

畜牧兽医学报 ›› 2025, Vol. 56 ›› Issue (10): 4759-4773.doi: 10.11843/j.issn.0366-6964.2025.10.001
曹雨1(
), 周铂涵1, 许琦1, 袁子翱1, 苏蕊1, 吕琦1, 李金泉1,2, 张燕军1, 王瑞军1, 王志英1,3,*(
)
收稿日期:2025-03-14
出版日期:2025-10-23
发布日期:2025-11-01
通讯作者:
王志英
E-mail:1825948624@qq.com;wzhy0321@126.com
作者简介:曹雨(2001-),女,四川资阳人,硕士生,主要从事羊遗传资源保护与育种研究,E-mail: 1825948624@qq.com
基金资助:
CAO Yu1(
), ZHOU Bohan1, XU Qi1, YUAN Zi'ao1, SU Rui1, LÜ Qi1, LI Jinquan1,2, ZHANG Yanjun1, WANG Ruijun1, WANG Zhiying1,3,*(
)
Received:2025-03-14
Online:2025-10-23
Published:2025-11-01
Contact:
WANG Zhiying
E-mail:1825948624@qq.com;wzhy0321@126.com
摘要:
精准识别与动物经济性状显著相关的潜在功能基因位点,对提升动物个体育种效果和改良生产性能具有重要的理论与实践意义。随着组学技术的发展,整合多组学数据已成为提高基因鉴定准确性的有效策略。其中,表达数量性状基因座(expression quantitative trait locus, eQTL)分析为解析基因型与经济性状的关联机制以及基因表达的遗传调控网络提供了新的研究视角,使其成为后全基因组关联分析(post-genome-wide association studies, Post-GWAS)时代的重要研究方向。本文首先介绍了eQTL分析的基本原理与方法,然后重点探讨了整合eQTL与GWAS数据提升功能基因位点鉴定效率的策略与方法及其在重要家畜(牛、猪、鸡、羊)遗传育种方面的应用。最后归纳总结了在应用过程中存在的问题,为深入解析动物复杂性状的遗传机制提供了理论依据,同时为推进动物生物育种技术创新、助力种业振兴奠定了重要基础。
中图分类号:
曹雨, 周铂涵, 许琦, 袁子翱, 苏蕊, 吕琦, 李金泉, 张燕军, 王瑞军, 王志英. 整合eQTL和GWAS数据识别潜在功能基因位点在动物遗传育种中的研究进展[J]. 畜牧兽医学报, 2025, 56(10): 4759-4773.
CAO Yu, ZHOU Bohan, XU Qi, YUAN Zi'ao, SU Rui, LÜ Qi, LI Jinquan, ZHANG Yanjun, WANG Ruijun, WANG Zhiying. Research Progress on Integrated eQTL-GWAS Data Analysis for Potential Functional Genetic Loci Identification in Animal Breeding[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(10): 4759-4773.
| 1 | 王建刚. 山羊多羔性状基因网络构建及其关键功能基因筛选[D]. 杨凌: 西北农林科技大学, 2015. |
| WANG J G. Construction of genetic network on goat prolificacy and Selection of the key functional genes[D]. Yangling: Northwest: A&F University, 2015. (in Chinese) | |
| 2 |
XU M , TANG Q , QI J , et al. Integration of GWAS and transcriptomic analyses reveal candidate genes for duck gonadal development during puberty onset[J]. BMC Genomics, 2024, 25 (1): 1151.
doi: 10.1186/s12864-024-11079-3 |
| 3 |
GUO Y , GU X , SHENG Z , et al. A complex structural variation on chromosome 27 leads to the ectopic expression of HOXB8 and the muffs and beard phenotype in chickens[J]. PLOS Genet, 2016, 12 (6): e1006071.
