Association of Twelve Candidate Gene Polymorphisms with the Intramuscular Fat Content and Average Backfat Thickness of Chinese Suhuai Pigs

Simple Summary

Appropriate intramuscular fat content (IFC) is the goal of consumers and the direction that breeders must pursue. However, it is difficult to improve the IFC but not average backfat thickness (ABT) by traditional breeding methods, and pigs must be slaughtered to accurately measure IFC. Marker-assisted selection (MAS) provides an economic and efficient method to improve the IFC in pigs. Our research indicated that the FABP3 (rs1110770079) single nucleotide polymorphism (SNP) could be a candidate gene associated with IFC (but not ABT), and IFC could be improved by selecting the individuals with a favorable genotype (GG) of FABP3 (rs1110770079) SNP for pig breeding.

Abstract

The present study aimed to identify the molecular markers for genes that influence intramuscular fat content (IFC), but not average backfat thickness (ABT). A total of 330 Suhuai pigs were slaughtered, and measurements of IFC and ABT were obtained. Phenotypic and genetic correlations between IFC and ABT were calculated. Thirteen single nucleotide polymorphisms (SNPs) among 12 candidate genes for IFC were analyzed, including FABP3, LIPE, IGF1, IGF2, LEP, LEPR, MC4R, PHKG1, RETN, RYR1, SCD, and UBE3C. Associations of the evaluated SNPs with IFCIFC and ABT were performed. Our results showed that the means of IFC and ABT were 1.99 ± 0.03 % and 26.68 ± 0.28 mm, respectively. The coefficients of variation (CVs) of IFC and ABT were 31.21% and 19.36%, respectively. The phenotypic and genetic correlations between IFC and ABT were moderate. Only the FABP3 (rs1110770079) was associated with IFC (p < 0.05) but not with ABT. Besides, there was a tendency for associations of RYR1 (rs344435545) and SCD (rs80912566) with IFC (p < 0.1). Our results indicated that the FABP3 (rs1110770079) SNP could be used as a marker to improve IFC without changing ABT in the Suhuai pig breeding system.

1. Introduction

Meat is widely consumed as an important protein source for humans; pork occupies a dominant position in the consumption structure of meat in China [1], and its consumption is about 40 kg per person per year in recent years. During the past decades, breeders have mainly focused on the increase of growth rate, muscle deposition, and lean meat yield, resulting in a reduction in meat quality and fat deposition [2,3]. Intramuscular fat content (IFC) is a key indicator of the meat quality assessment system. Previous studies have indicated that IFC is closely related to flavor, juiciness, tenderness, and water-holding capacity [4,5]. IFC refers to the amount of fat accumulated between muscle fibers or within muscle cells, which could be extracted from muscle samples by using chemical methods [6]. The main components of IFC include phospholipids and triglycerides [7]. Recently, some studies have shown that the increase in triglycerides contributes to increases in IFC [8,9].

At present, improving IFC has become an important objective in the modern pig breeding program [10], especially in China [11]. However, it is difficult to improve IFC by traditional breeding methods. Although the heritability of IFC is relatively high (h² = 0.25–0.50) [12], it is still impossible to assess exact IFC in the offspring. Accurate measurement of IFC still requires slaughtering pigs, which is costly to implement. As compared to traditional breeding methods, marker-assisted selection (MAS) and genome selection (GS) technology are ideal ways to improve IFC in pigs. However, in comparison with MAS, many pig farms cannot cover the cost of GS technology. Thus, MAS is an attractive approach to improve IFC in pigs. Several candidate genes and causative mutations for IFC have been identified in previous studies, such as FABP3 (rs1110770079) [13], LIPE (rs328830166) [14], IGF1 (rs322131043), IGF1 (rs341412920) [15], IGF2 (g.3072G > A) [16], LEP (rs45431504) [17], LEPR (rs45435518) [18], MC4R (rs81219178) [19], PHKG1 (rs697732005) [20], RETN (rs327132149) [21], RYR1 (rs344435545) [22], SCD (rs80912566) [18] and UBE3C (rs81329544) [23].

In general, the phenotypic correlation between backfat (BF) thickness and IFC is moderate and positive. Therefore, increasing IFC often leads to increased BF thickness [24]. The increased BF thickness in turn could result in a decrease in lean meat percentage, which adversely affects the lean meat yield. Thus, it is especially necessary to find SNPs that could increase IFC but not the BF thickness.

Suhuai pig is a new lean-type pig breed. Briefly, after 23 years of successive breeding of the crossbred offspring of the Large White Pig and Huai Pig, a new breed was developed, called the **

