Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations

Abstract

Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes (GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.

Background

The pig, which has abundant phenotypes, is a model animal that not only intimately resembles man in physiology, anatomy, and genetic architecture[1] but also contributes to meat consumption[2]. In the past decades, lean pigs account for a large share of the pork consumption market, and the traits that affect swine growth have been important breeding targets in the pig industry[2]. Loin muscle area (LMA) and loin muscle depth (LMD) play important roles in the determination of growth traits (e.g., back fat and carcass lean rate)[3]. Thus, prediction of growth traits by investigating LMA and LMD is important. It is generally accepted that both of nutrition and growth environment of the pig can affect the growth and development of loin muscle[4, 5], and muscle growth may be subject to number of muscle fibers in swine muscle composition[6]. However, genetics is widely considered to be the most important factor for the loin muscle development. Hence, it is the primary means to achieve this goal in terms of genetics, such as finding causal genes or mutations that affect muscle development[7, 8]. LMA and LMD are heritable and moderate-heritability above traits, where the estimated heritabilities range from 0.35 to 0.47[9–11], showing that these two traits can be improved via genetic approach.

To dissect the molecular basis of the divergent phenotypes of LMA and LMD among different individuals, researchers have selected various populations[12–14] using multiple strategies[15, 16]. For instance, using exiguous microsatellite markers across the pig entire genome, genome scans have detected a limited number of quantitative trait loci (QTLs) for LMA[17] and LMD[18]. Despite some progress in the traditional genetic improvement of carcass and growth-related traits, inadequacies and challenges remain to be overcome to elucidate the biological mechanisms of complex traits[19].

To date, 27,878 QTLs associated with 718 traits have been reported in the pig QTL database (http://www.animalgenome.org/cgi-bin/QTLdb/SS/index, Jul 24, 2018). Among them, 330 and 41 QTLs are associated with LMA and LMD, respectively. These discoveries have provided a certain number of molecular markers to the breeding for swine LMA and LMD. For instance, Edwards et al.[20] used 510 Duroc × Pietrain F2 animals genotyped for 124 microsatellite markers that are uniformly spaced across the genome and detected a significant QTL that affects LMA and related backfat trait. A similar approach was employed in a study related to LMD[21]. The researchers detected a significant QTL on Sus scrofa chromosome (SSC) 11 that explains approximately 6% of the phenotypic variance of LMD. Although many QTLs have been detected, these findings are still deficient due to the inferior power of QTL mapping and poor resolution in the majority of QTLs[22]. Another factor that cannot be ignored is that the confidence intervals are commonly in the order of ~30 cM, and such a large region may contain several genes, which hamper the optimization of plausible candidate genes[23, 24]. With the development of genomic tools and high-density single nucleotide polymorphism (SNP) marker panels in recent years, genome-wide association studies (GWAS), a powerful strategy in detecting genetic variants associated with complex traits, have been widely used in humans[25], and livestock[26, 27]. In pigs, GWAS have been conducted to explore the genetic variants and genetic architecture on a great diversity of importantly economic traits[28]. These studies successfully identified causal mutation and verified the causal genes[29]. For LMA and LMD, GWAS have also been performed to detect the underlying genetic mechanisms[4, 30], but the sample size of these studies is smaller compared with that of the current study. Although GWAS have obtained remarkable findings[31], the use of association studies in the genetics of human and other species is bound by population structure and limited sample size[32]. Therefore, to enhance the detection power and verify the significant discoveries identified in single-population GWAS, meta-analysis of GWAS was widely used in human[33] and livestock[34] owing to the powerful statistical efficiency.

In this study, we performed a GWAS, based on phenotype records using Porcine SNP50 Beadchip in two Duroc pig populations with different genetic contexts (American origin and Canadian origin). The American and Canadian original populations comprised 3916 and 2127 individuals, respectively. Moreover, a meta-analysis combining the full GWAS results from the two populations was conducted and uncovered additional significant SNPs. This study aimed to identify genomic regions and plausible candidate genes that lead to the phenotypic diversity of LMA and LMD. This study can serve as a basis to elucidate the molecular architecture underlying muscle development in pigs, humans, and other mammals.

Materials and methods

Ethics statement

All procedures involving animals in this study met the guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. The ethics committee of South China Agriculture University (SCAU) (Guangzhou, China) approved this study (Approval number SCAU#0017).

Animals and phenotype

In this study, experimental animals, including American and Canadian origin Duroc populations, were raised in the Wens Foodstuff Group Co., Ltd. (Guangdong, China) following the same feeding standards. Briefly, a total of 6043 pigs of these two Duroc populations were used in the present study, including 3916 American origin pigs and 2127 Canadian origin pigs. The pigs of the American original population were born between 2013 and 2017, and the other was born between 2015 and 2017. All pigs of the two populations sustained uniform feeding conditions and fine fodder and consistent management during the fattening period from 30 to 100 kg live weight to minimize the impact of non-genetic factors. Phenotypic records included LMA and LMD. For American and Canadian populations, phenotypes of LMA and LMD were collected by practiced investigators from the 10th-rib to 11th-rib of pigs at the weight of 100 ± 5 kg by an Aloka 500V SSD B ultrasound (Corometrics Medical Systems, USA), which employed diagnostic ultrasound system and transducers to acquire high resolution images, and computer software was used to ascertain the LMA[11].

