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Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect

Overview of attention for article published in BMC Bioinformatics, January 2018
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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2 tweeters
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1 patent

Citations

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9 Dimensions

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23 Mendeley
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1 CiteULike
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Title
Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-017-2004-2
Pubmed ID
Authors

M. Shi, D. M. Umbach, A. S. Wise, C. R. Weinberg

Abstract

To evaluate statistical methods for genome-wide genetic analyses, one needs to be able to simulate realistic genotypes. We here describe a method, applicable to a broad range of association study designs, that can simulate autosome-wide single-nucleotide polymorphism data with realistic linkage disequilibrium and with spiked in, user-specified, single or multi-SNP causal effects. Our construction uses existing genome-wide association data from unrelated case-parent triads, augmented by including a hypothetical complement triad for each triad (same parents but with a hypothetical offspring who carries the non-transmitted parental alleles). We assign offspring qualitative or quantitative traits probabilistically through a specified risk model and show that our approach destroys the risk signals from the original data. Our method can simulate genetically homogeneous or stratified populations and can simulate case-parents studies, case-control studies, case-only studies, or studies of quantitative traits. We show that allele frequencies and linkage disequilibrium structure in the original genome-wide association sample are preserved in the simulated data. We have implemented our method in an R package (TriadSim) which is freely available at the comprehensive R archive network. We have proposed a method for simulating genome-wide SNP data with realistic linkage disequilibrium. Our method will be useful for developing statistical methods for studying genetic associations, including higher order effects like epistasis and gene by environment interactions.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 22%
Student > Postgraduate 5 22%
Student > Ph. D. Student 3 13%
Other 2 9%
Student > Master 1 4%
Other 3 13%
Unknown 4 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 35%
Computer Science 4 17%
Decision Sciences 2 9%
Agricultural and Biological Sciences 2 9%
Medicine and Dentistry 2 9%
Other 2 9%
Unknown 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 12 March 2020.
All research outputs
#5,110,226
of 17,366,233 outputs
Outputs from BMC Bioinformatics
#2,133
of 6,152 outputs
Outputs of similar age
#135,144
of 415,197 outputs
Outputs of similar age from BMC Bioinformatics
#143
of 445 outputs
Altmetric has tracked 17,366,233 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 6,152 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 64% 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 415,197 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 67% of its contemporaries.
We're also able to compare this research output to 445 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 66% of its contemporaries.