Genome
Whole-genome sequencing association analysis reveals the genetic architecture of meat quality traits in Chinese Qingyu pigs
Journal: Genome
Manuscript ID gen-2019-0227.R3
Manuscript Type: Article
Date Submitted by the 22-May-2020 Author:
Complete List of Authors: Wu, Pingxian; Sichuan Agricultural University - Chengdu Campus Wang, Kai; Sichuan Agricultural University Zhou, Jie; Sichuan Agricultural University Chen, Dejuan;Draft Sichuan Agricultural University - Chengdu Campus Yang, Xidi ; Sichuan Agricultural University - Chengdu Campus Jiang, Anan; Sichuan Agricultural University - Chengdu Campus Shen, Linyuan ; Sichuan Agricultural University - Chengdu Campus Zhang, Shunhua ; Sichuan Agricultural University - Chengdu Campus Xiao, Weihang ; Sichuan Agricultural University - Chengdu Campus Jiang, Yanzhi; Sichuan Agricultural University Zhu, Li; Sichuan Agricultural University - Chengdu Campus Li, Xuewei ; Sichuan Agricultural University - Chengdu Campus Xu, Xu; Sichuan Provincial Animal Husbandry and Food Bureau Zeng, Yangshuang; Sichuan Provincial Animal Husbandry and Food Bureau Tang, Guoqing ; Sichuan Agricultural University - Chengdu Campus
Keyword: Pork color, Pork pH, GWAS, SNPs, Qingyu pigs
Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? :
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1 Title Page
2 Article Title
3 Whole-genome sequencing association analysis reveals the genetic architecture of
4 meat quality traits in Chinese Qingyu pigs
5 Authors
6 Pingxian Wu 1,†, Kai Wang 1,†, Jie Zhou 1, Dejuan Chen 1 , Xidi Yang 1, Anan Jiang 1,
7 Linyuan Shen1, Shunhua Zhang 1, Weihang Xiao 1, Yanzhi Jiang 2, Li Zhu 1 , Yangshuang
8 Zeng3, Xu Xu3, Xuewei Li 1, Guoqing Tang 1,*
9 1 Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of
10 Sichuan Province, Sichuan Agricultural University, Chengdu 611130, Sichuan, China
11 2 College of Life Science, Sichuan Agricultural University, Yaan 625014, Sichuan, China
12 3 Sichuan Animal Husbandry Station, Chengdu, 610041, Sichuan, China 13 * Corresponding author : GuoqingDraft Tang, [email protected]. 14 † Co-first author : These authors contributed equally to this work.
15 Authors’ Email
16 Pingxian Wu, Email: [email protected].
17 Kai Wang, Email: [email protected].
18 Jie Zhou, Email: [email protected].
19 Dejuan Chen, Email: [email protected].
20 Xidi Yang, Email: [email protected].
21 Shunhua Zhang, Email: [email protected].
22 Anan Jiang, Email: [email protected].
23 Linyuan Shen, Email: [email protected].
24 Weihang Xiao, Email: [email protected].
25 Yanzhi Jiang, Email: [email protected].
26 Li Zhu, Email: [email protected].
27 Xuewei Li, Email: [email protected].
28 Yangshuang Zeng, Email: [email protected].
29 Xu Xu, Email: [email protected].
30 Guoqing Tang, Email: [email protected].
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31 Whole-genome sequencing association analysis reveals the
32 genetic architecture of meat quality traits in Chinese Qingyu
33 pigs
34 Abstract
35 The Chinese Qingyu pig breed is an invaluable indigenous genetic resource.
36 However, few studies have investigated the genetic architecture of meat
37 quality traits in Qingyu pigs. Here, 30 purebred Qingyu pigs were
38 subjected to whole-genome sequencing. After quality control, 18,436,759
39 SNPs were retained. Genome-wide association studies (GWAS) were then 40 performed for meat pH and Draftcolor at three postmortem time points (45 41 min, 24 h and 48 h) using single-marker regression analysis. In total, 11
42 and 69 SNPs were associated with meat pH and color of the longissimus
43 thoracis muscle (LTM), respectively, while 54 and 29 SNPs were associated
44 with meat pH and color of the semimembranosus muscle (SM),
45 respectively. Seven SNPs associated with pork pH were shared by all three
46 postmortem time points. Several candidate genes for meat traits were
47 identified, including four genes (CXXC5, RYR3, BNIP3, and MYCT1) related
48 to skeletal muscle development, regulation of Ca2+ release in the muscle,
49 and anaerobic respiration, which are promising candidates for selecting
50 superior meat quality traits in Qingyu pigs. To our knowledge, this is the
51 first study investigating the postmortem genetic architecture of pork pH
52 and color in Qingyu pigs. Our findings further the current understanding
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53 of the genetic factors influencing meat quality.
54 Keywords: Pork color, Pork pH, GWAS, SNPs, Qingyu pigs
55
56 Introduction
57 The Chinese Qingyu pig breed is an invaluable Chinese genetic resource
58 (China National Commission of Animal Genetic Resources, 2012). Qingyu
59 pigs are mainly distributed in a narrow region within the mountainous
60 areas of Sichuan Province, China. It is one of the most famous indigenous
61 pig breeds for its tender meat and tasty flavor(Linyuan Shen, 2014). 62 Previously, these pigs wereDraft mainly subjected to long-term natural 63 selection and short-term traditional selection programs. Thus, their
64 genome is likely markedly different from that of other pig breeds. Recent
65 investigations have focused on meat quality traits in pigs(Verardo, Sevón-
66 Aimonen, Serenius, Hietakangas, & Uimari, 2017; Zhou et al., 2014),
67 however, because of their limited population size, no similar study has
68 been performed on Qingyu pigs at whole-genome level.
69 Meat quality is one of the most important economic factors in pig
70 production. However, because meat quality is measured after slaughter,
71 it is difficult and expensive to obtain the phenotypes of meat quality.
72 Previous studies have reported that many pig meat qualities exhibit low
73 to moderate heritability, such as meat color (0.1 < h2 < 0.4), pH (h2 = 0.15),
74 shear force (h2 = 0.40), and drip loss (h2 = 0.20) in pigs(Miar et al., 2014).
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75 More recently, several studies have been performed to identify the
76 candidate genes and genetic variants influencing meat quality
77 traits(Schmid, Maushammer, Preuß, & Bennewitz, 2018; Vidal, Noguera,
78 Amills, Varona, & Sánchez, 2005). A total of 16,071 quantitative trait loci
79 (QTLs) (PigQTLdb, https://www.animalgenome.org/cgi-
80 bin/QTLdb/SS/index, 2019) associated with meat and carcass traits in pigs.
81 Among these QTLs, 722 and 632 were found to be associated with meat
82 pH and color, respectively. However, most of these QTLs had large
83 confidence intervals and lacked reproducibility in different populations. 84 To date, no QTLs have been Draftidentified for complex traits in Qingyu pigs 85 specifically. However, identifying variants in key genomic regions is
86 important for genetic improvement of this breed, because traditional
87 selection methods do not allow selection of highly specific meat quality
88 traits.
