Integrating genome- and transcriptome-wide association studies to uncover the host–microbiome interactions in bovine rumen methanogenesis

Wei Wang , Zhenyu Wei , Zhuohui Li , Jianrong Ren , Yanliang Song , Jingyi Xu , Anguo Liu , Xinmei Li , Manman Li , Huimei Fan , Liangliang Jin , Zhannur Niyazbekova , Wen Wang , Yuanpeng Gao , Yu Jiang , Junhu Yao , Fuyong Li , Shengru Wu , Yu Wang

iMeta ›› 2024, Vol. 3 ›› Issue (5) : e234

PDF
iMeta ›› 2024, Vol. 3 ›› Issue (5) :e234 DOI: 10.1002/imt2.234
RESEARCH ARTICLE
Integrating genome- and transcriptome-wide association studies to uncover the host–microbiome interactions in bovine rumen methanogenesis
Author information +
History +
PDF

Abstract

The ruminal microbiota generates biogenic methane in ruminants. However, the role of host genetics in modifying ruminal microbiota-mediated methane emissions remains mysterious, which has severely hindered the emission control of this notorious greenhouse gas. Here, we uncover the host genetic basis of rumen microorganisms by genome- and transcriptome-wide association studies with matched genome, rumen transcriptome, and microbiome data from a cohort of 574 Holstein cattle. Heritability estimation revealed that approximately 70% of microbial taxa had significant heritability, but only 43 genetic variants with significant association with 22 microbial taxa were identified through a genome-wide association study (GWAS). In contrast, the transcriptome-wide association study (TWAS) of rumen microbiota detected 28,260 significant gene–microbe associations, involving 210 taxa and 4652 unique genes. On average, host genetic factors explained approximately 28% of the microbial abundance variance, while rumen gene expression explained 43%. In addition, we highlighted that TWAS exhibits a strong advantage in detecting gene expression and phenotypic trait associations in direct effector organs. For methanogenic archaea, only one significant signal was detected by GWAS, whereas the TWAS obtained 1703 significant associated host genes. By combining multiple correlation analyses based on these host TWAS genes, rumen microbiota, and volatile fatty acids, we observed that substrate hydrogen metabolism is an essential factor linking host–microbe interactions in methanogenesis. Overall, these findings provide valuable guidelines for mitigating methane emissions through genetic regulation and microbial management strategies in ruminants.

Keywords

GWAS / Holstein cattle / host genetics / methanogenesis / rumen microbiota / TWAS

Cite this article

Download citation ▾
Wei Wang, Zhenyu Wei, Zhuohui Li, Jianrong Ren, Yanliang Song, Jingyi Xu, Anguo Liu, Xinmei Li, Manman Li, Huimei Fan, Liangliang Jin, Zhannur Niyazbekova, Wen Wang, Yuanpeng Gao, Yu Jiang, Junhu Yao, Fuyong Li, Shengru Wu, Yu Wang. Integrating genome- and transcriptome-wide association studies to uncover the host–microbiome interactions in bovine rumen methanogenesis. iMeta, 2024, 3(5): e234 DOI:10.1002/imt2.234

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Roques, Simon, Gonzalo Martinez-Fernandez, Yuliaxis Ramayo-Caldas, Milka Popova, Stuart Denman, Sarah J. Meale, and Diego P. Morgavi. 2024. “Recent Advances in Enteric Methane Mitigation and the Long Road to Sustainable Ruminant Production.” Annual Review of Animal Biosciences 12: 321-343. https://doi.org/10.1146/annurev-animal-021022-024931

[2]

Wang, Yue, Zhiping Zhu, Hongmin Dong, Xiuming Zhang, Sitong Wang, and Baojing Gu. 2024. “Mitigation Potential of Methane Emissions in China's Livestock Sector Can Reach One-Third by 2030 at Low Cost.” Nature Food 5: 603-614. https://doi.org/10.1038/s43016-024-01010-0

[3]

Mizrahi, Itzhak, R. John Wallace, and Sarah Moraïs. 2021. “The Rumen Microbiome: Balancing Food Security and Environmental Impacts.” Nature Reviews Microbiology 19: 553-566. https://doi.org/10.1038/s41579-021-00543-6

