Evidence map›Paper›PMID 29973141›Full record

ArticleBMC genomics2018

Genome variants associated with RNA splicing variations in bovine are extensively shared between tissues.

Ruidong Xiang, Ben J Hayes, Christy J Vander Jagt, Iona M MacLeod, Majid Khansefid, Phil J Bowman, Zehu Yuan, Claire P Prowse-Wilkins, Coralie M Reich, Brett A Mason and 7 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
5.2field-weighted citation impact, top 4% 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  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. Novel functional sequences uncovered through a bovine multiassembly graph.Proceedings of the National Academy of Sciences of the United States of America · 2021
    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

17 authors at 5 institutions in 1 country.

Ruidong XiangFaculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia. ruidong.xiang@unimelb.edu.au.
Ben J HayesAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Christy J Vander JagtAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Iona M MacLeodAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Majid KhansefidAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Phil J BowmanAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Zehu YuanAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Claire P Prowse-WilkinsAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Coralie M ReichAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Brett A MasonAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Josie B GarnerAgriculture Victoria, Dairy Production Science, Ellinbank, VIC, 3821, Australia.
Leah C MarettAgriculture Victoria, Dairy Production Science, Ellinbank, VIC, 3821, Australia.
Yizhou ChenElizabeth Macarthur Agricultural Institute, New South Wales Department of Primary Industries, Camden, NSW, 2570, Australia.
Sunduimijid BolormaaAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Hans D DaetwylerAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Amanda J ChamberlainAgriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
Michael E GoddardFaculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia.
Agriculture Victoria · AUAgriBio · AUAgriculture and Food · AUNew South Wales Department of Primary Industries · AUUniversity of Melbourne · AU

Funding

Australian Research Council DP160101056DairyBio na
6 · The paper itself

Abstract

backgroundMammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues.

resultsUsing whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding (±1 Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits.

conclusionsUsing novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.

Indexed as

Genetic VariationRNA SplicingAnimalsBlood CellsCaseinsCattleExonsFemaleLiverMammary Glands, AnimalMusclesPolymorphism, Single NucleotideQuantitative Trait LociTranscriptomeCaseinsBovineExpression QTLGene expressionGenetic correlationsLocal genomic relationship matrices (LGRM)RNA splicingsQTLTranscriptome meta-analysis

Identifiers

PMID29973141
PMCPMC6032541
OpenAlexW2809985011

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.