Evidence map›Paper›PMID 32680461›Full record

ArticleBMC genomics2020

Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle.

Ze Yan, Hetian Huang, Ellen Freebern, Daniel J A Santos, Dongmei Dai, Jingfang Si, Chong Ma, Jie Cao, Gang Guo, George E Liu and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
6.5field-weighted citation impact, top 2% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.

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  4. Veterinary sciences · 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

13 authors at 6 institutions in 4 countries.

Ze YanNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Hetian HuangNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Ellen FreebernDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
Daniel J A SantosDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
Dongmei DaiNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Jingfang SiNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Chong MaDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China.
Jie CaoDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China.
Gang GuoBeijing Sunlon Livestock Development Co Ltd., Beijing, 100076, China.
George E LiuAnimal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
Li MaDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA. lima@umd.edu.
Lingzhao FangMRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. Lingzhao.fang@igmm.ed.ac.uk.ORCID http://orcid.org/0000-0003-1103-3679
Yi ZhangNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. yizhang@cau.edu.cn.
Ministry of Agriculture and Rural Affairs · CNUniversity of Maryland, College Park · USChina Agricultural University · CNAgricultural Research Service · USInstitute of Genetics and Cancer · GBMinistry of Agriculture · ID

Funding

Agriculture Research System of China CARS-37Beijing Dairy Industry Innovation Team Fund BAIC06
6 · The paper itself

Abstract

backgroundKetosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood.

resultsWe collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans.

conclusionsIn summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.

Indexed as

AnimalsCattleCattle DiseasesFemaleGenome-Wide Association StudyHumansKetosisLeukocytesRNA-SeqGWASHolsteinKetosisPhe-WASRNA-SeqWGCNA

Identifiers

PMID32680461
PMCPMC7367229
OpenAlexW3043399373

What OpenQuestion holds

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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.