89 Genome-wide association studies (GWAS) provide a systematic and
90 powerful approach for gaining deeper insights into complex traits in
91 livestock. Using SNP arrays, GWAS for meat quality traits have identified
92 many QTLs and candidate genes in commercial populations(Casiró et al.,
93 2017; Nonneman et al., 2013; Sanchez, Tribout, Iannuccelli, Bouffaud,
94 Servin, Tenghe, Dehais, Muller, Del Schneider, et al., 2014; Uimari, Sironen,
95 & Sevón-Aimonen, 2013; C. Zhang et al., 2015). However, SNP arrays are
96 limited in that they only identify a small fraction of genetic variants,
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97 resulting in low power of the GWAS, whereas whole-genome sequencing
98 (WGS) can potentially detect all genetic variants. Using WGS data, the
99 accuracy and power of GWAS are significantly improved, allowing credible
100 associations to be obtained(Daetwyler et al., 2014). Such advances make
101 genetic improvement achievable for meat quality traits in pigs.
102 Previous studies identified several candidate genes (such as PRKAG3,
103 RYR1 and IGF2 gene) affecting meat quality traits in lean-type western
104 pigs, and these findings facilitated considerable improvements in meat
105 quality(Gao, Zhang, Hu, & Li, 2007; Vries, Faucitano, Sosnicki, & Plastow, 106 2000). However, the Chinese DraftQingyu pig breed is a fat-type indigenous pig 107 line, and there are obvious differences between common commercial pigs
108 and Qingyu pigs in meat quality and productivity. Consequently, it is likely
109 that these breeds would exhibit significant genetic differences.
110 In this study, we performed a GWAS on meat pH and color at three
111 different times after slaughter (45 min, 24 h, and 48 h postmortem), using
112 WGS data from a purebred Qingyu pig population. The objective was to
113 identify candidate genes and genetic variants associated with meat pH
114 and color in Qingyu pigs.
115 Materials and methods
116 Ethics statement
117 All animals procedures related to this study were approved by the
118 Institutional Review Board (IRB14044) and the Institutional Animal Care
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119 and Use Committee of the Sichuan Agricultural University under permit
120 number DKY–B20140302.
121 Animals and phenotypes
122 A total of 30 purebred Qingyu pigs (including 15 females and 15 males)
123 from the Bashan animal husbandry technology Co., LTD. were used in this
124 study. During the period of 2018-2019, all pigs were subjected to same
125 feeding conditions. These pigs were slaughtered at approximately body
126 weight of 110 kg in a same commercial abattoir. The average age at
127 slaughter was 270 days. 128 The longissimus thoracis muscleDraft (LTM) between the 3rd and 4th last 129 ribs and semimembranosus muscle (SM) from the left-side carcass were
130 collected and stored at 4℃. Using the portable pH meter (model 720A;
131 Orion Research Inc., Boston, MA, USA), meat pH values were measured
132 on LTM and SM at 45 min, 24 h and 48 h postmortem. Meat color was
133 assessed using the three parameters (L* for lightness, a* for redness and
134 b* for yellowness) of the CIELAB color space with Minolta CR-300
135 colorimeter (Minolta Camera, Osaka, Japan) on the cut surface of LTM and
136 SM at 45 min, 24 h and 48 h postmortem. Three readings were taken for
137 meat pH and color values, then the average values were used for further
138 analysis.
139 Whole-genome sequencing and quality control
140 Ear tissues for 30 Qingyu pigs were collected and stored in 75% alcohol.
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141 Genomic DNA was extracted from ear tissue samples by the phenol-
142 chloroform method. Whole-genome sequencing was conducted using the
143 Illumina HiSeq PE150 platform. For raw data, the initial quality control was
144 conducted using FastQC software
145 (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/), with a Phred
146 score of 20 as the minimum to remove the adaptor polluted reads and
147 multiple N reads (where N > 10% of one read) to produce clean reads.
148 Then the clean reads were aligned to the Sscrofa11.1 reference genome
149 using BWA(Li & Durbin, 2009) (version 0.7.15) software with the 150 parameters mem –t 4 –k 32 -M.Draft Then SNPs were called by GATK(Depristo 151 et al., 2011) (version 4.1.3.0) software with default settings. The detected
152 variants were filtered with QualByDepth (QD) < 2.0, FisherStrand (FS) <
153 60.0, RMSMappingQuality (MQ) < 40.0, MappingQualityRankSumTest < –
154 12.5 and ReadPosRankSumTest < –8.0 by GATK software. Initially, a total
155 of 32,182,112 raw SNPs were found. Subsequently, raw SNPs were filtered
156 by the quality control criteria of minor allele frequency (MAF) > 0.1,
157 missing rate < 0.1, Hardy–Weinberg equilibrium (HWE) < 10-6, and a mean
158 depth greater than or equal to 3 (dp3), using VCFtools(Danecek et al.,
159 2011) software (version 4.2). Finally, a total of 18,436,759 SNPs were kept
160 for further study.
161 Association analysis
162 In this study, the sex, birth year and month were taken as fixed factors,
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163 and the DMU(Madsen, Jensen, Labouriau, Christensen, & Sahana, 2014)
164 software was used to adjust these phenotype data. Then a single-marker
165 regression analysis was performed GWAS with a univariate linear mixed
166 model using GEMMA(Depristo et al., 2011) software. The statistical model
167 was as follows:
168 퐲 = 퐙훃 + 퐖퐚 + 퐞,
169 where 퐲 is the vector of phenotypic values; 훃 is the vector of marker
170 effects; 퐚 is the vector of remaining polygene effects following the multi-
2 171 normal distribution (a~ MVN (0，A휎푎)), A is the numerator 172 relationship matrix; 퐞 is theDraft vector of residual effects, following the 2 173 multi-normal distribution (e~ MVN (0，I휎푒)); Z and W are incidence
174 matrices for 훃 and 퐚, respectively. The strict Bonferroni correction
175 method(Bland & Altman, 1995) was used to determine the significance
176 level. The genome-wide and suggestive significance values were
177 calculated as 0.05 18,436,759 = 2.71 × 10 ―9and 1.00 18,436,759
178 = 5.42 × 10 ―8, respectively.
179 The Manhattan and Q-Q plots were drawn using qqman(Turner, 2014)
180 package in R software environment. The genomic inflation factor the observed 푃 values 181 (λ = the expected 푃 values) was calculated to judge the false positive singles
182 with the estlambda function in GenABEL package(Aulchenko, Ripke,
183 Isaacs, & Van Duijn, 2007).
184 Linkage disequilibrium (LD) analysis
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185 To detect the LD between significant loci, the 40 kb region centering on
186 each significant SNP was used to conduct LD analysis using Haploview
187 software(Barrett, Fry, Maller, & Daly, 2005).
