大白猪眼肌面积、估计瘦肉率和背膘厚的加权一步法全基因组关联分析

doi:10.11843/j.issn.0366-6964.2023.04.006

摘要/Abstract

摘要： 旨在使用加权一步法全基因组关联分析方法，充分利用群体的表型、系谱和基因型信息，探索与大白猪眼肌面积、估计瘦肉率和背膘厚相关的候选基因。本研究收集21 754头大白猪眼肌面积、估计瘦肉率和背膘厚的表型记录数据，其中基因型数据个体共1 259头。通过方差分析和加权一步法全基因组关联分析，确定性状显著相关的数量性状基因座（quantitative trait locus，QTL）。并进行基因注释、GO （gene ontology）功能和KEGG （kyoto encyclopedia of genes and genomes）通路富集分析。计算结果表明，大白猪眼肌面积、估计瘦肉率和背膘厚的遗传力分别为0.450 6±0.017 3、0.496 8±0.017 4和0.475 8±0.017 2。利用加权一步法全基因组关联分析，定位到与眼肌面积显著相关的候选QTL区域10个，与估计瘦肉率显著相关的候选QTL区域8个，与背膘厚显著相关的候选QTL区域12个。后续基因注释、GO功能和KEGG通路富集分析显示，与眼肌面积相关的候选基因36个，共富集到7个条目；与估计瘦肉率相关的候选基因29个，共富集到2个条目；与背膘厚相关的候选基因41个，共富集到11个条目。分析这些条目中所包含的基因，结果显示其中部分候选基因与生长发育、肌肉脂肪和骨骼发育有关。如ADAL基因影响脂肪细胞分化等活动；FGF9基因被报道参与骨骼发育等过程。本研究通过加权一步法关联分析定位到与大白猪生长性状相关的重要候选基因，结果扩展了大白猪生长性状的相关研究，并为今后该品种生长性状的基因组遗传改良提供重要分子标记。

关键词: 大白猪, 眼肌面积, 估计瘦肉率, 背膘厚, 加权一步法全基因组关联分析

Abstract: The purpose was to explore candidate genes related to the eye muscle area, predicted lean meat percentage and average backfat thickness of Large White pigs by using the weighted single-step genome wide association study and making full use of the phenotype, pedigree and genotype information of the population. The phenotypic record data of eye muscle area, predicted lean meat percentage and average backfat thickness of 21 754 Large White pigs were collected, including 1 259 individuals with genotype data. Through analysis of variance and the weighted single-step genome wide association study, quantitative trait loci (QTL) that are significantly related to traits were determined. The gene annotation, gene ontology (GO) and kyoto Encyclopedia of genes and genes (KEGG) pathway enrichment analysis were carried out. The results showed that the heritability of eye muscle area, predicted lean meat percentage and average backfat thickness in Yorkshire pigs were 0.450 6±0.017 3, 0.496 8±0.017 4 and 0.475 8±0.017 2, respectively. Using the weighted single-step genome wide association study, 10 candidate QTL regions significantly associated with eye muscle area, 8 candidate QTL regions significantly associated with predicted lean meat percentage, and 12 candidate QTL regions significantly associated with average backfat thickness were localized. Follow-up gene annotation, GO function and KEGG pathway enrichment analysis revealed 36 candidate genes significantly associated with eye muscle area, with a total of 7 terms enriched; 29 candidate genes significantly associated with predicted lean meat percentage, with a total of 2 terms enriched; and 41 candidate genes significantly associated with average backfat thickness, with a total of 11 terms enriched. Analysis of the genes contained in these terms showed that some of these candidate genes were associated with growth and development, muscle fat and bone development. For example, ADAL gene affects adipocyte differentiation and other activities; FGF9 gene is reported to be involved in bone development and other processes. In this study, the important candidate genes related to growth traits of Yorkshire pigs were located through the weighted single-step genome wide association study. The results extended the related research of growth traits of Yorkshire pigs, and provide important molecular markers for genetic improvement of growth traits in the future.