Genotyping and quality control

Genomic DNA was extracted from ear tissue following the traditional phenol/chloroform method. The quality and quantity of the DNA samples were measured with a NanoDropTM 2000 (Thermo Fisher Scientific, Waltham, MA, USA) as described by Wang et al.[35]. In brief, all DNA samples were assessed by ratios of light absorption (A260/A280) in the range of 1.8–2.0 and diluted to a final concentration of 50ng/μL. Genotyping was conducted using Geneseek Porcine 50K SNP chip (Neogen, Lincoln, NE, United States), which contains 50,703 SNP markers across the entire genome. Quality control (QC) procedures were carried out using PLINK v1.07 software[36] with the parameters of animal call rates > 0.95, SNP call rates > 0.9, minor allele frequencies > 0.01, and P > 10⁻⁶ for Hardy–Weinberg equilibrium test. SNPs with no position information and located on the sex chromosomes were also removed. Notably, the two populations follow the same quality control criteria. After QC, a final set of 38,790 and 35,845 informative SNPs and 3916 and 2127 individuals were retained for single-trait GWAS in the American and Canadian original Duroc populations, respectively. Furthermore, in the meta-analysis of GWAS, a common set of 40,030 differently eligible SNPs for LMA and LMD across the populations were later used.

Population structure analysis

In consideration of the different genetic contexts of the two Duroc populations, principal component analysis (PCA) was performed to detect the possible population stratification using GCTA tool[37]. We initially conducted a blended PCA by combing the two populations with the first two eigenvectors belonging to each population and then drew a PCA plot. The top five eigenvectors were embedded subsequently as covariates in the association analysis model to correct and decrease the confounding effect of population structure in the massive sample association study herein[38]. Meanwhile, genomic control, a prevailing method for solving stratification, was also performed to acquire the genomic inflation factors (λ). Then, a quantile-quantile (Q-Q) plot was constructed using R script to assess the influence of population stratification on the GWAS.

Single-trait GWAS

The entire processes in the single-trait GWAS were performed using the software of genome-wide efficient mixed-model Analysis (GEMMA)[39] and genome-wide complex trait analysis (GCTA)[37]. The genomic relatedness matrix between individuals within each population was used to perform single-trait GWAS by GEMMA. GEMMA provides two alternative options (the centered genotypes or standardized genotypes) to estimate the relatedness matrix from genotypes. Although the choice of which way to use relies on the underlying genetic mechanism of a given trait, the two matrices exert negligible effects[39]. In the present study, the standardized option (“-gk 2”) was used to estimate a n × n relatedness matrix. GWAS was performed independently for LMA and LMD in each Duroc pig population with a univariate linear mixed model fitted in GEMMA. The estimated n × n standardized relatedness matrices (K) between the individuals within populations as well as the calculated top five eigenvectors were used in the model as follows:

$$\mathrm{y}=\mathrm{W}\mathrm{\alpha }+\mathrm{X}\mathrm{\beta }+\mathrm{u}+\mathrm{\epsilon }$$

where y is the vector of phenotypic values; W is the incidence matrix of covariates (fixed effects) that comprise the top five eigenvectors derived from PCA, sex and live weight; α is the vector of corresponding coefficients including the intercept; X is the vector of marker genotypes; β is the corresponding effect size of the marker; u is an m × 1 vector of random effects, with u ~ MVNm(0, λ τ⁻¹K); and ε is the vector of random residuals, with ε ~ MVNn(0, τ⁻¹In). τ⁻¹ is the variance of the residual errors; λ refers to the ratio between the two variance components; K is a known n × n relatedness matrix calculated in previous step; and I refers to the identity matrix. MVNn denotes the n-dimensional multivariate normal distribution. Bonferroni correction was applied to determine the genome-wide and chromosome-wide significance thresholds, which were defined as 0.05/N and 1/N, respectively. N is the number of filtered SNPs for each trait of American and Canadian original Duroc populations.

Meta-analysis

A meta-analysis was performed across the different genetic contexts of Duroc pig populations for a trait on the strength of single-trait analysis using METAL software[40]. METAL converts the direction of effect and the P value observed from each study ί into Z-scores. The key statistic formulae for the approach used herein was previously described[40, 41]. Notably, 40,030 common SNPs were detected in the American and Canadian origin populations. Bonferroni correction was applied to determine the genome-wide significance threshold (0.05/40,030) and the chromosome-wide significance threshold (1/40,030).

SNP-based heritability estimation and contribution to phenotypic variance

The GCTA software was used to estimate the variance explained by genome-wide SNPs (SNP-based heritability) for each trait in the two populations via the restricted maximum likelihood approach, relying on the genomic relationship matrix estimated from all the autosomal SNPs. To partition the phenotypic variance onto each of the autosomes, we also appointed a model to investigate the contributions by each chromosome as described by Yang et al.[37]. Furthermore, the GCTA tool was used to calculate the phenotypic variance explained by each suggestive or significant SNP, which was detected by single-trait GWAS. Notably, the fixed effects including the first five PCA eigenvectors, sex and live weight were embedded as covariates to adjust the population stratification and latent relatedness[42].

LD analysis and conditional analysis

Many SNPs, which were located on several narrow regions, were associated with LMA and LMD in the Canadian original Duroc population, and they were detected both by single-trait and meta-analysis GWAS. To further verify the linkage disequilibrium among these SNPs and detect candidate regions connected with the two traits, we employed PLINK v1.07[36] and Haploview v4.2[43] for haplotype block analysis. We also performed conditional analysis to determine the potential associated SNPs that might be masked in the putative region by intense signal. In brief, the peak SNP or the one that explained the largest phenotypic variance was selected to perform a univariate linear mixed model via converting its genotypes into covariates[28, 44]. Keeping that in mind, the other covariates used previously were consistent. Regional association plots were generated using the R package v3.4.3.