doi: 10.1371/journal.pgen.1006071 |
| 4 |
ZHANG L , GUO Y , WANG L , et al. Genomic variants associated with the number and diameter of muscle fibers in pigs as revealed by a genome-wide association study[J]. Animal, 2020, 14 (3): 475- 481.
doi: 10.1017/S1751731119002374 |
| 5 | 田敏. 奶山羊群体遗传多样性及重要经济性状关键基因鉴定[D]. 杨凌: 西北农林科技大学, 2024. |
| TIAN M. Assessing genetic diversity and identification of key genes for important economic traits in dairy goats[D]. Yangling: Northwest A&F University, 2024. (in Chinese) | |
| 6 | 马蒙, 朱军. 数量性状位点定位和全基因组关联分析的方法与软件综述[J]. 浙江大学学报, 2014, 40 (4): 379- 386. |
| MA M , ZHU J . Tools for quantitat trait locus mapping and genome-wide association study mapping: a review[J]. Journal of Zhejiang University, 2014, 40 (40): 379- 386. | |
| 7 | 澈力木格. 全基因组关联分析挖掘中国西门塔尔牛生长性状候选功能基因[D]. 呼和浩特: 内蒙古大学, 2023. |
| CHE L M G. Genome-wideassociation study for growth traits-related candidate functional gene in Chinese simmental cattle[D]. Hohhot: Inner Mongolia University, 2023. (in Chinese) | |
| 8 |
WEI C , ZENG H , ZHONG Z , et al. Integration of non-additive genome-wide association study with a multi-tissue transcriptome analysis of growth and carcass traits in Duroc pigs[J]. Animal, 2023, 17 (6): 100817.
doi: 10.1016/j.animal.2023.100817 |
| 9 | HOU L , ZHAO H . A review of post-GWAS prioritization approaches[J]. Front Genet, 2013, 4, 280. |
| 10 |
MAJEWSKI J , PASTINEN T . The study of eQTL variations by RNA-seq: From SNPs to phenotypes[J]. Trends Genet, 2011, 27 (2): 72- 79.
doi: 10.1016/j.tig.2010.10.006 |
| 11 |
LEAL-GUTIÉRREZ J D , ELZO M A , MATEESCU R G . Identification of eQTLs and sQTLs associated with meat quality in beef[J]. BMC Genomics, 2020, 21 (1): 104.
doi: 10.1186/s12864-020-6520-5 |
| 12 | 赵真坚, 王凯, 陈栋, 等. 基因组和DNA甲基化组联合分析筛选猪肉质性状关键基因[J]. 中国农业科学, 2024, 57 (7): 1394- 1406. |
| ZHAO Z J , WANG K , CHEN D , et al. Integrated analysis of genome and DNA methylation for screening key genes related to pork quality traits[J]. Scientia Agricultura Sinica, 2024, 57 (7): 1394- 1406. | |
| 13 |
YANG L , YIN H , BAI L , et al. Mapping and functional characterization of structural variation in 1060 pig genomes[J]. Genome Biol, 2024, 25 (1): 116.
doi: 10.1186/s13059-024-03253-3 |
| 14 |
FENG Y , ZHANG M , LIU Y , et al. Quantitative microbiome profiling reveals the developmental trajectory of the chicken gut microbiota and its connection to host metabolism[J]. Imeta, 2023, 2 (2): e105.
doi: 10.1002/imt2.105 |
| 15 |
LIU X , ZHANG J , XIONG X , et al. An integrative analysis of transcriptome and GWAS data to identify potential candidate genes influencing meat quality traits in pigs[J]. Front Genet, 2021, 12, 748070.
doi: 10.3389/fgene.2021.748070 |
| 16 | 王钟毓. 湖羊肌内脂肪沉积的关键基因鉴定及其分子调控机制研究[D]. 兰州: 兰州大学, 2024. |
| WANG Z Y. Identification of key genes and their molecular regulatory mechanisms in intramuscular fat deposition in Hu sheep[D]. Lanzhou: Lanzhou University, 2024. (in Chinese) | |
| 17 |
CHIANG C , SCOTT A J , DAVIS J R , et al. The impact of structural variation on human gene expression[J]. Nat Genet, 2017, 49 (5): 692- 699.