Key words: Yorkshire pigs, eye muscle area, predicted lean meat percentage, average backfat thickness, weighted single-step GWAS

中图分类号:

S828.2

吴骏, 蔡晓钿, 林清, 钟展明, 叶浩强, 魏趁, 徐志婷, 吴细波, 司景磊, 张哲, 李加琪. 大白猪眼肌面积、估计瘦肉率和背膘厚的加权一步法全基因组关联分析[J]. 畜牧兽医学报, 2023, 54(4): 1403-1414.

WU Jun, CAI Xiaodian, LIN Qing, ZHONG Zhanming, YE Haoqiang, WEI Chen, XU Zhiting, WU Xibo, SI Jinglei, ZHANG Zhe, LI Jiaqi. Weighted Single-step GWAS of Eye Muscle Area, Predicted Lean Meat Percentage and Average Backfat Thickness in A Yorkshire Pig Population[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(4): 1403-1414.

参考文献 45

[1]	陈辉.猪重要经济性状选择的遗传进展[J].猪业科学, 2019, 36(1):116-118.CHEN H.Genetic progress in the selection of important economic traits in pigs[J].Swine Industry Science, 2019, 36(1):116-118.(in Chinese)
[2]	MATHER K.Variation and selection of polygenic characters[J].J Genet, 1941, 41(2-3):159-193.
[3]	RISCH N, MERIKANGAS K.The future of genetic studies of complex human diseases[J].Science, 1996, 273(5281):1516-1517.
[4]	HIRSCHHORN J N, DALY M J.Genome-wide association studies for common diseases and complex traits[J].Nat Rev Genet, 2005, 6(2):95-108.
[5]	汤香, 何俊, 蒋隽.全基因组关联分析在畜禽中的研究进展[J].畜牧与兽医, 2020, 52(5):147-152.TANG X, HE J, JIANG J.Progress in research on whole genome association studies in livestock and poultry[J].Animal Husbandry & Veterinary Medicine, 2020, 52(5):147-152.(in Chinese)
[6]	石丽丹, 王小女, 张孟浩, 等.大白母猪乳头数全基因组关联分析[J].中国畜牧杂志, 2021, 57(S1):69-75, 83.SHI L D, WANG X N, ZHANG M H, et al.Genome wide association analysis of teat number in Yorkshire Pig[J].Chinese Journal of Animal Science, 2021, 57(S1):69-75, 83.(in Chinese)
[7]	胡斌, 蔡淑芳, 孟繁明, 等.藏猪出生重的全基因组关联分析[J].广东畜牧兽医科技, 2021, 46(6):40-44.HU B, CAI S F, MENG F M, et al.Genome-wide association study of birth weight in Tibetan pigs[J].Guangdong Journal of Animal and Veterinary Science, 2021, 46(6):40-44.(in Chinese)
[8]	KORTE A, FARLOW A.The advantages and limitations of trait analysis with GWAS:a review[J].Plant Methods, 2013, 9(4):29.
[9]	WANG H, MISZTAL I, AGUILAR I, et al.Genome-wide association mapping including phenotypes from relatives without genotypes[J].Genet Res (Camb), 2012, 94(2):73-83.
[10]	HOWARD J T, JIAO S H, TIEZZI F, et al.Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars[J].BMC Genet, 2015, 16:59.
[11]	ZHANG Z, ZHANG Z, OYELAMI F O, et al.Identification of genes related to intramuscular fat independent of backfat thickness in Duroc pigs using single-step genome-wide association[J].Anim Genet, 2021, 52(1):108-113.
[12]	R CORE TEAM.R:a language and environment for statistical computing[CP/OL].Vienna, Austria:R Core Team, 2022.
[13]	PURCELL S, NEALE B, TODD-BROWN K, et al.PLINK:a tool set for whole-genome association and population-based linkage analyses[J].Am J Hum Genet, 2007, 81(3):559-575.
[14]	HAMMAMI H, REKIK B, SOYEURT H, et al.BLUPF90 and related programs (BGF90)[J].J Anim Breed Genet, 2009, 126(5):366-377.