The genomic DNA was isolated from ear tissue using the standard phenol-chloroform protocol method [30]. The DNA concentration was measured with a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific Inc, Waltham, MA, USA) and the integrity of the DNA was checked on 1.5% agarose gel with an FR-250 electrophoresis apparatus (Furi Technology Co., Ltd., Shanghai, China). Only high-quality genomic DNAs from all samples were used for subsequent genoty**. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method and the improved multiplex ligation detection reaction (iMLDR) technique [31] were used to analyze the polymorphisms of 13 SNPs from the 12 candidate genes (Supplementary Table S1). Among these SNPs, 11 were genotyped through iMLDR in one ligation reaction, including LIPE (rs328830166), IGF1 (rs341412920), IGF1 (rs322131043), IGF2 (g.3072G > A), LEP (rs45431504), LEPR (rs45435518), MC4R (rs81219178), RETN (rs327132149), RYR1 (rs344435545), SCD (rs80912566) and UBE3C (rs81329544). Fragments covering these 11 SNPs were amplified using a multiplex of PCR reactions. The multiplex PCR reaction, performed in a 20 μL total volume, contained 1× GC-I buffer (Takara, Dalian, China), 3.0 mM Mg²⁺, 0.3 mM dNTPs, 1 U of Hot-Start Taq DNA polymerase (Takara, Dalian, China), 1 μL of primer mixture, and 20 ng of genomic DNA. The PCR program was as follow: 95 °C for 2 min, 11 cycles (94 °C for 20 s, 65 °C−0.5 °C/cycle for 40 s, and 72 °C for 1 min and 30 s), 24 cycles (94 °C for 20 s, 59 °C for 30 s, and 72 °C for 1 min and 30 s), 72 °C for 2 min, and hold at 4 °C. The purification reaction, performed in a new volume, contained 10 μL of each PCR product, 5U SAP and 2U Exonuclease I at 37 °C for 1 h and at 75 °C for 15 min. The ligation reaction, performed in a 10 μL final volume, contained 1 μL of 10× ligation buffer, 0.25 μL of Taq DNA Ligase (NEB Biotechnology, Bei**g, China), 0.4 μL of 5′ ligation primer mixture, 0.4 μL of 3′ ligation primer mixture, 2 μL of purified PCR product mixture and 6 μL of double distilled water (ddH₂O). The ligation cycling program was 38 cycles (94 °C for 1 min and 56 °C for 4 min), and a hold at 4 °C. A total of 0.5 μL of ligation product was loaded into an ABI3730XL, and the raw data were analyzed using the GeneMapper 4.1 software program (Thermo Fisher Scientific Inc, Waltham, MA, USA). All of the iMLDR primers are presented in Supplementary Table S1.

Only the polymorphisms of FABP3 (rs1110770079) and PHKG1 (rs697732005) were assessed using the PCR-RFLP method due to the failure of the iMLDR technique. For PCR-RFLP, the sequences for these 2 genes were obtained from the GenBank database (NLM, Bethesda, MD, USA). The primers of these 2 SNPs were designed by Primer Premier 6.0 software (Premier Biosoft International, Palo Alto, CA, USA) (Supplementary Table S1). PCR reaction was performed in a final volume of 25 μL, containing 1 μL of the template DNA (50 ng/μL), 1 μL of each primer, 22 μL 1.3 × Taq buffer. Cycling conditions were 98 °C for 2 min; 40 cycles at 98 °C for 10 s, 57 °C for 40 s, 72 °C for 1 min; and a final extension at 72 °C for 5 min. The quality of the PCR product was analyzed by using 1.5 % agarose gel electrophoresis. Genoty** was performed by the TSINGKE Company (Nan**g, China).

2.4. Statistical Analysis

Statistical analysis of IFC and ABT of the 330 Suhuai pigs was performed using GraphPad Prism 6 (GraphPad Software Inc, La Jolla, CA, USA) with the quartile method.

Phenotypic correlations of IFC and ABT were calculated using SAS 9.2 software (SAS Institute lnc, Cary, NC, USA). To estimate genetic correlations of IFC and ABT, all the phenotypes were corrected for sex and batch as fixed effects and sample identity (ID) as a random variable using DMU software [32].

In this study, genetic diversity indices were calculated to explore the genetic structure of all SNPs. The minimal number of genotypes used in the analysis was 3, and the total number of animals analyzed here was 327–330. The genotype and allele frequencies were calculated using Microsoft Excel 2013. The heterozygosity (He), homozygosity (Ho) and polymorphic information content (PIC) were estimated using Power Marker V3.0 software [33].

Here, variance analyses were performed using SAS 9.2 software (SAS Institute lnc, Cary, NC, USA). Sex and batch were considered as fixed effects in the models (1) and (2), because sex and batch had significantly effects on both IFC and ABT in variance analyses. We also found that age had significant effects on IFC, and carcass weight had significantly effects on ABT. Thus, age was used as the covariate for IFC in model (1), and carcass weight was used as the covariate for ABT in model (2).

Association analysis of each SNP with IFC and ABT was performed using the PROC GLM procedure of module of SAS 9.2 software.

For IFC, the following model was applied:

Y_ijklm = μ + G_i +B_j + S_k + D_l + K_m + e_ijklm,

(1)

For ABT, the following model was applied:

Y_ijklm = μ + G_i +B_j + S_k + W_l + K_m + e_ijklm,

(2)

In models (1) and (2), Y_ijlkm represents the vector of the phenotypic value of the trait under study, μ in these 2 models is the population mean of IFC and ABT, respectively, G_i, B_j and S_k refer to fixed effects of SNPs, batch and sex, respectively, D_l represents the covariate of age for IFC, W_l is the covariate of carcass weight for ABT, K_m is the random variable of kinship matrix using pedigree, and e_ijklm represents the random error. All statistical analyses were considered significant at p < 0.05.