doi: 10.1038/ng.3834 |
| 18 |
NICIURA S C M , CARDOSO T F , IBELLI A M G , et al. Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortusin sheep[J]. Parasit Vectors, 2024, 17 (1): 102.
doi: 10.1186/s13071-024-06205-9 |
| 19 |
CONESA A , MADRIGAL P , TARAZONA S , et al. A survey of best practices for RNA-seq data analysis[J]. Genome Biol, 2016, 17 (1): 13.
doi: 10.1186/s13059-016-0881-8 |
| 20 |
RANJAN A , BUDKE J M , ROWLAND S D , et al. eQTL regulating transcript levels associated with diverse biological processes in tomato[J]. Plant Physiol, 2016, 172 (1): 328- 340.
doi: 10.1104/pp.16.00289 |
| 21 |
ONGEN H , BUIL A , BROWN A A , et al. Fast and efficient QTL mapper for thousands of molecular phenotypes[J]. Bioinformatics, 2016, 32 (10): 1479- 1485.
doi: 10.1093/bioinformatics/btv722 |
| 22 | PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014: 701-710. |
| 23 |
SUL J H , HAN B , YE C , et al. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches[J]. PLOS Genet, 2013, 9 (6): e1003491.
doi: 10.1371/journal.pgen.1003491 |
| 24 |
CARITHERS L J , MOORE H M . The genotype-tissue expression (GTEx) project[J]. Biopreservation and Biobanking, 2015, 13 (5): 307- 308.
doi: 10.1089/bio.2015.29031.hmm |
| 25 |
DAVIS C A , HITZ B C , SLOAN C A , et al. The encyclopedia of DNA elements (ENCODE): Data portal update[J]. Nucleic Acids Res, 2018, 46 (D1): D794- D801.
doi: 10.1093/nar/gkx1081 |
| 26 |
VÕSA U , CLARINGBOULD A , WESTRA H J , et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression[J]. Nat Genet, 2021, 53 (9): 1300- 1310.
doi: 10.1038/s41588-021-00913-z |
| 27 |
XIANG R , BREEN E J , BOLORMAA S , et al. Mutant alleles differentially shape fitness and other complex traits in cattle[J]. Commun Biol, 2021, 4 (1): 1353.
doi: 10.1038/s42003-021-02874-9 |
| 28 |
OTA M , NAGAFUCHI Y , HATANO H , et al. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases[J]. Cell, 2021, 184 (11): 3006- 3021.
doi: 10.1016/j.cell.2021.03.056 |
| 29 |
ZHANG Y , WANG M , LI Z , et al. An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs[J]. Sci China Life Sci, 2024, 67 (6): 1133- 1154.
doi: 10.1007/s11427-023-2522-8 |
| 30 |
ZHAO S , CROUSE W , QIAN S , et al. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits[J]. Nat Genet, 2024, 56 (2): 336- 347.
doi: 10.1038/s41588-023-01648-9 |
| 31 |
HORMOZDIARI F , VAN DE BUNT M , SEGRÈ A V , et al. Colocalization of GWAS and eQTL signals detects target genes[J]. Am J Hum Genet, 2016, 99 (6): 1245- 1260.
doi: 10.1016/j.ajhg.2016.10.003 |
| 32 |
GIAMBARTOLOMEI C , VUKCEVIC D , SCHADT E E , et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics[J]. PLOS Genet, 2014, 10 (5): e1004383.
doi: 10.1371/journal.pgen.1004383 |
| 33 |
GIAMBARTOLOMEI C , ZHENLI LIU J , ZHANG W , et al. A Bayesian framework for multiple trait colocalization from summary association statistics[J]. Bioinformatics, 2018, 34 (15): 2538- 2545.
