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Meat Science

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A whole-genome sequence based association study on pork eating qualitytraits and cooking loss in a specially designed heterogeneous F6 pigpopulation

Jiuxiu Ji, Lisheng Zhou, Yizhong Huang, Min Zheng, Xianxian Liu, Yifeng Zhang, Cong Huang,Song Peng, Qingjie Zeng, Liepeng Zhong, Bin Yang, Wanbo Li, Shijun Xiao, Junwu Ma⁎,Lusheng Huang⁎

State Key Laboratory for Swine Genetics, Breeding and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China

A R T I C L E I N F O

Keywords:PigGWASCandidate geneEating quality traitsCooking loss

A B S T R A C T

To determine the genetic basis of pork eating quality traits and cooking loss, we herein performed a genome-wide association study (GWAS) for tenderness, juiciness, oiliness, umami, overall liking and cooking loss byusing whole genome sequences of heterogeneous stock F6 pigs which were generated by crossing 4 typicalwestern pig breeds (Duroc, Landrace, Large White and Pietrain) and 4 typical Asian pig breeds (Erhualian,Laiwu, Bamaxiang and Tibetan). We identified 50 associated loci (QTLs) and most of them are novel. Seven locialso showed pleiotropic associations with different traits. In addition, we identified multiple promising candidategenes for these traits, including PAK1 and AQP11 for cooking loss, EP300 for tenderness, SDK1 for juiciness,FITM2 and 5-linked MYH genes for oiliness, and TNNI2 and TNNT3 for overall liking. Our results provide notonly a better understanding of the genetic basis for meat quality, but also a potential application in futurebreeding for these complex traits.

1. Introduction

Pork quality, including eating quality and technological character-istics, has an important impact on the meat purchasing decision ofconsumers (Lee et al., 2012). More and more consumers are willing topay extra for superior pork which has good flavor and mouth feelmaking it delicious (Omana et al., 2014). Eating quality is produced as aresult of the raw meat qualities and the formation of soluble tastesubstances from cooking (Aaslyng et al., 2003), and there are thousandsof meat flavor and flavor precursors contributing to taste (Khan et al.,2015). Because the relationship between objective and sensory mea-surements, under many cases, does not keep consistency (Watson et al.,2008), eating quality traits are usually evaluated by consumers ortrained taste panels rather than physical or chemical methods. Meateating quality trait properties are firstly described in terms of tender-ness, juiciness, and flavor scores (Forrest, 1975). Genetic and non-ge-netic factors can both affect meat quality (Warner et al., 2010). Toconsistently improve quality and homogeneity of meat, it is essential toenhance the understanding of genetic factors that cause inter- and intra-breed variation.

Many molecular markers have been reported to be associated withmeat quality. Effects of genetic variants of the calpain gene on meattenderness have been found in bovine (Chung et al., 2014; Lee et al.,2014). The double muscling gene in cattle has very large effects oncarcass attributes and meat tenderness (Grobet et al., 1997). The cal-lipyge gene mapped to chromosome 18 results in the callipyge muscularhypertrophy (Cockett et al., 1994). The SNP c.1843C > T in the RYR1gene causes malignant hyperthermia and porcine stress syndrome,consequently influencing pork quality (Fujii et al., 1991). Milan et al.first identified R200Q mutation in the PRKAG3 gene responsible for RN(Rendment Napole) phenotype in Hampshire breed (Milan et al., 2000),and then Ciobanu et al. reported three novel PRKAG3 missense sub-stitutions (G52S, I199V and T30 N) associated with muscle glycogencontent in Berkshire × Yorkshire pigs (Ciobanu et al., 2001). Using thesame population, Ciobanu et al. also found that one CAST haplotypewas significantly associated with higher juiciness and improved ten-derness in pork (Ciobanu et al., 2004; Ciobanu et al., 2003). Zhang et al.discovered some candidate genes associated with meat quality (such asAMPD1, ADIPOQ, COPB1, FTO, LEPR, MC4R for sensory traits andHMGA2 and NME1 for cooking loss) by using a custom 96-SNP panel on

https://doi.org/10.1016/j.meatsci.2018.08.013Received 4 March 2018; Received in revised form 20 August 2018; Accepted 20 August 2018

⁎ Corresponding authors.E-mail addresses: mjwjxlab@jxau.edu.cn (J. Ma), lushenghuang@hotmail.com (L. Huang).

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Available online 23 August 20180309-1740/ © 2018 Elsevier Ltd. All rights reserved.

