Genome-Wide Identification of Quantitative Trait Loci and Candidate Genes for Seven Carcass Traits in a Four-Way Intercross Porcine Population

doi:10.21203/rs.3.rs-3137622/v1

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Research Article

Genome-Wide Identification of Quantitative Trait Loci and Candidate Genes for Seven Carcass Traits in a Four-Way Intercross Porcine Population

https://doi.org/10.21203/rs.3.rs-3137622/v1

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published 09 Jun, 2024

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BackgroundCarcass traits are essential economic traits in the commercial pig industry. However, the genetic mechanism of carcass traits is still unclear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to study seven carcass traits on 223 four-way intercross pigs.

Results A total of 227,921 high-quality single nucleotide polymorphisms (SNPs) were detected to perform GWAS. A total of 53 SNPs were identified for seven carcass traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43% to 16.32%. The heritability estimates based on SNP for seven carcass traits ranged from 0.23 (loin eye thickness) to 0.48 (dressing percentage). Furthermore, 11 candidate genes (LYPLAL1, EPC1, MATN2, ZFAT, ZBTB10, ZNF704, INHBA, SMYD3, PAK1, SPTBN2, and ACTN3) were found to be associated with carcass traits in pigs.

Conclusions A total of 30 significant SNPs, two QTLs on SSC8and SSC10, and 11 candidate genes were identified as being associated with carcasstraits of pigs. The GWAS results will improve our understanding of the genetic basis of carcass traits. We hypothesized that the candidate genes associated with these discovered SNPs would offer a biological basis for enhancing the carcass quality of pigs in swine breeding.

GWAS

SLAF-seq

Pigs

Carcass traits

Candidate genes

Carcass traits, such as dressing percentage (DP), number of ribs (RIB), skin thinkness (ST), carcass length (CL) and loin eye traits (LE), are important economic traits in pig production, and are also the main target traits for pig breeding and improvement. Recently, a study showed that the number of vertebrae was related to carcass length and meat production [1]. From the perspective of pork consumption and the economic value of vertebrae, sparerib is one of the most valuable parts of the pork carcass [2]. Because these carcass traits are controlled by multiple genes, their genetic basis is complex, and they are difficult to accurately measure for live hogs, the improvement effect of conventional breeding methods is limited. With the rapid development of molecular markers and the completion of pig genome sequencing, molecular breeding has become the most promising way to improve carcass traits of pigs. In recent years, significant progress has been made in the quantitative trait loci (QTLs) and candidate genes for pig carcass traits. To date, a total of 30,480 QTLs associated with carcass and meat quality traits have been added to the pig QTL database (https://www.animalgenome.org/cgi-bin/QTLdb/index, accessed on 25 April 2023). These findings have considerably improved our knowledge of the genetic architecture of pig carcass traits. However, the fine mapping of QTLs and the discovery of new candidate genes still need to be strengthened, and the molecular genetic basis of some carcass traits of pigs is still unclear. Based on this, three typical Western commercial pig breeds, Landrace, Yorkshire and Duroc, were hybridized with Saba pig, a Chinese local pig breed distributed in Yunnan Province, China [3], to establish (Duroc×Saba)×[Yorkshire×(Landrace×Saba)] (DSYLS) hybrid segregation population. The offspring showed great differences in several carcass traits.

Genome-wide association study (GWAS) was used to identify SNPs associated with pig economic traits. In recent years, GWAS based on SNP array for carcass-related traits of pigs has identified many QTLs and candidate genes [4]. Liu et al. [5] genotyped 576 Large White × Minzhu intercross pigs using the Illumina Porcine SNP60K Beadchip and performed GWAS for CL. GWAS results showed that a total of 31 genome-wide significant SNPs on SSC7 were detected to be associated with CL. Additionally, a total of 836 Duroc pigs were genotyped using the Illumina Porcine SNP60 K BeadChip. Furthermore, the study found that the vertnin gene (VRTN) was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths [6]. Zhuang et al. [7] used Illumina Porcine SNP50K Beadchip to genotype 6,043 Duroc pigs and conducted a GWAS for loin muscle area (LMA) and loin muscle depth (LMD). Several QTLs and candidate genes related to LMA and LMD were identified. However, only a modest number of known SNPs could be found using GWAS based on the porcine SNP array. Furthermore, another genotyping technique employed recently was GWAS based on whole-genome sequencing (WGS). Nevertheless, GWAS for Sus scrofa (Sscrofa) based on WGS with large populations is still too expensive at the moment. Therefore, reduced representation sequencing technology known as specific-locus amplified fragment sequencing (SLAF-seq) was created to produce large-scale SNP data fast, reliably, efficiently, and inexpensively [8]. In contrast to SNP arrays, SLAF-seq technology, which is based on high-throughput sequencing, may produce millions of high-density SNP loci that cover the entire genome. Using SLAF-seq-based GWAS, several SNPs and potential genes have been found for a variety of economic traits in various animals [9–13]. SLAF-seq for pig genotyping effectively found numerous new mutation sites [14–16].

On 223 four-way intercross pigs raised in the same environmental conditions, seven carcass traits, DP, RIB, ST, carcass straight length (CSL), carcass diagonal length (CDL), loin eye width (LEW), and loin eye thickness (LET) were examined. Then, using SLAF-seq technology, GWAS was carried out to find significant SNPs linked to these traits. The findings provide a basis for pig breeding and the use of molecular markers to improve carcass quality.

Phenotype description and correlation among growth traits

The statistical information on the seven carcass traits is shown in Table 1. The mean values for DP, RIB, ST, CSL, CDL, LEW and LET were 74.87%, 14.05, 3.47 mm, 96.86 cm, 83.44 cm, 8.21 cm and 6.17 cm, respectively. Coefficients of variation for the seven carcass traits were 3.87%, 4.12%, 32.45%, 5.97%, 7.52%, 11.28%, and 14.38%, respectively. The heritability estimates based on SNPs for seven carcass traits ranged from 0.23 (LET) to 0.48 (DP). The frequency distributions of these carcass traits are shown in Figure S1. These carcass traits appear to conform to the normal distribution.

The phenotypic correlation coefficients for these carcass traits are shown in Table 2. The results revealed that CSL had the strongest positive correlation with CDL (r = 0.82, p < 0.001). LEW had the strongest negative correlation with LET (r = − 0.23, p < 0.001). Genetic correlations for seven carcass traits are shown in Table 3. The results of genetic correlations are similar to which of phenotypic correlations. There is a positive genetic correlation between CSL and CDL (r = 0.95 ± 0.02), and a negative genetic correlation between LEW and LET (r = − 0.59 ± 0.15).