188 Candidate genes
189 The SNPs reached the suggestive level (P = 5.42 × 10−8) were selected
190 as promising loci. The candidate genes were searched within a 40 Kb
191 region centering each SNP based on the pig genes Sscrofa11.1. The gene
192 function was searched by NCBI (https://www.ncbi.nlm.nih.gov) database
193 and through the previous literatures. 194 Results Draft 195 GWAS for meat pH
196 The GWAS results are presented in Figure 1, Figure S1 and Table 1. At
197 45 min postmortem, 17 suggestive SNPs were identified for the SM pH.
198 The SNP SSC1:13,687,743 was most strongly associated with the meat pH
199 of SM (P = 3.32 × 10−9) at this time point. At 24 h postmortem, 22 SNPs
200 were detected for SM, of which four reached the genome-wide
201 significance level. In the genomic region of SSC4:58.54–58.58 Mb, three
202 genome-wide significant SNPs with P = 1.17 × 10−9 (SSC4:58,564,986;
203 SSC4:58,564,988; and SSC4:58,564,988) were associated with the meat
204 pH of SM. In the region of SSC8:127.18–127.22 Mb, the SNP
205 SSC8:127,196,057 was significantly associated with the meat pH of SM (P
206 = 1.32 × 10−9).
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207 At 48 h postmortem, 11 SNPs (P < 5.10 × 10−8) distributed on the
208 chromosomes 1, 2, 6, 14, and X, were identified to associate with the meat
209 pH of LTM. Of these, two genome-wide significant SNPs
210 (SSC14:139,229,307 and SSC14:139,229,315) were detected on SSC14 (P
211 = 1.92 × 10−9). For SM, 15 SNPs reaching the suggestive level were found
212 to associate with meat pH. Of these SNPs, three genome-wide significant
213 SNPs were located on SSC4 (P = 3.87 × 10−10).
214 Seven common SNPs, including SSC1:11,686,586; SSC4:58,564,986;
215 SSC4:58,564,988; SSC4:58,564,989; SSC8:127,196,057; 216 SSC13:188,752,539, and SSC14:48,838,026,Draft were associated with the 217 meat pH of SM across all three postmortem time points in Qingyu pigs
218 (Table 1).
219 GWAS for meat color L*
220 For LTM, a total of 14, one, and three SNPs were identified for color L*
221 at 45 min, 24 h, and 48 h, respectively (Figure 2, Figure S2 and Table 2).
222 The most significant SNP (SSCX:73,780,667, P = 1.89 × 10−10) was identified
223 at 48 h, and this SNP also associated with color L* at 45 min. In the region
224 of SSC2:141.46–141.50 Mb, a genome-wide significant SNP
225 (SSC2:141,482,540, P = 2.33 × 10−9) was found to associate with color L*
226 at 45 min.
227 For SM, a total of 17 SNPs were found to associate with color L* across
228 the three postmortem time points (Figure 2, Figure S2 and Table 2). Of
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229 these SNPs, a genome-wide significant SNP (SSC15:91,959,422; P = 5.34 ×
230 10−10) was identified to associate with color L* at 45 min.
231 GWAS for meat color a*
232 For LTM, a total of one and 29 suggestive SNPs were detected at 45 min
233 and 48 h, respectively (Figure 3, Figure S3 and Table 3). In the region of
234 SSC1:10.49–10.58 Mb, nine consecutive SNPs (P = 3.24 × 10−8) were
235 associated with color a* at 48 h. In the region of SSC6:7.89–7.93 Mb, 20
236 consecutive SNPs were identified to associate with color a* at 48 h (P =
237 6.28 × 10−9). For SM, 11 suggestive SNPs (P < 4.29 × 10−9) distributed on 238 seven different chromosomesDraft were found across the three time points 239 (Figure 3, Figure S3 and Table 3).
240 GWAS for meat color b*
241 For LTM, a total of eight and 13 SNPs were detected at 24 h and 48 h,
242 respectively, for color b* (Figure 4, Figure S4 and Table 4). The most
243 significant SNP (SSC13:3,219,566; P = 3.78 × 10−9) was associated with
244 color b* at 48 h. For SM, only one suggestive SNP (SSC15:38,140,718; P =
245 3.80 × 10−8) was identified at 45 min (Figure 4, Figure S4 and Table 4).
246 Haplotype block analysis
247 A total of five haplotype blocks were detected for meat quality using
248 the solid spin algorithm (Figure 5). In the region of SSC14:139.21–
249 139.25Mb, three significant SNPs for the pH of LTM at 48 h were situated
250 in a 0.18 Kb block (Figure 5A). For the pH of SM across the three time
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251 points, three SNPs (SSC4:58,564,986; SSC4:58,564,988; and
252 SSC4:58,564,989) were located in a 3 bp block (Figure 5B). In the region of
253 SSC8:126.23–126.61Mb, four suggestive SNPs were situated in a 0.78 Kb
254 block for the pH of SM at 24 h (Figure 5C). For color a* of LTM at 48 h, two
255 blocks were detected in SSC1 and SSC6. In the region of SSC1:10.49–
256 10.58Mb, a 49 Kb block was found (Figure 5D). In the region of SSC6:7.89–
257 7.93Mb, 20 SNPs with a high LD were located in a block. However, no
258 genes were found in this chromosome region within 40 Kb of each SNP
259 (Figure 5E). 260 Candidate genes Draft 261 Based on the pig genome assembly 11.1, a total of 13 and 50 candidate
262 genes were found for meat pH and color, respectively (Table 1–4). Of
263 these genes, two genes, MYCT1 and BINP3, were found to associate with
264 both meat pH and color. Using the DAVID functional annotation tool,
265 functional annotations in NCBI, and previous studies, four genes were
266 identified as promising candidate genes for improving meat quality in
267 Chinese Qingyu pigs.
268 Discussion
269 Meat color and pH are important indices used to evaluate pig meat
270 quality, and these characteristics play a crucial role in consumer
271 acceptance. Although numerous studies have been performed to
272 investigate the genetic architecture of meat quality in commercial pigs,
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273 no studies have been performed to date to detect the genetic loci and
274 genes associated with meat quality in Chinese Qingyu pigs. Moreover,
275 there are obvious genetic differences between commercial pigs and
276 Chinese Qingyu pigs. Thus, it is essential to understand the genetic basis
277 of meat color and pH to facilitate Qingyu pig breeding programs. GWAS is
278 an efficient method for identifying potential genetic loci and candidate
279 genes in domestic animal breeding, particularly for dissecting the
280 molecular architecture of important economical traits(Cho et al., 2015;
281 Hérault, Damon, Cherel, & Le Roy, 2018; H. Zhang, Wang, & Wang…, 2012). 282 In this study, we performedDraft a GWAS of an indigenous Qingyu pig 283 population. The Qingyu pigs are distributed in a narrow region with a very
284 small population size (historically never exceeding 200 pigs in total).
