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Bioinformatics
Article . 2017 . Peer-reviewed
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Bioinformatics
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Bioinformatics
Article . 2018
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Article . 2024
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GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification

Authors: Zachary A. Szpiech; Alexandra Blant; Trevor J. Pemberton;

GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification

Abstract

Abstract Summary Runs of homozygosity (ROH) are important genomic features that manifest when identical-by-descent haplotypes are inherited from parents. Their length distributions and genomic locations are informative about population history and they are useful for mapping recessive loci contributing to both Mendelian and complex disease risk. Here, we present software implementing a model-based method (Pemberton et al., 2012) for inferring ROH in genome-wide SNP datasets that incorporates population-specific parameters and a genotyping error rate as well as provides a length-based classification module to identify biologically interesting classes of ROH. Using simulations, we evaluate the performance of this method. Availability and Implementation GARLIC is written in C ++. Source code and pre-compiled binaries (Windows, OSX and Linux) are hosted on GitHub (https://github.com/szpiech/garlic) under the GNU General Public License version 3. Supplementary information Supplementary data are available at Bioinformatics online.

Keywords

Likelihood Functions, Haplotypes, Homozygote, Humans, Computer Simulation, Genomics, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Software

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
30
Top 10%
Top 10%
Top 10%
gold