Table 1

Descriptive statistics of seven carcass traits in the crossbred pigs
Trait	N^a	Min^b	Max^c	Mean	SD^d	CV^e	h² ± SE^f
Dressing percentage, DP (%)	223	63.68	86.00	74.87	2.89	3.87	0.48 ± 0.17
Number of ribs, RIB	211	12	15	14.05	0.58	4.12	0.28 ± 0.16
Skin thickness, ST (mm)	223	1.30	8.90	3.47	0.11	32.45	0.36 ± 0.16
Carcass straight length, CSL (cm)	223	79.00	117.00	96.86	5.78	5.97	0.26 ± 0.14
Carcass diagonal length, CDL (cm)	223	63.00	101.00	83.44	6.27	7.52	0.40 ± 0.16
Loin eye width, LEW (cm)	221	5.84	10.84	8.21	0.93	11.28	0.34 ± 0.17
Loin eye thickness, LET (cm)	221	3.82	9.58	6.17	0.89	14.38	0.23 ± 0.14
^a Number of samples
^b Minimum
^c Maximum
^d Standard deviation
^e Coefficient of variation
^f Heritability estimate ± Standard error

Table 2

Phenotypic correlation coefficients among seven carcass traits in the crossbred pigs
Trait*	DP	RIB	CSL	CDL	ST	LEW
RIB	0.06
CSL	–0.05	–0.05
CDL	–0.17^*	–0.09	0.82^***
ST	–0.05	0.04	0.03	0.15^*
LEW	0.02	0.14	0.21^**	0.16^*	-0.05
LET	0.15^*	0.01	0.05	–0.04	-0.16^*	–0.23^***

* DP, dressing percentage; RIB, number of ribs; ST, skin thickness; CSL, carcass straight length; CDL, carcass diagonal length; LEW, loin eye width; LET, loin eye thickness; ^* significant at p < 0.05, ^** significant at p < 0.01, ^*** significant at p < 0.001. The maximum value and minimum value of the phenotypic correlation coefficient between traits are in bold.

Table 3

Genetic correlations among seven carcass traits in the crossbred pigs
Trait*	DP	RIB	CSL	CDL	ST	LEW
RIB	0.13 (0.19)
CSL	–0.11 (0.18)	–0.08 (0.23)
CDL	–0.29 (0.16)	–0.14 (0.19)	0.95 (0.02)
ST	–0.06 (0.17)	0.15 (0.18)	0.02 (0.20)	0.27 (0.13)
LEW	0.05 (0.16)	0.43 (0.22)	0.89 (0.26)	0.50 (0.21)	–0.08 (0.19)
LET	0.44 (0.27)	–0.04 (0.25)	0.20 (0.24)	–0.03 (0.21)	–0.44 (0.25)	–0.59 (0.15)

* DP, dressing percentage; RIB, number of ribs; ST, skin thickness; CSL, carcass straight length; CDL, carcass diagonal length; LEW, loin eye width; LET, loin eye thickness; The numbers in brackets are standard errors. The maximum and minimum values of the genetic correlation between traits are in bold.

Identification of SLAFs and SNPs

In our previous study, 223 individuals were genotyped and descriptive statistics of the sequence data were presented [17–19]. In brief, the development of SLAF tags (314–344 bp sequence length) was accomplished using the restriction enzyme pair RsaI and HaeIII. The total number of paired-end reads obtained was 1109.92 Million. The average Q30 and GC content values were 90.74% and 44.83%, respectively (Table S1), showing the accuracy of our sequencing results for 223 accessions. With sequencing to an average depth of 11.94, a total of 1,552,377 SLAF tags (an average of 331,608 SLAFs for each individual) were identified from all individuals (Table S1). Additionally, Oryza sativa indica was employed as a control throughout the sequencing process. The results demonstrated that the normal construction of SLAF libraries was demonstrated by the enzyme digestion normally efficiency and paired-end comparison efficiency of control data, which were 90.77% and 95.4%, respectively.

After genomic mapping and SNP calling, a total of 16,997 polymorphic SLAF tags and 10,784,484 SNPs were obtained. After the genotyping results were filtered for a minimum MAF of 0.05 and locus integrity of 0.8, a total of 227,921 highly consistent SNPs were discovered. The density distributions of the SLAF, total SNPs and filtered SNPs across Sscrofa genome are shown in Figure S2. SNPs were found in almost all of the non-overlapping 1 Mb regions of the genome. The density distribution of total SNPs and filtered SNPs were calculated on each Sscrofa autosome and are shown in Table S2. The filtered SNP density across the 18 Sscrofa chromosomes was one SNP every 10.28 kb on average, which indicated that the data was reliable.

Genome-wide association study and identification of candidate genes

Since population stratification might affect GWAS, Q-Q plots of all traits were created. The observed –log10 p values calculated by the association study using MLM matched those that were anticipated, suggesting that the MLM did a good job of controlling for false positives. The Q-Q plot of each growth trait was displayed after the corresponding manhattan plot (Fig. 1–2). Using MLM, a total of 30 SNPs were found to be significant (p < 1.0×10^− 5) for seven carcass traits (Table 4). The phenotypic variation explained (PVE) by the significant SNPs was from 3.37–16.17% (Table 4). Seven, two, six, six, three, five, and two SNPs were significantly associated with DP, RIB, ST, CSL, CDL, LEW and LET, respectively. These detected SNPs were distributed in twelve Sscrofa chromosomes (SSC), except for SSC1, SSC6, SSC11, SSC12, SSC16 and SSC17. Additionally, 38 genes total were thought to be prospective candidate genes and were situated 100 kb upstream and downstream of these significant SNPs (Table 4).