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15 traits collected from 400 commercial pigs (Zhang et al., 2014).So far, most of reported QTLs for eating quality traits and cooking

loss have been mapped by linkage analyses with low-density markersand three-generation experimental crosses between two distinct breeds,thereby their confidence interval (CI) generally span a large chromo-somal region. During the past decade, with the development of high-throughput genotyping and sequencing technologies, the genome-wideassociation (GWA) analysis method has been widely applied to detectQTL at high-resolution location. However, to our knowledge, few GWAstudies for eating quality traits and cooking loss traits have been per-formed. Because of cost-effectiveness, SNP arrays have been used in themajority of initial or first-generation GWAs. Thanks to the significantdecrease in the cost of genome-resequencing, the next-generationGWAS based on whole-genome variants data has been proposed.Obviously, whole-genome resequencing can further improve QTL re-solving power than SNP array genotyping (Xin et al., 2018; Zheng et al.,2015).

Many Chinese local pig breeds, such as Erhualian and Tibetan, arewell known for their rich marbling and favorable flavor, texture andtaste characteristics compared with western commercial pig breeds (Liuet al., 2015; Shen et al., 2014). Therefore, the object of our study was toreveal genetic variants that may modulate the eating quality propertiesof pork through whole genome sequence-based GWAS using a large-scale F6 population from a pig heterogeneous stock, containing themixture of 4 western commercial pig breeds (Duroc, Landrace, LargeWhite and Pietrain) and 4 Chinese local pigs (Erhualian, Laiwu, Ba-maxiang and Tibetan).

2. Materials and methods

All samples were collected according to the guidelines for the careand use of experimental animals approved by the State Council of thePeople's Republic of China. The ethics committee of JiangxiAgricultural University specifically approved this study.

2.1. Animals and phenotypic measurement

The heterogeneous stock was derived from eight pig breeds con-sisting of 4 western commercial pig breeds (including Duroc, Landrace,Large White and Pietrain) and 4 Chinese local breeds (includingErhualian, Laiwu, Bamaxiang and Tibetan), which resulted in high le-vels of variation distributed roughly uniformly throughout the genome.This stock was bred using a disc rotation breeding design. This designcan reduce the increasing rate of inbreeding coefficient and ensure thegenetic contribution of each of eight founder breeds is equivalent. Foreach founder breed, eight unrelated F0 individuals including four boarsand four sows were selected, resulting in 8 initial groups (each groupcorresponded to one breed) or 32 initial subgroups (each subgroupcomprised of one boar and one sow from a breed). Initially, the fourboars and four sows of one Chinese local pig breed were mated to allsows of one western breed and all boars of another western breed re-spectively. Reciprocal matings involved all combinations of the eightfounder breeds: F1 hybrids were intercrossed, a balanced subset ofnonoverlapping 4- and 8-way progeny were subsequently mated, andthe obtained 8-way animals at the third generation (F3) were con-tinually intercrossed to generate the subsequent generations. In eachgeneration, 2 young boars and 2–4 gilts as reproducers were randomlychosen from the litters of each subgroup. The young boars always re-tained in their original subgroups, while gilts were assigned to othersubgroups to mate with boars. Supposing that the 8 groups (each al-ways contained 4 subgroups with similar consanguinity) or the 32subgroups form a circle, in every three generations (e.g. F0-F2, F3-F5),the pairing relationship between boars and sows from different sub-groups followed the rotation pattern of 1/8, 1/4 and 1/2 disks, re-spectively (Fig. S1). All F6 animals were raised under uniform indoorconditions at the experimental farm in Jiangxi Agricultural University

(Nanchang, China), and were fed ad libitum on a diet containing 16%crude protein, 3100 kJ digestible energy, 0.78% lysine, 0.6% calciumand 0.5% phosphorus. All piglets were weaned at 46 days and the maleswere castrated at 90 days.

At 240 ± 3 days of age, a total of 836 progeny including 448 fe-males and 388 males in 23 batches were slaughtered and measured formeat quality traits. The average carcass weight of these pigs was58.6 kg. After slaughter, muscle samples were collected from the long-issimus dorsi (LD) between the 11th-rib and the first lumbar vertebra ofthe left side of each carcass, and stored at 4 °C for 48 h. For measuringcooking loss and eating quality traits, meat blocks were cut into12× π×1 cm3 slices follow the direction of muscle fibers. Each samplewas repeated 6 times.

Cooking loss (CO) was determined using an electric frying pan(LIVEN, China) preheated to 115 °C, which was monitored by a handheld infrared thermometer (BENETECH, China). In this study, bothsides of the samples were fried for 1min and then turned every 30s,until the meat was “well done” and the internal temperature around75 °C. The meat slices were cooled for 3min at room temperature, thenthey were weighed after using paper napkin to gently dry the surface ofthe meat.

Each meat slice was cut into halves and served to taste panelist. Atrained eating quality traits panel comprised 7 trained panelists wasused to quantify pork eating quality traits including tenderness, juici-ness, oiliness, umami and overall liking. Panelists were asked to eval-uate the eating quality traits using 5-point scales where tenderness(1= tough; 5= tender), juiciness (1= dry; 5= juicy), oiliness(1= lean; 5= fatty), umami (1=weak umami; 5= strong umami),overall liking (1=dislike extremely; 5= like extremely). Each meatslice was chewed> 25 times. Mineral water was served for mouthrinsing between samples. Sample evaluations were averaged acrosspanelists for subsequent analysis.