Candidate gene search and functional annotation

The functional genes were searched on the strength of Sscrofa 11.1 genome version (http://asia.ensembl.org/Sus_scrofa/Info/Index). Gene sets with the criteria of including or nearest the significant SNPs were selected to conduct KEGG pathways and Gene Ontology (GO) analysis. Pathway analysis and GO analysis was conducted using the DAVID bioinformatics resource (https://david.ncifcrf.gov/, Aug 6, 2018, version 6.8). Fisher’s exact test was used to assess the significance of the enriched terms with P < 0.05 and detect the genes involved in biological processes[2, 45]. In addition, GeneCards (http://www.genecards.org/) and the NCBI (https://www.ncbi.nlm.nih.gov/) database were used to query gene functions and determine promising candidates.

Results

Phenotype and heritability analysis

We acquired four phenotypic data from two Duroc populations of 6043 pigs, which met the quality control standards, including LMA and LMD. Descriptive statistics of phenotypes and the SNP-based heritability of LMA and LMD are shown in Table 1. The coefficients of variation (CV) of LMA and LMD in the American and Canadian original populations ranged from 7.10% to 8.94% and 7.89% to 11.22%, respectively. Results showed that different scopes of CV values indicated different levels of sample variability between the two pig populations. The SNP-based heritability of each trait was estimated using a univariate model, where the phenotypic variance was partitioned into the variance explained by genetic components and residual variance with the genetic relationship matrix. The genomic heritabilities of the traits in the two populations are moderate-heritability, ranging from 0.37 to 0.39, showing that genetic technology could effectively promote the genetic improvement of these traits.

Table 1. Descriptive statistics and heritability for loin muscle area (LMA) and loin muscle depth (LMD) traits in two Duroc populations.

Traits1	Source	Unit	N	Mean (±SD)	Min	Max	C.V./%	h2 (±SE)
LMA	American origin	cm2	3916	42.28±3.78	26.40	54.80	8.94	0.37±0.02
LMD		mm	3916	52.36±3.72	37.30	65.00	7.10	0.39±0.02
LMA	Canada origin	cm2	2127	39.14±4.39	27.30	56.60	11.22	0.37±0.03
LMD		mm	2127	47.91±3.78	37.00	61.20	7.89	0.38±0.03

Number (N), Mean (standard deviation), Minimum (Min), Maximum (Max), coefficient of variation (C.V.), heritability (standard error) of LMA and LMD traits values.

¹Loin muscle area (LMA); Loin muscle depth (LMD)

Single-trait GWAS

The effects of potential population stratification were corrected using PCA. In this study, the two Duroc pig populations were clearly identified via PCA, as shown in Fig 1. Although almost all individuals were classified into one cluster within each population, the plot implied a slight population stratification. The Manhattan and QQ plots for LMA and LMD are shown in Fig 2 and S1 Fig. The genomic inflation factor (λ) at each trait ranged from 1.03 to 1.10, and none of the Q-Q plots showed any sign of inflation, indicating consistent consequence with PCA.

Fig 1. Population structure for two Duroc populations.

pc1 = first principal component; pc2 = second principal component. The first two PCs derived from the genomic kinship matrix were extracted to assess the population structure. In this study, the two Duroc pig populations can be clearly identified via principal component analysis.

Fig 2. Manhattan plots of genome-wide association studies for LMA and LMD in the two Duroc pig populations.

The x-axis represents the chromosomes, and the y-axis represents the -log10(P-value). Different colors indicate various chromosomes. The solid and dashed lines indicate the 5% genome-wide and chromosome-wide Bonferroni corrected thresholds, respectively. The thresholds of the genome-wide and the chromosome-wide levels are P < 1.29E-06 and P < 2.58E-05 (American population); P < 1.39E-06 and P < 2.79E-05 (Canadian population); P < 1.25E-06 and P < 2.50E-05 (meta-analysis). Manhattan plots on the left are shown for the single-trait association analysis in (A) American original population, (B) Canadian original population, and (C) meta-analysis for LMA. Manhattan plots on the right are shown for the single-trait association analysis in (D) American original population, (E) Canadian original population, and (F) meta-analysis for LMD.

We herein performed four separate association analyses for LMA and LMD in the two populations using a univariate linear mixed model. Multiple significant SNPs associated with LMA and LMD for the American and Canadian original populations of Duroc breed are listed in Tables 2 and 3, respectively. In summary, 24 SNPs were detected on SSC6, 7, 12, 16, and 18. Notably, eight SNPs showed significant association with more than one trait, suggesting that they exert pleiotropic effects on multiple carcass traits.

Table 2. Significant SNP and associated genes for LMA in the single-trait association analysis.