Table 4

The description of significant SNPs and candidate genes for seven carcass traits
Trait^a	SNP rs^b	Location (bp)^c	p-value^d	–log₁₀p^e	MAF^f	Allele	PEV (%)^g	Gene^h	Distance (bp)ⁱ
DP	rs334674154	SSC10:9047896	5.80×10^− 7	6.24	0.46	G/C	13.95	LYPLAL1	Within
	rs81321697	SSC10:42821846	2.19×10^− 6	5.66	0.46	C/A	9.88	EPC1	D_13,439
	rs693030537	SSC10:42821848	2.63×10^− 6	5.58	0.44	A/G	10.52	EPC1	D_13,437
	rs343827180	SSC10:42821905	8.77×10^− 7	6.06	0.48	A/T	10.40	EPC1	D_13,380
	rs1109743164	SSC14:133704571	9.48×10^− 6	5.02	0.08	T/G	7.78	NA	NA
		SSC14:133704611	7.90×10^− 6	5.10	0.08	T/C	7.82	NA	NA
		AEMK020004 00.1:3892	8.68×10^− 6	5.06	0.07	G/A	6.45	NA	NA
RIB	rs708233859	SSC9:29825285	2.14×10^− 6	5.67	0.25	A/G	3.01	NA	NA
		SSC4:38814467	8.77×10^− 7	6.06	0.05	A/G	10.79	RPL30	U_93,687
		SSC4:38814467	8.77×10^− 7	6.06	0.05	A/G	10.79	MATN2	Within
ST	rs330806505	SSC4:7108760	9.67×10^− 6	5.01	0.32	G/A	7.98	ZFAT	Within
	rs320372958	SSC15:35899597	6.27×10^− 6	5.20	0.41	C/T	8.98	ENSSSCG00 000044137	D_94,956
	rs340678157	SSC15:35899625	2.71×10^− 6	5.57	0.42	C/T	9.77	ENSSSCG00 000044137	D_94,928
	rs328617466	SSC7:51823917	3.28×10^− 6	5.48	0.09	C/T	12.40	HDGFL3	U_1,539
								TM6SF1	Within
								BTBD1	D_53,437
	rs318262569	SSC4:56089144	2.54×10^− 6	5.60	0.29	A/T	16.17	ZBTB10	D_46,889
	rs318262569	SSC4:56089144	2.54×10^− 6	5.60	0.29	A/T	16.17	ZNF704	U_84,835
	rs325200532	SSC13:188457884	1.66×10^− 6	5.78	0.07	C/T	7.40	NA	NA
CSL	rs324898647	SSC8:7830994	7.80×10^− 6	5.11	0.36	A/G	10.69	NA	NA
	rs340085518	SSC8:7831068	8.59×10^− 6	5.07	0.11	A/G	10.41	NA	NA
	rs322537544	SSC8:8263513	9.87×10^− 6	5.01	0.31	C/T	4.19	ENSSSCG00 000039778	U_9,422
	rs330152743	SSC5:10772879	7.98×10^− 6	5.10	0.47	T/C	9.93	TMPRSS6	U_67,399
								TEX33	Within
								TST	U_13,256
								KCTD17	U_53,920
								CSF2RB	D_43,492
								MPST	U_23,148
	rs690857471	SSC7:36193764	2.23×10^− 7	6.65^*	0.10	T/A	6.20	NA	NA
		SSC18:52920094	4.78×10^− 6	5.32	0.07	T/G	9.39	INHBA	U_19,127
CDL		SSC10:18327923	2.75×10^− 6	5.56	0.09	A/C	12.63	SMYD3	Within
		SSC18:52920094	4.45×10^− 7	6.35^*	0.07	T/G	9.39	INHBA	U_19,127
	rs338797354	SSC4:122997439	8.21×10^− 6	5.09	0.13	C/T	3.37	ARHGAP29	D_88,483
	rs338797354	SSC4:122997439	8.21×10^− 6	5.09	0.13	C/T	3.37	ABCD3	U_44,182
LEW		SSC9:12068165	8.84×10^− 6	5.05	0.08	G/A	12.83	PAK1	Within
								AQP11	Within
								RSF1	D_53,660
	rs708657805	SSC9:12068393	2.69×10^− 6	5.57	0.08	A/G	13.41	PAK1	Within
								AQP11	Within
								RSF1	D_53,432
	rs324713326	SSC3:15128630	8.79×10^− 6	5.06	0.09	G/A	6.68	NA	NA
	rs341980415	SSC3:27538828	8.34×10^− 6	5.08	0.07	A/G	13.36	XYLT1	U_45,936
	rs337711004	SSC3:74664280	5.14×10^− 6	5.29	0.32	A/G	9.79	ENSSSCG00 000045421	U_32,729
	rs337711004	SSC3:74664280	5.14×10^− 6	5.29	0.32	A/G	9.79	ETAA1	D_56,789
LET	rs1113904511	SSC2:5773845	1.64×10^− 6	5.79	0.27	G/A	7.51	C11orf80	U_76,046
								RBM14	Within
								RBM4B	Within
								ACTN3	D_85,000
								SPTBN2	D_34,428
								CCS	D_45,626
								CTSF	D_81,266
								ENSSSCG00 000040263	D_42,630
								CCDC87	D_59,619
	rs1112016337	SSC4:27406931	1.45×10^− 6	5.84	0.10	A/G	12.19	ENSSSCG00 000050037	Within
^a DP, dressing percentage; RIB, number of ribs; ST, skin thickness; CSL, carcass straight length; CDL, carcass diagonal length; LEW, loin eye width; LET, loin eye thickness
^b SNP rs ID from Ensembl
^c Locations of the significant SNP according to the Sus Scrofa Build 11.1 assembly; SSC, Sus Scrofa chromosome ^dMinor Allele Frequency
^e Genome-wide significant associations are underlined
^e,f and* represented the 10% and 1% genome-wide significance, respectively
^g Phenotypic Variation Explained
^h The gene located with 100 kb upstream and downstream of the significant SNP
ⁱ U/D represented the gene located upstream or downstream of the SNP (Intergenic region), within represented the SNP located with the gene

DP, RIB, and ST

GWAS results and candidate genes for DP, RIB, and ST are showed in Table 4 and Fig. 1.

On SSC10, four SNPs were significantly related to DP. Among, the rs334674154 was located in the LYPLAL1 gene. Another three adjacent SNPs, including rs81321697, rs693030537 and rs343827180, were located 13.4 kb downstream of the EPC1 gene. For RIB, the significant SNP (SSC4:38814467) was located with the MATN2 gene.

For ST, a total of six significant SNPs were detected on SSC4, SSC7, SSC13 and SSC15. Among, the rs330806505 was located within the ZFAT gene. The rs318262569 was located 46.9 kb downstream and 84.8 kb upstream of ZBTB10 and ZNF704, respectively.

CSL, CDL, LEW and LET

GWAS results and candidate genes for CSL, CDL, LEW and LET are showed in Table 4 and Fig. 2.

Interestingly, one significant SNP (SSC18:52920094) were found to be associated with CSL and CDL. The SNP was located 19.1 kb upstream of the INHBA gene. Additionally, the SNP SSC10:18327923 was significantly associated with CDL, which was located with the SMYD3 gene.

Furthermore, a total of five significant SNPs were detected to be related to LEW. Among, two adjacent genes (SSC9:12068165 and rs708657805) on SSC9 were located with PAK1 and AQP11. For LET, one significant SNP (rs1113904511) on SSC2 was located 34.4 kb and 85.0 kb downstream of SPTBN2 and ACTN3, respectively.

Comparison with previously mapped QTL in pigs

Based on SNP and QTL locations, the Pig QTL Database (Pig QTLdb, https://www.animalgenome.org/cgi-bin/QTLdb/SS/index, accessed on 25 April 2023) was searched to see if any of the QTLs connected to carcass traits in the study replicate any other QTLs that have been previously identified. In total, 30 SNPs significantly associated with carcass traits were identified, of which 9 SNPs were located in previously reported QTL regions in pigs. The remaining 21 SNPs had not been incorporated into any QTLs linked to carcass traits that had been previously reported. Interestingly, two QTLs on SSC10 (42.82–42.82 Mb, 59-bp) and SSC8 (78.31–82.64Mb, 4.33-Mb) were found to be associated with DP and CSL, respectively. The results of QTLs comparison are shown in Table S3.

