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BMC Genomics. 2011 Sep 23;12:460. doi: 10.1186/1471-2164-12-460.

Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms.

Author information

  • 1Department of Psychology, University of Colorado at Boulder, 1416 Broadway, Boulder, CO 80301, USA. daniel.howrigan@gmail.com

Abstract

BACKGROUND:

A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.

METHOD:

We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.

RESULTS:

Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).

CONCLUSION:

Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.

PMID:
21943305
[PubMed - indexed for MEDLINE]
PMCID:
PMC3188534
Free PMC Article
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