[4]

Rojas-Downing, M. Melissa, A. Pouyan Nejadhashemi, Timothy Harrigan and Sean A. Woznicki. 2017. “Climate Change and Livestock: Impacts, Adaptation, and Mitigation.” Climate Risk Management 16: 145-163. https://doi.org/10.1016/j.crm.2017.02.001

[5]

Patra, Amlan Kumar. 2017. “Accounting Methane and Nitrous Oxide Emissions, and Carbon Footprints of Livestock Food Products in Different States of India.” Journal of Cleaner Production 162: 678-686. https://doi.org/10.1016/j.jclepro.2017.06.096

[6]

Matthews, Chloe, Fiona Crispie, Eva Lewis, Michael Reid, Paul W. O'Toole, and Paul D. Cotter. 2018. “The Rumen Microbiome: A Crucial Consideration When Optimising Milk and Meat Production and Nitrogen Utilisation Efficiency.” Gut Microbes 10: 115-132. https://doi.org/10.1080/19490976.2018.1505176

[7]

Seshadri, Rekha, Sinead C. Leahy, Graeme T. Attwood, Koon Hoong Teh, Suzanne C. Lambie, Adrian L. Cookson, Emiley A. Eloe-Fadrosh, et al. 2018. “Cultivation and Sequencing of Rumen Microbiome Members From the Hungate1000 Collection.” Nature Biotechnology 36: 359-367. https://doi.org/10.1038/nbt.4110

[8]

Zhou, M., Y.-H. Chung, K. A. Beauchemin, L. Holtshausen, M. Oba, T. A. McAllister, and L. L. Guan. 2011. “Relationship between Rumen Methanogens and Methane Production in Dairy Cows Fed Diets Supplemented With a Feed Enzyme Additive.” Journal of Applied Microbiology 111: 1148-1158. https://doi.org/10.1111/j.1365-2672.2011.05126.x

[9]

Danielsson, R., A. Schnürer, V. Arthurson, and J. Bertilsson. 2012. “Methanogenic Population and CH4 Production in Swedish Dairy Cows Fed Different Levels of Forage.” Applied and Environmental Microbiology 78: 6172-6179. https://doi.org/10.1128/AEM.00675-12

[10]

Tapio, Ilma, Timothy J. Snelling, Francesco Strozzi, and R. John Wallace. 2017. “The Ruminal Microbiome Associated With Methane Emissions from Ruminant Livestock.” Journal of Animal Science and Biotechnology 8: 7. https://doi.org/10.1186/s40104-017-0141-0

[11]

Smith, Paul E., Alan K. Kelly, David A. Kenny, and Sinéad M. Waters. 2022. “Differences in the Composition of the Rumen Microbiota of Finishing Beef Cattle Divergently Ranked for Residual Methane Emissions.” Frontiers in Microbiology 13: 855565. https://doi.org/10.3389/fmicb.2022.855565

[12]

Shi, Weibing, Christina D. Moon, Sinead C. Leahy, Dongwan Kang, Jeff Froula, Sandra Kittelmann, Christina Fan, et al. 2014. “Methane Yield Phenotypes Linked to Differential Gene Expression in the Sheep Rumen Microbiome.” Genome Research 24: 1517-1525. https://doi.org/10.1101/gr.168245.113

[13]

Lee, Jong-Hwan, Sanjay Kumar, Geun-Hye Lee, Dong-Ho Chang, Moon-Soo Rhee, Min-Ho Yoon, and Byoung-Chan Kim. 2013. “Methanobrevibacter boviskoreani sp. Nov., Isolated From the Rumen of Korean Native Cattle.” International Journal of Systematic and Evolutionary Microbiology 63: 4196-4201. https://doi.org/10.1099/ijs.0.054056-0

[14]

Greening, Chris, Renae Geier, Cecilia Wang, Laura C. Woods, Sergio E. Morales, Michael J. McDonald, Rowena Rushton-Green, et al. 2019. “Diverse Hydrogen Production and Consumption Pathways Influence Methane Production in Ruminants.” The ISME Journal 13: 2617-2632. https://doi.org/10.1038/s41396-019-0464-2