2.2. Genotyping, imputation and quality control

Genomic DNA was extracted from ear tissues using a standardphenol-chloroform method. For each individual, 10 μg of DNA wassheared into fragments of 200–800 bp using the Covaris system (LifeTechnologies). According to the Illumina DNA sample preparationprotocol, DNA fragments were end repaired, A-tailed, ligated to paired-end adaptors and PCR amplified for library construction. The quality oflibrary was check by Agilent 2100 Bioanalyzer. Paired-end sequencingwas performed by using the Illumina Xten platform (Illumina Inc., SanDiego, CA) at Novogene company (Beijing, China) according to themanufacture's standard protocols. The average genome coverage wasapproximately 7.8×. All clean reads of individuals were aligned againstNCBI Sus scrofa 11.1 genome using BMA (Li et al., 2010). The mappedreads were further sorted by SAMTools (Li et al., 2009). Then the sortedreads were processed with indel realignment and duplicate markingusing Picard (http://broadinstitute.github.io/picard/). SNPs werecalled by using Platypus (Rimmer et al., 2014). A total of 30.1 millionSNPs, including 13,985 non-synonymous SNPs, were identified in theF6 population, and their average call rate was 89%. Missing genotypeswere imputed by the Beagle v.4 program (Browning et al., 2007). Out ofthe 836 pigs, 16 were randomly selected and sequenced twice. Bycomparing the data from replicated samples, we found that the meanratio of genotype concordance was 98.7%. We also compared genotypesobtained from genome sequence and those identified from the NeogenGGP procine BeadChip (the third generation BeadChip features>51,000 evenly distributed SNPs) in 9 F6 pigs and obtained an averageconcordance ratio of 98.9%. So the results suggest the SNP data ob-tained in this study were reliable.

Quality control were carried out by Plink v1.9 (Purcell et al., 2007).Briefly, SNPs were removed if they had minor allele frequencies(MAF) < 0.03. Samples were removed on low (< 95%) call rate. Inaddition, we discarded some individuals because their phenotypic data

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were not very reliable. These include pigs who were measured in firstbatch (as pre-test samples for eating quality measurement) and notaccurately measured, as well as those with small carcass weights(< 30 kg). Consequently, a total of 771 individuals measured for eatingquality traits and 791 individuals measured for cooking loss passed thefilter and a final set of 23,509,300 SNPs were selected for subsequentassociation analysis.

2.3. Statistical analysis

The phenotypic data was adjusted for sex and batch using a linearregression model. Then the residuals were used to examine the corre-lation coefficients among all investigated traits by the R software.

The association analyses were conducted using the Genome-wideEfficient Mixed-Model Association (GEMMA) approach developed byZhou and Stephens (Zhou et al., 2012). GEMMA executes a generalizedlinear mixed model regression to evaluate the association between SNPsand phenotypic values. It can efficiently take into account the relat-edness among individuals, population stratification and systematic ef-fects (e.g. sex, batch and slaughter age included in the model as cov-ariates). GEMMA obtains either the maximum likelihood estimate(MLE) or the restricted maximum likelihood estimate (REML) in thetest, and provides a P value for corresponding test. In this study, weconsidered P < 5×10−8 as genome-wide significance level becausethis Bonferroni-corrected signficance threshold is often used in humanGWASs (Johnson et al., 2010; Xin et al., 2018). In addition, Tachma-zidou et al. also observed the significant association signals atP < 5×10−8 and suggestive association signals at P < 10−5 in awhole genome sequence-imputed GWAS (Tachmazidou et al., 2017). Toreduce the false positive rate, we preferred to present results for all lociwith P < 10−6. GWA peaks with P < 10−6 at a distance of> 3Mbwere considered as different QTLs, because the level of linkage dis-equilibrium (LD) between SNPs positioned> 3Mb apart was gen-erally< 0.1 (r2≤ 0.1).

The influence of population stratification was assessed by ex-amining the distribution of test statistics and assessing their deviationfrom the null distribution (that expected under the null hypothesis of noSNP associated with the trait) in a quantile-quantile (Q-Q) plot (Pearsonet al., 2008). The Q-Q plot was constructed using R software.

Haplotype block or LD analysis was performed for the chromosomalregions with multiple significant SNPs clustered around the peak SNP.The haplotype blocks were identified using the HAPLOVIEW v4.2software with default settings (Barrett et al., 2005).

2.4. Bioinformatics analyses

SNP positions on chromosomes and the closest genes to tag (sig-nificant and suggestive) SNPs associated with traits were obtained byusing Sscrofa 11.1 genome assembly from Ensembl website (http://asia.ensembl.org/index.html?redirect=no). The overlap between ourGWAS data and previously mapped QTL data was assessed using thePigQTLdb (http://www.animalgenome.org/cgi-bin/QTLdb/SS/index).To identify the candidate genes, we manually queried GeneCards andPubMed for the information about the associations between all candi-date genes within 1Mb bin size on either side of GWAS lead SNPs andthe studied traits.