[15]	JENSEN J, MÄNTYSAARI E A, MADSEN P, et al.Residual maximum likelihood estimation of (Co)variance components in multivariate mixed linear models using average information[J].J Indian Soc Agric Stat, 1997, 49:215-236.
[16]	CHRISTENSEN O F, LUND M S.Genomic prediction when some animals are not genotyped[J].Genet Select Evol, 2010, 42(1):2.
[17]	VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci, 2008, 91(11):4414-4423.
[18]	FANGMANN A, SHARIFI R A, HEINKEL J, et al.Empirical comparison between different methods for genomic prediction of number of piglets born alive in moderate sized breeding populations[J].J Anim Sci, 2017, 95(4):1434-1443.
[19]	AGUILAR I, MISZTAL I, TSURUTA S, et al.PREGSF90-POSTGSF90:Computational tools for the implementation of single-step genomic selection and genome-wide association with Ungenotyped individuals in BLUPF90 programs[C]//Proceedings of the 10th World Congress of Genetics Applied to Livestock Production.2014.
[20]	ZHANG Z, LIU J F, DING X D, et al.Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix[J].PLoS One, 2010, 5(9):e12648.
[21]	WANG H Y, MISZTAL I, AGUILAR I, et al.Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens[J].Front Genet, 2014, 5:134.
[22]	MARQUES D B D, BASTIAANSEN J W M, BROEKHUIJSE M L W J, et al.Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs[J].Genet Sel Evol, 2018, 50(1):40.
[23]	VERONEZE R, BASTIAANSEN J W, KNOL E F, et al.Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations[J].BMC Genet, 2014, 15:126.
[24]	ZHUANG Z, DING R, QIU Y, et al.A large-scale genome-wide association analysis reveals QTL and candidate genes for intramuscular fat content in Duroc pigs[J].Anim Genet, 2021, 52(4):518-522.
[25]	GAO N, CHEN Y L, LIU X H, et al.Weighted single-step GWAS identified candidate genes associated with semen traits in a Duroc boar population[J].BMC Genomics, 2019, 20(1):797.
[26]	SHERMAN B T, HAO M, QIU J, et al.DAVID:a web server for functional enrichment analysis and functional annotation of gene lists (2021 update)[J].Nucleic Acids Res, 2022, 50(W1):W216-W221.
[27]	HUANG D W, SHERMAN B T, LEMPICKI R A.Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J].Nat Protocols, 2009, 4(1):44-57.
[28]	张广杰.大白猪主要生长性状遗传参数估计及其与相关基因多态性的关联分析[D].南宁:广西大学, 2019.ZHANG G J.Estimation of genetic patameters of major growth traits in Yorkshire pig and its association with related gene pilymorphisms[D]. Nanning:Guangxi University, 2019.(in Chinese)
[29]	黄叶.大白猪胴体性状全基因组关联分析及遗传参数估计[D].南宁:广西大学, 2020.HUANG Y.Genome wide association analysis and genetic parameter estimation of carcass traits in Yorkshire pigs[D]. Nanning:Guangxi University, 2020.(in Chinese)
[30]	贺婕妤, 王斌虎, 廖柱, 等.长白和大白猪主要生长性状的遗传参数估计[J].畜牧兽医学报, 2021, 52(8):2115-2123.HE J Y, WANG B H, LIAO Z, et al.Estimation of genetic parameters of main growth traits in Landrace and Large White pigs[J].Acta Veterinaria et Zootechnica Sinica, 2021, 52(8):2115-2123.(in Chinese)
[31]	SUWANNASING R, DUANGJINDA M, BOONKUM W, et al.The identification of novel regions for reproduction trait in landrace and large white pigs using a single step genome-wide association study[J].Asian-Australas J Anim Sci, 2018, 31(12):1852-1862.