[15]

Johnson, K. A., and D. E. Johnson. 1995. “Methane Emissions From Cattle.” Journal of Animal Science 73: 2483-2492. https://doi.org/10.2527/1995.7382483x

[16]

O'Hara, Eóin, André L. A. Neves, Yang Song, and Le Luo Guan. 2020. “The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger?” Annual Review of Animal Biosciences 8: 199-220. https://doi.org/10.1146/annurev-animal-021419-083952

[17]

Zhang, Chenguang, Huifeng Liu, Lei Sun, Yue Wang, Xiaodong Chen, Juan Du, Åsa Sjöling, Junhu Yao, and Shengru Wu. 2023. “An Overview of Host-Derived Molecules That Interact With Gut Microbiota.” iMeta 2: e88. https://doi.org/10.1002/imt2.88

[18]

Bonder, Marc Jan, Alexander Kurilshikov, Ettje F. Tigchelaar, Zlatan Mujagic, Floris Imhann, Arnau Vich Vila, Patrick Deelen, et al. 2016. “The Effect of Host Genetics on the Gut Microbiome.” Nature Genetics 48: 1407-1412. https://doi.org/10.1038/ng.3663

[19]

Yang, Hui, Jinyuan Wu, Xiaochang Huang, Yunyan Zhou, Yifeng Zhang, Min Liu, Qin Liu, et al. 2022. “ABO Genotype Alters the Gut Microbiota by Regulating GalNAc Levels in Pigs.” Nature 606: 358-367. https://doi.org/10.1038/s41586-022-04769-z

[20]

Goodrich, Julia K., Jillian L. Waters, Angela C. Poole, Jessica L. Sutter, Omry Koren, Ran Blekhman, Michelle Beaumont, et al. 2014. “Human Genetics Shape the Gut Microbiome.” Cell 159: 789-799. https://doi.org/10.1016/j.cell.2014.09.053

[21]

Goodrich, Julia K., Emily R. Davenport, Michelle Beaumont, Matthew A. Jackson, Rob Knight, Carole Ober, Tim D. Spector, et al. 2016. “Genetic Determinants of the Gut Microbiome in UK Twins.” Cell Host & Microbe 19: 731-743. https://doi.org/10.1016/j.chom.2016.04.017

[22]

Li, Fuyong, Changxi Li, Yanhong Chen, Junhong Liu, Chunyan Zhang, Barry Irving, Carolyn Fitzsimmons, Graham Plastow, and Le Luo Guan. 2019. “Host Genetics Influence the Rumen Microbiota and Heritable Rumen Microbial Features Associate With Feed Efficiency in Cattle.” Microbiome 7: 92. https://doi.org/10.1186/s40168-019-0699-1

[23]

Wang, Weimin, Yukun Zhang, Xiaoxue Zhang, Chong Li, Lvfeng Yuan, Deyin Zhang, Yuan Zhao, et al. 2023. “Heritability and Recursive Influence of Host Genetics on the Rumen Microbiota Drive Body Weight Variance in Male Hu Sheep Lambs.” Microbiome 11: 197. https://doi.org/10.1186/s40168-023-01642-7

[24]

Difford, Gareth Frank, Damian Rafal Plichta, Peter Løvendahl, Jan Lassen, Samantha Joan Noel, Ole Højberg, André-Denis G. Wright, et al. 2018. “Host Genetics and the Rumen Microbiome Jointly Associate With Methane Emissions in Dairy Cows.” PLoS Genetics 14: e1007580. https://doi.org/10.1371/journal.pgen.1007580

[25]

Kurilshikov, Alexander, Carolina Medina-Gomez, Rodrigo Bacigalupe, Djawad Radjabzadeh, Jun Wang, Ayse Demirkan, Caroline I. Le Roy, et al. 2021. “Large-Scale Association Analyses Identify Host Factors Influencing Human Gut Microbiome Composition.” Nature Genetics 53: 156-165. https://doi.org/10.1038/s41588-020-00763-1

[26]