SSC1	SNP ID	Position(bp)2	P value3	Population	Minor/major Allele	MAF4	EPV/%5	Candidate gene	Distance/bp6
6	Hal_2	47357966	6.05E-07	American	T/C	0.094	1.18	RASGRP4	24726
	12784654	54079560	9.84E-07	American	T/C	0.094	1.18	FGF21	-3046
7	ALGA0040260	30342161	8.40E-06	Canadian	A/G	0.216	1.77	NUDT3	-1092
	ALGA0040263	30356985	8.48E-06	Canadian	G/A	0.216	1.77	NUDT3	within
	ASGA0032536	30476054	1.06E-05	Canadian	C/A	0.217	1.74	PACSIN1	within
	ASGA0032526	30497305	1.06E-05	Canadian	C/T	0.217	1.74	PACSIN1	within
	INRA0024788	30317219	1.35E-05	Canadian	T/C	0.217	1.72	ENSSSCG00000032242	-3195
	DRGA0007345	21886880	1.39E-05	Canadian	G/A	0.362	1.74	ENSSSCG00000032646	474
16	WU_10.2_16_35829257	33757844	5.75E-06	Canadian	C/A	0.328	1.83	SNX18	-18902
	ASGA0072998	33589982	1.12E-05	Canadian	T/C	0.328	1.81	ARL15	24383
	ALGA0090190	33515233	1.34E-05	Canadian	A/G	0.328	1.78	ARL15	within
	ALGA0090184	33467573	1.35E-05	Canadian	G/A	0.328	1.78	ARL15	within
	MARC0103451	33493718	1.35E-05	Canadian	T/C	0.328	1.78	ARL15	within
	MARC0074818	33559933	1.35E-05	Canadian	T/C	0.328	1.78	ARL15	within
	ASGA0073002	33636702	1.57E-05	Canadian	T/G	0.328	1.79	ARL15	71103
	ALGA0090276	34334858	1.76E-05	Canadian	A/G	0.303	1.79	GPX8	-650
	ALGA0090273	34364519	2.07E-05	Canadian	A/G	0.303	1.78	MCIDAS	-13441
	ALGA0090242	34031872	2.26E-05	Canadian	C/T	0.303	1.78	ENSSSCG00000016898	-9810

¹Sus scrofa chromosome.

²SNP position in Ensembl.

³The bold data in this column represent the significant SNP surpass the genome-wide significant threshold, otherwise at the chromosome-wide significant level.

⁴Minor allelic frequency.

⁵The proportion of the phenotypic variance explained by significant SNP.

⁶The location of SNP in upstream/downstream of the nearest gene.

Table 3. Significant SNP and associated genes for LMD in the single-trait analysis.

SSC1	SNP ID	Position(bp)2	P value3	Population	Minor/major Allele	MAF4	EPV/%5	Candidate gene	Distance/bp6
6	Hal	47357966	3.88E-06	American	T/C	0.094	1.04	RASGRP4	24726
	12784654	54079560	4.97E-06	American	T/C	0.094	1.05	FGF21	-3046
7	MARC0061142	30716800	1.91E-05	American	T/C	0.158	0.61	SNRPC	within
12	ALGA0065784	26376192	2.98E-06	American	T/C	0.27	1.2	ENSSSCG00000034682	-555
18	WU_10.2_18_58542037	53382393	9.98E-06	American	T/C	0.165	0.59	INHBA	477180
7	ALGA0040263	30356985	4.96E-09	Canadian	G/A	0.216	2.48	NUDT3	within
	ALGA0040260	30342161	5.47E-09	Canadian	A/G	0.216	2.48	NUDT3	-1092
	ASGA0032536	30476054	7.05E-09	Canadian	C/A	0.217	2.45	PACSIN1	within
	ASGA0032526	30497305	7.05E-09	Canadian	C/T	0.217	2.45	PACSIN1	within
	INRA0024788	30317219	8.60E-09	Canadian	T/C	0.217	2.43	ENSSSCG00000032242	-3195
	DRGA0007345	21886880	1.39E-05	Canadian	T/C	0.105	2.05	ENSSSCG00000032646	474
	ALGA0039866	26451150	8.72E-06	Canadian	G/A	0.362	2.15	TINAG	within
	ALGA0039447	21940478	1.57E-05	Canadian	T/C	0.361	1.98	ZNF165	within
	M1GA0027226	26125115	2.21E-05	Canadian	A/G	0.274	1.67	FAM83B	within

¹Sus scrofa chromosome.

²SNP position in Ensembl.

³The bold data in this column represent the significant SNP surpass the genome-wide significant threshold, otherwise at the chromosome-wide significant level.

⁴Minor allelic frequency.

⁵The proportion of the phenotypic variance explained by significant SNP.

⁶The location of SNP in upstream/downstream of the nearest gene.

Five SNPs were significantly associated with LMA and LMD in the American original Duroc population, of which two common SNPs were associated with LMA and LMD at different significance levels (Tables 2 and 3). These two SNPs were located close to genes RAS guanyl releasing protein 4 (RASGRP4) (SNP on SSC6: 47.35Mb) and fibroblast growth factor 21 (FGF21) (SNP on SSC6:54.07Mb). They both explained 1.18% of the phenotypic variance of LMA. Three SNPs were associated with LMD at the chromosome-wide significance level. Of these SNPs, the most significant was ALGA0065784 (SSC12: 26.37Mb) and was located close to gene ENSSSCG00000034682, and it explained 1.20% of the phenotypic variance of LMD.