[32]	RUAN D L, ZHUANG Z W, DING R R, et al.Weighted single-step GWAS identified candidate genes associated with growth traits in a Duroc pig population[J].Genes (Basel), 2021, 12(1):117.
[33]	ZHANG X, XUE C Y, LIN J N, et al.Interrogation of nonconserved human adipose lincRNAs identifies a regulatory role of linc-ADAL in adipocyte metabolism[J].Sci Transl Med, 2018, 10(446):eaar5987.
[34]	ZHANG D W, WU W J, HUANG X, et al.Comparative analysis of gene expression profiles in differentiated subcutaneous adipocytes between Jiaxing Black and Large White pigs[J].BMC Genomics, 2021, 22(1):61.
[35]	CHEN S R, AN J Y, LIAN L, et al.Polymorphisms in AKT3, FIGF, PRKAG3, and TGF-β genes are associated with myofiber characteristics in chickens[J].Poult Sci, 2013, 92(2):325-330.
[36]	OCARANZA P, LAMMOGLIA J J, ÍÑIGUEZ G, et al.Effects of thyroid hormone on the GH signal transduction pathway[J]. Growth Horm IGF Res, 2014, 24(1):42-46.
[37]	ZHAO X Y, WANG C, WANG Y P, et al.Comparative gene expression profiling of muscle reveals potential candidate genes affecting drip loss in pork[J].BMC Genet, 2019, 20(1):89.
[38]	SAMSEL Z, SEKRETARSKA J, OSINKA A, et al.Central apparatus, the molecular kickstarter of ciliary and flagellar nanomachines[J].Int J Mol Sci, 2021, 22(6):3013.
[39]	唐益, 周林, 周宇鑫, 等.精子鞭毛蛋白以及精子表面蛋白17的研究进展[J].浙江海洋大学学报:自然科学版, 2021, 40(3):260-265.TANG Y, ZHOU L, ZHOU Y X, et al.Research progress of sperm flagellar protein and sperm surface protein 17[J].Journal of Zhejiang Ocean University:Natural Science, 2021, 40(3):260-265.(in Chinese)
[40]	ZHAO X, MO D L, LI A N, et al.Comparative analyses by sequencing of transcriptomes during skeletal muscle development between pig breeds differing in muscle growth rate and fatness[J].PLoS One, 2011, 6(5):e19774.
[41]	李若梅, 翁梦佳, 孙一丹, 等.Fgf9在小鼠下颌骨发育软骨成骨及膜内成骨过程中的作用探讨[J].中国口腔颌面外科杂志, 2021, 19(4):309-314.LI R M, WENG M J, SUN Y D, et al.Fgf9 regulated murine mandibular development by promoting endochondral and intramembranous ossification[J].China Journal of Oral and Maxillofacial Surgery, 2021, 19(4):309-314.(in Chinese)
[42]	SENTCHORDI-MONTANÉ L, DIAZ-GONZALEZ F, CATEDRA-VALLES E V, et al.Identification of the third FGF9 variant in a girl with multiple synostosis-comparison of the genotype:phenotype of FGF9 variants in humans and mice[J].Clin Genet, 2021, 99(2):309-312.
[43]	ALFADHEL M, UMAIR M, ALMUZZAINI B, et al.Identification of the TTC26 splice variant in a novel complex ciliopathy syndrome with biliary, renal, neurological, and skeletal manifestations[J].Mol Syndromol, 2021, 12(3):133-140.
[44]	WANG Z, IIDA A, MIYAKE N, et al.Axial spondylometaphyseal dysplasia is caused by C21orf2 mutations[J].PLoS One, 2016, 11(3):e0150555.
[45]	GU Y R, LI M Z, ZHANG K, et al.Evaluation of endogenous control genes for gene expression studies across multiple tissues and in the specific sets of fat- and muscle-type samples of the pig[J].J Anim Breed Genet, 2011, 128(4):319-325.