Table 5

Effect of the genotypes on several carcass traits
Trait^a	SNP rs^b	Location (bp)^c	Genotype^d	N^e	Phenotype value^f	Additive effect	Dominance effect	Gene
DP (%)	rs334674154	SSC10: 9047896	GG	69	73.77 ± 0.34 ^B	1.41	-0.68	LYPLAL1
			GC	59	74.50 ± 0.37 ^B
			CC	55	76.59 ± 0.38 ^A
			NN	40
RIB		SSC4: 38814467	AA	166	14.10 ± 0.04 ^A	0.55	0.01	MATN2
			AG	16	13.56 ± 0.13 ^B
			GG	13.00 ± 0.38 ^B
			NN	27
ST (mm)	rs330806505	SSC4: 7108760	GG	94	3.25 ± 0.11 ^cB	0.48	-0.11	ZFAT
			GA	65	3.62 ± 0.14 ^bAB
			AA	26	4.20 ± 0.22 ^aA
			NN	38
CSL (cm)		SSC18: 52920094	TT	174	97.47 ± 0.43 ^A	4.99	0.3	INHBA
			TG	25	92.78 ± 1.13 ^B
			GG	2	87.50 ± 3.99 ^B
			NN	22
CDL (cm)		SSC18: 52920094	TT	174	84.16 ± 0.46 ^A	6.08	1.22	INHBA
			TG	25	79.30 ± 1.23 ^B
			GG	2	72.00 ± 4.33 ^B
			NN	22
		SSC10: 18327923	AA	158	82.70 ± 0.45 ^B	4.15	1.48	SMYD3
			AC	26	88.33 ± 1.11 ^A
			CC	91.00 ± 3.27 ^A
			NN	36
LEW (cm)		SSC9: 12068165	GG	162	8.13 ± 0.07 ^B	0.90	-0.48	PAK1
			GA	13	8.55 ± 0.25 ^B
			AA	9.92 ± 0.34 ^A
			NN	39
	rs708657805	SSC9: 12068393	AA	164	8.11 ± 0.07 ^B	0.91	-0.41	PAK1
			AG	12	8.61 ± 0.26 ^B
			GG	7	9.92 ± 0.34 ^A
			NN	38
^a DP, dressing percentage; RIB, number of ribs; ST, skin thickness; CSL, carcass straight length; CDL, carcass diagonal length; LEW, loin eye width; ^b SNP: rs ID from Ensembl. ^c SSC: Sus scrofa chromosome; Locations of the significant SNP according to the Sus scrofa Build 11.1 assembly. ^d NN represents no genotype. ^e N: Number of pig accessions. ^f Different capital letters indicate an extremely significant difference (p < 0.01), and different lowercase letters indicate a significant difference (p < 0.05).

Functional annotation of candidate genes

The results of GO annotation showed that INHBA was involved in the negative regulation of cell proliferation. SMYD3 participated in myotube cell development and negative regulation of protein kinase activity. PAK1 participated in mitotic cell cycle. GO annotation results of other genes are shown in Table S4.

Association analysis between SNP marker genotypes and carcass traits

The association analysis between candidate SNPs genotypes and carcass traits was performed. The result revealed that the genotype (G–A) of SNP rs330806505, presented significant associated with ST (p < 0.05). The allele G was favorable for thinner ST on rs330806505. Two homozygous genotypes of other SNPs presented extremely significant associated with corresponding traits (p < 0.01). Table 5 displays the genotype effects as well as additive and dominant effects.

In this study, we used SLAF-seq technology to obtain 227,921 highly consistent SNPs. These SNPs were used to calculate genetic parameters for seven carcass traits. The genomic heritability estimates based on SNPs for seven carcass traits ranged from 0.23 (LET) to 0.48 (DP) (Table 1), which was similar to the results of previous studies [7, 20]. The heritability estimates for CSL (0.26 ± 0.14) and CDL (0.40 ± 0.16) lower than the results of previous studies [20–23]. Estimated heritabilities of these seven carcass traits were low or moderate magnitude, showing that these carcass traits could be genetically improved. There were high positive genetic correlations (r = 0.95) between CSL with CDL (Table 3), which was similar to the results of a previous study [2].

QTLs identified for carcass traits

A total of 30 SNPs were identified as significant for seven carcass traits investigated, of which 9 SNPs had located in previously reported QTLs for pig carcass traits. Three adjacent SNPs on SSC10 (59-bp internal) were associated with DP, which was located in a previously reported QTL region related to DP [24]. Additionally, three SNPs on SSC8 (78.31–82.64Mb, 4.33-Mb) were found to be associated with CSL, which was located in a previously reported QTL region related to CSL [25].

Candidate genes for DP, RIB, and ST

On SSC10, four SNPs were significantly related to DP. Among the SNPs, the rs334674154 was located in the LYPLAL1 gene (lysophospholipase like 1). Two homozygous genotypes of the SNP presented significant association with DP (p < 0.01). The DP of the CC genotype was 2.82% higher than that of GG (Table 5). The results of GO annotation revealed that LYPLAL1 has hydrolase activity. Studies have found that common variants within or near the LYPLAL1 gene are related to a variety of human metabolic characteristics, including obesity and fatty liver. Knockout of the LYPLAL1 gene in mice would not lead to any significant phenotypic or metabolic physiological abnormalities in mice [26]. A recent study showed that the knockout of LYPLAL1 gene could lead to the reduction of AKT2 phosphorylation and glucose uptake in human adipocytes induced by insulin [27]. Another three adjacent SNPs (rs81321697, rs693030537 and rs343827180), were located 13.4 kb downstream of the EPC1 gene (enhancer of polycomb homolog 1). The EPC1 gene was found to be involved in skeletal muscle differentiation [28].

For RIB, the significant SNP (SSC4:38814467) was located with the MATN2 gene. Two homozygous genotypes of the SNP presented significant association with RIB (p < 0.01). The RIB of the AA genotype was 1.1 more than that of GG genotype (Table 5). MATN2 gene encodes matrix protein 2 (MATN2), an extracellular matrix (ECM) protein. ECM plays an important role in differentiation, maintenance and remodeling of tissues during development and regeneration. As a multiadhesion adaptor protein, MATN2 interacts with other ECM proteins and integrins to promote neurite outgrowth, Schwann cell migration, neuromuscular junction formation, skeletal muscle and liver regeneration and skin wound healing, and can also regulate multiple signal transduction pathways, including transforming growth factor beta, bone morphogenetic protein 7 and Smad signaling pathway, which are critical for muscle tissue regeneration [29]. Zhang et al. [30] found that MATN2 gene was widely distributed in many connective tissues of mice, including tracheal cartilage, developing bone and adult bone. Compared with normal healthy tissues, the expression level of MATN2 in developing articular cartilage tissue of osteoarthritis was significantly increased. This study suggested that MATN2 might play an important role in the communication between matrix and matrix and between matrix and cells, and it could be used as a potential biomarker of early osteoarthritis of articular cartilage. In view of the extensive expression of MATN2 in bone tissue and the role of its coding protein in tissue regeneration and osteoarthritis, we believe that MATN2 could be a potential candidate gene for RIB.

