Goodness‐of‐fit tests for disorder detection in NGS experiments

Next‐generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high‐dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experime...

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Bibliographic Details
Published in:Biometrical journal 2019-03, Vol.61 (2), p.424-441
Main Authors: Jiménez‐Otero, Norman, de Uña‐Álvarez, Jacobo, Pardo‐Fernández, Juan Carlos
Format: Article
Language:English
Subjects:
Biostatistics - methods
Computer simulation
Cytosine
Deoxyribonucleic acid
Disorders
DNA
DNA sequencing
Experiments
GC bias
Guanine
Heterozygosity
Heterozygote
High-Throughput Nucleotide Sequencing
Homozygosity
Homozygote
Mathematical models
Models, Statistical
Monte Carlo Method
multiple hypothesis testing
Poisson distribution
Sequence Analysis, DNA
sequencing
Statistical models
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Summary:Next‐generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high‐dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness‐of‐fit procedure for disorder detection is derived. The method combines the proper evaluation of local p‐values (one for each DNA base) with suitable corrections for multiple comparisons and the discrete nature of the p‐values. A global test for the detection of disorders in the whole DNA region is proposed too. The performance of the introduced procedures is investigated through simulations. A real data illustration is provided.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201700284