3. Result and discussion

3.1. Phenotype statistics and correlations between traits

The descriptive statistics of 5 eating quality traits and cooking lossare summarized in Table 1. The heritability of eating quality traitsranged from 0.14 ± 0.08 (for umami) to 0.51 ± 0.08 (for oiliness),while the heritability of cooking loss was 0.27 ± 0.07. Table 2 showsthe high positive and significant correlation coefficients among eating

quality traits (0.49≤ r≤ 0.86, P < .01). As reported by other studies(Josell et al., 2004), juiciness was negatively correlated with cookingloss. Cooking loss had significantly negative correlation with othereating quality traits (−0.65≤ r≤−0.37, P < .01), Therefore, redu-cing the loss during meat preparation may improve the palatability ofpork.

3.2. Assessment of population stratification and the level of linkagedisequilibrium

The Q-Q plots of the test statistics in GWAS are shown in Fig. S2.The lambda values for all traits are close to 1, indicating that there areno very strong stratification in our samples.

We assessed the LD extent pattern in the heterogeneous stock F6population. The average r2 of 0.3 or more was observed for SNPs<40Kb apart (unpublished data). Our previous study demonstrated that theaverage LD values (r20.3) ranged from ~100 Kb to ~700 Kb in Chinesenative breeds and Western commercial breeds (Ai et al., 2013). Thus,we are sure that the heterogeneous stock F6 animals had a much lowerLD extent compared with previously studied outbreds, which is helpfulfor mapping QTLs at high resolution. The relatively low degree of LD inthe F6 population was mainly due to the fact that the number of theirhaplotypes derived from 8 founder breeds was normally much higherthan that of haplotypes in any individual purebred. Nevertheless, theLD level varied considerably among different GWAS regions, probablydue to their different allelic/haplotype diversity in F0 and F6 animals.

3.3. GWAS signals for traits

In total, 118 tag SNPs for eating quality traits and 48 tag SNPs forcooking loss were identified in this study (Table 3). Out of them, twoSNPs reached a genome-wide significance level, including one foroverall liking on SSC2 and another for cooking loss on SSC9 (Fig. 1). Allthe significant SNPs represented 50 QTLs, of which only several QTLshave been reported previously.

There were 48 SNPs significantly associated with cooking loss, ofwhich 34 fell into the region from 5.11Mb to 15.06Mb on SSC9(Fig. 2A). However, these SNP were clustered into seven distinct LDblocks (Fig. 2B). Furthermore, after correcting the top SNP

Table 1Descriptive statistics of cooking loss and eating quality traits in the hetero-geneous stock F6 pigs.

Traits (Units) Number Mean SD Minimum Maximum Estimatedheritability

Cooking loss(%)

791 30.89 4.17 13.96 43.12 0.27 ± 0.07

Tenderness(1–5)

771 2.72 0.54 1.20 4.40 0.39 ± 0.08

Juiciness(1–5)

771 2.43 0.50 1.30 4.40 0.31 ± 0.08

Oiliness (1–5) 771 2.00 0.37 1.20 3.80 0.51 ± 0.08Umami (1–5) 771 2.86 0.35 1.80 4.00 0.14 ± 0.08Overall liking

(1–5)771 2.84 0.53 1.50 4.60 0.41 ± 0.08

Table 2The correlation coefficients between cooking loss and eating quality traits.

Trait Cooking loss Tenderness Juiciness Oiliness Umami

Tenderness −0.62⁎⁎Juiciness −0.65⁎⁎ 0.74⁎⁎Oiliness −0.52⁎⁎ 0.66⁎⁎ 0.73⁎⁎Umami −0.37⁎⁎ 0.49⁎⁎ 0.53⁎⁎ 0.50⁎⁎Overall liking −0.64⁎⁎ 0.86⁎⁎ 0.85⁎⁎ 0.75⁎⁎ 0.64⁎⁎

⁎⁎ P < .01.

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rs321693140 (P=1.77×10−8) at about 11.97Mb, another SNPrs1113848771 at 5.97Mb was still significant (P < 10−6; Fig. 2C).This suggests that the two GWA tag SNPs represented different trait-associated loci in the SSC9 region. The top SNP rs321693140 resides atthe ~1 Kb upstream of the P21 (RAC1) Activated Kinase 1 (PAK1) gene.Given that the peak SNP identified by the whole-genome sequence(WGS) based GWAS is very likely in strong LD with the underlying QTL,the QTL effect on phenotype could be estimated by the SNP effect. Atthe SNP rs321693140 (G > T), the GG genotype had higher cooking

loss (31.87 ± 0.22) compared to the TG (30.28 ± 0.20) and TT(29.59 ± 0.52) genotypes (P < .01), thereby accounting for 5.26% ofthe phenotypic variation in cooking loss. Obviously, the favorable “T”allele (decreasing cooking loss) has partial dominant effect over un-favorable allele “G” (Fig. 3). The QTLs on SSC12 and SSC 15 overlappedwith those for cooking loss trait reported by Li et al. (Li et al., 2011).