doi: 10.1093/bioinformatics/bty147 |
| 34 |
WALLACE C . A more accurate method for colocalisation analysis allowing for multiple causal variants[J]. PLOS Genet, 2021, 17 (9): e1009440.
doi: 10.1371/journal.pgen.1009440 |
| 35 |
PIVIDORI M , RAJAGOPAL P S , BARBEIRA A , et al. PhenomeXcan: Mapping the genome to the phenome through the transcriptome[J]. Sci Adv, 2020, 6 (37): eaba2083.
doi: 10.1126/sciadv.aba2083 |
| 36 | 江珍珍, 李娜. 一种整合转录组和基因组数据的关联检验[J]. 数理统计与管理, 2024, 43 (6): 995- 1009. |
| JIANG Z Z , LI N . An association test for integrating transcriptome and genomic data[J]. Journal of Applied Statistics and Management, 2024, 43 (6): 995- 1009. | |
| 37 | 靳秀媛. 全转录组关联分析中连续结局网络回归模型构建策略与统计方法研究[D]. 济南: 山东大学, 2023. |
| JIN X Y. Construction strategy and statistical methods for network regression model with continuous outcome in transcriptomewide association studies[D]. Jinan: Shandong University, 2023. (in Chinese) | |
| 38 | 郭萍, 刘璐, 燕冉, 等. 全转录组关联研究的设计、分析与展望[J]. 中国卫生统计, 2023, 40 (1): 144-148, 152. |
| GUO P , LIU L , YAN R , et al. Design analysis and outlook of transcriptome-wide association studies[J]. Chinese Health Statistics, 2023, 40 (1): 144-148, 152. | |
| 39 |
GAMAZON E R , WHEELER H E , SHAH K P , et al. A gene-based association method for mapping traits using reference transcriptome data[J]. Nat Genet, 2015, 47 (9): 1091- 1098.
doi: 10.1038/ng.3367 |
| 40 |
GUSEV A , KO A , SHI H , et al. Integrative approaches for large-scale transcriptome-wide association studies[J]. Nat Genet, 2016, 48 (3): 245- 252.
doi: 10.1038/ng.3506 |
| 41 |
LUNINGHAM J M , CHEN J , TANG S , et al. Bayesian genome-wide TWAS method to leverage both cis- and trans-eQTL information through summary statistics[J]. Am J Hum Genet, 2020, 107 (4): 714- 726.
doi: 10.1016/j.ajhg.2020.08.022 |
| 42 |
BARBEIRA A N , PIVIDORI M , ZHENG J , et al. Integrating predicted transcriptome from multiple tissues improves association detection[J]. PLOS Genet, 2019, 15 (1): e1007889.
doi: 10.1371/journal.pgen.1007889 |
| 43 |
XIE Y , SHAN N , ZHAO H , et al. Transcriptome-wide association studies: General framework and methods[J]. Quant Biol, 2021, 9 (2): 141- 150.
doi: 10.15302/J-QB-020-0228 |
| 44 | ZHANG Z, CHEN Z, TENG J, et al. FarmGTEx TWAS-server: An interactive web server for customized TWAS analysis[J/OL]. Genomics, Proteomics and Bioinformatics, 2025. DOI: 10.1093/gpbjnl/qzaf006. |
| 45 | 王晶, 张国燕, 程杉. 孟德尔随机化的良好实践——孟德尔随机化分析的常见设计、关键挑战及优化[J]. 首都医科大学学报, 2023, 44 (6): 1087- 1094. |
| WANG J , ZHANG G Y , CHENG S . Good practices in mendelian randomization: common designs, key chal-lenges, and optimization in mendelian randomization analysis[J]. Journal of Capital Medical University, 2023, 44 (6): 1087- 1094. | |
| 46 | DAVIES N M , HOLMES M V , SMITH G D . Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362, k601. |
| 47 |
BARFIELD R , FENG H , GUSEV A , et al. Transcriptome-wide association studies accounting for colocalization using Egger regression[J]. Genet Epidemiol, 2018, 42 (5): 418- 433.
