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For: Choi JS, Jin SK, Jeong YH, Jung YC, Jung JH, Shim KS, Choi YI. Relationships between Single Nucleotide Polymorphism Markers and Meat Quality Traits of Duroc Breeding Stocks in Korea. Asian-Australas J Anim Sci 2016;29:1229-38. [PMID: 27507182 PMCID: PMC5003982 DOI: 10.5713/ajas.16.0158] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/09/2016] [Accepted: 08/10/2016] [Indexed: 11/27/2022]

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Cited by Other Article(s)

Green HE, Oliveira HRD, Alvarenga AB, Scramlin-Zuelly S, Grossi D, Schinckel AP, Brito LF. Genomic background of biotypes related to growth, carcass and meat quality traits in Duroc pigs based on principal component analysis. J Anim Breed Genet 2024;141:163-178. [PMID: 37902119 DOI: 10.1111/jbg.12831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/14/2023] [Accepted: 10/14/2023] [Indexed: 10/31/2023]

Abstract

As the swine industry continues to explore pork quality traits alongside growth, feed efficiency and carcass leanness traits, it becomes imperative to understand their underlying genetic relationships. Due to this increase in the number of desirable traits, animal breeders must also consider methods to efficiently perform direct genetic changes for each trait and evaluate alternative selection indexes with different sets of phenotypic measurements. Principal component analysis (PCA) and genome-wide association studies (GWAS) can be combined to understand the genetic architecture and biological mechanisms by defining biological types (biotypes) that relate these valuable traits. Therefore, the main objectives of this study were to: (1) estimate genomic-based genetic parameters; (2) define animal biotypes utilizing PCA; and (3) utilize GWAS to link the biotypes to candidate genes and quantitative trait loci (QTL). The phenotypic dataset included 2583 phenotypic records from female Duroc pigs from a terminal sire line. The pedigree file contained 193,764 animals and the genotype file included 21,309 animals with 35,651 single nucleotide polymorphisms (SNPs). Eight principal components (PCs), accounting for a total of 99.7% of the population variation, were defined for three growth, eight conventional carcass, 10 pork quality and 18 novel carcass traits. The eight biotypes defined from the PCs were found to be related to growth rate, maturity, meat quality and body structure, which were then related to candidate genes. Of the 175 candidate genes found, six of them [LDHA (SSC1), PIK3C3 (SSC6), PRKAG3 (SSC15), VRTN (SSC7), DLST (SSC7) and PAPPA (SSC1)] related to four PCs were found to be associated with previously defined QTL, linking the biotypes with biological processes involved with muscle growth, fat deposition, glycogen levels and skeletal development. Further functional analyses helped to make connections between biotypes, relating them through common KEGG pathways and gene ontology (GO) terms. These findings contribute to a better understanding of the genetic relationships between growth, carcass and meat quality traits in Duroc pigs, enabling breeders to better understand the biological mechanisms underlying the phenotypic expression of these traits.

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Xu J, Jiang AM, Zhang C, Zheng Y, Zhang T, Zhou L. Potential of eight mutations for marker-assisted breeding in Chinese Lulai black pigs. Can J Anim Sci 2022. [DOI: 10.1139/cjas-2021-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]

Liu Y, Long H, Feng S, Ma T, Wang M, Niu L, Zhang X, Wang L, Lei Y, Chen Y, Wang Q, Xu X. Trait correlated expression combined with eQTL and ASE analyses identified novel candidate genes affecting intramuscular fat. BMC Genomics 2021;22:805. [PMID: 34749647 PMCID: PMC8577010 DOI: 10.1186/s12864-021-08141-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open

Affiliation(s)

Yan Liu Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China.,Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070, China
Huan Long Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Simin Feng Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Tingting Ma Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Mufeng Wang Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Lizhu Niu Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Xinyi Zhang Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Lianni Wang Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Yu Lei Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Yilong Chen Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Qiankun Wang Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China
Xuewen Xu Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China. .,The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070, China. .,Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070, China.

