↓ Skip to main content

Identification of candidate protective variants for common diseases and evaluation of their protective potential

Overview of attention for article published in BMC Genomics, August 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
63 Mendeley
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
Identification of candidate protective variants for common diseases and evaluation of their protective potential
Published in
BMC Genomics, August 2017
DOI 10.1186/s12864-017-3964-3
Pubmed ID
Authors

Joe M. Butler, Neil Hall, Niro Narendran, Yit C. Yang, Luminita Paraoan

Abstract

Human polymorphisms with derived alleles that are protective against disease may provide powerful translational opportunities. Here we report a method to identify such candidate polymorphisms and apply it to common non-synonymous SNPs (nsSNPs) associated with common diseases. Our study also sought to establish which of the identified protective nsSNPs show evidence of positive selection, taking this as indirect evidence that the protective variant has a beneficial effect on phenotype. Further, we performed an analysis to quantify the predicted effect of each protective variant on protein function/structure. An initial analysis of eight SNPs previously identified as associated with age-related macular degeneration (AMD), revealed that two of them have a derived allele that is protective against developing the disease. One is in the complement component 2 gene (C2; E318D) and the other is in the complement factor B gene (CFB; R32Q). Then, combining genomewide ancestral allele information with known common disease-associated nsSNPs from the GWAS catalog, we found 32 additional SNPs which have a derived allele that is disease protective. Out of the total 34 identified candidate protective variants (CPVs), we found that 30 show stronger evidence of positive selection than the protective variant in lipoprotein lipase (LPL; S447X), which has already been translated into gene therapy. Furthermore, 11 of these CPVs have a higher probability of affecting protein structure than the lipoprotein lipase protective variant (LPL; S447X). We identify 34 CPVs from the human genome. Diseases they confer protection against include, but are not limited to, type 2 diabetes, inflammatory bowel disease, age-related macular degeneration, multiple sclerosis and rheumatoid arthritis. We propose that those 30 CPVs with evidence of stronger positive selection than the LPL protective variant, may be considered as priority candidates for therapeutic approaches. The next step towards translation will require testing the hypotheses generated by our analyses, specifically whether the CPV arose from a gain-of-function or a loss-of-function mutation.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 19%
Researcher 11 17%
Student > Bachelor 7 11%
Student > Ph. D. Student 7 11%
Student > Doctoral Student 4 6%
Other 6 10%
Unknown 16 25%
Readers by discipline Count As %
Medicine and Dentistry 15 24%
Biochemistry, Genetics and Molecular Biology 15 24%
Agricultural and Biological Sciences 7 11%
Neuroscience 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 6%
Unknown 18 29%
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 07 September 2017.
All research outputs
#13,565,862
of 22,996,001 outputs
Outputs from BMC Genomics
#5,028
of 10,692 outputs
Outputs of similar age
#160,840
of 317,594 outputs
Outputs of similar age from BMC Genomics
#93
of 223 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,692 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 50% 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 317,594 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 223 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.