Tenderness is an important element of eating quality affecting thedecision of consumers to repurchase (Maltin et al., 2003). TendernessQTLs were identified on SSC2, 5, 6 and 12 with significances at the

Table 3Description of SNPs significantly associated with cooking loss and eating quality traits.

Traits SharedQTLs1)

Peak SNP2) No3) Chr4) Pos (bp)5) Alleles6) Beta7) Se8) P value9) Nearest genes Candidate genes10)

Cooking loss rs1109158662 1 7 24,391,799 C/T 1.40 0.28 7.66×10−7 C6orf10rs1113848771 5 9 5,978,512 A/G 1.00 0.19 1.78×10−7 SSU72P8rs321693140 29 9 11,973,549 G/T 1.27 0.22 1.77×10−8 PAK1 PAK1, AQP11

5 rs333782759 1 11 60,892,508 G/T 1.77 0.35 7.88×10−7 GPC56 rs327393550 1 12 55,442,744 G/T 1.01 0.20 8.89×10−7 TMEM220

rs340571362 1 13 67,355,364 C/T 1.04 0.21 7.36×10−7 HRH1rs334164987 1 15 115,844,715 G/A 1.86 0.38 9.96×10−7 SPAG16

7 rs1112735343 2 15 125,568,941 T/A 1.14 0.23 7.43×10−7 AP1S3rs710696671 7 15 128,477,884 T/A 2.46 0.46 1.44×10−7 RHBDD1 COL4A4

Tenderness rs320508322 1 2 119,674,062 G/A 0.19 0.04 9.56×10−7 FEM1C5_2,673,438 1 5 2,673,438 G/A 0.16 0.03 9.38×10−7 TBC1D22Ars333879170 1 5 6,979,659 A/T 0.17 0.03 3.86×10−7 CSDC2 EP300rs319963994 1 6 17,575,264 A/G 0.19 0.04 2.59×10−7 CYB5B

4 6_35,798,064 9 6 35,798,064 G/T 0.23 0.04 1.01×10−7 CBLN1rs329835906 1 6 133,796,796 C/T 0.22 0.04 7.81×10−7 EIF5rs331490457 1 12 35,707,063 G/A 0.18 0.03 2.10×10−7 YPEL2

6 rs335927354 1 12 54,357,319 G/A 0.35 0.07 5.34×10−7 STX8Juiciness 1 2_1,523,582 3 2 1,523,582 C/T 0.16 0.03 2.08×10−7 TH

2 rs322037886 2 3 2,573,286 A/T 0.20 0.04 3.93×10−7 SDK1 SDK13 rs329746236 1 5 16,180,600 A/G 0.23 0.05 3.02×10−7 LIMA1

rs337116853 1 6 21,963,448 G/A 0.18 0.04 4.95×10−7 HMGN1rs341841968 1 7 77,800,188 G/C 0.13 0.03 9.10×10−7 SUPT16Hrs342964369 1 12 5,547,093 A/G 0.27 0.05 2.69×10−7 TRIM47rs1113367268 1 13 27,596,364 T/A 0.28 0.06 7.70×10−7 TOPAZ1rs694213416 1 13 160,477,828 G/C 0.21 0.04 2.05×10−7 OR5K3rs321790260 2 15 136,544,582 G/C 0.14 0.03 8.34×10−7 ACKR3rs332580467 4 16 10,181,681 G/A 0.22 0.04 3.03×10−7 CDH12

Oiliness rs338054415 1 1 96,893,572 G/A 0.11 0.02 9.27×10−7 SKOR22 3_795,142 1 3 795,142 A/G 0.23 0.04 2.80×10−7 UNCX3 rs325998926 1 5 16,152,979 T/C 0.12 0.02 5.24×10−7 LIMA1

rs709923578 1 9 72,301,220 T/C 0.16 0.03 6.83×10−7 ANKIB1rs331467110 1 10 24,049,981 A/G 0.10 0.02 3.22×10−7 NAV1

6 12_55,531,774 3 12 55,531,774 A/G 0.24 0.05 2.09×10−7 PIRT MYH2, MYH3, MYH4, MYH8,MYH13

rs81305225 3 13 16,140,799 T/G 0.14 0.03 2.14×10−7 RBMS37 rs335370197 1 15 126,049,864 C/G 0.22 0.04 6.54×10−7 FAM124B

16_55,408,975 1 16 55,408,975 T/C 0.12 0.02 2.94×10−7 RARS17_46,517,757 7 17 46,517,757 A/G 0.16 0.03 0.64×10−7 TOX2 FITM2