For the Canadian original Duroc pig population, 19 SNPs were associated with LMA and LMD. As shown in Tables 2 and 3, six common SNPs were associated with these two traits at different significance levels. Among them, six SNPs associated with LMA reached the chromosome-wide significance level (P < 2.79E-05), and five SNPs associated LMD reached the genome-wide significance level (P < 1.39E-06). The most significant SNP, ALGA0040260, was located at 30.34Mb and was near the gene Nudix hydrolase 3 (NUDT3) on SSC7. This SNP explained 1.77% of the phenotypic variance of LMA. As for LMD, the peak SNP in this region, ALGA0040263, was located at 30.35 Mb within the NUDT3 gene on SSC7 and contributed 2.48% of the phenotypic variance of LMD. The remaining 13 SNPs reached the chromosome-wide significance level, of which 10 were associated with LMA (all located on SSC16) and three with LMD (all located on SSC7). The most significant SNP of the 10 SNPs, WU_10.2_16_35829257 contributed 1.83% of the phenotypic variance of LMA. This SNP was not located within any gene; nonetheless, it was in close proximity to gene sorting nexin 18 (SNX18). In addition, the remaining SNPs, which were close to each other on SSC16 between 33.46 Mb and 34.37 Mb, were located within or near several genes, such as ADP ribosylation factor like GTPase 15 (ARL15), glutathione peroxidase 8 (putative) (GPX8), and multiciliate differentiation and DNA synthesis associated cell cycle protein (MCIDAS), and a not openly reported gene (ENSSSCG00000016898) (Table 2).

Meta-analysis across populations by trait

We conducted a meta-analysis of GWAS for each trait across these two Duroc pig populations. The meta-analysis integrates all association signals from the two populations. Thus, when the two populations show consistent association directions with the traits of interest, the detection power for these traits improve via the meta-analysis. In the current study, 68 SNPs associated with LMA and LMD were identified in the across-population meta-analysis. Among them, 56 SNPs were associated with LMA, and 11 of these 56 SNPs reached the genome-wide significance level (P < 1.25E-06). In addition, 37 SNPs were associated with LMD, and 15 of these 37 SNPs reached the genome-wide significance level (P < 1.25E-06) (Fig 2; S1 Table). Among these 68 SNPs, 25 common SNPs were associated with LMA and LMD simultaneously. In addition to all the significant SNPs, 17 significant SNPs that were detected in the single-trait association analysis were confirmed and 51 loci with novel candidate genes were identified in the meta-analysis. To evaluate whether SNPs associated with LMA and LMD traits in present study replicate any previously known QTLs, we searched the pigQTLdb based on SNP locations (S2 Table). Although a large number of QTLs that were mainly detected via QTL mapping using sparse microsatellite markers were identified for LMA and LMD in multiple pig breeds, few QTLs were reported within a narrow interval using the GWAS strategy. Compared with previous results, our findings revealed many loci in several confined genetic regions and highlighted two QTLs that influence LMA and LMD.

Haplotype block analysis and conditional analysis

As mentioned above, among the 19 significant SNPs in the Canadian original population, we found eight significant SNPs on SSC16 in close proximity to be associated with LMA and detected a haplotype block that spanned 709 kb (Fig 3A) containing the eight SNPs. In particular, we observed five significant SNPs associated with LMA and LMD, and these significant SNPs were situated in a 283 kb block on SSC7 (Fig 3B). To examine whether linkage disequilibrium (LD) masks certain independently associated SNPs and/or potential signals, we performed a step-wise conditional analysis. Then we fitted the peak SNP ALGA0040260 on SSC7 that was significantly associated with LMA and LMD in the Canadian original population into the univariate linear mixed model as a covariate to inspect these associations. For LMA, the P values of previous significant SNPs that had strong LD status with lead SNP ALGA0040260 decreased below the minimum threshold line (Fig 4A and 4B). The same pattern was found for the same peak SNP for LMD trait (Fig 4C and 4D). Analogously, the peak SNP WU_10.2_16_35829257 on SSC16 that was merely significantly associated with LMA in the Canadian original population was also fitted into the model as a covariate in the same manner. The same pattern was also observed as shown in Fig 5A and 5B.

Fig 3.

Linkage disequilibrium (LD) blocks in the significant region on SSC16 (A) and SSC7. (B). LD blocks are marked with triangles. Values in boxes are LD (r²) between SNP pairs and the boxes are colored according to the standard Haploview color scheme. The complete red boxes with no numbers indicated that SNP pairs have complete linkage disequilibrium. Annotated genes in the chromosomal region were retrieved from the Ensemble genome browser (www.ensembl.org/Sus_scrofa/Info/Index).

Fig 4. Regional association plot of the primary signal (ALGA0040260) associated with LMA and LMD at SSC7.

For each plot, the -log10(observed P-values) of SNPs (y-axis) are presented according to their chromosomal position (x-axis). The red line and darkblue line indicate the genome-wide significance level (P < 1.39E-06) and the chromosome-wide significance level (P < 2.79E-05), respectively. The primary SNPs are denoted by large blue triangles. SNPs are represented by colored rhombi according to the target SNP with which they were in strongest LD. The left panel of the figure shows the association results for LMA (A) before and (B) after conditional analysis on ALGA0040260. The right panel of the figure shows the association results for LMD (C) before and (D) after conditional analysis on ALGA0040260. The P-value of association results for (B) LMA and (D) LMD after conditional analysis on ALGA0040260 fell below the predicted threshold.

Fig 5. Regional association plot of the primary signal (WU_10.2_16_35829257) associated with LMA at SSC16.