GWAS results showed that two SNPs on SSC4 were significantly associated with ST, and these two loci were located near zinc finger protein gene, in which the SNP rs330806505 was located within the ZFAT gene (zinc finger protein ZFAT). Genotype (G–A) of the SNP presented significant association (p < 0.05) with ST. The ST of genotype GG of SSC14:42805887 was 0.37 mm and 0.95 mm thinner than that of GA and AA, respectively. The rs318262569 was located 46.9 kb downstream and 84.8 kb upstream of ZBTB10 (zinc finger and BTB domain containing 10) and ZNF704 (zinc finger protein), respectively. The results of GO annotation showed that ZBTB10 and ZNF704 had the function of metal ion binding. Zinc finger protein (ZFP) refers to a class of proteins that contain short, stable proteins that can self fold to form a "finger" structure by binding zinc²⁺(Zn²⁺). Zinc is a cofactor for more than 1,000 kinds of enzymatic reactions [31, 32], and it is necessary for more than 2,000 kinds of transcription factors [33]. Therefore, zinc is associated with a variety of organic activities, such as development, differentiation and cell growth. The functions of ZFP have DNA interaction, RNA packaging, transcriptional activation, apoptosis regulation, protein folding and assembly, and lipid binding [34–36]. About 10% of human proteins can bind with zinc [37]. Skin is the third highest zinc content tissue in the body [38]. Zinc plays a very important role in skin. Keratinocyte (KC) in the skin accounts for about 97% of the skin epidermis. It can be divided into four layers according to the degree of differentiation and keratinization, including the basal layer, acanthous layer, granular layer and cuticle. Zinc is most abundant in the acanthous layer [39]. In vitro experiments showed that exposure of HaCaT KCs (HaCaT keratinocytes) to non-toxic concentrations of zinc could promote their survival and proliferation. A large number of experiments proved that zinc is necessary for keratinocytes to proliferate [40–42]. In addition, mutations or disorders of zinc transporters and zinc deficiency are closely related to human related skin diseases (including acquired acrodermatitis, necrotic transitional erythema, pellagra, etc.) [43]. To sum up, ZFP plays an important role in the proliferation and differentiation of skin by combining with zinc. Therefore, zinc finger protein genes ZFAT, ZBTB10 and ZNF704 could be considered as potential candidate genes for pig ST.

Candidate genes for CDL and CSL

Interestingly, one significant SNP (SSC18:52920094) was found to be associated with CSL and CDL. The SNP was located 19.1 kb upstream of the INHBA gene (inhibin beta A subunit). Two homozygous genotypes of the SNP presented significant association with CSL and CDL (p < 0.01). Genotype TT was 9.97 cm and 12.16 cm longer than genotype GG in CSL and CDL, respectively (Table 5). GO annotation results revealed that the gene had the function of growth factor activity and transforming growth factor beta receptor binding, and was involved in the negative regulation of cell proliferation. INHBA gene encodes INHBA, which is a member of TGF-β superfamily. The known TGF-β signal pathway is involved in cell proliferation and apoptosis [44], which is very important for female reproductive process [45]. A large number of studies have proved that INHBA gene had a significant impact on the fecundity of livestock [46–48]. At present, there is no report indicating that INHBA gene is associated with carcass length. Additionally, the SNP SSC10:18327923 was significantly associated with CDL, which was located with the SMYD3 gene (SET and MYND domain containing 3). Two homozygous genotypes of the SNP presented significant association with CDL (p < 0.01). The CDL of genotype CC was 8.3 cm longer than that of genotype AA (Table 5). GO annotation results showed that SMYD3 gene was involved in myotube cell development and negative regulation of protein kinase activity. SMYD3 encodes SMYD3, a histone methyltransferase, which has been found to be related to muscle volume and skeletal muscle atrophy [49]. The study found that SMYD3 gene might be a potential candidate gene for hind leg muscle atrophy of canine hip dysplasia [50]. Therefore, SMYD3 gene could be considered as a potential candidate gene for CDL.

Candidate genes for LEW and LET

Two adjacent SNPs (SSC9:12068165 and rs708657805) on SSC9 were detected to be related to LEW. Two homozygous genotypes of these two SNPs presented significant association with LEW (p < 0.01). The LEW of genotype AA for two SNPs was 0.79 cm and 0.81 cm wider than that of GG genotype, respectively (Table 5). These two loci were located in two overlapping genes, including PAK1 (p21 RAC1 activated kinase 1) and AQP11 (aquaporin 11). GO annotation results showed that PAK1 gene had the function of ATP binding and participated in mitotic cell cycle. PAK1 is a member of PAK family, which includes 6 members and is divided into 2 groups. The first group includes PAK1, PAK2 and PAK3, and the second group includes PAK4, PAK5 and PAK6. Among them, PAK1 is expressed in myoblasts and specifically activated during mammalian myoblastic differentiation. PAK1 is a regulator of myoblast differentiation in vitro and in vivo, and participates in the promyogenic lNcad/Cdo/Cdc42 signal pathway [51]. Studies have found that PAK1 and AQP1 were candidate genes for cooking loss [52]. In conclusion, PAK1 gene was more likely to be a candidate gene for the loci.

For LET, one significant SNP (rs1113904511) on SSC2 was located 34.4 kb and 85.0 kb downstream of SPTBN2 (spectrin beta chain, non-erythrocytic 2) and ACTN3 (α-actinin-3), respectively. GO analysis found that SPTBN2 gene had the function of actin binding and structural component of cytoskeleton (Table 5). De León et al. [53] believed that SPTBN2 gene might have a negative impact on the muscle development of cattle. Furthermore, GO analysis results showed that ACTN3 gene had the function of calcium ion binding, and participated in the regulation of the force of skeletal muscle contraction, positive regulation of skeletal muscle tissue growth, and positive regulation of skeletal muscle fiber development. ACTN3 encodes α-actinin-3 (ACTN3), which is only expressed in type II muscle fibers (fast, glycolytic), while α-actinin-2 presents in all skeletal muscle fibers [54, 55]. α-actinin performs static functions in skeletal muscle, maintains myofibril network and coordinates muscle contraction [55, 56]. Studies have found that the loss of ACTN3 gene function could change muscle metabolism in mice [57]. Since ACTN3 is only distributed in type II muscle fibers and its function in muscle, ACTN3 gene should be considered as a strong candidate gene for loin eye traits, including LET trait.

However, more pig populations must be used to verify these SNPs and candidate genes in future studies, and more pig biological tests must be carried out to confirm the roles and functions of these loci and genes.

In conclusion, we conducted a GWAS based on SLAF-seq for seven carcass traits in 223 four-way intercross pigs using MLM. A total of 30 significant SNPs, two QTLs on SSC8 and SSC10, and 11 candidate genes (LYPLAL1, EPC1, MATN2, ZFAT, ZBTB10, ZNF704, INHBA, SMYD3, PAK1, SPTBN2, and ACTN3) were identified as being associated with carcass traits of pigs. Overall, our study provided new evidences that multiple genes were involved in regulating carcass traits in pigs. These SNPs and corresponding candidate genes served as a biological foundation for improving carcass quality in swine breeding.

Animals, phenotypic collection and statistical analysis

As described previously [17–19], a population of four-way crossbred pigs was set up. To put it briefly, a total of 223 four-way crossbred animals (DSYLS) were created by mating 7 hybrid boars (Duroc × Saba, DS) and 37 hybrid sows (Yorkshire × (Landrace × Saba), YLS). The diets and environmental conditions for all test pigs were kept the same, and they all had unlimited access to food and water. 223 pigs were injected 10% potassium chloride injection rapidly intravenously at a rate of 0.3–0.5 milliliters per kilogram of body weight when they weighed 105.25 ± 15.75 kg on average. All animals were slaughtered after euthanasia. During this time, ear tissue samples were gathered.