Umami rs327488990 4 5 47,406,256 C/T 0.15 0.03 1.67×10−7 ITPR2rs319005918 2 7 85,744,386 G/A 0.09 0.02 8.82×10−7 RGMA

5 rs329178505 1 11 60,705,230 T/C 0.13 0.03 6.81×10−7 GPC513_207,002,771 23 13 207,002,771 T/C 0.08 0.02 1.30×10−7 TRAPPC10rs346073248 8 14 128,021,562 G/T 0.10 0.02 1.61×10−7 RAB11FIP2rs332484936 1 18 46,323,201 G/A 0.10 0.02 9.37×10−7 NFE2L3

Overall liking rs788718131 1 1 23,575,880 A/G 0.20 0.04 3.31×10−7 NMBR1 2_1,655,159 18 2 1,655,159 G/A 0.26 0.05 2.12×10−8 TRPM5 TNNT3, TNNI22 rs337902638 2 3 2,620,816 C/A 0.22 0.04 1.70×10−7 SDK14 6_35,798,014 1 6 35,798,014 G/A 0.21 0.04 8.70×10−7 CBLN1

rs329958585 1 9 125,767,805 G/T 0.20 0.04 1.97×10−7 C1orf215 11_61,032,983 2 11 61,032,983 T/C 0.30 0.06 4.74×10−7 GPC5

rs710526074 1 12 39,700,308 A/G 0.23 0.05 9.47×10−7 TAF15

1 The QTL regions shared by different traits are indicated by numbers.2 Ten SNPs that do not possess rs ID were named by the author based on the physical positions that they are mapped to the pig reference genome.3 The number of significant SNPs within the QTL regions.4,5 The positions of the associated SNPs on the Sus scrofa Build 11.1 assembly.6 The favorable allele that increases the phenotype was placed ahead.7 Beta estimates.8 Standard errors for beta.9 The P value in bold surpassed the genome-wise significance threshold (5× 10−8).10 Genes within 1Mb upstream and downstream of the peak SNP were related to meat quality in other GWA studies and/or functional tests.

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suggestive level. Previously, one QTL for tenderness was mapped onSSC2 by Ciobanu et al. (Ciobanu et al., 2004), who reported threemissense mutations including Ser66Asn, Arg249Lys, and Ser638Arg inthe CAST gene associated with tenderness. In addition, Nonneman et al.(Nonneman et al., 2011) identified additional 4 SNPs (67853_270,67855_230, 67855_289, and 77013_98) in CAST that were consistentlyassociated with tenderness in four different commercial pig popula-tions, and 4 polymorphic sites (67831_429/430, 67841_556,67855_230, 67853_306+77013_98) in the promoter region of thisgene that were allele specific in binding nuclear proteins. Six of theseSNPs were found to be segregating in our population (Ser66Asn,Ser638Arg, 67841_556 and 67855_230, 67853_270 and 67855_289with MAF≥ 0.03). But they were all in low LD (r2 < 0.01) with thepresent GWA top SNP rs320508322 (P=9.56×10−7) on SSC2, andonly one of them (SNP 67841_556) were significant at P < .005 (Fig.S3). In fact, even the top SNP rs344846824 identified in the CAST re-gion did not reached the suggestive level of P < 10−6, suggesting thatthe CAST unlikely harbored a causative mutation(s) underlying thedetected QTL. The lead SNP rs320508322 (at 119.6 Mb) lies nearby theFem-1 Homolog C (FEM1C) gene, encoding a component of an E3ubiquitin-protein ligase complex. On SSC5, the peak SNP rs333879170associated with tenderness was located in the intron of the gene ColdShock Domain Containing C2 (CSDC2).

Seventeen significant SNPs in 10 chromosomal regions were de-tected to be associated with juiciness. Seven of the regions have notbeen reported previously except those on SSC13 and SSC15 (Choi et al.,2011; Rohrer et al., 2006; Zhang et al., 2014). Three of the peak SNPsfall in the introns of genes: SPT16 Homolog, Facilitates ChromatinRemodeling Subunit (SUPT16H) on SSC7 Testis And Ovary Specific PAZDomain Containing 1 (TOPAZ1) on SSC13 and Cadherin 12 (CDH12) onSSC16.

Twenty SNPs significantly associated with oiliness were found in theregions of SSC1, 3, 5, 9, 10, 12, 13, 15, 16 and 17. Oiliness may bestrongly related to the intramuscular fat content (IMF). Consistent withthat, at least 4 QTLs identified here were just present at the regionsharboring previously reported IMF QTL (Luo et al., 2012; Ma et al.,2013; Nii et al., 2005; Nonneman et al., 2013). Especially, within theSSC12 region (from 45.6Mb to 56.1Mb) that was demonstrated to havea large effect on IMF (Luo et al., 2012; Ma et al., 2013), we identified asignificant SNP (denoted as SNP 12_55,531,774, located at 55.5 Mb)

associated with oiliness, which is close to the Phosphoinositide Inter-acting Regulator Of Transient Receptor Potential Channels (PIRT) gene.The top SNPs on the SSC5, 9, 10, 13 and 16 were found in the LIMDomain And Actin Binding 1 (LIMA1), Ankyrin Repeat And IBR DomainContaining 1 (ANKIB1), Neuron Navigator 1 (NAV1), RNA BindingMotif Single Stranded Interacting Protein 3 (RBMS3) and Arginyl-TRNASynthetase (RARS) genes respectively.