285 Therefore, an available population containing 30 pure Qingyu pigs was
286 slaughtered and used for measurements of meat color and pH at three
287 time points postmortem. Although our sample size is smaller than that
288 used in previous studies (>100)(Cho et al., 2015; Hérault et al., 2018), the
289 GWAS results from limited sample sizes (<100) can also provide important
290 information regarding complex traits in pigs(Wang, Liu, Deng, Yu, & Li,
291 2016; P. Wu et al., 2019). Because of the limited population size, it is
292 already ideal value in Chinese local pigs slaughter experiment. To
293 minimize the number of false positives and provide credible results, a
294 strict quality control criterion (MAF > 0.1)(P. Wu et al., 2019) was used in
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295 our GWAS. Furthermore, for the WGS data, a strict Bonferroni correction
296 method(Bland & Altman, 1995) was used to determine significant SNPs.
297 Despite these stringent quality controls, we still obtained significant
298 associations for the complex traits of meat color and pH, with seven
299 common SNPs and four important candidate genes identified to associate
300 with these traits in Qingyu pigs.
301 Previous studies have mainly focused on the phenotypes at 45 min and
302 24 h after slaughter(Cho et al., 2015; Hérault et al., 2018; Sanchez, Tribout,
303 Iannuccelli, Bouffaud, Servin, Tenghe, Dehais, Muller, Schneider, et al., 304 2014), and the literature showsDraft that different genetic variants are 305 detected at these time points(Hérault et al., 2018). In the current study,
306 we made full used of the meat quality phenotypic data from the three
307 different time points to detect the genetic variation affecting phenotypic
308 variability over time, and we identified common loci associated with meat
309 quality traits at different time points in Qingyu pigs. These results indicate
310 the possible existence of genetic temporal effects on meat quality traits
311 after slaughter.
312 Population structure
313 Population stratification is a major factor generating false-positive
314 results in GWAS. Numerous studies have reported that ignoring
315 population stratification can result in the inflation of false positives and
316 reduce the statistical power(Aulchenko et al., 2007; Matthieu, Christophe,
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317 Mickael, & Thomas, 2011). In this study, the genomic inflation factor was
318 < 1.22, which is close to the ideal value of 1.00 and indicates that no
319 population stratification was apparent(Pearson & A., 2008). However, we
320 still identified several genome-wide significant SNPs for meat color and
321 pH at three time points after slaughter. Meat quality traits are low to
322 moderately heritable traits controlled by polygenes(Falconer, 1960; Miar
323 et al., 2014). The strict Bonferroni correction method(Bland & Altman,
324 1995) and WGS data comprising a large number of genetic variants (more
325 than 18 million) were used in this study. Therefore, the SNPs reaching the 326 genome suggestive level wereDraft considered to be promising SNPs. However, 327 the study sample size was small, and future investigations of Chinese
328 Qingyu pigs using larger populations are required to further elucidate the
329 genetic traits influencing meat color and pH post-slaughter.
330 Promising candidate genes
331 For meat color L*, three genome-wide significant regions
332 (SSC2:141.46–141.50 Mb, SSCX:73.76–73.80 Mb and SSC15:91.94–91.98
333 Mb) were detected. In the region of SSC2:141.46–141.50 Mb, a genome-
334 wide significant SNP SSC2:141482540bp was identified close to the CXXC
335 Finger Protein 5 (CXXC5) gene, which is a member of the CXXC-type zinc
336 finger domain containing protein family (https://www.genecards.org/).
337 Previous studies have shown that the CXXC5 gene is expressed in skeletal
338 muscle, where it regulates the promoter activities of key skeletal muscle
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339 genes(Guangming Li, Yongqing Li, & Tingfang Chen, 2014). Additionally,
340 this gene is involved in cellular energy metabolism(Aras et al., 2013) , and
341 it may also regulate skeletal myogenesis, thereby playing an important
342 role in meat color.
343 For meat color a*, the chromosome region 80.92–80.96 Mb on SSC7
344 was found to be associated with SM color a* at 24 h in Qingyu pigs. An
345 important candidate gene, ryanodine receptor 3 (RYR3), is located in this
346 region. The RYR3 gene encodes a ryanodine receptor that releases
347 calcium from intracellular storage for use in cellular processes 348 (https://www.genecards.org/).Draft The three known ryanodine receptor (RYR) 349 isoforms, including RYR1, RYR2, and RYR3, are widely expressed in
350 mammalian skeletal muscles(Perez, López, & Allen, 2005; Sonnleitner &
351 A., 1998). Previous studies have shown that RYR3 is often co-expressed
352 with RYR1 and RYR2 to regulate Ca2+ release in the muscle(Perez et al.,
353 2005; Sonnleitner & A., 1998). The RYR1 gene, also known as the
354 halothane gene, significantly influences meat color in pigs(Fujii et al., 1991;
355 Geldermann et al., 2015; Guàrdia, Estany, Balasch, Oliver, & Diestre, 2008).
356 Furthermore, interactions between RYR1 and RYR3 may play a key role in
357 the modulation of Ca2+ release, thereby resulting in changes in pork color.
358 Three common SNPs (SSC4: 58,564,986; SSC4: 58,564,988; and SSC4:
359 58,564,989) located in the region of SSC4:58.54–58.58 Mb were found to
360 be associated with meat pH across all three postmortem time points. Two
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361 reported QTLs (QTL: 3683(Evans et al., 2003) and QTL: 4130(de Koning et
362 al., 2003)) in this region are known to affect meat pH. However, within
363 this chromosome region, no genes with an influence on meat pH in pigs
364 have been identified.
365 Two genes, BCL2 interacting protein 3 (BNIP3) and MYC target 1
366 (MYCT1), in the SSC14:140.34–140.41 Mb region were significantly
367 associated with both meat pH and color in Qingyu pigs, and significant
368 SNPs were located on the BNIP3 gene. This gene plays important roles in
369 mitochondrial function, lipid metabolism, and anaerobic respiration in 370 mice(Glick et al., 2012; NamasDraft et al., 2011). Furthermore, a previous study 371 reported that the BNIP3 gene was associated with meat pH at 72 h
372 postmortem in Chinese Sutai pigs(Zhou et al., 2014). Therefore, this gene
373 may play an important role in meat pH and color in other indigenous pigs.
374 The SSC1:13.67–13.71 Mb, chromosome region was found to be
375 significantly associated with meat color L* and pH in this study. In this
376 region, a candidate gene, MYCT1, was identified. This gene is involved in
377 transcriptional regulation(S. Wu et al., 2016).
378 Conclusions
379 This is the first study to investigate the genetic architecture of meat
380 quality traits in Chinese Qingyu pigs. Although a limited population size
381 was used in this study, our findings provide new insights regarding the
382 genetic loci and candidate genes for meat color and pH at three
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383 postmortem time points in Qingyu pigs. These results will help improve
384 Qingyu pig breeding programs. However, functional studies of these
385 identified SNPs and genes are still needed.
386 Competing interests
387 The authors declare that they have no competing interests.
388 Authors’ contributions
389 PW, GT, and KW performed experiments; PW and KW analyzed data
390 and prepared figures and tables; GT and PW edited and revised
391 manuscript; GT, PW, JZ, DC, XY, AJ, LS, SZ, WX, YJ, LZ, YZ, XX and XL 392 conceived, designed researchDraft and wrote this paper. PW, KW, JZ, DC, XY, 393 AJ, LS, SZ, WX, YJ, LZ, YZ, XX, XL and GT approved final version of this
394 manuscript.