doi: 10.1002/gepi.22131 |
| 48 |
YUAN Z , ZHU H , ZENG P , et al. Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies[J]. Nat Commun, 2020, 11 (1): 3861.
doi: 10.1038/s41467-020-17668-6 |
| 49 |
ZHU Z , ZHANG F , HU H , et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets[J]. Nat Genet, 2016, 48 (5): 481- 487.
doi: 10.1038/ng.3538 |
| 50 |
ZHU H , ZHOU X . Transcriptome-wide association studies: A view from Mendelian randomization[J]. Quant Biol, 2021, 9 (2): 107- 121.
doi: 10.1007/s40484-020-0207-4 |
| 51 |
WAINBERG M , SINNOTT-ARMSTRONG N , MANCUSO N , et al. Opportunities and challenges for transcriptome-wide association studies[J]. Nat Genet, 2019, 51 (4): 592- 599.
doi: 10.1038/s41588-019-0385-z |
| 52 |
ZOU Y , CARBONETTO P , WANG G , et al. Fine-mapping from summary data with the "sum of single effects" model[J]. PLOS Genet, 2022, 18 (7): e1010299.
doi: 10.1371/journal.pgen.1010299 |
| 53 |
BERISA T , PICKRELL J K . Approximately independent linkage disequilibrium blocks in human populations[J]. Bioinformatics, 2016, 32 (2): 283- 285.
doi: 10.1093/bioinformatics/btv546 |
| 54 |
WANG G , SARKAR A , CARBONETTO P , et al. A simple new approach to variable selection in regression, with application to genetic fine mapping[J]. J Royal Stat Soc Ser B (Statistical Methodology), 2020, 82 (5): 1273- 1300.
doi: 10.1111/rssb.12388 |
| 55 |
LI J , MUKIIBI R , JIMINEZ J , et al. Applying multi-omics data to study the genetic background of bovine respiratory disease infection in feedlot crossbred cattle[J]. Front Genet, 2022, 13, 1046192.
doi: 10.3389/fgene.2022.1046192 |
| 56 |
BADIA-BRINGUÉ G , CANIVE M , FERNANDEZ-JIMENEZ N , et al. Summary-data based Mendelian randomization identifies gene expression regulatory polymorphisms associated with bovine paratuberculosis by modulation of the nuclear factor kappa β (NF-κß)-mediated inflammatory response[J]. BMC Genomics, 2023, 24 (1): 605.
doi: 10.1186/s12864-023-09710-w |
| 57 |
WANG T , NIU Q , ZHANG T , et al. Cis-eQTL analysis and functional validation of candidate genes for carcass yield traits in beef cattle[J]. Int J Mol Sci, 2022, 23 (23): 15055.
doi: 10.3390/ijms232315055 |
| 58 |
SILVA-VIGNATO B , CESAR A S M , AFONSO J , et al. Integrative analysis between genome-wide association study and expression quantitative trait loci reveals bovine muscle gene expression regulatory polymorphisms associated with intramuscular fat and backfat thickness[J]. Front Genet, 2022, 13, 935238.
doi: 10.3389/fgene.2022.935238 |
| 59 |
CAI W , ZHANG Y , CHANG T , et al. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle[J]. J Anim Sci Biotechnol, 2023, 14 (1): 78.
doi: 10.1186/s40104-023-00876-7 |
| 60 |
LIU S , GAO Y , CANELA-XANDRI O , et al. A multi-tissue atlas of regulatory variants in cattle[J]. Nat Genet, 2022, 54 (9): 1438- 1447.
doi: 10.1038/s41588-022-01153-5 |
| 61 |
TANG Y , ZHANG J , LI W , et al. Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle[J]. BMC Genomics, 2024, 25 (1): 445.