Seven carcass traits, including DP, RIB, ST, CSL, CDL, LEW and LET were measured according to the method of “Technical regulation for testing of carcass traits in lean-type pig” (NYT825-2004).

The SAS (SAS Institute, Inc., Cary, NC) MEANS procedure was used to generate descriptive statistics for seven carcass traits under study, including the total number, minimum, maximum, mean, standard deviation, and coefficient of variation. Using the R package "ggpubr", the sample distribution was visualized as a frequency distribution histogram. The phenotypic correlation study was conducted using the R function "PerformanceAnalytics". Using GCTA software, the genomic heritabilities and genetic correlations for seven carcass traits were calculated [58].

SLAF library construction and high-throughput sequencing

As described previously, molecular markers throughout the entire pig genome were generated and SLAF library were constructed [17–19]. Meanwhile, the control genome (Oryza sativa spp. japonica; 374.30 Mb; http://rapdb.dna.affrc.go.jp/) was used to verify the reliability of the experimental process. Ultimately, SLAF-seq for each individual was conducted on an Illumina HiSeq 2500 platform (Illumina, Inc., San Diego, CA, USA) at Beijing Biomarker Technologies Corporation in Beijing, China.

Data processing and SNP calling

As described previously, data processing and SNP calling were further constructed [17–19]. Briefly stated, Dual-Index software [59] was used to further analyze the raw SLAF-seq data in order to produce the raw paired-end sequencing reads for each accession. Then, using BWA software [60], raw paired-end reads were aligned to the Sus scrofa reference genome (Sscrofa 11.1_102). Afterward, polymorphic SLAF tags were obtained. SNP loci were discovered based on data from polymorphic SLAF tags, primarily using GATK [61]. Clean reads that matched to the reference genome were used as the foundation for local realignments, base recalibration, and SNP detection with GATK [61]. The accuracy of the SNPs found using GATK was ensured by utilizing SAMtools [62]. The overlap of SNPs discovered by both GATK and SAMtools were selected as the reliable collection of SNPs to be submitted to the following analysis. Using PLINK 2 [63], a total of 227, 921 SNPs were acquired for further research by filtering for minor allele frequency (MAF: 0.05) and integrity (int: 0.8).

Genome-wide association study (GWAS)

227,921 total filtered SNPs detected from 223 accessions were used for GWAS. The mixed linear model (MLM) of GEMMA software [64] was employed for association analysis between carcass traits and reliable SNP markers. The MLM formula of GEMMA software was as follows:

y = Wα + Xβ + Zµ + ε

Where y was a n×1 vector of phenotype in crossbred pigs, X was a n×1 vector of marker genotypes, W was the matrix of population structure calculated by the ADMIXTURE software [65], and Z was the matrix of the kinship relationship calculated using GCTA software [66]. α was the vector of fixed effects; β was the marker effects; µ was random effects and ε was the vector of residuals. The association result could then be determined for each variation site. Bonferroni correction (BC) approach [64] was used for multiple tests in the study. Markers with adjusted − log10 (p) > 5 (control threshold) were deemed to be significant SNPs for carcass traits of interest [17–19]. Based on the number of filtered SNPs (n = 227,921), the threshold p-value for genome-wide 1% and 10% significance were 4.39 × 10^− 8 (0.01/227,921) and 4.39 × 10^− 7 (0.1/227,921), respectively. Using the R package “qqman”, the manhattan and quantile-quantile (Q-Q) plots of GWAS were drawn.

Identification and annotation of candidate genes

Based on the previous references [67, 68], the genes within 100 kb upstream or downstream of significant associated SNPs were deemed as potential candidate genes for carcass traits. The relevant information on these potential genes was downloaded from the Ensembl Sscrofa11.1 database (www.ensembl.org). Afterward, GO annotation of candidate genes was conducted using Gene Ontology Consortium (http://geneontology.org).

Association analysis between SNP marker genotypes and growth traits

Proc GLM in SAS 9.4 (SAS Institute, Inc., Cary, NC) was used to estimate the associations between SNP marker genotypes and carcass traits. Additive genetic effects were calculated by comparing the two homozygous genotypes in pairs, and the dominance effects were computed as the deviation of the heterozygote effect from the mean of the two homozygous genotypes.

GWAS

Genome-wide association study

SLAF-seq

Specific-locus amplified fragment sequencing

SNP

Single nucleotide polymorphism

QTL

Quantitative trait loci

Dressing percentage

RIB

Number of ribs

Skin thickness

CSL

Carcass straight length

CDL

Carcass diagonal length

LEW

Loin eye width

LET

Loin eye thickness

SSC

Sus scrofa chromosome

MAF

Minor allele frequency

PVE

Phenotypic variation explained

Q-Q plot

Quantile-quantile plot

MLM

Mixed linear model

Gene ontology

WGS

Whole-genome sequencing

Guanine-cytosine

Acknowledgments

Not applicable.

Author Contributions

S.L. and M.L. conceived and designed the experiments. X.W., Y.Y., Y.Z., S.W., Q.C., D.Y. and X.D. determined the phenotypic data and collected the sample. H.W. performed the experiment and processed and analyzed the data. Y.Y., Y.Z. S.W. and Q.C. assisted with the processing of data. H.W. and X.W. wrote the manuscript. M.L. and S.L subsequently revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Yunnan Swine Industry Technology System Program (2020KJTX0016), and Yunnan Province Important National Science & Technology Specific Projects (202104BI090022, 202102AE090039). These funding agencies had no role in the research design, data gathering, data analysis, or writing the manuscript.

Availability of data and materials

The genome sequencing raw data was deposited in NCBI’s SRA database https://www.ncbi.nlm.nih.gov/bioproject/?, Accession: PRJNA842083).

Ethics approval and consent to participate

All of the animals utilized in this study were handled and used in accordance with the guidelines for experimental animal care and use set forth by China's Ministry of Agriculture and Rural Affairs. The entire study was authorized by the Yunnan Agricultural University (YNAU, Kunming, China) ethics committee. We confirmed that the study was conducted in accordance with ARRIVE guidelines 2.0 [69].

