Evidence map›Paper›PMID 35986427›Full record

ArticleJournal of animal science and biotechnology2022

Weighted single-step GWAS and RNA sequencing reveals key candidate genes associated with physiological indicators of heat stress in Holstein cattle.

Hanpeng Luo, Lirong Hu, Luiz F Brito, Jinhuan Dou, Abdul Sammad, Yao Chang, Longgang Ma, Gang Guo, Lin Liu, Liwei Zhai and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of animal science and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
8.5field-weighted citation impact, top 1% 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

31 citing papers in PubMed, 58 citations in OpenAlex.

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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

12 authors at 7 institutions in 3 countries.

Hanpeng LuoLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China.
Lirong HuLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China.
Luiz F BritoDepartment of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA.
Jinhuan DouCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing, 100096, China.
Abdul SammadLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China.
Yao ChangLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China.
Longgang MaCollege of Animal Science, Xinjiang Agricultural University, Urumqi, 830052, China.
Gang GuoBeijing Sunlon Livestock Development Company Limited, Beijing, 100029, China.
Lin LiuBeijing Dairy Cattle Center, Beijing, 100192, China.
Liwei ZhaiLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China.
Qing XuCollege of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, 100044, China.
Yachun WangLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No.2, Yuanmingyuanxi Road, Haidian District, Beijing, 100193, China. wangyachun@cau.edu.cn.ORCID http://orcid.org/0000-0003-3629-2802
China Agricultural University · CNBeijing Automotive Group (China) · CNBeijing Jiaotong University · CNBeijing University of Agriculture · CNMinistry of Agriculture · IDPurdue University West Lafayette · USXinjiang Agricultural University · CN

Funding

National Agricultural Genetic Improvement Program 2130135Program for Changjiang Scholar and Innovation Research Team in Universit IRT_15R62
6 · The paper itself

Abstract

backgroundThe study of molecular processes regulating heat stress response in dairy cattle is paramount for developing mitigation strategies to improve heat tolerance and animal welfare. Therefore, we aimed to identify quantitative trait loci (QTL) regions associated with three physiological indicators of heat stress response in Holstein cattle, including rectal temperature (RT), respiration rate score (RS), and drooling score (DS). We estimated genetic parameters for all three traits. Subsequently, a weighted single-step genome-wide association study (WssGWAS) was performed based on 3200 genotypes, 151,486 phenotypic records, and 38,101 animals in the pedigree file. The candidate genes located within the identified QTL regions were further investigated through RNA sequencing (RNA-seq) analyses of blood samples for four cows collected in April (non-heat stress group) and four cows collected in July (heat stress group).

resultsThe heritability estimates for RT, RS, and DS were 0.06, 0.04, and 0.03, respectively. Fourteen, 19, and 20 genomic regions explained 2.94%, 3.74%, and 4.01% of the total additive genetic variance of RT, RS, and DS, respectively. Most of these genomic regions are located in the Bos taurus autosome (BTA) BTA3, BTA6, BTA8, BTA12, BTA14, BTA21, and BTA24. No genomic regions overlapped between the three indicators of heat stress, indicating the polygenic nature of heat tolerance and the complementary mechanisms involved in heat stress response. For the RNA-seq analyses, 2627 genes were significantly upregulated and 369 downregulated in the heat stress group in comparison to the control group. When integrating the WssGWAS, RNA-seq results, and existing literature, the key candidate genes associated with physiological indicators of heat stress in Holstein cattle are: PMAIP1, SBK1, TMEM33, GATB, CHORDC1, RTN4IP1, and BTBD7.

conclusionsPhysiological indicators of heat stress are heritable and can be improved through direct selection. Fifty-three QTL regions associated with heat stress indicators confirm the polygenic nature and complex genetic determinism of heat tolerance in dairy cattle. The identified candidate genes will contribute for optimizing genomic evaluation models by assigning higher weights to genetic markers located in these regions as well as to the design of SNP panels containing polymorphisms located within these candidate genes.

Indexed as

Climatic resilienceDairy cattleQTL mappingRNA sequencingWssGWAS

Identifiers

PMID35986427
PMCPMC9392250
OpenAlexW4293098524

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.