Evidence map›Paper›PMID 38891742›Full record

ArticleAnimals : an open access journal from MDPI2024

Genome-Wide Gene-Environment Interaction Analysis Identifies Novel Candidate Variants for Growth Traits in Beef Cattle.

Tianyu Deng, Keanning Li, Lili Du, Mang Liang, Li Qian, Qingqing Xue, Shiyuan Qiu, Lingyang Xu, Lupei Zhang, Xue Gao and 3 more

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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.

Tianyu DengInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Keanning LiInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Lili DuInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Mang LiangInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Li QianInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Qingqing XueInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Shiyuan QiuInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Lingyang XuInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Lupei ZhangInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0001-7701-2331
Xue GaoInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Xianyong LanShaanxi Key Laboratory of Molecular Biology for Agriculture, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang 712100, China.
Junya LiInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Huijiang GaoInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.

Funding

National Natural Science Foundations of China 32172693Program of National Beef Cattle and Yak Industrial Technology System CARS-37Science and Technology Project of Inner Mongolia Autonomous Region 2020GG0210
6 · The paper itself

Abstract

Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10

Indexed as

cattlegene-based analysisgene-set analysisgenotype-by-environment interactionrobust estimator

Identifiers

PMID38891742
PMCPMC11171348

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.