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Tan Y, Gan M, Shen L, Li L, Fan Y, Chen Y, Chen L, Niu L, Zhao Y, Jiang A, Jiang D, Zhang S, Zhu L. Profiling and Functional Analysis of Long Noncoding RNAs and mRNAs during Porcine Skeletal Muscle Development. Int J Mol Sci 2021;22:ijms22020503. [PMID: 33419093 PMCID: PMC7825455 DOI: 10.3390/ijms22020503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 12/28/2020] [Accepted: 01/01/2021] [Indexed: 11/16/2022] Open

Abstract

Gene transcripts or mRNAs and long noncoding RNAs (lncRNAs) are differentially expressed during porcine skeletal muscle development. However, only a few studies have been conducted on skeletal muscle transcriptome in pigs based on timepoints according to the growth curve for porcine. Here, we investigated gene expression in Qingyu pigs at three different growth stages: the inflection point with the maximum growth rate (MGI), the inflection point of the gradually increasing stage to the rapidly increasing stage (GRI), and the inflection point of the rapidly increasing stage to the slowly increasing stage (RSI). Subsequently, we explored gene expression profiles during muscle development at the MGI, GRI and RSI stages by Ribo-Zero RNA sequencing. Qingyu pigs reached the MGI, GRI and RSI stages at 156.40, 23.82 and 288.97 days of age with 51.73, 3.14 and 107.03 kg body weight, respectively. A total of 14,530 mRNAs and 11,970 lncRNAs were identified at the three stages, and 645, 323 differentially expressed genes (DEGs) and 696, 760 differentially expressed lncRNAs (DELs) were identified in the GRI vs. MGI, and RSI vs. MGI, comparisons. Functional enrichment analysis revealed that genes involved in immune system development and energy metabolism (mainly relate to amino acid, carbohydrate and lipid) were enriched at the GRI and MGI stages, respectively, whereas genes involved in lipid metabolism were enriched at the RSI stage. We further characterized G1430, an abundant lncRNA. The full-length sequence (316 nt) of lncRNA G1430 was determined by rapid amplification of cDNA ends (RACE). Subcellular distribution analysis by quantitative real-time PCR (qRT-PCR) revealed that G1430 is a cytoplasmic lncRNA. Binding site prediction and dual luciferase assay showed that lncRNA G1430 directly binds to microRNA 133a (miR-133a). Our findings provide the basis for further investigation of the regulatory mechanisms and molecular genetics of muscle development in pigs.

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Affiliation(s)

Ya Tan Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.) Institute of Animal Husbandry and Veterinary, Guizhou Academy of Agricultural Science, Guiyang 550005, China
Mailin Gan Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Linyuan Shen Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Liang Li Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.) Institute of Animal Husbandry and Veterinary, Guizhou Academy of Agricultural Science, Guiyang 550005, China
Yuan Fan Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Ying Chen Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Lei Chen Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Lili Niu Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Ye Zhao Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Anan Jiang Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Dongmei Jiang Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.)
Shunhua Zhang Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.) Correspondence: (S.Z.); (L.Z.); Tel.: +86-28-8629-1133 (S.Z. & L.Z.)
Li Zhu Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; (Y.T.); (M.G.); (L.S.); (L.L.); (Y.F.); (Y.C.); (L.C.); (L.N.); (Y.Z.); (A.J.); (D.J.) Correspondence: (S.Z.); (L.Z.); Tel.: +86-28-8629-1133 (S.Z. & L.Z.)

