利用基因组信息提高畜禽生产性能研究进展

doi:10.16431/j.cnki.1671-7236.2020.01.019

摘要/Abstract

摘要： 为了满足人们对畜产品需求的快速增长，必须在加快畜禽产业发展的同时把对环境的影响降到最低，提高畜禽遗传特性有望促进这一问题的解决。进入21世纪以来，以基因组选择为核心的分子育种技术迎来了发展机遇，利用该技术可实现早期准确选择，从而大幅度缩短世代间隔，加快群体遗传进展，并显著降低育种成本。虽然在某些畜种中（如奶牛），基因组选择取得了成功，群体也获得较大遗传进展，但仍无法满足快速增长的需求。因此，亟需寻找能够进一步加快遗传进展的方法。研究表明，在SNP标记数据中加入目标性状的已知功能基因信息，可以提高基因组育种值预测的准确性，进而加快遗传进展。而挖掘更多基因组信息的同时，开发更优化的分析方法可以更有助于目标的实现。文章总结了主要畜禽物种的可用基因组数据，包括牛、绵羊、山羊、猪和鸡以及这些数据是如何有助于鉴定影响重要性状的遗传标记和基因，从而进一步提高基因组选择的准确性。

关键词: 基因组信息; 基因组选择; 畜禽生产性能

Abstract: In order to meet the rapid growth of demand,we must speed up the development of livestock and poultry industry and minimize the impact on the environment.Improving the genetic characteristics of livestock and poultry is expected to promote the solution of this problem.Since the 21st century,the molecular breeding technology with genome selection as the core has provided opportunities.Early and accurate selection can be realized using this technology,which can greatly shorten the generation interval,accelerate the progress of population genetics,and significantly reduce the breeding cost.Although in some breeds,such as dairy cows,genome selection has been successful,and the population has also made great genetic progress,but still can not meet the rapid growth needs.Therefore,it is necessary to find ways to further accelerate genetic progress.The results showed that adding the known functional gene information of target traits to SNP marker data could improve the accuracy of gene breeding value prediction and accelerate the genetic progress.While mining more genomic information,developing more optimized analysis methods can be more conducive to the realization of the goal.This paper summarizes the available genomic data of major livestock and poultry species,including cattle,sheep,goats,pigs and chickens,and how these data can help identify genetic markers and genes that affect important traits,so as to further improve the accuracy of genomic selection.

Key words: genomic information; genomic selection; livestock improvement

中图分类号:

S8-1

周喜荣, 于丽娟, 张玉华, 玛尔孜亚·亚森, 蒋晓梅, 拉扎特·艾尼瓦尔, 张艳花. 利用基因组信息提高畜禽生产性能研究进展[J]. 中国畜牧兽医, 2020, 47(1): 156-163.

ZHOU Xirong, YU Lijuan, ZHANG Yuhua, MAERZIYA·Yasen, JIANG Xiaomei, LAZHATI·Ainiwaer, ZHANG Yanhua. Research Progress on Genomic Information for Livestock Improvement[J]. China Animal Husbandry and Veterinary Medicine, 2020, 47(1): 156-163.