Thirty-nine SNPs in 6 regions were associated with umami. OnSSC5, the peak SNP rs327488990 was in the intron of the Inositol 1, 4,5-Trisphosphate Receptor Type 2 (ITPR2) gene. The top SNP13_207,002,771 on SSC13 was also inside the Trafficking ProteinParticle Complex 10 (TRAPPC10) gene. The positions of the other 37SNPs were close to the locations of 4 genes including RepulsiveGuidance Molecule Family Member A (RGMA), Glypican 5 (GPC5),RAB11 Family Interacting Protein 2(RAB11FIP2) and Nuclear Factor,Erythroid 2 Like 3 (NFE2L3).

For overall liking, a total of 26 significant SNPs, were found onseven chromosomal regions. The top SNP 6_35,798,014 on SSC6 is closeto the QTL for overall liking that was found by Markljung (Markljunget al., 2008). Among all the significant SNPs for this trait, the mostsignificant one was the SNP 2_1,655,159, reaching genome-wide sig-nificance (P=2.12× 10−8). At this locus, pigs with GG (2.90 ± 0.02)genotype had significantly (P < .01) higher phenotypic values thanpigs carrying the GA (2.68 ± 0.04) and AA (2.28 ± 0.20) genotypes,which explains ~ 4.24% of the phenotypic variance in this population.Accordingly, this QTL for overall liking exhibited partial dominant ef-fect (Fig. 3).

3.4. Possible pleiotropic QTLs

This study showed that some QTLs affected more than one trait. TheQTLs on SSC2 and 3 for overall liking were co-localized with those forjuiciness (Table 3). The QTL region (60.70–61.03Mb) on SSC11 wasshared by cooking loss, umami and overall liking. Additionally, the SNP6_35,798,064 at 35.79Mb on SSC6 was found to be associated withboth tenderness and overall liking. The result indicates that most (4 outof 7) QTLs influencing overall liking are derived from the shared effectson other eating quality traits, which is consistent with the strong andsignificant correlations (r > 0.6, P < .01) between overall liking andeating quality traits. In addition, juiciness and oiliness shared the QTL

Fig. 1. Manhattan plot of the genome-wide association study (GWAS) result. Chromosomes 1–18 were shown in different colors. The X-axis represent the chro-mosomes, and the Y- axis shows the –log10 (P-value). The horizontal, dashed line represents the chromosome-wide significance thresholds and the solid line reflectsthe genome-wide significance thresholds.

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regions of 16.15–16.18Mb on SSC5 and 0.79–2.57Mb on SSC3. Andtwo loci on SSC12 and SSC15 might be common to cooking loss andoiliness.

3.5. Candidate genes for major QTLs

The peak SNP rs321693140 on SSC9 for cooking loss (at 11.9 Mb)was located 994 bp away from the P21 (RAC1) Activated Kinase 1(PAK1) gene. PAK1 is one member of PAK family which includes six

members and is divided into two groups: group I (PAK1, PAK2, andPAK3) and group II (PAK4, PAK5, and PAK6). PAK1 is expressed inmyoblasts and activated specifically during mammalian myoblast dif-ferentiation. PAK1 is a redundant regulator of myoblast differentiationin vitro and in vivo and involved in the promyogenic lNcad/Cdo/Cdc42signaling pathway (Joseph et al., 2017). Another gene Aquaporin 11(AQP11) was located 91.6 Kb away from the peak SNP rs321693140.AQPs plays a key role in selectively transporting water across themembranes of cells. AQP11 proteins are localized in the endoplasmicreticulum of proximal tubules (Holmes, 2012). A previous study foundthat the water permeability of AQP11 was 8-fold lower than that ofAQP1 and 3-fold higher than that of mock-infected cell membrane(Yakata et al., 2011). Therefore, PAK1 and AQP11 should be both re-garded as candidate genes for cooking loss.

The peak SNP rs333879170 on SSC5 for tenderness (at 6.9Mb) waslocated 332.6 Kb away from E1A Binding Protein P300 (EP300) gene.This gene involves the development of myoblasts that is a complexedand ordered process controlled by many myogenic regulatory factors,including Myf5, MyoD and myogenin (Berkes et al., 2005; Franceticet al., 2011). Genetic evidence from the mouse and ES cell model sys-tems has been provided that the histone acetyltransferase activity ofEP300 is essential for the expression of Myf5 and MyoD, and conse-quently for skeletal muscle development (Roth et al., 2003). Duringmuscle regeneration, AKT1 and 2 can promote the association of MyoDwith EP300 and PCAF acetyltransferases through direct phosphoryla-tion of EP300 (Serra et al., 2007). The regulation of EP300 during thestages of myoblast differentiation appears to be mediated through AKTsignaling and blunting of EP300 inhibits myoblast differentiation.(Chenet al., 2015). In addition, several target genes of transcription factorEP300 are related with lipid metabolism in pigs (Ramayo-Caldas et al.,2014). Therefore, EP300 may be related to the SSC5 QTL effect ontenderness.