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Shan H, Song X, Cao Y, Xiong P, Wu J, Jiang J, Jiang Y. Association of the melanocortin 4 receptor (MC4R) gene polymorphism with growth traits of Hu sheep. Small Rumin Res 2020. [DOI: 10.1016/j.smallrumres.2020.106206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]

Zhang J, Li J, Wu C, Hu Z, An L, Wan Y, Fang C, Zhang X, Li J, Wang Y. The Asp298Asn polymorphism of melanocortin‐4 receptor (MC4R) in pigs: evidence for its potential effects on MC4R constitutive activity and cell surface expression. Anim Genet 2020;51:694-706. [DOI: 10.1111/age.12986] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 05/04/2020] [Accepted: 06/23/2020] [Indexed: 12/14/2022]

Tiezzi F, Brito LF, Howard J, Huang YJ, Gray K, Schwab C, Fix J, Maltecca C. Genomics of Heat Tolerance in Reproductive Performance Investigated in Four Independent Maternal Lines of Pigs. Front Genet 2020;11:629. [PMID: 32695139 PMCID: PMC7338773 DOI: 10.3389/fgene.2020.00629] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/26/2020] [Indexed: 12/12/2022] Open

Abstract

Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.

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Yu DY, Wu RZ, Zhao Y, Nie ZH, Wei L, Wang TY, Liu ZP. Polymorphisms of four candidate genes and their correlations with growth traits in blue fox (Alopex lagopus). Gene 2019;717:143987. [PMID: 31362037 DOI: 10.1016/j.gene.2019.143987] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Revised: 07/16/2019] [Accepted: 07/16/2019] [Indexed: 11/16/2022]

Wang B, Li P, Zhou W, Gao C, Liu H, Li H, Niu P, Zhang Z, Li Q, Zhou J, Huang R. Association of Twelve Candidate Gene Polymorphisms with the Intramuscular Fat Content and Average Backfat Thickness of Chinese Suhuai Pigs. Animals (Basel) 2019;9:E858. [PMID: 31652864 DOI: 10.3390/ani9110858] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 12/16/2022] Open

Abstract

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.

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Wang C, Wang X, Tang J, Chen H, Zhang J, Li Y, Lei S, Ji H, Yang B, Ren J, Ding N. Genome-wide association studies for two exterior traits in Chinese Dongxiang spotted pigs. Anim Sci J 2018;89:868-875. [DOI: 10.1111/asj.13003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/11/2018] [Indexed: 12/29/2022]

Pena RN, Ros-Freixedes R, Tor M, Estany J. Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs. Int J Mol Sci 2016;17:E2100. [PMID: 27983643 DOI: 10.3390/ijms17122100] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 12/06/2016] [Accepted: 12/07/2016] [Indexed: 12/11/2022] Open

Abstract

Among the large number of attributes that define pork quality, fat content and composition have attracted the attention of breeders in the recent years due to their interaction with human health and technological and sensorial properties of meat. In livestock species, fat accumulates in different depots following a temporal pattern that is also recognized in humans. Intramuscular fat deposition rate and fatty acid composition change with life. Despite indication that it might be possible to select for intramuscular fat without affecting other fat depots, to date only one depot-specific genetic marker (PCK1 c.2456C>A) has been reported. In contrast, identification of polymorphisms related to fat composition has been more successful. For instance, our group has described a variant in the stearoyl-coA desaturase (SCD) gene that improves the desaturation index of fat without affecting overall fatness or growth. Identification of mutations in candidate genes can be a tedious and costly process. Genome-wide association studies can help in narrowing down the number of candidate genes by highlighting those which contribute most to the genetic variation of the trait. Results from our group and others indicate that fat content and composition are highly polygenic and that very few genes explain more than 5% of the variance of the trait. Moreover, as the complexity of the genome emerges, the role of non-coding genes and regulatory elements cannot be disregarded. Prediction of breeding values from genomic data is discussed in comparison with conventional best linear predictors of breeding values. An example based on real data is given, and the implications in phenotype prediction are discussed in detail. The benefits and limitations of using large SNP sets versus a few very informative markers as predictors of genetic merit of breeding candidates are evaluated using field data as an example.

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