参考文献

[1] THORNTON P K.Livestock production:Recent trends,future prospects[J].Philosophical Transactions of the Royal Society B:Biological Sciences,2010,365(1554):2853-2867.
[2] HAVENSTEIN G B,FERKET P R,QURESHI M A.Growth,livability,and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets[J].Poultry Science,2003,82(10):1500-1508.
[3] CAPPER J L,BAUMAN D E.The role of productivity in improving the environmental sustainability of ruminant production systems[J].Annual Review of Animal Biosciences,2013,1:469-489.
[4] PRYCE J E,ROYAL M D,GARNSWORTHY P C,et al.Fertility in the high-producing dairy cow[J].Livestock Production Science,2004,86(1-3):125-135.
[5] GODDARD M E,HAYES B J.Mapping genes for complex traits in domestic animals and their use in breeding programmes[J].Nature Reviews Genetics,2009,10(6):381-391.
[6] MEUWISSEN T H E,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829.
[7] HILL W G,GODDARD M E,VISSCHER P M.Data and theory point to mainly additive genetic variance for complex traits[J].PLoS Genetics,2008,4(2):e1000008.
[8] WIGGANS G R,COLE J B,HUBBARD S M,et al.Genomic selection in dairy cattle:The USDA experience[J].Annual Review of Animal Biosciences,2017,5(1):309-327.
[9] 阿地力江·卡德尔,刘雪雪,杨敏,等.全基因组选择信号检测方法在家畜研究中的应用进展[J].中国畜牧兽医,2016,43(6):1641-1646. ADILJAN K,LIU X X,YANG M,et al.Development and application of genomic selection signature methods in livestock species[J].China Animal Husbandry & Veterinary Medicine,2016,43(6):1641-1646.
[10] CROSSA J,PÉREZ-RODRÍGUEZ P,CUEVAS J,et al.Genomic selection in plant breeding:Methods,models,and perspectives[J].Trends in Plant Science,2017,22(11):961-975.
[11] MARIO HERRERO S W B H.Livestock and the environment:What have we learned in the past decade[J].Annual Review of Environment and Resources,2015,40:177-202.
[12] CONSORTIUM S A,ELSIK C G,TELLAM R L,et al.The genome sequence of taurine cattle:A window to ruminant biology and evolution[J].Science,2009,324(5926):522-528.
[13] JIANG Y,XIE M,CHEN W,et al.The sheep genome illuminates biology of the rumen and lipid metabolism[J].Science,2014,344(6188):1168-1173.
[14] DONG Y,XIE M,JIANG Y,et al.Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus)[J].Nature Biotechnology,2013,31(2):135-141.
[15] GROENEN M A,ARCHIBALD A L,UENISHI H,et al.Analyses of pig genomes provide insight into porcine demography and evolution[J].Nature,2012,491(7424):393-398.
[16] INTERNATIONAL CHICKEN GENOME SEQUENCING CONSORTIUM.Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution[J].Nature,2004,432(7018):695-716.
[17] DAETWYLER H D,CAPITAN A,PAUSCH H,et al.Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle[J].Nature Genetics,2014,46(8):858-865.
[18] KORLACH J,MARKS P J,CICERO R L,et al.Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in Zero-Mode waveguide nanostructures[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(4):1176-1181.
[19] CLARKE J,WU H C,JAYASINGHE L,et al.Continuous base identification for single-molecule nanopore DNA sequencing[J].Nature Nanotechnology,2009,4(4):265-270.
[20] BICKHART D M,ROSEN B D,KOREN S,et al.Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome[J].Nature Genetics,2017,49(4):643-650.
[21] GEORGES M,CHARLIER C,HAYES B.Harnessing genomic information for livestock improvement[J].Nature Reviews Genetics,2019,20(3):135-156.
[22] WONG G K,LIU B,WANG J,et al.A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms[J].Nature,2004,432(7018):717-722.
[23] AUTON A,BROOKS L D,DURBIN R M,et al.A global reference for human genetic variation[J].Nature,2015,526(7571):68-74.
[24] CHARLIER C,LI W,HARLAND C,et al.NGS-based reverse genetic screen for common embryonic lethal mutations compromising fertility in livestock[J].Genome Research,2016,26(10):1333-1341.
[25] MACLEOD I M,BOWMAN P J,VANDER JAGT C J,et al.Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits[J].BMC Genomics,2016,17:144.