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Burgos C, Latorre P, Altarriba J, Carrodeguas JA, Varona L, López-Buesa P. Allelic frequencies of NR6A1 and VRTN, two genes that affect vertebrae number in diverse pig breeds: a study of the effects of the VRTN insertion on phenotypic traits of a Duroc × Landrace-Large White cross. Meat Sci. 2015;100:150–5.
Jiang N, Liu C, Lan T, Zhang Q, Cao Y, Pu G, Niu P, Zhang Z, Li Q, Zhou J, et al. Polymorphism of VRTN Gene g.20311_20312ins291 was associated with the number of ribs, carcass diagonal length and cannon bone circumference in suhuai pigs. Anim (Basel). 2020;10(3):484.
Diao S, Huang S, Chen Z, Teng J, Ma Y, Yuan X, Chen Z, Zhang H, Li J, Zhang Z. Genome-wide signatures of selection detection in three South China indigenous pigs. Genes (Basel). 2019;10(5):346.
Blaj I, Tetens J, Preuss S, Bennewitz J, Thaller G. Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs. PLoS ONE. 2018;13(10):e0205576.
Liu X, Wang LG, Liang J, Yan H, Zhao KB, Li N, Zhang LC, Wang LX. Genome-wide association study for certain carcass traits and organ weights in a Large White × Minzhu intercross porcine population. J Integr Agr. 2014;13(12):2721–30.
Sato S, Uemoto Y, Kikuchi T, Egawa S, Kohira K, Saito T, Sakuma H, Miyashita S, Arata S, Kojima T, et al. SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population. BMC Genet. 2016;17:60.
Zhuang Z, Li S, Ding R, Yang M, Zheng E, Yang H, Gu T, Xu Z, Cai G, Wu Z, et al. Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations. PLoS ONE. 2019;14(6):e0218263.
Sun X, Liu D, Zhang X, Li W, Liu H, Hong W, Jiang C, Guan N, Ma C, Zeng H, et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE. 2013;8(3):e58700.
Wang WH, Wang JY, Zhang T, Wang Y, Zhang Y, Han K. Genome-wide association study of growth traits in Jinghai Yellow chicken hens using SLAF-seq technology. Anim Genet. 2019;50(2):175–6.
Yang X, Deng F, Wu Z, Chen SY, Shi Y, Jia X, Hu S, Wang J, Cao W, Lai SJ. A genome-wide association study identifying genetic variants associated with growth, carcass and meat quality traits in rabbits. Anim (Basel). 2020;10(6):1068.
Li F, Liu J, Liu W, Gao J, Lei Q, Han H, Yang J, Li H, Cao D, Zhou Y. Genome-wide association study of body size traits in Wenshang Barred chickens based on the specific-locus amplified fragment sequencing technology. J Anim Sci. 2021;92(1):e13506.
Melak S, Wang Q, Tian Y, Wei W, Zhang L, Elbeltagy A, Chen J. Identification and validation of marketing weight-related SNP markers using SLAF sequencing in male Yangzhou Geese. Genes (Basel). 2021;12(8):1203.
Xi Y, Xu Q, Huang Q, Ma S, Wang Y, Han C, Zhang R, Wang J, Liu H, Li L. Genome-wide association analysis reveals that EDNRB2 causes a dose-dependent loss of pigmentation in ducks. BMC Genomics. 2021;22(1):381.
Li Z, Wei S, Li H, Wu K, Cai Z, Li D, Wei W, Li Q, Chen J, Liu H, et al. Genome-wide genetic structure and differentially selected regions among Landrace, Erhualian, and Meishan pigs using specific-locus amplified fragment sequencing. Sci Rep. 2017;7(1):10063.
Qin M, Li C, Li Z, Chen W, Zeng Y. Genetic diversities and differentially selected regions between Shandong indigenous pig breeds and western pig breeds. Front genet. 2020;10:1351.
Tong SF, Zhu M, Xie R, Li DF, Zhang LF, Yang L. Genome-wide detection for runs of homozygosity analysis in three pig breeds from Chinese Taihu Basin and Landrace pigs by SLAF-seq data. J Integr Agr. 2022;21(11):3293–301.
Wang H, Wang X, Li M, Sun H, Chen Q, Yan D, Dong X, Pan Y, Lu S. Genome-wide association study of growth traits in a four-way crossbred pig population. Genes (Basel). 2022; 13(11): 1990.
Wang H, Wang X, Li M, Sun H, Chen Q, Yan D, Dong X, Pan Y, Lu S. Genome-wide association study reveals genetic loci and candidate genes for meat quality traits in a four-way crossbred pig population. Front Genet. 2023;14:1001352.
Wang H, Wang X, Yan D, Sun H, Chen Q, Li M, Dong X, Pan Y, Lu S. Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology. BMC Genomics. 2022;23(1):594.
Gilbert H, Bidanel JP, Gruand J, Caritez JC, Billon Y, Guillouet P, Lagant H, Noblet J, Sellier P. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits. J Anim Sci. 2007;85(12):3182–8.
Bereskin B, Steele NC. Estimates of genetic parameters for carcass measures of body composition and growth in swine. J Anim Sci. 1988;66(10):2498–507.
Chimonyo M, Dzama K. Estimation of genetic parameters for growth performance and carcass traits in Mukota pigs. Animal. 2007;1(3):317–23.
Gong H, Xiao S, Li W, Huang T, Huang X, Yan G, Huang Y, Qiu H, Jiang K, Wang X, et al. Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array. J Anim Breed Genet. 2019;136(1):3–14.
Harmegnies N, Davin F, De Smet S, Buys N, Georges M, Coppieters W. Results of a whole-genome quantitative trait locus scan for growth, carcass composition and meat quality in a porcine four-way cross. Anim Genet. 2006;37(6):543–53.
Anderson-Eklund L, Marklund L, Lundström K, Haley CS, Andersson K, Hansson I, Moller M, Andersson L. Mapping quantitative trait loci for carcass and meat quality traits in a Wild boar ×Large white. J Anim Sci. 1998;76(3):694–700.
Watson RA, Gates AS, Wynn EH, Calvert FE, Girousse A, Lelliott CJ, Barroso I. Lyplal1 is dispensable for normal fat deposition in mice. Dis Model Mech. 2017;10(12):1481–8.
Chen Z, Yu H, Shi X, Warren CR, Lotta LA, Friesen M, Meissner TB, Langenberg C, Wabitsch M, Wareham N. Functional screening of candidate causal genes for insulin resistance in human preadipocytes and adipocytes. Circ Res. 2020;126(3):330–46.
Doyon Y, Selleck W, Lane WS, Tan S, Côté J. Structural and functional conservation of the NuA4 histone acetyltransferase complex from yeast to humans. Mol Cell Biol. 2004;24(5):1884–96.
Korpos E, Deak F, Kiss I. Matrilin-2, an extracellular adaptor protein, is needed for the regeneration of muscle, nerve and other tissues. Neural Regen Res. 2015;10(6):866–9.
Zhang S, Peng J, Guo Y, Javidiparsijani S, Wang G, Wang Y, Liu H, Liu J, Luo J. Matrilin-2 is a widely distributed extracellular matrix protein and a potential biomarker in the early stage of osteoarthritis in articular cartilage. Biomed Res Int. 2014; 2014: 986127.
Andreini C, Bertini I, Cavallaro G. Minimal functional sites allow a classification of zinc sites in proteins. PLoS ONE. 2011;6(10):e26325.
Andreini C, Bertini I. A bioinformatics view of zinc enzymes. J Inorg Biochem. 2012;111:150–6.
Berg JM, Shi Y. The galvanization of biology: a growing appreciation for the roles of zinc. Science. 1996;271(5252):1081–5.
Laity JH, Lee BM, Wright PE. Zinc finger proteins: new insights into structural and functional diversity. Curr Opin Struct Biol. 2001;11(1):39–46.