The closest gene to the GWA top SNP rs322037886 at 2.5 Mb onSSC3 for juiciness, is the Sidekick Cell Adhesion Molecule 1 (SDK1)gene. A GWA study has demonstrated that SDK1 is a candidate gene forIMF in pork (Davoli et al., 2016). IMF can greatly influence the ten-derness, juiciness, oiliness and umami intensity in the meat qualityevaluation (Iida et al., 2015), so it is reasonable to regard SDK1 as acandidate gene also for juiciness. However, the mechanism of SDK1involving in lipid metabolism has not been studied clearly.

The top SNP 17_46,517,757 on SSC17 for oiliness is close to theFITM2 gene, encoding Fat Storage Inducing Transmembrane Protein 2.FITM2 is one member of conserved gene family important for lipiddroplet formation named fat-inducing transcript (FIT). A study showedthat shRNA silencing of FITM2 in 3 T3-LI adipocytes prevents accu-mulation of lipid droplets (Kadereit et al., 2008). Therefore, FITM2 isimportant in the fundamental process of storing fat. On SSC12, the peakSNP 12_55,531,774 associated with oiliness was closely linked withMYH2, MYH3, MYH4, MYH8 and MYH13 genes (Fig. 4A), which allbelong to the myosin heavy chain gene family (MYH).MYH2 andMYH4were reported to be associated with IMF (Luo et al., 2012). Linkagedisequilibrium (LD) analysis showed that the most probable QTL con-fidence interval spans 464 Kb from rs320701961 to 12_55,531,774(Fig. 4). The five MYH members were present in the LD block so theywere as strong candidate genes for oiliness.

The top SNP 2_1,655,159 for overall liking resides at 1.1 Mb onSSC2, nearby the TNNI2 and TNNT3 gene. TNNI2 and TNNT3 are strongcandidate genes for overall liking. The TNNI2 gene encodes a fast-twitch skeletal muscle protein, a member of the troponin I gene family,and a component of the troponin complex including troponin T, tro-ponin C and troponin I subunits. The troponin complex, along withtropomyosin, is responsible for the calcium-dependent regulation ofstriated muscle contraction. The TNNT3 gene encodes fast skeletalmuscle troponin T protein. Favorable meat traits such as flavor andtenderness have been found to be closely related with a higher contentof oxidative fibers in muscles (Hocquette et al., 2012). The SNP

Fig. 2. The existence of two distinct genetic loci (represented by the SNPrs1113848771 at 5978512 bp and the SNP rs321693140 at 11973549 bp, re-spectively) for cooking loss on SSC9 was indicated by panel A) the regionalassociation plot, panel B) the plot of LD blocks constructed from the significant(P < 10−6) SNPs detected in the region, and panel C) the Manhattan plot ofconditional association analysis conditioning on the lead SNP rs321693140.

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g.1167C > T polymorphism in TNNI2 gene was found to have sig-nificant effect on fat percentage in pigs (Xu et al., 2010), while TNNT3was predicted to regulate muscle growth and muscle fiber according toprotein interaction network (Wang et al., 2017).

It should be noted that the vast majority of the detected GWAS tagSNPs lie in intergenic and intronic regions, suggesting that they arelikely to influence gene regulation. Therefore, an integrative multi-omic

approach should be applied to infer the causality of these proposedcandidate genes and trait-associated variants in the formation of phe-notypes.

4. Conclusion

This GWA study identified a total of 166 SNPs significantly asso-ciated with 5 eating quality traits and cooking loss. Eight QTLs reportedin previous studies were validated here. Another 42 QTLs for thesetraits were detected for the first time. Two genome-wide significantQTLs affected cooking loss and overall liking, respectively. Somegenomic regions were found to be significantly associated with two ormore traits, supporting the reliability of these loci. In addition, a set ofcandidate genes adjacent to the GWA signals were proposed here due totheir potential influence on the corresponding traits. These results willadvance our understanding of the genetic basis of cooking loss andeating quality traits in pigs.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.meatsci.2018.08.013.

Acknowledgements

The study was supported by the Major Program of National NatureScience Foundation of China (No. 31790413), the National KeyResearch and Development Program of China (No. 2018YFD0500401),the Cultivation Programs for Young Scientists of Jiangxi province (No.20133BCB23013) and the 12th Five-year plan of National Science andTechnology in Rural Areas (No. 2015BAD03B02).

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