395 Author details
396 1 Farm Animal Genetic Resources Exploration and Innovation Key
397 Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu
398 611130, Sichuan, China
399 2 College of Life Science, Sichuan Agricultural University, Yaan 625014,
400 Sichuan, China
401 3 Sichuan Animal Husbandry Station, Chengdu, 610041, Sichuan, China
402 Acknowledgements
403 Not applicable.
404 Consent for publication
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405 Not applicable.
406 Funding
407 The study was supported by grants from the Sichuan Science and
408 Technology Program (20ZDYF1241), the Sichuan Innovation Team of Pig
409 (SCSZTD-3-002), the National key R&D Program of China
410 #2018YFD0501204, the National Natural Science Foundation of China
411 (31530073), the National Natural Science Foundation of China #C170102,
412 the Chinese National Science and Tech Support Program
413 (No.2015BAD03B01, 2015GA810001), the earmarked fund for the China 414 Agriculture Research System Draft(No.CARS-35-01A). 415 Ethics approval
416 All experimental procedures were performed in accordance with the
417 Institutional Review Board (IRB14044) and the Institutional Animal Care
418 and Use Committee of the Sichuan Agricultural University under permit
419 number DKY-B20140302.
420 References
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477 Madsen, P., Jensen, J., Labouriau, R., Christensen, O. F., & Sahana, G. (2014). DMU - A Package for 478 Analyzing Multivariate Mixed Models in quantitative Genetics and Genomics. World Congress 479 on Genetics Applied to Livestock. 480 Matthieu, B., Christophe, A., Mickael, G., & Thomas, M. (2011). Accounting for Population 481 Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide 482 Association Studies. Plos One, 6(12), e28845-. 483 Miar, Y., Plastow, G. S., Moore, S. S., Manafiazar, G., Charagu, P., Kemp, R. A., . . . McKay, R. M. (2014). 484 Genetic and phenotypic parameters for carcass and meat quality traits in commercial 485 crossbred pigs. Journal of Animal Science, 92(7), 2869-2884. 486 Namas, R. A., Metukuri, M. R., Dhupar, R., Velosa, C., Jefferson, B. S., Myer, E., . . . Zamora, R. (2011). 487 Hypoxia-Induced Overexpression of BNIP3 is Not Dependent on Hypoxia-Inducible Factor 1α 488 in Mouse Hepatocytes. Shock, 36(2), 196-202. 489 Nonneman, D. J., Shackelford, S. D., King, D. A., Wheeler, T. L., Wiedmann, R. T., Snelling, W. M., & 490 Rohrer, G. A. (2013). Genome-wide association of meat quality traits and tenderness in 491 swine. 91(9), 4043-4050. 492 Pearson, & A., T. (2008). How to Interpret a Genome-wide Association Study. Jama, 299(11), 1335. 493 Perez, C. F., López, J. R., & Allen, P. D. (2005). Expression levels of RyR1 and RyR3 control resting free 494 Ca2+ in skeletal muscle. Am J Physiol Cell Physiol, 288(3), C640-649. 495 Sanchez, M. P., Tribout, T., Iannuccelli, N., Bouffaud, M., Servin, B., Tenghe, A., . . . Mercat, M.-J. 496 (2014). A genome-wide associationDraft study of production traits in a commercial population of 497 Large White pigs: evidence of haplotypes affecting meat quality. Genetics Selection Evolution, 498 46(1), 12. 499 Sanchez, M. P., Tribout, T., Iannuccelli, N., Bouffaud, M., Servin, B., Tenghe, A., . . . Mercat, M. J. 500 (2014). A genome-wide association study of production traits in a commercial population of 501 Large White pigs: evidence of haplotypes affecting meat quality. Genetics Selection Evolution, 502 46(1), 12. 503 Schmid, M., Maushammer, M., Preuß, S., & Bennewitz, J. (2018). Mapping QTL for production traits in 504 segregating Piétrain pig populations using genome-wide association study results of F2 505 crosses. Animal Genetics, 49(4). 506 Sonnleitner, & A. (1998). Functional properties of the ryanodine receptor type3 (RyR3) Ca2+ release 507 channel. Embo Journal, 17(10), 2790-2798. 508 Turner, S. D. (2014). qqman: an R package for visualizing GWAS results using Q-Q and manhattan 509 plots. Biorxiv. 510 Uimari, P., Sironen, A., & Sevón-Aimonen, M.-L. (2013). Evidence for three highly significant QTL for 511 meat quality traits in the Finnish Yorkshire pig breed. Journal of Animal Science, 91(5), 2001- 512 2011. 513 Verardo, L. L., Sevón-Aimonen, M.-L., Serenius, T., Hietakangas, V., & Uimari, P. (2017). Whole- 514 genome association analysis of pork meat pH revealed three significant regions and several 515 potential genes in Finnish Yorkshire pigs. BMC genetics, 18(1), 13. 516 Vidal, O., Noguera, J. L., Amills, M., Varona, L., & Sánchez, A. (2005). Identification of carcass and meat 517 quality quantitative trait loci in a Landrace pig population selected for growth and leanness. 518 Journal of Animal Science, 83(2), 293-300. 519 Vries, A. G. D., Faucitano, L., Sosnicki, A., & Plastow, G. S. (2000). The use of gene technology for 520 optimal development of pork meat quality. 69(4), 397-405.
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521 Wang, X., Liu, X., Deng, D., Yu, M., & Li, X. (2016). Genetic determinants of pig birth weight variability. 522 BMC genetics, 17(S1), S15. 523 Wu, P., Wang, K., Yang, Q., Zhou, J., Chen, D., Liu, Y., . . . Xiao, W. (2019). Whole-genome re- 524 sequencing association study for direct genetic effects and social genetic effects of six growth 525 traits in Large White pigs. Scientific reports, 9(1), 1-12. 526 Wu, S., Gui, J., Yin, X., Pan, Q., Liu, X., & Chu, L. (2016). Transmembrane domain is crucial to the 527 subcellular localization and function of Myc target 1. Journal of cellular and molecular 528 medicine, 20(3), 471-481. 529 Zhang, C., Wang, Z., Bruce, H., Kemp, R. A., Charagu, P., Miar, Y., . . . Plastow, G. (2015). Genome-wide 530 association studies (GWAS) identify a QTL close to PRKAG3 affecting meat pH and colour in 531 crossbred commercial pigs. Bmc Genetics, 16(1), 33. 532 Zhang, H., Wang, Z., & Wang…, S. (2012). Progress of genome wide association study in domestic 533 animals. 3(1), 26. 534 Zhou, L. S., Yang, J., Liu, X. X., Zhang, Z. Y., Yang, B., & Jun-Wu, M. A. (2014). Genome-Wide 535 Association Analyses for Musle pH 72 h Value and Meat Color Traits in Sutai Pigs. Scientia 536 Agricultura Sinica, 198(10), 3277–3284.