[26] BOURNEUF E,OTZ P,PAUSCH H,et al.Rapid Discovery of de novo deleterious mutations in cattle enhances the value of livestock as model species[J].Scientific Reports,2017,7(1):11466.
[27] KADRI N K,HARLAND C,FAUX P,et al.Coding and noncoding variants in HFM1,MLH3,MSH4,MSH5,RNF212,and RNF212B affect recombination rate in cattle[J].Genome Research,2016,26(10):1323-1332.
[28] SCHAUB M A,BOYLE A P,KUNDAJE A,et al.Linking disease associations with regulatory information in the human genome[J].Genome Research,2012,22(9):1748-1759.
[29] ANDERSSON L,ARCHIBALD A L,BOTTEMA C D,et al.Coordinated international action to accelerate genome-to-phenome with FAANG,the functional annotation of animal genomes project[J].Genome Biology,2015,16:57.
[30] VILLAR D,BERTHELOT C,ALDRIDGE S,et al.Enhancer evolution across 20 mammalian species[J].Cell,2015,160(3):554-566.
[31] ZHOU Y,XU L,BICKHART D M,et al.Reduced representation bisulphite sequencing of ten bovine somatic tissues reveals DNA methylation patterns and their impacts on gene expression[J].BMC Genomics,2016,17(1):779.
[32] LITTLEJOHN M D,TIPLADY K,FINK T A,et al.Sequence-based association analysis reveals an MGST1 eQTL with pleiotropic effects on bovine milk composition[J].Scientific Reports,2016,6:25376.
[33] KEMPER K E,LITTLEJOHN M D,LOPDELL T,et al.Leveraging genetically simple traits to identify small-effect variants for complex phenotypes[J].BMC Genomics,2016,17(1):858.
[34] BODO B,MARKUS O S,JULIANE F,et al.Adrenal cortex expression quantitative trait loci in a German Holstein×Charolais cross[J].BMC Genetics,2016,17(1):135.
[35] THOMAS J L,KATHRYN T,MAKSIM S,et al.DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content[J].BMC Genomics,2017,18(1):968.
[36] CHEN C,YANG B,ZENG Z,et al.Genetic dissection of blood lipid traits by integrating genome-wide association study and gene expression profiling in a porcine model[J].BMC Genomics,2013,14:848.
[37] JUNWU M,JIE Y,LISHENG Z,et al.A splice mutation in the PHKG1 gene causes high glycogen content and low meat quality in pig skeletal muscle[J].PLoS Genetics,2014,10(10):e1004710.
[38] GONZÁLEZ-PRENDES R,QUINTANILLA R,CÁNOVAS A,et al.Joint QTL mapping and gene expression analysis identify positional candidate genes influencing pork quality traits[J].Scientific Reports,2017,7:39830.
[39] MAROILLEY T,LEMONNIER G,LECARDONNEL J,et al.Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis[J].BMC Genomics,2017,18(1):967.
[40] YUNA B,GUILLAUME L M,DAVID C,et al.Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken[J].BMC Genomics,2011,12:567.
[41] JOHNSSON M,JONSSON K B,ANDERSSON L,et al.Quantitative trait locus and genetical genomics analysis identifies putatively causal genes for fecundity and brooding in the chicken[J].G3(Bethesda),2015,6(2):311-319.
[42] JOHNSSON M,WILLIAMS M J,JENSEN P,et al.Genetical genomics of behavior:A novel chicken genomic model for anxiety behavior[J].Genetics,2016,202(1):327-340.
[43] FALLAHSHAROUDI A,DE KOCK N,JOHNSSON M,et al.QTL mapping of stress related gene expression in a cross between domesticated chickens and ancestral red junglefowl[J].Molecular and Cellular Endocrinology,2017,446:52-58.
[44] BATTLE A,BROWN C D,ENGELHARDT B E,et al.Genetic effects on gene expression across human tissues[J].Nature,2017,550(7675):204-213.
[45] MARTINEZ V,BVNGER L,HILL W G.Analysis of response to 20 generations of selection for body composition in mice:Fit to infinitesimal model assumptions[J].Genetics Selection Evolution,2000,32(1):3-21.
[46] MICHAEL L,BRUCE W.Genetics and Analysis of Quantitative Traits[M].Sinauer:Sunderland,1998.
[47] HENDERSON C R.Sire evaluation and genetic trends[J].Journal of Animal Science,1973,1973(Symposium):10-41.
[48] WRIGHT S.Coefficients of inbreeding and relationship[J].The American Naturalist,1922,56:330-338.
[49] GARANT D,KRUUK L E.How to use molecular marker data to measure evolutionary parameters in wild populations[J].Molecular Ecology,2005,14(7):1843-1859.
