↓ Skip to main content

Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data

Overview of attention for article published in Genome Biology (Online Edition), May 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

blogs
1 blog
twitter
39 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
71 Dimensions

Readers on

mendeley
124 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data
Published in
Genome Biology (Online Edition), May 2017
DOI 10.1186/s13059-017-1216-0
Pubmed ID
Authors

Yang Wu, Zhili Zheng, Peter M. Visscher, Jian Yang

Abstract

Understanding the mapping precision of genome-wide association studies (GWAS), that is the physical distances between the top associated single-nucleotide polymorphisms (SNPs) and the causal variants, is essential to design fine-mapping experiments for complex traits and diseases. Using simulations based on whole-genome sequencing (WGS) data from 3642 unrelated individuals of European descent, we show that the association signals at rare causal variants (minor allele frequency ≤ 0.01) are very unlikely to be mapped to common variants in GWAS using either WGS data or imputed data and vice versa. We predict that at least 80% of the common variants identified from published GWAS using imputed data are within 33.5 Kbp of the causal variants, a resolution that is comparable with that using WGS data. Mapping precision at these loci will improve with increasing sample sizes of GWAS in the future. For rare variants, the mapping precision of GWAS using WGS data is extremely high, suggesting WGS is an efficient strategy to detect and fine-map rare variants simultaneously. We further assess the mapping precision by linkage disequilibrium between GWAS hits and causal variants and develop an online tool (gwasMP) to query our results with different thresholds of physical distance and/or linkage disequilibrium ( http://cnsgenomics.com/shiny/gwasMP ). Our findings provide a benchmark to inform future design and development of fine-mapping experiments and technologies to pinpoint the causal variants at GWAS loci.

Twitter Demographics

The data shown below were collected from the profiles of 39 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 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 124 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 27%
Student > Ph. D. Student 31 25%
Student > Master 13 10%
Other 10 8%
Student > Bachelor 9 7%
Other 16 13%
Unknown 12 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 37 30%
Agricultural and Biological Sciences 37 30%
Medicine and Dentistry 13 10%
Neuroscience 7 6%
Immunology and Microbiology 3 2%
Other 9 7%
Unknown 18 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 23 March 2018.
All research outputs
#972,396
of 20,943,519 outputs
Outputs from Genome Biology (Online Edition)
#860
of 3,994 outputs
Outputs of similar age
#22,106
of 283,240 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 20,943,519 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,994 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has done well, scoring higher than 78% of its peers.
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 283,240 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them