Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

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...

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Bibliographic Details
Published in:BMC genomics Vol. 12; no. 1; p. 460
Main Authors: Howrigan, Daniel P, Simonson, Matthew A, Keller, Matthew C
Format: Journal Article
Language:English
Published: London BioMed Central 23.09.2011
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Alzheimer's disease
Analysis
Animal Genetics and Genomics
Autophagy (Cytology)
Biomedical and Life Sciences
Bipolar disorder
Chromosome Mapping
Chromosomes
Colorectal cancer
Comparative and evolutionary genomics
Computational Biology - methods
Computer Simulation
Genetics
Genome, Human
Genomes
Homozygosity
Homozygote
Humans
Leukemia
Life Sciences
Linkage Disequilibrium
Mental health
Methodology
Methodology Article
Microarrays
Microbial Genetics and Genomics
Phenotype
Physiological aspects
Plant Genetics and Genomics
Polymorphism, Single Nucleotide
Prostate cancer
Proteomics
Regression Analysis
Schizophrenia
Sequence Analysis, DNA - methods
Single nucleotide polymorphisms
Studies
Zygote
United States
Linkage Disequilibrium Pattern
Minor Allele Frequency
Simulated Phenotype
Linkage Disequilibrium
Homozygous SNPs
ISSN:1471-2164, 1471-2164
Online Access:Get full text
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Summary: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.
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ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-12-460