[50] ROHAN L F,HAO C,BRUCE L,et al.Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals[J].Genetics Selection Evolution,2016,48(1):96.
[51] GARCÍA-RUIZ A,COLE J B,VANRADEN P M,et al.Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection[J].Proceedings of the National Academy of Sciences of the United States of America,2016,113(28):E3995-E4004.
[52] LOURENCO D A L,TSURUTA S,FRAGOMENI B O,et al.Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus[J].Journal of Animal Science,2015,93(6):2653-2662.
[53] CAITLYN E A,JACK C M D,MAX F R,et al.Total cost estimation for implementing genome-enabled selection in a multi-level swine production system[J].Genetics Selection Evolution,2014,46:32.
[54] SANTOS B F S,AMER P R,GRANLEESE T,et al.Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations[J].Journal of Animal Breeding and Genetics,2018,135(3):221-237.
[55] WOLC A,ZHAO H H,ARANGO J,et al.Response and inbreeding from a genomic selection experiment in layer chickens[J].Genetics Selection Evolution,2015,47:59.
[56] GONZALEZ-RECIO O,PRYCE J E,HAILE-MARIAM M,et al.Incorporating heifer feed efficiency in the Australian selection index using genomic selection[J].Journal of Dairy Science,2014,97(6):3883-3893.
[57] 伏智亮,沈如玉,王绮绮,等.肌肉生长抑制素调控肌肉和脂肪组织代谢的研究进展[J].畜牧与兽医,2019,51(7):121-126. FU Z L,SHEN R Y,WANG Q Q,et al.Advances in researches on myostatin in the regulation of muscle and adipose tissue metabolism[J].Animal Husbandry & Veterinary Medicine,2019,51(7):121-126.(in Chinese)
[58] 吴东旺,孙丽媛,袁再美,等.动物DGAT基因的研究进展[J].中国畜牧兽医,2019,46(7):1945-1952. WU D W,SUN L Y,YUAN Z M,et al.Research progress on animal DGAT gene[J].China Animal Husbandry & Veterinary Medicine,2019,46(7):1945-1952.(in Chinese)
[59] BLOTT S,KIM J J,MOISIO S,et al.Molecular dissection of a quantitative trait locus:A phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition[J].Genetics,2003,163(1):253-266.
[60] COHEN-ZINDER M,SEROUSSI E,LARKIN D M,et al.Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle[J].Genome Research,2005,15(7):936-944.
[61] KARIM L,TAKEDA H,LIN L,et al.Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature[J].Nature Genetics,2011,43(5):405-413.
[62] BOUWMAN A C,DAETWYLER H D,CHAMBERLAIN A J,et al.Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals[J].Nature Genetics,2018,50(3):362-367.
[63] DAVID H,ROHAN L F,KADIR K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[64] ERBE M,HAYES B J,MATUKUMALLI L K,et al.Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels[J].Journal of Dairy Science,2012,95(7):4114-4129.
[65] MIKE G.Genomic selection:Prediction of accuracy and maximisation of long term response[J].Genetica,2009,136(2):245-257.
[66] PSZCZOLA M,CALUS M P L.Updating the reference population to achieve constant genomic prediction reliability across generations[J].Animal,2016,10(6):1018-1024.
[67] BRΦNDUM R F,SU G,JANSS L,et al.Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction[J].Journal of Dairy Science,2015,98(6):4107-4116.
[68] HUANG H,FANG M,JOSTINS L,et al.Fine-mapping inflammatory bowel disease loci to single-variant resolution[J].Nature,2017,547(7663):173-178.
[69] WANG H,YANG H,SHIVALILA C S,et al.One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering[J].Cell,2013,153(4):910-918.
[70] JENKO J,GORJANC G,CLEVELAND M A,et al.Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs[J].Genetics Selection Evolution,2015,47:55.
[71] KASINATHAN P,WEI H,XIANG T,et al.Acceleration of genetic gain in cattle by reduction of generation interval[J].Scientific Reports,2015,5:8674.
[72] ROSS E M,MOATE P J,MARETT L C,et al.Metagenomic predictions:From microbiome to complex health and environmental phenotypes in humans and cattle[J].PLoS One,2013,8(9):e73056.
[73] KITTELMANN S,PINARES-PATIÑO C S,SEEDORF H,et al.Two different bacterial community types are linked with the low-methane emission trait in sheep[J].PLoS One,2014,9(7):e103171.