Rühlemann, Malte Christoph, Britt Marie Hermes, Corinna Bang, Shauni Doms, Lucas Moitinho-Silva, Louise Bruun Thingholm, Fabian Frost, et al. 2021. “Genome-Wide Association Study in 8,956 German Individuals Identifies Influence of ABO Histo-Blood Groups on Gut Microbiome.” Nature Genetics 53: 147-155. https://doi.org/10.1038/s41588-020-00747-1

[27]

Lopera-Maya, Esteban A., Alexander Kurilshikov, Adriaan van der Graaf, Shixian Hu, Sergio Andreu-Sánchez, Lianmin Chen, Arnau Vich Vila, et al. 2022. “Effect of Host Genetics on the Gut Microbiome in 7,738 Participants of the Dutch Microbiome Project.” Nature Genetics 54: 143-151. https://doi.org/10.1038/s41588-021-00992-y

[28]

Qin, Youwen, Aki S. Havulinna, Yang Liu, Pekka Jousilahti, Scott C. Ritchie, Alex Tokolyi, Jon G. Sanders, et al. 2022. “Combined Effects of Host Genetics and Diet on Human Gut Microbiota and Incident Disease in a Single Population Cohort.” Nature Genetics 54: 134-142. https://doi.org/10.1038/s41588-021-00991-z

[29]

Zhang, Qianqian, Gareth Difford, Goutam Sahana, Peter Løvendahl, Jan Lassen, Mogens Sandø Lund, Bernt Guldbrandtsen, and Luc Janss. 2020. “Bayesian Modeling Reveals Host Genetics Associated With Rumen Microbiota Jointly Influence Methane Emission in Dairy Cows.” The ISME Journal 14: 2019-2033. https://doi.org/10.1038/s41396-020-0663-x

[30]

Fan, Peixin, Corwin D. Nelson, J. Danny Driver, Mauricio A. Elzo, Francisco Peñagaricano, and Kwangcheol C. Jeong. 2021. “Host Genetics Exerts Lifelong Effects Upon Hindgut Microbiota and Its Association With Bovine Growth and Immunity.” The ISME Journal 15: 2306-2321. https://doi.org/10.1038/s41396-021-00925-x

[31]

Martínez-Álvaro, Marina, Marc D. Auffret, Carol-Anne Duthie, Richard J. Dewhurst, Matthew A. Cleveland, Mick Watson, and Rainer Roehe. 2022. “Bovine Host Genome Acts on Rumen Microbiome Function Linked to Methane Emissions.” Communications Biology 5: 350. https://doi.org/10.1038/s42003-022-03293-0

[32]

Lu, Mingming, Yadong Zhang, Fengchun Yang, Jialin Mai, Qianwen Gao, Xiaowei Xu, Hongyu Kang, et al. 2023. “TWAS Atlas: A Curated Knowledgebase of Transcriptome-Wide Association Studies.” Nucleic Acids Research 51: D1179-D1187. https://doi.org/10.1093/nar/gkac821

[33]

Wainberg, Michael, Nasa Sinnott-Armstrong, Nicholas Mancuso, Alvaro N. Barbeira, David A. Knowles, David Golan, Raili Ermel, et al. 2019. “Opportunities and Challenges for Transcriptome-Wide Association Studies.” Nature Genetics 51: 592-599. https://doi.org/10.1038/s41588-019-0385-z

[34]

Tang, Shan, Hu Zhao, Shaoping Lu, Liangqian Yu, Guofang Zhang, Yuting Zhang, Qing-Yong Yang, et al. 2021. “Genome- and Transcriptome-Wide Association Studies Provide Insights into the Genetic Basis of Natural Variation of Seed Oil Content in Brassica napus.” Molecular Plant 14: 470-487. https://doi.org/10.1016/j.molp.2020.12.003

[35]

Zhang, Yuting, Hui Zhang, Hu Zhao, Yefan Xia, Xiangbo Zheng, Ruyi Fan, Zengdong Tan, et al. 2022. “Multi-Omics Analysis Dissects the Genetic Architecture of Seed Coat Content in Brassica Napus.” Genome Biology 23: 86. https://doi.org/10.1186/s13059-022-02647-5

[36]