doi: 10.1186/s12864-024-10346-7 |
| 62 |
QADRI Q R , LAI X , ZHAO W , et al. Exploring the interplay between the hologenome and complex traits in bovine and porcine animals using genome-wide association analysis[J]. Int J Mol Sci, 2024, 25 (11): 6234.
doi: 10.3390/ijms25116234 |
| 63 |
ZHU Z , CHEN X , ZHANG S , et al. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective[J]. Hum Genet, 2023, 142 (11): 1543- 1560.
doi: 10.1007/s00439-023-02602-9 |
| 64 |
MAPEL X M , KADRI N K , LEONARD A S , et al. Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle[J]. Nat Commun, 2024, 15 (1): 674.
doi: 10.1038/s41467-024-44935-7 |
| 65 | 徐盼, 张震, 章峰, 等. 整合数字基因表达谱与全基因组关联分析鉴定猪血液性状候选基因[J]. 中国农业科学, 2016, 49 (2): 348- 360. |
| XU P , ZHANG Z , ZHANG F , et al. Identification of candidate genes for hematological traits by integrating gene expression profiling and genome-wide association study in a porcine model[J]. Scientia Agricultura Sinica, 2016, 49 (2): 348- 360. | |
| 66 |
徐盼, 张震, 崔磊磊, 等. 白色杜洛克×二花脸资源家系猪血液性状的系统遗传学研究[J]. 畜牧兽医学报, 2016, 47 (2): 232- 240.
doi: 10.11843/j.issn.0366-6964.2016.02.004 |
|
XU P , ZHANG Z , CUI L L , et al. A systems genetics study of hematological traits in a white duroc×erhualian pigs F2 resource population[J]. Acta Veterinaria et Zootechnica Sinica, 2016, 47 (2): 232- 240.
doi: 10.11843/j.issn.0366-6964.2016.02.004 |
|
| 67 |
DAZA K R , VELEZ-IRIZARRY D , CASIRÓ S , et al. Integrated genome-wide analysis of microRNA expression quantitative trait loci in pig longissimus dorsi muscle[J]. Front Genet, 2021, 12, 644091.
doi: 10.3389/fgene.2021.644091 |
| 68 | 刘炎. 整合多种分子表型QTL解析eQTL的形成机制及鉴定猪肉质性状候选基因的功能突变[D]. 武汉: 华中农业大学, 2022. |
| LIU Y. Integrating multiple molecular QTL to resolve the formation mechanism of eQTL and identify functional mutations of candidate genes for pig meat quality traits[D]. Wuhan: Huazhong Agricultural University, 2022. (in Chinese) | |
| 69 |
TENG J , GAO Y , YIN H , et al. A compendium of genetic regulatory effects across pig tissues[J]. Nat Genet, 2024, 56 (1): 112- 123.
doi: 10.1038/s41588-023-01585-7 |
| 70 |
黄雅妮, 唐熹, 李井泉, 等. 大规模群体解析猪日增重及达百千克体重日龄的潜在因果基因[J]. 畜牧兽医学报, 2025, 56 (3): 1100- 1109.
doi: 10.11843/j.issn.0366-6964.2025.03.012 |
|
HUANG Y N , TANG X , LI J Q , et al. Large-scale population analysis of potential causal genes for daily weight gain and age at 100 kg in pigs[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56 (3): 1100- 1109.
doi: 10.11843/j.issn.0366-6964.2025.03.012 |
|
| 71 | 赵迪. 基于转录组鉴定天农麻鸡胴体性状关键基因[D]. 佛山: 佛山科学技术学院, 2022. |
| ZHAO D. Identification of key genes for carcass traits in Tiannong partridge chickens based on transcriptome[D]. Foshan: Foshan University, 2022. (in Chinese) | |
| 72 |
TAN X , LIU R , ZHAO D , et al. Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens[J]. J Adv Res, 2024, 55, 1- 16.
