Dataset Information

GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification.

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.

Contact

zachary.szpiech@ucsf.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Szpiech ZA

PROVIDER: S-EPMC5870576 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification.

Szpiech Zachary A ZA Blant Alexandra A Pemberton Trevor J TJ

Bioinformatics (Oxford, England) 20170701 13

<h4>Summary</h4>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 po ...[more]

PMID: 28205676

Similar Datasets

Project description:Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Dataset Information

GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification.

Summary

Availability and implementation

Contact

Supplementary information

Publications

GARLIC: Genomic Autozygosity Regions Likelihood-based Inference and Classification.

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