537 538 Figure Captions Draft 539 Figure 1. Manhattan plots for meat pH at three time points postmortem in Qingyu pigs. The
540 horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8)
541 level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of longissimus
542 thoracis muscle (LTM), respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem
543 of semimembranosus muscle (SM), respectively.
544
545 Figure 2. Manhattan plots for meat color L* at three time points postmortem in Qingyu pigs. The
546 horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8)
547 level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM,
548 respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem of SM, respectively.
549
550 Figure 3. Manhattan plots for meat color a* at three time points postmortem in Qingyu pigs. The
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551 horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8)
552 level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM,
553 respectively. (D), (E) and (F) for pH after 45 min), 24 h and 48 h postmortem of SM, respectively.
554
555 Figure 4. Manhattan plots for meat color b* at three time points postmortem in Qingyu pigs. The
556 horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8)
557 level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM,
558 respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem of SM, respectively.
559 560 Figure 5. Haplotype blocks for five importantDraft chromosome regions. (A) indicate a haplotype block 561 located on SSC14: 139.21-139.25 Mb; (B) indicate a haplotype block located on SSC4: 58.54-58.58
562 Mb; (C) indicate a haplotype block located on SSC8: 126.23-126.61 Mb; (D) indicate a haplotype
563 block located on SSC1: 10.49-10.58 Mb; (E) indicate a haplotype block located on SSC6: 7.89-7.93
564 Mb. The black lines mark the detected blocks.
565
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Figures 1-5
Draft
Figure 1. Manhattan plots for meat pH at three time points postmortem in Qingyu pigs. The horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8) level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of longissimus thoracis muscle (LTM), respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem of semimembranosus muscle (SM), respectively.
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Draft
Figure 2. Manhattan plots for meat color L* at three time points postmortem in Qingyu pigs. The horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8) level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM, respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem of SM, respectively.
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Draft
Figure 3. Manhattan plots for meat color a* at three time points postmortem in Qingyu pigs. The horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8) level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM, respectively. (D), (E) and (F) for pH after 45 min), 24 h and 48 h postmortem of SM, respectively.
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Draft
Figure 4. Manhattan plots for meat color b* at three time points postmortem in Qingyu pigs. The horizontal red and blue lines indicate the genome-wide (2.71 × 10−9) and suggestive (5.42 × 10−8) level, respectively. (A), (B) and (C) for pH after 45 min, 24 h and 48 h postmortem of LTM, respectively. (D), (E) and (F) for pH after 45 min, 24 h and 48 h postmortem of SM, respectively.
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Draft
Figure 5. Haplotype blocks for five important chromosome regions. (A) indicate a haplotype block located on SSC14: 139.21-139.25 Mb; (B) indicate a haplotype block located on SSC4: 58.54-58.58 Mb; (C) indicate a haplotype block located on SSC8: 126.23-126.61 Mb; (D) indicate a haplotype block located on SSC1: 10.49-10.58 Mb; (E) indicate a haplotype block located on SSC6: 7.89-7.93 Mb. The black lines mark the detected blocks.
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1 Table 1. The SNPs and candidate genes for meat pH at 40 min, 24 hours and 48 hours in Chinese Qingyu pigs
Trait Chr Position Region Allele MAF P–value Candidate gene
LTM pH48 1 61054125 61.03–61.07 C/T 0.15 2.70E–08
LTM pH48 2 10348472 10.33–10.37 A/C 0.467 1.02E–08 LOC100522849/LOC100524479
LTM pH48 6 56465091 56.45–56.49 T/C 0.467 1.02E–08 U6/LOC100626505/LOC100626318 LTM pH48 6 82707315 82.69–82.73 DraftC/A 0.121 2.94E–08 LTM pH48 6 136454147 136.43–136.47 A/T 0.15 5.10E–08
LTM pH48 14 139229307 139.21–139.25 A/G 0.143 1.92E–09
LTM pH48 14 139229315 139.21–139.25 C/T 0.143 1.92E–09
LTM pH48 14 139229483 139.21–139.25 T/C 0.121 3.27E–08
LTM pH48 14 140389050 140.37–140.41 T/C 0.133 2.52E–08 BNIP3/JAKMIP3
LTM pH48 X 404183 0.38–0.42 G/A 0.467 1.02E–08
LTM pH48 X 71981674 71.96–72.00 T/C 0.467 1.02E–08
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SM pH45 1 9137980 9.12–9.16 A/G 0.333 3.38E–08 SNX9
SM pH45 1 9137997 9.12–9.16 G/C 0.333 3.38E–08 SNX9
SM pH45 1 11686586 11.67–11.71 G/A 0.133 4.00E–08 TIAM2
SM pH45 1 13687743 13.67–13.71 C/T 0.383 3.32E–09 MYCT1