Li, Long, Zhitao Tian, Jie Chen, Zengdong Tan, Yuting Zhang, Hu Zhao, Xiaowei Wu, et al. 2023. “Characterization of Novel Loci Controlling Seed Oil Content in Brassica napus by Marker Metabolite-Based Multi-Omics Analysis.” Genome Biology 24: 141. https://doi.org/10.1186/s13059-023-02984-z

[37]

Bossé, Yohan, Zhonglin Li, Jun Xia, Venkata Manem, Robert Carreras-Torres, Aurélie Gabriel, Nathalie Gaudreault, et al. 2020. “Transcriptome-Wide Association Study Reveals Candidate Causal Genes for Lung Cancer.” International Journal of Cancer 146: 1862-1878. https://doi.org/10.1002/ijc.32771

[38]

Gusev, Alexander, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W. J. H. Penninx, Rick Jansen, et al. 2016. “Integrative Approaches for Large-Scale Transcriptome-Wide Association Studies.” Nature Genetics 48: 245-252. https://doi.org/10.1038/ng.3506

[39]

Lyu, Zhe, Nana Shao, Taiwo Akinyemi, and William B. Whitman. 2018. “Methanogenesis.” Current Biology 28: R727-R732. https://doi.org/10.1016/j.cub.2018.05.021

[40]

Lan, Wei, and Chunlei Yang. 2019. “Ruminal Methane Production: Associated Microorganisms and the Potential of Applying Hydrogen-Utilizing Bacteria for Mitigation.” Science of The Total Environment 654: 1270-1283. https://doi.org/10.1016/j.scitotenv.2018.11.180

[41]

Janssen, Peter H. 2010. “Influence of Hydrogen on Rumen Methane Formation and Fermentation Balances Through Microbial Growth Kinetics and Fermentation Thermodynamics.” Animal Feed Science and Technology 160: 1-22. https://doi.org/10.1016/j.anifeedsci.2010.07.002

[42]

Aguilar-Marin, Sandra Bibiana, Claudia Lorena Betancur-Murillo, Gustavo A. Isaza, Henry Mesa, and Juan Jovel. 2020. “Lower Methane Emissions Were Associated With Higher Abundance of Ruminal Prevotella in a Cohort of Colombian Buffalos.” BMC Microbiology 20: 364. https://doi.org/10.1186/s12866-020-02037-6

[43]

Wallace, R. John, Goor Sasson, Philip C. Garnsworthy, Ilma Tapio, Emma Gregson, Paolo Bani, Pekka Huhtanen, et al. 2019. “A Heritable Subset of the Core Rumen Microbiome Dictates Dairy Cow Productivity and Emissions.” Science Advances 5: eaav8391. https://doi.org/10.1126/sciadv.aav8391

[44]

Veneman, Jolien B., Stefan Muetzel, Kenton J. Hart, Catherine L. Faulkner, Jon M. Moorby, Hink B. Perdok, and Charles J. Newbold. 2015. “Does Dietary Mitigation of Enteric Methane Production Affect Rumen Function and Animal Productivity in Dairy Cows?” PloS One 10: e0140282. https://doi.org/10.1371/journal.pone.0140282

[45]

Betancur-Murillo, Claudia Lorena, Sandra Bibiana Aguilar-Marín, and Juan Jovel. 2022. “Prevotella: A Key Player in Ruminal Metabolism.” Microorganisms 11: 1. https://doi.org/10.3390/microorganisms11010001

[46]

Schlau, N., L. L. Guan, and M. Oba. 2012. “The Relationship between Rumen Acidosis Resistance and Expression of Genes Involved in Regulation of Intracellular pH and Butyrate Metabolism of Ruminal Epithelial Cells in Steers.” Journal of Dairy Science 95: 5866-5875. https://doi.org/10.3168/jds.2011-5167

[47]

Xue, Mingyuan, Jiajin Wu, Yunyi Xie, Senlin Zhu, Yifan Zhong, Jianxin Liu, and Huizeng Sun. 2022. “Investigation of Fiber Utilization in the Rumen of Dairy Cows Based on Metagenome-Assembled Genomes and Single-Cell RNA Sequencing.” Microbiome 10: 11. https://doi.org/10.1186/s40168-021-01211-w

[48]

