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Bioinformatics. 2014 Aug 1;30(15):2179-88. doi: 10.1093/bioinformatics/btu196. Epub 2014 Apr 14.

Association analysis using next-generation sequence data from publicly available control groups: the robust variance score statistic.

Author information

1
Department of Statistical Science, University of Toronto, Toronto, ON, Canada, Program in Child Health Evaluative Sciences, the Hospital for Sick Children Research Institute, Toronto, ON, Canada, Department of Clinical Neuroscience, Institute of Psychiatry, King's College London, London, Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, Molecular and Population Genetics and NIHR Comprehensive Biomedical Research Centre, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
2
Department of Statistical Science, University of Toronto, Toronto, ON, Canada, Program in Child Health Evaluative Sciences, the Hospital for Sick Children Research Institute, Toronto, ON, Canada, Department of Clinical Neuroscience, Institute of Psychiatry, King's College London, London, Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, Molecular and Population Genetics and NIHR Comprehensive Biomedical Research Centre, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaDepartment of Statistical Science, University of Toronto, Toronto, ON, Canada, Program in Child Health Evaluative Sciences, the Hospital for Sick Children Research Institute, Toronto, ON, Canada, Department of Clinical Neuroscience, Institute of Psychiatry, King's College London, London, Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, Molecular and Population Genetics and NIHR Comprehensive Biomedical Research Centre, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Abstract

MOTIVATION:

Sufficiently powered case-control studies with next-generation sequence (NGS) data remain prohibitively expensive for many investigators. If feasible, a more efficient strategy would be to include publicly available sequenced controls. However, these studies can be confounded by differences in sequencing platform; alignment, single nucleotide polymorphism and variant calling algorithms; read depth; and selection thresholds. Assuming one can match cases and controls on the basis of ethnicity and other potential confounding factors, and one has access to the aligned reads in both groups, we investigate the effect of systematic differences in read depth and selection threshold when comparing allele frequencies between cases and controls. We propose a novel likelihood-based method, the robust variance score (RVS), that substitutes genotype calls by their expected values given observed sequence data.

RESULTS:

We show theoretically that the RVS eliminates read depth bias in the estimation of minor allele frequency. We also demonstrate that, using simulated and real NGS data, the RVS method controls Type I error and has comparable power to the 'gold standard' analysis with the true underlying genotypes for both common and rare variants.

AVAILABILITY AND IMPLEMENTATION:

An RVS R script and instructions can be found at strug.research.sickkids.ca, and at https://github.com/strug-lab/RVS.

CONTACT:

lisa.strug@utoronto.ca

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
24733292
PMCID:
PMC4103600
DOI:
10.1093/bioinformatics/btu196
[Indexed for MEDLINE]
Free PMC Article

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