Klug A. The discovery of zinc fingers and their applications in gene regulation and genome manipulation. Annu Rev Biochem. 2010;79:213–31.
Fukada T, Yamasaki S, Nishida K, Murakami M, Hirano T. Zinc homeostasis and signaling in health and diseases: zinc signaling. J Biol Inorg Chem. 2011;16(7):1123–34.
Andreini C, Banci L, Bertini I, Rosato A. Counting the zinc-proteins encoded in the human genome. J Proteome Res. 2006;5(1):196–201.
Jackson MJ. Physiology of zinc: general aspects. Zinc in Human Biology. 1989: 1–14.
Inoue Y, Hasegawa S, Ban S, Yamada T, Date Y, Mizutani H, Nakata S, Tanaka M, Hirashima N. ZIP2 protein, a zinc transporter, is associated with keratinocyte differentiation. J Biol Chem. 2014;289(31):21451–62.
Hsu DJ, Daniel JC, Gerson SJ. Effect of zinc deficiency on keratins in buccal epithelium of rats. Arch Oral Biol. 1991;36(10):759–63.
Chai F, Truong-Tran AQ, Evdokiou A, Young G, Zalewski P. Intracellular zinc depletion induces caspase activation and p21Waf1/Cip1 cleavage in human epithelial cell lines. J Infect Dis. 2000;182(Supplement1):85–S92.
Wilson D, Varigos G, Ackland ML. Apoptosis may underlie the pathology of zinc-deficient skin. Immunol Cell Biol. 2006;84(1):28–37.
Ogawa Y, Kinoshita M, Shimada S, Kawamura T. Zinc and skin disorders. Nutrients. 2018;10(2):199.
Massagué J. TGFβ signalling in context. Nat Rev Mol Cell Biol. 2012;13(10):616–30.
Chang H, Brown CW, Matzuk MM. Genetic analysis of the mammalian transforming growth factor-beta superfamily. Endocr Rev. 2002;23(6):787–23.
Hou J, An X, Li G, Wang Y, Song Y, Cao B. Exploring polymorphisms and their effects on reproductive traits of the INHA and INHβA genes in three goat breeds. J Anim Sci. 2012;83(4):273–8.
Li X, Ye J, Han X, Qiao R, Li X, Lv G, Wang K. Whole-genome sequencing identifies potential candidate genes for reproductive traits in pigs. Genomics. 2020;112(1):199–206.
Bao Y, Yao X, Li X, Ei-Samahy M, Yang H, Liang Y, Liu Z, Wang F. INHBA transfection regulates proliferation, apoptosis and hormone synthesis in sheep granulosa cells. Theriogenology. 2021;175:111–22.
Proserpio V, Fittipaldi R, Ryall JG, Sartorelli V, Caretti G. The methyltransferase SMYD3 mediates the recruitment of transcriptional cofactors at the myostatin and c-Met genes and regulates skeletal muscle atrophy. Genes Dev. 2013;27(11):1299–312.
Bartolomé N, Segarra S, Artieda M, Francino O, Sánchez E, Szczypiorska M, Casellas J, Tejedor D, Cerdeira J, Martínez A. A genetic predictive model for canine hip dysplasia: integration of genome wide association study (GWAS) and candidate gene approaches. PLoS ONE. 2015;10(4):e0122558.
Joseph GA, Lu M, Radu M, Lee JK, Burden SJ, Chernoff J, Krauss RS. Group I Paks promote skeletal myoblast differentiation in vivo and in vitro. Mol Cell Biol. 2017;37(4):e00222–00216.
Ji J, Zhou L, Huang Y, Zheng M, Liu X, Zhang Y, Huang C, Peng S, Zeng Q, Zhong L, et al. A whole-genome sequence based association study on pork eating quality traits and cooking loss in a specially designed heterogeneous F6 pig population. Meat Sci. 2018;146:160–7.
De León C, Martínez R, Rocha JF, Darghan AE. Selection of genomic regions and genes associated with adaptation and fertility traits in two Colombian creole cattle breeds. Genet Mol Res. 2021;20(3):GMR18882.
Mills M, Yang N, Weinberger R, Vander Woude DL, Beggs AH, Easteal S, North K. Differential expression of the actin-binding proteins, alpha-actinin-2 and – 3, in different species: implications for the evolution of functional redundancy. Hum Mol Genet. 2001;10(13):1335–46.
MacArthur DG, North KN. A gene for speed? The evolution and function of alpha-actinin-3. BioEssays. 2004;26(7):786–95.
Lek M, Quinlan KG, North KN. The evolution of skeletal muscle performance: gene duplication and divergence of human sarcomeric α-actinins. BioEssays. 2010;32(1):17–25.
MacArthur DG, Seto JT, Raftery JM, Quinlan KG, Huttley GA, Hook JW, Lemckert FA, Kee AJ, Edwards MR, Berman Y, et al. Loss of ACTN3 gene function alters mouse muscle metabolism and shows evidence of positive selection in humans. Nat Genet. 2007;39(10):1261–5.
Lee S, Yang J, Goddard M, Visscher P, Wray N. Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012;28(19):2540–2.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79(17):5112–20.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, et al. The Genome Analysis Toolkit: a mapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.
Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–4.
Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19(9):1655–64.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.
Wang RJ, Gao XF, Yang J, Kong XR. Genome-wide association study to identify favorable SNP allelic variations and candidate genes that control the timing of Spring bud flush of Tea (Camellia sinensis) using SLAF-seq. J Agric Food Chem. 2019;67(37):10380–91.
Yin T, Halli K, König S. Direct genetic effects, maternal genetic effects, and maternal genetic sensitivity on prenatal heat stress for calf diseases and corresponding genomic loci in German Holsteins. J Dairy Sci. 2022;105(8):6795–808.
Percie du Sert N, Ahluwalia A, Alam S, Avey MT, Baker M, Browne WJ, Clark A, Cuthill IC, Dirnagl U, Emerson M, et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 2020;18(7):e3000411.

No competing interests reported.

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published 09 Jun, 2024

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Genome-Wide Identification of Quantitative Trait Loci and Candidate Genes for Seven Carcass Traits in a Four-Way Intercross Porcine Population

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Journal Publication

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Abstract

Figures

Background

Results

Phenotype description and correlation among growth traits

Identification of SLAFs and SNPs

Genome-wide association study and identification of candidate genes

DP, RIB, and ST

CSL, CDL, LEW and LET

Comparison with previously mapped QTL in pigs

Functional annotation of candidate genes

Association analysis between SNP marker genotypes and carcass traits

Discussion

QTLs identified for carcass traits

Candidate genes for DP, RIB, and ST

Candidate genes for CDL and CSL

Candidate genes for LEW and LET

Conclusions

Materials and Methods

Animals, phenotypic collection and statistical analysis

SLAF library construction and high-throughput sequencing

Data processing and SNP calling

Genome-wide association study (GWAS)

Identification and annotation of candidate genes

Association analysis between SNP marker genotypes and growth traits

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1