Martinez-Fernandez, Gonzalo, Stuart E. Denman, Chunlei Yang, Jane Cheung, Makoto Mitsumori, and Christopher S. McSweeney. 2016. “Methane Inhibition Alters the Microbial Community, Hydrogen Flow, and Fermentation Response in the Rumen of Cattle.” Frontiers in Microbiology 7: 1122. https://doi.org/10.3389/fmicb.2016.01122

[49]

Ungerfeld, Emilio M. 2020. “Metabolic Hydrogen Flows in Rumen Fermentation: Principles and Possibilities of Interventions.” Frontiers in Microbiology 11: 589. https://doi.org/10.3389/fmicb.2020.00589

[50]

Li, Qiushuang, Rong Wang, Zhi Yuan Ma, Xiu Min Zhang, Jin Zhen Jiao, Zhi Gang Zhang, Emilio M. Ungerfeld, et al. 2022. “Dietary Selection of Metabolically Distinct Microorganisms Drives Hydrogen Metabolism in Ruminants.” The ISME Journal 16: 2535-2546. https://doi.org/10.1038/s41396-022-01294-9

[51]

Wolf, Patricia G., Ambarish Biswas, Sergio E. Morales, Chris Greening, and H. Rex Gaskins. 2016. “H2 Metabolism Is Widespread and Diverse Among Human Colonic Microbes.” Gut Microbes 7: 235-245. https://doi.org/10.1080/19490976.2016.1182288

[52]

Peoples, Jessica N., Anita Saraf, Nasab Ghazal, Tyler T. Pham, and Jennifer Q. Kwong. 2019. “Mitochondrial Dysfunction and Oxidative Stress in Heart Disease.” Experimental & Molecular Medicine 51: 1-13. https://doi.org/10.1038/s12276-019-0355-7

[53]

Mailloux, Ryan J. 2021. “An Update on Methods and Approaches for Interrogating Mitochondrial Reactive Oxygen Species Production.” Redox Biology 45: 102044. https://doi.org/10.1016/j.redox.2021.102044

[54]

Wikström, Mårten, Klaas Krab, and Vivek Sharma. 2018. “Oxygen Activation and Energy Conservation by Cytochrome C Oxidase.” Chemical Reviews 118: 2469-2490. https://doi.org/10.1021/acs.chemrev.7b00664

[55]

Zhang, Peipei, Hao Lu, Yuan Wu, Danbo Lu, Chenguang Li, Xiangdong Yang, Zhangwei Chen, Juying Qian, and Junbo Ge. 2023. “COX5A Alleviates Doxorubicin-Induced Cardiotoxicity by Suppressing Oxidative Stress, Mitochondrial Dysfunction and Cardiomyocyte Apoptosis.” International Journal of Molecular Sciences 24: 10400. https://doi.org/10.3390/ijms241210400

[56]

Zhang, Wei, Yu Wang, Junzhe Wan, Pengbo Zhang, and Fei Pei. 2019. “COX6B1 Relieves Hypoxia/Reoxygenation Injury of Neonatal Rat Cardiomyocytes by Regulating Mitochondrial Function.” Biotechnology Letters 41: 59-68. https://doi.org/10.1007/s10529-018-2614-4

[57]

Kühlbrandt, Werner. 2015. “Structure and Function of Mitochondrial Membrane Protein Complexes.” BMC Biology 13: 89. https://doi.org/10.1186/s12915-015-0201-x

[58]

Xue, Mingyuan, Yunyi Xie, Yifan Zhong, Xiaojiao Ma, Huizeng Sun, and Jianxin Liu. 2022. “Integrated Meta-Omics Reveals New Ruminal Microbial Features Associated With Feed Efficiency in Dairy Cattle.” Microbiome 10: 32. https://doi.org/10.1186/s40168-022-01228-9

[59]

Shabat, Sheerli Kruger Ben, Goor Sasson, Adi Doron-Faigenboim, Thomer Durman, Shamay Yaacoby, Margret E. Berg Miller, Bryan A. White, Naama Shterzer, and Itzhak Mizrahi. 2016. “Specific Microbiome-Dependent Mechanisms Underlie the Energy Harvest Efficiency of Ruminants.” The ISME Journal 10: 2958-2972. https://doi.org/10.1038/ismej.2016.62