doi: 10.1016/j.jare.2023.02.016 |
| 73 | 徐燕萍, 陈璐祎, 刘玮丽, 等. 肝疾病中炎症衰老机制研究进展[J]. 浙江大学学报, 2025, 54 (1): 90- 98. |
| XU Y Q , CHEN L Y , LIU W L , et al. Advances in inflammatory senescence in liver disease[J]. Journal of Zhejiang University, 2025, 54 (1): 90- 98. | |
| 74 |
SUN C , LAN F , ZHOU Q , et al. Mechanisms of hepatic steatosis in chickens: Integrated analysis of the host genome, molecular phenomics and gut microbiome[J]. GigaScience, 2024, 13, giae023.
doi: 10.1093/gigascience/giae023 |
| 75 |
ZHANG W , LAN F , ZHOU Q , et al. Host genetics and gut microbiota synergistically regulate feed utilization in egg-type chickens[J]. J Anim Sci Biotechnol, 2024, 15 (1): 123.
doi: 10.1186/s40104-024-01076-7 |
| 76 | 袁泽湖. 整合GWAS和eQTL先验的绵羊部分肉用性状全基因组选择研究[D]. 兰州: 兰州大学, 2020. |
| YUAN Z H. The prior information from GWAS and eQTL increase the accuracy of genomic selection in several sheep meat traits[D]. Lanzhou: Lanzhou University, 2020. (in Chinese) | |
| 77 |
YUAN Z , SUNDUIMIJID B , XIANG R , et al. Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits[J]. Genet Select Evolut, 2021, 53 (1): 8.
doi: 10.1186/s12711-021-00602-9 |
| 78 | 孙燕勇. 整合eGWAS和eQTL分析绵羊全血转录组与繁殖激素的关联[D]. 呼和浩特: 内蒙古农业大学, 2021. |
| SUN Y Y. The association of whole blood transcriptome and reproductive hormones in sheep by eGWAS and eQTL[D]. Hohhot: Inner Mongolia Agricultural University, 2021. (in Chinese) | |
| 79 | 宋梓辰. 基于全基因组和转录组关联分析鉴定肉兔生长发育和屠宰性状的关键基因[D]. 泰安: 山东农业大学, 2023. |
| SONG Z C. Association analyses on growth and slaughter traits of meat rabbits based on genome and transcriptome sequencing data[D]. Taian: Shandong Agricultural University, 2023. (in Chinese) | |
| 80 |
NEUMEYER S , HEMANI G , ZEGGINI E . Strengthening causal inference for complex disease using molecular quantitative trait loci[J]. Trends Mol Med, 2020, 26 (2): 232- 241.
doi: 10.1016/j.molmed.2019.10.004 |
| 81 |
UMANS B D , BATTLE A , GILAD Y . Where are the disease-associated eQTLs?[J]. Trends in Genetics, 2021, 37 (2): 109- 124.
doi: 10.1016/j.tig.2020.08.009 |
| 82 |
WANG T , LIU Y , YIN Q , et al. Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation[J]. Briefings Bioinformat, 2022, 23 (1): bbab370.
doi: 10.1093/bib/bbab370 |
| 83 |
MAI J , LU M , GAO Q , et al. Transcriptome-wide association studies: Recent advances in methods, applications and available databases[J]. Commun Biol, 2023, 6 (1): 899.
doi: 10.1038/s42003-023-05279-y |
| 84 |
MAI J , QIAN Q , GAO H , et al. scTWAS atlas: An integrative knowledgebase of single-cell transcriptome-wide association studies[J]. Nucleic Acids Res, 2025, 53 (D1): D1195- D1204.
doi: 10.1093/nar/gkae931 |
| 85 |
BURGESS S , DAVEY SMITH G , DAVIES N M , et al. Guidelines for performing Mendelian randomization investigations[J]. Wellcome Open Res, 2020, 4, 186.