For each plot, the -log10(observed P-values) of SNPs (y-axis) are presented according to their chromosomal position (x-axis). The red line and darkblue line indicate the genome-wide significance level (P < 1.39E-06) and the chromosome-wide significance level (P < 2.79E-05), respectively. The primary SNPs are denoted by large blue triangles. SNPs are represented by colored rhombi according to the target SNP with which they were in strongest LD. The left panel of the figure shows the association results for LMA (A) before conditional analysis on ALGA0040260. In the right panel of the figure (B), the P-value of corresponding SNPs fell below the predicted threshold.

SNP effects and partitioning phenotypic variance by chromosomes

The phenotypic variance explained by significant SNPs, which were detected by single-trait association analysis, and autosomes were estimated for LMA and LMD in the two populations using GCTA tool[37] (Tables 2 and 3, Fig 6). For LMA, the peak SNP, Hal_2, located on SSC6, accounted for 1.18% of the phenotypic variance in the American original population. However, the significant SNPs in the Canadian original population accounted for 1.72%-1.83% of the phenotypic variance, and the lead SNP, WU_10.2_16_35829257, located on SSC16, had the largest contribution (1.83%) to the phenotypic variance. For LMD, the significant SNPs in the American original population explained 0.59%-1.20% of the phenotypic variance. The most significant SNP, ALGA0065784, located on SSC12, accounted for 1.20% of the phenotypic variance. In the Canadian original population, nine significant SNPs explained 1.67%-2.48% of the phenotypic variance. Notably, the peak SNP, ALGA0040263, and the second most significant SNP, ALGA0040260, made an equal contribution to the phenotypic variance (2.48%), and they were located on a narrow region on SSC7 between 30.34 and 30.35 Mb.

Fig 6. Heritabilities of LMA and LMD by chromosome in the two populations.

Estimates of chromosome-wide heritability on LMA and LMD are drawn against the chromosome length (x-axis). The blue line represents heritability regressed on chromosome length. Grey area around the blue line is the 95% confidence level interval for prediction from the linear model. In the figure, (A) and (B) show the heritability that each chromosome explained for LMA in the American and Canadian original populations, respectively. (C) and (D) show the heritability that each chromosome explained for LMD in the American and Canadian original populations, respectively.

The phenotypic variance explained by each chromosome for LMA and LMD is shown in Fig 6. The contribution of autosomes differed from each other, and much new information was obtained. For instance, chromosomes SSC7, SSC8, and SSC16 accounted for 4.35%, 5.72%, and 3.71% of LMA variance in the Canadian original population, respectively. Although the contribution of SSC8 explained the largest fraction of LMA variance, single-trait association analysis or meta-analysis did not detect any significantly associated SNPs on this chromosome. Meanwhile, single-trait association analysis identified numerous SNPs significantly associated with LMD on SSC7 in the same population, and chromosome 7 explained approximately 6.61% of LMD variance. These findings are in line with the results of association analysis. The peak shown in Fig 6A and 6C implied that SSC7 should have identified a large number of significant SNPs, whereas few SNPs were detected on this chromosome in the American original population.

Candidate genes and function analysis

A total of 41 functional genes that were within or nearest the significant SNPs were detected based on the Sus scrofa 11.1 genome assembly (Tables 2 and 3 and S1 Table). These genes were used to conduct KEGG pathways and Gene Ontology (GO) analysis. In particular, the gene set enrichment analysis uncovered that 21 annotated genes had a highlight biology function with LMA and LMD (S3 Table). In brief, the most significant pathways for LMA and LMD were related to glutathione metabolism and MAPK signaling pathway, respectively. The top GO term for LMA was related to transcription and DNA templated whereas that for LMD was related to regulation of transcription and DNA templated. Given that numerous genes are involved in important pathways and biological processes, functional annotations in the GeneCards tools and NCBI database as well as copious literatures were investigated and verified. Hence, eight genes, including Glutamate-Cysteine Ligase Catalytic Subunit (GCLC), GPX8, death domain associated protein (DAXX), fibroblast growth factor 21 (FGF21), TATA-Box binding protein associated factor 11 (TAF11), SAM pointed domain containing ETS transcription factor (SPDEF), NUDT3, and protein kinase C and casein kinase substrate in neurons 1 (PACSIN1), with biological functions such as increasing available amino acids in skeletal muscle, mammalian growth, fat deposition, and body size of pig were selected as promising candidates for swine carcass traits and growth-related traits.