[60]

Chen, Shifu. 2023. “Ultrafast One-Pass FASTQ Data Preprocessing, Quality Control, and Deduplication Using Fastp.” iMeta 2: e107. https://doi.org/10.1002/imt2.107

[61]

Li, Heng, and Richard Durbin. 2010. “Fast and Accurate Long-Read Alignment With Burrows-Wheeler Transform.” Bioinformatics 26: 589-595. https://doi.org/10.1093/bioinformatics/btp698

[62]

Van der Auwera, Geraldine A., Mauricio O. Carneiro, Christopher Hartl, Ryan Poplin, Guillermo Del Angel, Ami Levy-Moonshine, and Tadeusz Jordan, et al. 2013. “From FastQ Data to High Confidence Variant Calls: the Genome Analysis Toolkit Best Practices Pipeline.” Current Protocols in Bioinformatics 43: 11.10.11-11.10.33. https://doi.org/10.1002/0471250953.bi1110s43

[63]

Purcell, Shaun, Benjamin Neale, Kathe Todd-Brown, Lori Thomas, Manuel A. R. Ferreira, David Bender, Julian Maller, et al. 2007. “PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses.” The American Journal of Human Genetics 81: 559-575. https://doi.org/10.1086/519795

[64]

Browning, Brian L., Xiaowen Tian, Ying Zhou, and Sharon R. Browning. 2021. “Fast Two-Stage Phasing of Large-Scale Sequence Data.” The American Journal of Human Genetics 108: 1880-1890. https://doi.org/10.1016/j.ajhg.2021.08.005

[65]

Magoč, Tanja and Steven L. Salzberg. 2011. “FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies.” Bioinformatics 27: 2957-2963. https://doi.org/10.1093/bioinformatics/btr507

[66]

Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson and Susan P. Holmes. 2016. “DADA2: High-Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13: 581-583. https://doi.org/10.1038/nmeth.3869

[67]

Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME2.” Nature Biotechnology 37: 852-857. https://doi.org/10.1038/s41587-019-0209-9

[68]

Quast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, Jörg Peplies, and Frank Oliver Glöckner. 2012. “The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools.” Nucleic Acids Research 41: D590-D596. https://doi.org/10.1093/nar/gks1219

[69]

Kim, Daehwan, Joseph M. Paggi, Chanhee Park, Christopher Bennett and Steven L. Salzberg. 2019. “Graph-Based Genome Alignment and Genotyping With HISAT2 and HISAT-Genotype.” Nature Biotechnology 37: 907-915. https://doi.org/10.1038/s41587-019-0201-4

[70]

Pertea, Mihaela, Geo M. Pertea, Corina M. Antonescu, Tsung-Cheng Chang, Joshua T. Mendell and Steven L. Salzberg. 2015. “StringTie Enables Improved Reconstruction of a Transcriptome from RNA-seq Reads.” Nature Biotechnology 33: 290-295. https://doi.org/10.1038/nbt.3122

[71]

Shannon, Paul, Andrew Markiel, Owen Ozier, Nitin S. Baliga, Jonathan T. Wang, Daniel Ramage, Nada Amin, Benno Schwikowski, and Trey Ideker. 2003. “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.” Genome Research 13: 2498-2504. https://doi.org/10.1101/gr.1239303

[72]

Bastian, Mathieu, Sebastien Heymann, and Mathieu Jacomy. 2009. “Gephi: An Open Source Software for Exploring and Manipulating Networks.” Proceedings of the International AAAI Conference on Web and Social Media 3: 361-362. https://doi.org/10.1609/icwsm.v3i1.13937

[73]

Douglas, Gavin M., Vincent J. Maffei, Jesse R. Zaneveld, Svetlana N. Yurgel, James R. Brown, Christopher M. Taylor, Curtis Huttenhower, and Morgan G. I. Langille. 2020. “PICRUSt2 for Prediction of Metagenome Functions.” Nature Biotechnology 38: 685-688. https://doi.org/10.1038/s41587-020-0548-6

[74]

