CJ, Megens HJ., & Martinez Barrio A. , et alStrong signatures of selection in the domestic pig genome. Proc Natl Acad Sci USA. 2012; 109:19529–36.
crossref pmid pmc
22. Ishii M, Fei H., & Friedman JM. Targeted disruption of GPR7, the endogenous receptor for neuropeptides B and W, leads to metabolic defects and adult-onset obesity. Proc Natl Acad Sci USA. 2003; 100:10540–5.
crossref pmid pmc
23. Federico A, Forzati F., & Esposito F. , et alHmga1/Hmga2 double knock-out mice display a “superpygmy” phenotype. Biol Open. 2014; 3:372–8.
crossref pmid pmc
24. Iiritano S, Chiefari E., & Ventura V. , et alThe HMGA1-IGF-I/IGFBP system: a novel pathway for modulating glucose uptake. Mol Endocrinol. 2012; 26:1578–89.
crossref pmid pmc
25. Zhang L-C, Li N., & Liu X. , et alA genome-wide association study of limb bone length using a Large White×Minzhu intercross population. Genet Sel Evol:GSE. 2014; 46:56

26. Beale EG, Harvey BJ., & Forest C. PCK1 and PCK2 as candidate diabetes and obesity genes. Cell Biochem Biophys. 2007; 48:89–95.
crossref pmid
27. Millward CA, Desantis D., & Hsieh CW. , et alPhosphoenolpyruvate carboxykinase (Pck1) helps regulate the triglyceride/fatty acid cycle and development of insulin resistance in mice. J Lipid Res. 2010; 51:1452–63.
crossref pmid pmc
28. Beederman M, Lamplot JD., & Nan G. , et alBMP signaling in mesenchymal stem cell differentiation and bone formation. J Biomed Sci Eng. 2013; 6:32–52.
crossref pmid pmc
29. Huang J, Liu Y, Filas B, Gunhaga L., & Beebe DC. Negative and positive auto-regulation of BMP expression in early eye development. Dev Biol. 2015; 407:256–64.
crossref pmid pmc
30. Manson SR, Austin PF, Guo Q., & Moore KH. BMP-7 signaling and its critical roles in kidney development, the responses to renal injury, and chronic kidney disease. Vitam Horm. 2015; 99:91–144.
crossref pmid
PDF Links  PDF Links
PubReader  PubReader
ePub Link  ePub Link
Full text via DOI  Full text via DOI
Download Citation  Download Citation
CrossRef TDM  CrossRef TDM
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
About |  Browse Articles |  Current Issue |  For Authors and Reviewers |  Privacy Policy
Copyright © 2014 by Asian-Australasian Journal of Animal Sciences. All rights reserved.                 powerd by m2community