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

Overview of attention for article published in BMC Genomics, July 2018
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4 tweeters

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23 Dimensions

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Title
Genome variants associated with RNA splicing variations in bovine are extensively shared between tissues
Published in
BMC Genomics, July 2018
DOI 10.1186/s12864-018-4902-8
Pubmed ID
Authors

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, Josie B. Garner, Leah C. Marett, Yizhou Chen, Sunduimijid Bolormaa, Hans D. Daetwyler, Amanda J. Chamberlain, Michael E. Goddard

Abstract

Mammalian 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. Using 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. Using 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 22%
Student > Ph. D. Student 5 12%
Student > Master 5 12%
Student > Doctoral Student 3 7%
Student > Postgraduate 3 7%
Other 5 12%
Unknown 11 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 29%
Biochemistry, Genetics and Molecular Biology 6 15%
Veterinary Science and Veterinary Medicine 5 12%
Engineering 2 5%
Medicine and Dentistry 2 5%
Other 3 7%
Unknown 11 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2018.
All research outputs
#11,899,194
of 19,432,554 outputs
Outputs from BMC Genomics
#5,147
of 9,792 outputs
Outputs of similar age
#158,576
of 291,388 outputs
Outputs of similar age from BMC Genomics
#4
of 6 outputs
Altmetric has tracked 19,432,554 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,792 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 291,388 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.