Yang, Jian, S. Hong Lee, Michael E. Goddard and Peter M. Visscher. 2011. “GCTA: A Tool for Genome-Wide Complex Trait Analysis.” The American Journal of Human Genetics 88: 76-82. https://doi.org/10.1016/j.ajhg.2010.11.011

[75]

Rothschild, Daphna, Omer Weissbrod, Elad Barkan, Alexander Kurilshikov, Tal Korem, David Zeevi, Paul I. Costea, et al. 2018. “Environment Dominates over Host Genetics in Shaping Human Gut Microbiota.” Nature 555: 210-215. https://doi.org/10.1038/nature25973

[76]

Wen, Chaoliang, Wei Yan, Chunning Mai, Zhongyi Duan, Jiangxia Zheng, Congjiao Sun and Ning Yang. 2021. “Joint Contributions of the Gut Microbiota and Host Genetics to Feed Efficiency in Chickens.” Microbiome 9: 126. https://doi.org/10.1186/s40168-021-01040-x

[77]

Zhang, Futao, Wenhan Chen, Zhihong Zhu, Qian Zhang, Marta F. Nabais, Ting Qi, Ian J. Deary, et al. 2019. “OSCA: A Tool for Omic-Data-Based Complex Trait Analysis.” Genome Biology 20: 107. https://doi.org/10.1186/s13059-019-1718-z

[78]

Xu, Lizhen, Andrew D. Paterson, Williams Turpin and Wei Xu. 2015. “Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.” PLoS One 10: e0129606. https://doi.org/10.1371/journal.pone.0129606

[79]

Zhou, Xiang and Matthew Stephens. 2012. “Genome-Wide Efficient Mixed-Model Analysis for Association Studies.” Nature Genetics 44: 821-824. https://doi.org/10.1038/ng.2310

[80]

Wang, K., M. Li and H. Hakonarson. 2010. “ANNOVAR: Functional Annotation of Genetic Variants From High-Throughput Sequencing Data.” Nucleic Acids Research 38: e164. https://doi.org/10.1093/nar/gkq603

[81]

Gamazon, Eric R., Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, et al. 2015. “A Gene-Based Association Method for Mapping Traits Using Reference Transcriptome Data.” Nature Genetics 47: 1091-1098. https://doi.org/10.1038/ng.3367

[82]

Ongen, Halit, Alfonso Buil, Andrew Anand Brown, Emmanouil T. Dermitzakis and Olivier Delaneau. 2016. “Fast and Efficient QTL Mapper for Thousands of Molecular Phenotypes.” Bioinformatics 32: 1479-1485. https://doi.org/10.1093/bioinformatics/btv722

[83]

Giambartolomei, Claudia, Damjan Vukcevic, Eric E. Schadt, Lude Franke, Aroon D. Hingorani, Chris Wallace and Vincent Plagnol. 2014. “Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics.” PLoS Genetics 10: e1004383. https://doi.org/10.1371/journal.pgen.1004383

[84]

Wang, Dangdang, Luyu Chen, Guangfu Tang, Junjian Yu, Jie Chen, Zongjun Li, Yangchun Cao, et al. 2023. “Multi-Omics Revealed the Long-Term Effect of Ruminal Keystone Bacteria and the Microbial Metabolome on Lactation Performance in Adult Dairy Goats.” Microbiome 11: 215. https://doi.org/10.1186/s40168-023-01652-5

[85]

Bu, Dechao, Haitao Luo, Peipei Huo, Zhihao Wang, Shan Zhang, Zihao He, Yang Wu, et al. 2021. “KOBAS-I: Intelligent Prioritization and Exploratory Visualization of Biological Functions for Gene Enrichment Analysis.” Nucleic Acids Research 49: W317-W325. https://doi.org/10.1093/nar/gkab447

[86]

Mu, Hongyan, Jianzhou Chen, Wenjie Huang, Gui Huang, Meiying Deng, Shimiao Hong, Peng Ai, Chuan Gao, and Huangkai Zhou. 2024. “OmicShare Tools: A Zero-Code Interactive Online Platform for Biological Data Analysis and Visualization.” iMeta 3: e228. https://doi.org/10.1002/imt2.228

RIGHTS & PERMISSIONS

2024 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

/