Discussion

QTL associated with LMA and LMD

Scientific and comprehensive determining indices are the basis of measuring the level of production performance and growth-related traits of swine. LMA and LMD are important traits influencing the production performance of commercial pigs. Thus, the genetic mechanisms of LMA and LMD should be elucidated to estimate the affecting factors and consider them for future pig breeding programs. In this study, we performed four single-trait GWAS in two Duroc pig populations containing 6043 individuals in total. To improve the power of detection for common SNPs, we conducted a meta-analysis combining the full GWAS results from the two populations and uncovered additional significant SNPs. To our knowledge, few studies used GWAS strategy to analyze LMA and LMD. The present study is the first GWAS for LMA and LMD focusing on Duroc breeding pigs. Based on a large sample size (n = 6043) and multiple statistical analysis strategies, a total of 75 SNPs associated with LMA and LMD were detected. Of these SNPs, only one third of the significant SNPs were found to be simultaneously associated with both LMA and LMD, although these two traits were highly positively correlated to each other in American (r = 0.94) and Canadian populations (r = 0.74). The partial overlap of significant SNPs between LMA and LMD may be because the muscle growth is not only depended on the muscle fiber density, but also the fiber diameter[6, 46, 47]. It means that the muscle fiber density and the fiber diameter may have great contribution to the development of loin muscle and consequently affect LMA and LMD. One QTL with pleiotropic effects on LMA and LMD on SSC7 was identified in our study. LD analysis uncovered one haplotype block of 283 kb, in which the most significant SNPs (ALGA0040260 and ALGA0040263) on SSC7 were associated with LMA and LMD in the Canadian original pig population, whereas a region that partially overlapped with the results herein was identified significantly associated with carcass length as described by Liu et al.[48]. Notably, previous studies demonstrated that a hot region that spans approximately 6.5 Mb (from 31.27Mb to 37.74Mb) on SSC7 exert strong QTL effects on carcass traits[14, 49]. Compared with previous studies, our findings indicated that the pleiotropic QTL for LMA and LMD was fine mapped within a narrow interval of 283 kb. In addition, on SSC16, a QTL that affects LMA was refined to a 709 kb interval in this study. In this QTL region, Cho et al.[12] identified a QTL for LMA at the more distal position from our QTL in an F2 intercross between Landrace and Korean native pigs (n = 830). In the same region, Choi et al.[13] reported a QTL for LMA that spanned from 24.3 cM to 44.8 cM using 954 F2 Duroc × Pietrain pigs. Our study increased the power of QTL detection and narrowed the QTL locations by using high-density molecular markers and large sample sizes. However, no common SNPs and QTL that were associated with the traits were shared with the two pig populations in this study. The results showed that the difference in genetic background greatly influenced the identification of significant SNPs. In subsequent studies, we will continue to increase the number of samples and the density of breed-specific markers or use re-sequencing data for the traits analyzed herein.

Population stratification control in different genetic backgrounds

Given their powerful detection capability, GWAS are widely adopted for studying the genetics of natural variation. However, the most basic problem of GWAS is the risk of false positive in structured populations that may cause spurious associations[50, 51]. In the present study, three methods were used to resolve this problem: genomic control[28], PC analysis[38], and a linear mixed model that includes genomic relatedness matrix among individuals[52]. In the linear mixed model, the top five eigenvectors that calculated separately in the two different populations via GCTA tool[37] were embedded as covariates in the association analysis model to reduce the influence of population sub-stratification. As illustrated in S1 Fig, the Q-Q plot implies that the population stratification was sufficiently controlled with the aid of relatedness matrix and principal components of the specific population. Nevertheless, only a few variants were detected in the American original population. Most traits of interest are multifactorial and many factors affect the detection power of statistical of genetics studies[52]. In this study, we selected two Duroc pig populations with different backgrounds to perform the same analysis. The qualified sample size of the American original population is much larger than that of the Canadian original population (3196 versus 2127). Theoretically, the larger the sample size, the more genetic variations detected. However, a notable exception was detected. The probable reason is that rare variant SNPs have lower r2 compared to the common alleles contained in commercial SNP arrays because these SNPs are selected to represent common variants[52]. Thus, additional breed-specific SNP arrays should be developed to boost more dense representative tag variations for association analysis.

Candidate genes

Basing from the results of KEGG pathway and GO analysis, we obtained a number of functional genes and performed further gene annotations. Eight genes related to LMA and LMD were selected as promising candidates, of which, two were relevant to LMA, one was relevant to LMD, and five were involved in both traits. For LMA, GCLC and GPX8 are highlighted as important candidate genes that play roles in the pathway of glutathione metabolism. GCLC, the first rate-limiting enzyme of glutathione synthesis, is related to the biological processes of protein heterodimerization activity and coenzyme binding. Wei et al.[53] found that GCLC participates in amino acid metabolism and might upregulate the expression of glutathione in porcine skeletal muscle via feeding pigs with a linseed-enriched diet. GPX8 functions in glutathione metabolism and thyroid hormone synthesis pathways. Damon et al.[54] discovered that GPX8 is highly expressed in the longissimus muscle of Large White pigs with the function of response to oxidative stress and oxidation reduction. It was also identified by genome-wide linkage analysis as a candidate gene for growth and carcass merit in an F2 pig population because expression QTL (eQTL) analysis of loin muscle tissue demonstrated that GPX8 eQTL is part of a network relevant to cell cycle and lipid metabolism[55]. For LMD, one candidate gene, DAXX, was related to LMD trait. DAXX participates in the MAPK signaling pathway and is also involved in the regulation of transcription and DNA templated. The DAXX gene encodes a multifunctional protein and interacts with various proteins, such as apoptosis antigen Fas. Yang et al.[56] found that DAXX, as a novel signaling protein, plays a critical role in Fas-binding domain, and the elevated expression of DAXX cements Fas-mediated apoptosis.