doi: 10.12688/wellcomeopenres.15555.2 |
| 86 |
BURGESS S , CRONJÉ H T . Incorporating biological and clinical insights into variant choice for Mendelian randomisation: Examples and principles[J]. Egastroenterology, 2024, 2 (1): e100042.
doi: 10.1136/egastro-2023-100042 |
| 87 | BURGESS S , O'DONNELL C J , GILL D . Expressing results from a Mendelian randomization analysis: Separating results from inferences[J]. JAMA Cardiol, 2021, 6 (1): 7- 8. |
| 88 |
LEVIN M G , BURGESS S . Mendelian randomization as a tool for cardiovascular research: A review[J]. JAMA Cardiol, 2024, 9 (1): 79- 89.
doi: 10.1001/jamacardio.2023.4115 |
| [1] | 周泰增, 杨祎挺, 朱悦华, 钱洪喜, 刘一辉, 甘麦邻, 朱砺, 沈林園. 母猪死胎和木乃伊全基因组关联分析[J]. 畜牧兽医学报, 2025, 56(3): 1231-1241. |
| [2] | 吴嘉浩, 吴姿仪, 窦腾飞, 白利瑶, 张永前, 董联合, 李鹏飞, 李新建, 韩雪蕾, 李秀领. 豫农黑猪生长相关性状的拷贝数变异全基因组关联分析研究[J]. 畜牧兽医学报, 2025, 56(3): 1110-1119. |
| [3] | 张硕, 周雨潇, 吴海波, 索伦. 长效CRISPR/Cas9基因编辑结局的动态追踪研究[J]. 畜牧兽医学报, 2023, 54(10): 4196-4208. |
| [4] | 杨雨婷, 张兴, 牛安然, 闫之春, 龚华忠, 丁偌楠, 马黎. 基于高密度SNP标记重构猪多品种群体系谱[J]. 畜牧兽医学报, 2022, 53(12): 4183-4196. |
| [5] | 杨欣婷, 郑麦青, 谭晓冬, 赵桂苹, 黄超, 李森, 李韦, 文杰, 刘冉冉. 快大型黄羽肉鸡肉品质性状的遗传参数估计和关键基因挖掘[J]. 畜牧兽医学报, 2021, 52(9): 2416-2428. |
| [6] | 刘晓静, 刘璐, 王杰, 崔焕先, 赵桂苹, 文杰. 鸡血糖性状的全基因组关联分析[J]. 畜牧兽医学报, 2020, 51(6): 1187-1195. |
| [7] | 常天鹏, 夏江威, 宝金山, 金生云, 朱波, 徐凌洋, 陈燕, 张路培, 高雪, 李俊雅, 高会江. 利用两种统计模型对中国肉用西门塔尔牛屠宰性状的全基因组关联分析[J]. 畜牧兽医学报, 2018, 49(4): 833-840. |
| [8] | 王杰, 刘璐, 刘杰, 赵桂苹, 刘冉冉, 郑麦青, 文杰. 基于通路分析方法诠释肉鸡体重性状全基因组关联研究[J]. 畜牧兽医学报, 2017, 48(5): 810-817. |
| [9] | 李延鹤,刘军,张涌,权富生. 动物胚胎育种及应用中的技术策略[J]. 畜牧兽医学报, 2016, 47(10): 1954-1960. |
| [10] | 赵谦,浦亚斌,关伟军,赵倩君,马月辉. 猪重要性状全基因组关联分析的研究进展[J]. 畜牧兽医学报, 2015, 46(6): 873-881. |
| [11] | 樊庆灿,王金玉,张跟喜,唐莹,张涛,顾玉萍,施会强. 运用四种线性模型对京海黄鸡上市体重进行全基因组关联分析[J]. 畜牧兽医学报, 2014, 45(7): 1053-1059. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||