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Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits

Overview of attention for article published in Genome Medicine, August 2016
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Title
Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits
Published in
Genome Medicine, August 2016
DOI 10.1186/s13073-016-0338-4
Pubmed ID
Authors

Jennifer M. Whitehead Pavlides, Zhihong Zhu, Jacob Gratten, Allan F. McRae, Naomi R. Wray, Jian Yang

Abstract

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex traits and diseases. However, elucidating the causal genes underlying GWAS hits remains challenging. We applied the summary data-based Mendelian randomization (SMR) method to 28 GWAS summary datasets to identify genes whose expression levels were associated with traits and diseases due to pleiotropy or causality (the expression level of a gene and the trait are affected by the same causal variant at a locus). We identified 71 genes, of which 17 are novel associations (no GWAS hit within 1 Mb distance of the genes). We integrated all the results in an online database ( http://www.cnsgenomics/shiny/SMRdb/ ), providing important resources to prioritize genes for further follow-up, for example in functional studies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Germany 1 <1%
Unknown 155 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 23%
Student > Ph. D. Student 29 18%
Student > Master 21 13%
Student > Bachelor 13 8%
Student > Doctoral Student 9 6%
Other 23 15%
Unknown 26 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 25%
Biochemistry, Genetics and Molecular Biology 38 24%
Medicine and Dentistry 12 8%
Neuroscience 9 6%
Computer Science 8 5%
Other 19 12%
Unknown 33 21%
Attention Score in Context

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 10 September 2016.
All research outputs
#15,740,505
of 25,374,917 outputs
Outputs from Genome Medicine
#1,385
of 1,585 outputs
Outputs of similar age
#220,885
of 376,071 outputs
Outputs of similar age from Genome Medicine
#19
of 24 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 11th percentile – i.e., 11% 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 376,071 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.