SM pH45 1 13688240 13.67–13.71 G/A 0.35 5.31E–08 MYCT1 SM pH45 1 40859921 40.84–40.88 DraftT/C 0.317 5.25E–08 SM pH45 4 23316 0.00–0.04 A/G 0.217 4.30E–08
SM pH45 4 58564986 58.54–58.58 C/G 0.133 4.12E–09
SM pH45 4 58564988 58.54–58.58 C/G 0.133 4.12E–09
SM pH45 4 58564989 58.54–58.58 A/G 0.133 4.12E–09
SM pH45 7 3976964 3.96–4.00 A/G 0.45 6.28E–09
SM pH45 8 127196057 127.18–127.22 G/A 0.217 3.51E–08
SM pH45 9 107575639 107.56–107.60 C/A 0.367 6.98E–09
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SM pH45 13 188752539 188.73–188.77 G/A 0.25 2.58E–08
SM pH45 14 48838026 48.82–48.86 C/T 0.133 1.78E–08
SM pH45 14 68437527 68.42–68.46 C/T 0.317 3.63E–08
SM pH45 17 7777641 7.76–7.80 A/G 0.233 8.53E–09
SM pH24 1 11686586 11.67–11.71 G/A 0.133 1.30E–08 TIAM2 SM pH24 1 119655069 119.64–119.68 DraftA/C 0.117 2.10E–08 SM pH24 4 58564986 58.54–58.58 C/G 0.133 1.17E–09
SM pH24 4 58564988 58.54–58.58 C/G 0.133 1.17E–09
SM pH24 4 58564989 58.54–58.58 A/G 0.133 1.17E–09
SM pH24 4 92914731 92.89–92.93 A/G 0.5 8.78E–09
SM pH24 4 92914737 92.89–92.93 G/T 0.414 1.88E–08
SM pH24 6 39368957 39.35–39.39 C/T 0.25 1.15E–08 UQCRFS1
SM pH24 8 126250654 126.23–126.27 A/G 0.367 1.38E–08 GRID2
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SM pH24 8 127196057 127.18–127.22 G/A 0.217 1.32E–09
SM pH24 8 127589737 127.57–127.61 T/C 0.3 2.35E–08
SM pH24 8 127590420 127.57–127.61 T/C 0.3 2.35E–08
SM pH24 8 127590458 127.57–127.61 C/G 0.3 2.35E–08
SM pH24 8 127590515 127.57–127.61 T/C 0.3 2.35E–08 SM pH24 9 107575639 107.56–107.60 DraftC/A 0.367 3.29E–08 SM pH24 13 160628402 160.61–160.65 G/A 0.214 2.28E–08
SM pH24 13 161487877 161.47–161.51 G/T 0.483 4.30E–08 LOC100514986
SM pH24 13 188752539 188.73–188.77 G/A 0.25 2.93E–08
SM pH24 14 22352333 22.33–22.37 G/C 0.117 2.70E–08
SM pH24 14 48838026 48.82–48.86 C/T 0.133 5.54E–09
SM pH24 15 134442379 134.42–134.46 A/G 0.383 2.20E–08
SM pH24 17 7777641 7.76–7.80 A/G 0.233 3.55E–08
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SM pH48 1 11686586 11.67–11.71 G/A 0.133 1.54E–08 TIAM2
SM pH48 1 40859921 40.84–40.88 T/C 0.317 3.50E–08
SM pH48 4 58564986 58.54–58.58 C/G 0.133 3.87E–10
SM pH48 4 58564988 58.54–58.58 C/G 0.133 3.87E–10
SM pH48 4 58564989 58.54–58.58 A/G 0.133 3.87E–10 SM pH48 4 92914731 92.89–92.93 DraftA/G 0.5 2.82E–08 SM pH48 6 39368957 39.35–39.39 C/T 0.25 1.05E–08 UQCRFS1
SM pH48 8 127196057 127.18–127.22 G/A 0.217 1.45E–08
SM pH48 13 160628402 160.61–160.65 G/A 0.214 1.16E–08
SM pH48 13 188752539 188.73–188.77 G/A 0.25 5.30E–08
SM pH48 14 22352333 22.33–22.37 G/C 0.117 2.78E–08
SM pH48 14 48838026 48.82–48.86 C/T 0.133 3.34E–09
SM pH48 15 134442379 134.42–134.46 A/G 0.383 7.19E–09
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SM pH48 X 68635245 68.62–68.66 A/G 0.3 3.76E–08
SM pH48 X 68635283 68.62–68.66 T/C 0.3 4.62E–08
2 Chr, Chromosome; Range, Range of significant chromosome region; MAF, Minor allele frequency; The bolded text shown the genome–wide
3 significant SNPs.
4 5 Table 2. The SNPs and candidate genes for meat color L* at 45Draft min, 24 hours and 48 hours in Chinese Qingyu pigs Trait Chr Position Region Allele MAF P–value Candidate gene
LTM L* 45min 1 262652496 262.63–262.67 A/G 0.167 1.74E–08 LOC100522178
LTM L* 45min 2 141482540 141.46–141.50 T/G 0.433 2.33E–09 CXXC5
LTM L* 45min 4 101441921 101.42–101.46 C/T 0.397 1.91E–08 HMGCS2/PHGDH
LTM L* 45min 5 11915458 11.90–11.94 C/T 0.464 1.09E–08 RBFOX2/LOC100518305
LTM L* 45min 5 24964188 24.94–24.98 C/A 0.467 6.58E–09
LTM L* 45min 5 56672021 56.65–56.69 G/A 0.133 2.04E–08 DERA
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LTM L* 45min 6 39486877 39.47–39.51 C/T 0.15 3.64E–08
LTM L* 45min 6 55944370 55.92–55.96 C/T 0.19 3.51E–08 RPS9/TSEN34/MBOAT7/TMC4
LTM L* 45min 6 55944371 55.92–55.96 A/G 0.19 3.51E–08 RPS9/TSEN34/MBOAT7/TMC4
LTM L* 45min 6 55944382 55.92–55.96 C/G 0.19 3.51E–08 RPS9/TSEN34/MBOAT7/TMC4
LTM L* 45min 7 14097777 14.08–14.12 T/C 0.467 4.63E–08 RNF144B LTM L* 45min 9 135544428 135.52–135.56DraftT/C 0.317 4.04E–09 LTM L* 45min 17 10456940 10.44–10.48 T/G 0.483 2.88E–08 SFRP1
LTM L* 45min X 73780667 73.76–73.80 T/C 0.217 3.45E–08
LTM L* 24h 8 18710213 18.69–18.73 G/A 0.117 1.52E–08
LTM L* 48h 2 141243855 141.22–141.26 T/C 0.15 2.06E–08 MATR3/PAIP2/SLC23A1
LTM L* 48h 2 143635313 143.62–143.66 T/C 0.15 2.06E–08 GNPDA1/NDFIP1
LTM L* 48h X 73780667 73.76–73.80 T/C 0.217 1.89E–09
SM L* 45min 11 78626322 78.61–78.65 A/G 0.233 5.24E–08 LAMP1/GRTP1
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SM L* 45min 11 78639685 78.62–78.66 G/C 0.233 5.24E–08 GRTP1
SM L* 45min 15 20377889 20.36–20.40 A/G 0.383 6.66E–09 U4
SM L* 45min 15 31653490 31.63–31.67 A/C 0.317 4.88E–08 CFAP221
SM L* 45min 15 31653492 31.63–31.67 A/T 0.317 4.88E–08 CFAP221
SM L* 45min 15 31653494 31.63–31.67 A/T 0.317 4.88E–08 CFAP221 SM L* 45min 15 91959422 91.94–91.98DraftA/G 0.417 5.34E–10 SM L* 45min 15 94398442 94.38–94.42 C/A 0.417 3.28E–08 PMS1
SM L* 24h 1 13687743 13.67–13.71 C/T 0.383 1.59E–08 MYCT1
SM L* 24h 7 96891071 96.87–96.91 T/C 0.133 4.84E–08 HEATR4/ACOT4