For LMA and LMD, many common significant SNPs that are involved in both traits were found to be close to or within numerous genes. Consequently, five genes were determined as major functional candidates: FGF21, TAF11, SPDEF, NUDT3, and PACSIN1. The FGF21 gene encodes a member of the fibroblast growth factor (FGF) family, which plays a critical role in lipid metabolism[57], cell growth and development, and wound healing[58]. Ayuso et al.[59] found that FGF21 is relevant to adipose and muscle tissue development, and functions in lipid metabolism in purebred Iberian pigs and in cellular and muscle growth in Duroc-crossbred Iberian pigs. FGF21 also induces PGG-1α and regulates carbohydrate and fatty acid metabolism[60]. TAF11 participates in RNA polymerase II transcription initiation and promoter clearance pathway and is related to the regulation of transcription and DNA templated. Robinson et al.[61] showed that TAF11 enhances TBP-TFIIA complex formation through protein-protein interactions, and the loss of interaction would encumber the cell growth and transcription in vivo. NUDT3 of the Nudix protein family and participates in insulin signaling and inositol phosphate metabolism pathway. Many studies proved that NUDT3 is the obesity-linked gene for human[62], Drosophila, and mouse[63], possibly because of its polymorphic activity against polyphosphate substrates[64]. SPDEF plays a role in DNA binding transcription factor activity, transcription, and DNA templated. Meanwhile, PACSIN1 is involved in phospholipid binding and cytoskeletal protein binding. Baranski et al.[65] found that SPDEF and PACSIN1 are associated with adiposity and the amount of fat/triglyceride in tissue-specific knocked down Drosophila. NUDT3, SPDEF, and PACSIN1 have been identified in a closely linked region that is associated with obesity or carcass traits, such as body growth and size, in human or pigs[48, 66, 67]. These candidate genes warrant further investigation.

Comparing the single-trait analysis with meta-analysis

In the present study, we performed four single-trait GWAS in American and Canadian original Duroc pig populations using the same linear mixed model and carried out a meta-analysis to improve the detection power. Comprehensive use of multiple methods improves the effectiveness of detection, but no common significant SNPs were identified between the two populations. For this case, some issues are worth discussing. First, significant SNPs detected by single-trait association analysis were significantly less than those detected by meta-analysis. The results suggest that the number of samples used in the experiment needs to be improved, although it far exceeds the number in a vast majority of previous studies; population specificity also possibly contributes to the occurrence of this event[31, 40]. Some informative SNPs that had large effects on the single-trait GWAS were eliminated during QC in the meta-analysis. Another intuitive difference between single-trait association analysis and meta-analysis is that the new P values for all significant SNPs were decreased, and numerous SNPs that were detected by single-trait association analysis were confirmed via meta-analysis. Similar findings have been discovered in previous studies that closely related to carcass traits. For instance, Jiang et al.[68] performed GWAS separately using American and British original Yorkshire populations to uncover significant variants and candidate genes related to growth and fatness traits, but they detected no overlapping significant SNPs in the two populations with different genetic backgrounds. Guo et al.[69] used four pig populations (a White Duroc × Erhualian F2 intercross population, Chinese Sutai, Laiwu, and Erhualian populations) and four methods (SS-GWAS, MS-GWAS, SM-GWAS, and MM-GWAS, the detailed introductions are shown in the article) to conduct an association analysis on nine growth and fatness traits. Results revealed no common loci across the four populations in the single-population GWAS. However, the meta-analysis was more powerful in detecting the association between markers and diverse phenotypes, and greater SNPs were perceived compared with the single-population association analysis. Despite is strong detection effectiveness, the meta-analysis might deduce a high false positive rate[40, 68]. Therefore, we focused on the overlapping SNPs of single-trait association analysis and meta-analysis, and conducted a subsequent functional analysis based on them.

Conclusions

In sum, we detected a set of significant SNPs for LMA and LMD in 6043 Duroc pigs from two populations with different genetic contexts. One pleiotropic QTL located on SSC7 with a 283 kb interval was identified to affect multiple traits. Another genetic region on SSC16 (709 kb) was also discovered to play an important role in LMA. A series of bioinformatics analysis strategies revealed many functional genes for LMA and LMD. For instance, GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1 with biological functions such as increasing available amino acids in skeletal muscle, mammalian growth, fat deposition, and body size of pig were selected as promising candidates for swine carcass and growth-related traits. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. These findings revealed the complexity of the genetic mechanism that forms the phenotypic diversity and provide essential insights into the future production of pigs in the context of marker-assisted selection.

Supporting information

S1 Fig. Quantile–quantile (Q–Q) plots of genome-wide association studies for LMA and LMD in the two populations.

Q–Q plots show the observed versus expected negative log10 P-values. The left panel of the figure shows Q–Q plots for LMA in (A) the American original population and (B) the Canadian original population, respectively. The right panel of the figure shows Q–Q plots for LMD in (C) the American original population and (D) the Canadian original population, respectively.

(TIF)

S1 Table. Significant SNPs and associated genes for LMA and LMD in the meta-analysis.

(DOCX)

S2 Table. Comparative mapping of tag SNPs with previous QTLs reported in the pig QTL database (as of September 26, 2018) and previous GWAS results.

(DOCX)

S3 Table. Significant KEGG pathway and GO terms with loin muscle area (LMA) and loin muscle depth (LMD) (P-value<0.05).

(XLSX)

Data Availability

All relevant data required to replicate the findings in this study are available on Figshare (https://doi.org/10.6084/m9.figshare.8019551.v1).

Funding Statement

We would like to thank the Natural Science Foundation of China (31601912 and 31790411), the Science and Technology Planning Project of Guangdong Province (Grant No. 2017B020201012), the Natural Science Foundation of Guangdong Province (Grant Nos. 2018B030315007 and 2017A030313213) and the Pearl River Nova Program of Guangzhou (201906010011) for supporting this study. We would also like to thank the Wens Foodstuffs Group Co., Ltd. for providing all phenotypic data and ear tissue samples.