Evidence map›Paper›PMID 32631327›Full record

ArticleBMC biology2020

Comparative whole genome DNA methylation profiling across cattle tissues reveals global and tissue-specific methylation patterns.

Yang Zhou, Shuli Liu, Yan Hu, Lingzhao Fang, Yahui Gao, Han Xia, Steven G Schroeder, Benjamin D Rosen, Erin E Connor, Cong-Jun Li and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMC biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
4.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 64 citations in OpenAlex.

  1. Effect of methylation on genome mutability in cattle.Genetics, selection, evolution : GSE · 2026
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  4. Emerging opportunities for DNA methylation biomarkers in cattle improvement.The Journal of reproduction and development · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 3 countries.

Yang ZhouKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China. yangzhou@mail.hzau.edu.cn.
Shuli LiuAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Yan HuKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
Lingzhao FangMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
Yahui GaoAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Han XiaKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
Steven G SchroederAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Benjamin D RosenAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Erin E ConnorDepartment of Animal and Food Sciences, University of Delaware, Newark, DE, 19716, USA.
Cong-Jun LiAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Ransom L BaldwinAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
John B ColeAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Curtis P Van TassellAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Liguo YangKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
Li MaDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
George E LiuAnimal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA. George.Liu@usda.gov.ORCID 0000-0003-0192-6705
Agricultural Research Service · USHuazhong Agricultural University · CNInstitute of Genetics and Cancer · GBUniversity of Delaware · USUniversity of Maryland, College Park · US

Funding

Agricultural Research Service 8042-31000-001-00-DAgricultural Research Service 8042-31000-002-00-DAgricultural Research Service 8042-31310-078-00-DFundamental Research Funds for the Central Universities 2662017QD016National Institute of Food and Agriculture 2013-67015-20951National Institute of Food and Agriculture 2016-67015-24886National Institute of Food and Agriculture 2019-67015-29321National Natural Science Foundation of China 31902148Natural Science Foundation of Hubei Province 2018CFB363United States - Israel Binational Agricultural Research and Development Fund US-4997-17
6 · The paper itself

Abstract

backgroundEfforts to improve animal health, and understand genetic bases for production, may benefit from a comprehensive analysis of animal genomes and epigenomes. Although DNA methylation has been well studied in humans and other model species, its distribution patterns and regulatory impacts in cattle are still largely unknown. Here, we present the largest collection of cattle DNA methylation epigenomic data to date.

resultsUsing Holstein cattle, we generated 29 whole genome bisulfite sequencing (WGBS) datasets for 16 tissues, 47 corresponding RNA-seq datasets, and 2 whole genome sequencing datasets. We did read mapping and DNA methylation calling based on two different cattle assemblies, demonstrating the high quality of the long-read-based assembly markedly improved DNA methylation results. We observed large differences across cattle tissues in the methylation patterns of global CpG sites, partially methylated domains (PMDs), hypomethylated regions (HMRs), CG islands (CGIs), and common repeats. We detected that each tissue had a distinct set of PMDs, which showed tissue-specific patterns. Similar to human PMD, cattle PMDs were often linked to a general decrease of gene expression and a decrease in active histone marks and related to long-range chromatin organizations, like topologically associated domains (TADs). We tested a classification of the HMRs based on their distributions relative to transcription start sites (TSSs) and detected tissue-specific TSS-HMRs and genes that showed strong tissue effects. When performing cross-species comparisons of paired genes (two opposite strand genes with their TSS located in the same HMR), we found out they were more consistently co-expressed among human, mouse, sheep, goat, yak, pig, and chicken, but showed lower consistent ratios in more divergent species. We further used these WGBS data to detect 50,023 experimentally supported CGIs across bovine tissues and found that they might function as a guard against C-to-T mutations for TSS-HMRs. Although common repeats were often heavily methylated, some young Bov-A2 repeats were hypomethylated in sperm and could affect the promoter structures by exposing potential transcription factor binding sites.

conclusionsThis study provides a comprehensive resource for bovine epigenomic research and enables new discoveries about DNA methylation and its role in complex traits.

Indexed as

DNA MethylationEpigenesis, GeneticGenomeAnimalsCattleCpG IslandsEpigenomicsFemaleMaleOrgan SpecificityWhole Genome SequencingCattleDNA methylationHypomethylated regionPartially methylated domainsSomatic tissuesWGBS (whole genome bisulfite sequencing)

Identifiers

PMID32631327
PMCPMC7339546
OpenAlexW3040365013

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.