SM L* 24h 7 96891075 96.87–96.91 T/C 0.133 4.84E–08 HEATR4/ACOT4
SM L* 24h 13 188752539 188.73–188.77 G/A 0.25 3.54E–09
SM L* 24h 15 139149221 139.13–139.17 A/G 0.241 1.99E–08 LOC106506441/LOC100525618
SM L* 24h 15 139149222 139.13–139.17 T/C 0.241 1.99E–08 LOC106506441/LOC100525618
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SM L* 48h 8 128655507 128.64–128.68 A/G 0.483 3.18E–08 CCSER1
SM L* 48h 8 128655510 128.64–128.68 A/G 0.483 3.18E–08 CCSER1
SM L* 48h 11 60425672 60.41–60.45 T/C 0.167 2.98E–08
6 Chr, Chromosome; Range, Range of significant chromosome region; MAF, Minor allele frequency; The bolded text shown the genome–wide
7 significant SNPs. 8 Draft 9 Table 3. The SNPs and candidate genes for meat color a* at 45 min, 24 hours and 48 hours in Chinese Qingyu pigs
Trait Chr Position Region Allele MAF P–value Candidate gene
LTM a* 45min 11 39939081 39.92–39.96 T/C 0.117 1.66E–08
LTM a* 48h 1 10511686 10.49–10.53 C/T 0.133 3.24E–08
LTM a* 48h 1 10516181 10.50–10.54 G/A 0.133 3.24E–08
LTM a* 48h 1 10531243 10.51–10.55 C/A 0.133 3.24E–08
LTM a* 48h 1 10531245 10.51–10.55 G/A 0.133 3.24E–08
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LTM a* 48h 1 10531254 10.51–10.55 C/T 0.133 3.24E–08
LTM a* 48h 1 10531283 10.51–10.55 C/A 0.133 3.24E–08
LTM a* 48h 1 10531296 10.51–10.55 T/C 0.133 3.24E–08
LTM a* 48h 1 10531489 10.51–10.55 G/A 0.133 3.24E–08
LTM a* 48h 1 10561423 10.54–10.58 T/A 0.133 3.24E–08 LTM a* 48h 6 7909526 7.89–7.93DraftT/C 0.5 6.28E–09 LTM a* 48h 6 7909547 7.89–7.93 A/G 0.5 6.28E–09
LTM a* 48h 6 7910329 7.89–7.93 C/T 0.5 6.28E–09
LTM a* 48h 6 7910368 7.89–7.93 T/G 0.5 6.28E–09
LTM a* 48h 6 7910883 7.89–7.93 C/T 0.5 6.28E–09
LTM a* 48h 6 7910999 7.89–7.93 C/A 0.5 6.28E–09
LTM a* 48h 6 7911197 7.89–7.93 C/T 0.5 6.28E–09
LTM a* 48h 6 7911275 7.89–7.93 G/A 0.5 6.28E–09
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LTM a* 48h 6 7911283 7.89–7.93 C/G 0.5 6.28E–09
LTM a* 48h 6 7911887 7.89–7.93 T/A 0.5 6.28E–09
LTM a* 48h 6 7912268 7.89–7.93 T/C 0.5 6.28E–09
LTM a* 48h 6 7912274 7.89–7.93 A/T 0.5 6.28E–09
LTM a* 48h 6 7912280 7.89–7.93 T/C 0.5 6.28E–09 LTM a* 48h 6 7912281 7.89–7.93DraftT/C 0.5 6.28E–09 LTM a* 48h 6 7912307 7.89–7.93 T/C 0.5 6.28E–09
LTM a* 48h 6 7912311 7.89–7.93 T/G 0.5 6.28E–09
LTM a* 48h 6 7912332 7.89–7.93 A/G 0.5 6.28E–09
LTM a* 48h 6 7912351 7.89–7.93 T/G 0.5 6.28E–09
LTM a* 48h 6 7912718 7.89–7.93 G/A 0.5 6.28E–09
LTM a* 48h 6 7912719 7.89–7.93 G/C 0.5 6.28E–09
SM a* 45min 13 1787693 1.77–1.81 A/G 0.15 2.29E–08 PIK3R4
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SM a* 45min 13 2452714 2.43–2.47 G/A 0.133 4.29E–09 CAPN7
SM a* 24h 6 45025624 45.01–45.05 T/G 0.446 5.28E–08 TMEM147/ATP4A
SM a* 24h 7 24597661 24.58–24.62 G/T 0.25 4.53E–08
SM a* 24h 7 80940554 80.92–80.96 A/G 0.3 4.12E–08 RYR3
SM a* 24h 9 135148030 135.13–135.17 T/C 0.183 4.03E–08 U6 SM a* 48h 2 129659035 129.64–129.68DraftA/G 0.283 3.67E–08 ALDH7A1 SM a* 48h 9 30052019 30.03–30.07 T/A 0.183 3.45E–08
SM a* 48h 9 55884909 55.86–55.90 A/G 0.167 4.57E–08 KCNJ5
SM a* 48h 12 57772709 57.75–57.79 T/C 0.183 5.67E–09
SM a* 48h 15 3137358 3.12–3.16 C/T 0.167 1.07E–08
10 Chr, Chromosome; Range, Range of significant chromosome region; MAF, Minor allele frequency; The bolded text shown the genome–wide
11 significant SNPs.
12
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13 Table 4. The SNPs and candidate genes for meat color b* at 45 min, 24 hours and 48 hours in Chinese Qingyu pigs
Trait Chr Position Region Allele MAF P–value Candidate gene
LTM b* 24h 1 223492350 223.47–223.51 A/G 0.267 5.28E–08 MAMDC2
LTM b* 24h 2 139543929 139.52–139.56 T/G 0.2 2.76E–08
LTM b* 24h 10 66077453 66.06–66.10 T/C 0.483 8.63E–09 LTM b* 24h 12 58102854 58.08–58.12DraftA/T 0.417 2.45E–08 COX10 LTM b* 24h 14 140362731 140.34–140.38 T/C 0.2 1.94E–08 BNIP3
LTM b* 24h 15 129718873 129.70–129.74 A/G 0.117 1.45E–08
LTM b* 24h 16 18982351 18.96–19.00 C/T 0.117 4.02E–08
LTM b* 24h 17 61680224 61.66–61.70 G/A 0.383 1.21E–08 OSBPL2/ADRM1
LTM b* 48h 2 69543184 69.52–69.56 G/T 0.117 3.82E–08 DNM2/MIR199A–2
LTM b* 48h 5 2502414 2.48–2.52 A/G 0.3 2.85E–09
LTM b* 48h 5 33472985 33.45–33.49 T/A 0.375 3.81E–08
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LTM b* 48h 5 62735391 62.72–62.76 T/G 0.2 1.93E–08 MFAP5
LTM b* 48h 9 16562269 16.54–16.58 A/G 0.241 2.55E–08
LTM b* 48h 11 21350905 21.33–21.37 T/G 0.2 3.37E–08 ZC3H13
LTM b* 48h 11 45856150 45.84–45.88 T/C 0.25 9.74E–09
LTM b* 48h 13 3219566 3.20–3.24 A/G 0.417 3.78E–09 LTM b* 48h 13 151224397 151.20–151.24DraftT/G 0.5 1.37E–08 HHLA2 LTM b* 48h 13 160674783 160.65–160.69 T/A 0.233 4.43E–09
LTM b* 48h 13 160674784 160.65–160.69 G/C 0.233 4.43E–09
LTM b* 48h 14 141640799 141.62–141.66 C/T 0.31 1.98E–08 LOC100152524/LOC100156664
LTM b* 48h 14 141640800 141.62–141.66 A/G 0.31 1.98E–08 LOC100152524/LOC100156664
SM b* 45min 15 38140718 38.12–38.16 G/A 0.3 3.80E–08 DEFB108B
14 Chr, Chromosome; Range, Range of significant chromosome region; MAF, Minor allele frequency; The bolded text shown the genome–wide
15 significant SNPs.
https://mc06.manuscriptcentral.com/genome-pubs