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MARV: a tool for genome-wide multi-phenotype analysis of rare variants

Overview of attention for article published in BMC Bioinformatics, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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10 tweeters

Citations

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11 Dimensions

Readers on

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62 Mendeley
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Title
MARV: a tool for genome-wide multi-phenotype analysis of rare variants
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1530-2
Pubmed ID
Authors

Marika Kaakinen, Reedik Mägi, Krista Fischer, Jani Heikkinen, Marjo-Riitta Järvelin, Andrew P. Morris, Inga Prokopenko

Abstract

Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes. We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (P TG+FI = 1.8 × 10(-9)) than observed in single phenotype rare variant analyses (P TG = 6.5 × 10(-8) and P FI = 0.27). MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 61 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 26%
Student > Master 13 21%
Student > Bachelor 9 15%
Student > Ph. D. Student 6 10%
Professor 5 8%
Other 6 10%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 29%
Biochemistry, Genetics and Molecular Biology 16 26%
Engineering 6 10%
Medicine and Dentistry 5 8%
Computer Science 3 5%
Other 4 6%
Unknown 10 16%

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 21 February 2017.
All research outputs
#3,667,930
of 15,626,055 outputs
Outputs from BMC Bioinformatics
#1,446
of 5,680 outputs
Outputs of similar age
#73,794
of 263,167 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 24 outputs
Altmetric has tracked 15,626,055 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,680 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 74% 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 263,167 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 71% of its contemporaries.
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 has done well, scoring higher than 83% of its contemporaries.