Evidence map›Paper›PMID 26589139›Full record

ArticleBMC genetics2015

Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle.

Liuhong Chen, Chinyere Ekine-Dzivenu, Michael Vinsky, John Basarab, Jennifer Aalhus, Mike E R Dugan, Carolyn Fitzsimmons, Paul Stothard, Changxi Li

Open access · diamondAbstract read
In one paragraph

Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

22 citing papers in PubMed, 39 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

9 authors at 3 institutions in 1 country.

Liuhong ChenDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada. liuhong@ualberta.ca.
Chinyere Ekine-DzivenuDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada. ekine@ualberta.ca.
Michael VinskyLacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, AB, T4L 1 W1, Canada. michael.vinsky@ualberta.ca.
John BasarabLacombe Research Centre, Alberta Agriculture and Forestry, 6000 C & E Trail, Lacombe, AB, T4L 1 W1, Canada. John.Basarab@gov.ab.ca.
Jennifer AalhusLacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, AB, T4L 1 W1, Canada. jennifer.aalhus@agr.gc.ca.
Mike E R DuganLacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, AB, T4L 1 W1, Canada. mike.dugan@agr.gc.ca.
Carolyn FitzsimmonsDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada. carolyn.fitzsimmons@agr.gc.ca.
Paul StothardDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada. stothard@ualberta.ca.
Changxi LiDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada. changxi.li@arg.gc.ca.
University of Alberta · CAAgriculture and Agri-Food Canada · CAAlberta Crop Industry Development Fund · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of genetic variants that are associated with fatty acid composition in beef will enhance our understanding of host genetic influence on the trait and also allow for more effective improvement of beef fatty acid profiles through genomic selection and marker-assisted diet management. In this study, 81 and 83 fatty acid traits were measured in subcutaneous adipose (SQ) and longissimus lumborum muscle (LL), respectively, from 1366 purebred and crossbred beef steers and heifers that were genotyped on the Illumina BovineSNP50 Beadchip. The objective was to conduct genome-wide association studies (GWAS) for the fatty acid traits and to evaluate the accuracy of genomic prediction for fatty acid composition using genomic best linear unbiased prediction (GBLUP) and Bayesian methods.

resultsIn total, 302 and 360 significant SNPs spanning all autosomal chromosomes were identified to be associated with fatty acid composition in SQ and LL tissues, respectively. Proportions of total genetic variance explained by individual significant SNPs ranged from 0.03 to 11.06% in SQ, and from 0.005 to 24.28% in the LL muscle. Markers with relatively large effects were located near fatty acid synthase (FASN), stearoyl-CoA desaturase (SCD), and thyroid hormone responsive (THRSP) genes. For the majority of the fatty acid traits studied, the accuracy of genomic prediction was relatively low (<0.40). Relatively high accuracies (> = 0.50) were achieved for 10:0, 12:0, 14:0, 15:0, 16:0, 9c-14:1, 12c-16:1, 13c-18:1, and health index (HI) in LL, and for 12:0, 14:0, 15:0, 10 t,12c-18:2, and 11 t,13c + 11c,13 t-18:2 in SQ. The Bayesian method performed similarly as GBLUP for most of the traits but substantially better for traits that were affected by SNPs of large effects as identified by GWAS.

conclusionsFatty acid composition in beef is influenced by a few host genes with major effects and many genes of smaller effects. With the current training population size and marker density, genomic prediction has the potential to predict the breeding values of fatty acid composition in beef cattle at a moderate to relatively high accuracy for fatty acids that have moderate to high heritability.

Indexed as

AnimalsBack MusclesCattleFatty AcidsFemaleGenome-Wide Association StudyMalePolymorphism, Single NucleotideRed MeatFatty Acids

Identifiers

PMID26589139
PMCPMC4654876
OpenAlexW2176236019

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.