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Calculating the statistical significance of rare variants causal for Mendelian and complex disorders

Overview of attention for article published in BMC Medical Genomics, June 2018
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
Calculating the statistical significance of rare variants causal for Mendelian and complex disorders
Published in
BMC Medical Genomics, June 2018
DOI 10.1186/s12920-018-0371-9
Pubmed ID
Authors

Aliz R. Rao, Stanley F. Nelson

Abstract

With the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. To calculate the significance of finding a rare protein-altering variant in a given gene, one must know the frequency of seeing a variant in the general population that is at least as damaging as the variant in question. We developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. Based on data from 2504 individuals in the 1000 Genomes Project dataset, we calculated the number of individuals who have a rare variant in a given gene for numerous filtering threshold scenarios, which may be used for calculating the significance of an observed rare variant being causal for disease. Additionally, we calculated mutational burden data on the number of individuals with rare variants in genic regions mapping to protein domains. We describe methods to use the mutational burden data for calculating the significance of observing rare variants in a given proportion of sequenced individuals. We present SORVA, an implementation of these methods as a web tool, and we demonstrate application to 20 relevant but diverse next-gen sequencing studies. Specifically, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and a large-scale study of a complex disorder, autism spectrum disorder. If we use the frequency counts to rank genes based on intolerance for variation, the ranking correlates well with pLI scores derived from the Exome Aggregation Consortium (ExAC) dataset (ρ = 0.515), with the benefit that the scores are directly interpretable. We have presented a strategy that is useful for vetting candidate genes from NGS studies and allows researchers to calculate the significance of seeing a variant in a given gene or protein domain. This approach is an important step towards developing a quantitative, statistics-based approach for presenting clinical findings.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 21%
Student > Ph. D. Student 14 19%
Student > Master 9 12%
Other 6 8%
Student > Bachelor 2 3%
Other 9 12%
Unknown 18 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 36%
Medicine and Dentistry 10 14%
Agricultural and Biological Sciences 4 5%
Psychology 3 4%
Nursing and Health Professions 2 3%
Other 5 7%
Unknown 23 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 22 November 2023.
All research outputs
#6,480,451
of 25,380,192 outputs
Outputs from BMC Medical Genomics
#362
of 1,912 outputs
Outputs of similar age
#102,019
of 335,543 outputs
Outputs of similar age from BMC Medical Genomics
#5
of 16 outputs
Altmetric has tracked 25,380,192 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,912 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 81% 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 335,543 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.