Evidence map›Paper›PMID 34399688›Full record

ArticleGenetics, selection, evolution : GSE2021

Large-scale transcriptome sequencing in broiler chickens to identify candidate genes for breast muscle weight and intramuscular fat content.

Huimin Kang, Di Zhao, Hai Xiang, Jing Li, Guiping Zhao, Hua Li

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
6.2field-weighted citation impact, top 3% 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

17 citing papers in PubMed, 33 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

6 authors at 4 institutions in 1 country.

Huimin KangGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China.
Di ZhaoGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China.
Hai XiangGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China.
Jing LiGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China.
Guiping ZhaoGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China. zhaoguiping@caas.cn.
Hua LiGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding; Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes; School of Life Science and Engineering, Foshan University, #33 Guang-yun-lu, Shishan, Nanhai, Foshan, 528231, Guangdong, People's Republic of China. okhuali@fosu.edu.cn.
Guangdong University of Education · CNInstitute of Animal Sciences · CNKey Laboratory of Guangdong Province · CNWen's Food Group (China) · CN

Funding

Guangdong Basic and Applied Basic Research Foundation 2019A1515110453Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding 2019B030301010Innovation Team of Precise Animal Breeding 2019KCXTD006Key-Area Research and Development Program of Guangdong Province 2020B020222002Key Laboratory of Animal Molecular Design and Precise Breeding of Guangdong Higher Education Institutes 2019KSYS011National Natural Science Foundation of China 31802037Qingyuan City Qingcheng District Science and Technology Planning Project 2020A01
6 · The paper itself

Abstract

backgroundIn broiler production, breast muscle weight and intramuscular fat (IMF) content are important economic traits. Understanding the genetic mechanisms that underlie these traits is essential to implement effective genetic improvement programs. To date, genome-wide association studies (GWAS) and gene expression analyses have been performed to identify candidate genes for these traits. However, GWAS mainly detect associations at the DNA level, while differential expression analyses usually have low power because they are typically based on small sample sizes. To detect candidate genes for breast muscle weight and IMF contents (intramuscular fat percentage and relative content of triglycerides, cholesterol, and phospholipids), we performed association analyses based on breast muscle transcriptomic data on approximately 400 Tiannong partridge chickens at slaughter age.

resultsFirst, by performing an extensive simulation study, we evaluated the statistical properties of association analyses of gene expression levels and traits based on the linear mixed model (LMM) and three regularized linear regression models, i.e., least absolute shrinkage and selection operator (LASSO), ridge regression (RR), and elastic net (EN). The results show that LMM, LASSO and EN with tuning parameters that are determined based on the one standard error rule exhibited the lowest type I error rates. Using results from all three models, we detected 43 candidate genes with expression levels that were associated with breast muscle weight. In addition, candidate genes were detected for intramuscular fat percentage (1), triglyceride content (2), cholesterol content (1), and phospholipid content (1). Many of the identified genes have been demonstrated to play roles in the development and metabolism of skeletal muscle or adipocyte. Moreover, weighted gene co-expression network analyses revealed that many candidate genes were harbored by gene co-expression modules, which were also significantly correlated with the traits of interest. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these modules are involved in muscle development and contraction, and in lipid metabolism.

conclusionsOur study provides valuable insight into the transcriptomic bases of breast muscle weight and IMF contents in Chinese indigenous yellow broilers. Our findings could be useful for the genetic improvement of these traits in broiler chickens.

Indexed as

Genetic Association StudiesTranscriptomeAdipose TissueAnimalsChickensFemaleGene Expression ProfilingMuscle, Skeletal

Identifiers

PMID34399688
PMCPMC8369645
OpenAlexW3194065432

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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.