Evidence map›Paper›PMID 32138655›Full record

ArticleBMC genomics2020

Genome-wide identification of loci associated with growth in rainbow trout.

Ali Ali, Rafet Al-Tobasei, Daniela Lourenco, Tim Leeds, Brett Kenney, Mohamed Salem

Open access · goldAbstract read
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 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.8field-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

24 citing papers in PubMed, 56 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

6 authors at 5 institutions in 1 country.

Ali AliDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
Rafet Al-TobaseiComputational Science Program, Middle Tennessee State University, Murfreesboro, TN, 37132, USA.
Daniela LourencoDepartment of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
Tim LeedsUnited States Department of Agriculture Kearneysville, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV, USA.
Brett KenneyDivision of Animal and Nutritional Sciences, West Virginia University, Morgantown, WV, 26506, USA.
Mohamed SalemDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA. mosalem@umd.edu.ORCID http://orcid.org/0000-0003-2142-6716
University of Maryland, College Park · USMiddle Tennessee State University · USUnited States Department of Agriculture · USUniversity of Georgia · USWest Virginia University · US

Funding

Agricultural Research Service 1930-31000-010National Institute of Food and Agriculture 2014-67015-21602
6 · The paper itself

Abstract

backgroundGrowth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.

resultsA previously developed 50 K gene-transcribed SNP chip, containing ~ 21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~ 6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R

conclusionThe majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

Indexed as

Allelic ImbalanceAnimalsAquacultureBody WeightCell Growth ProcessesGenome-Wide Association StudyGenomicsGenotypeLipid MetabolismOncorhynchus mykissPhenotypePolymorphism, Single NucleotideProteolysisQuantitative Trait LociBody weightFishGenomic selectionGWASQTLWssGBLUP

Identifiers

PMID32138655
PMCPMC7059289
OpenAlexW3009351765

What OpenQuestion 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 OpenQuestion graph.