Strategies to improve the performance of rare variant association studies by optimizing the selection of controls | Zendy

Na Zhu | Zendy; Verena Heinrich | Zendy; Thorsten Dickhaus | Zendy; Jochen Hecht | Zendy; Peter N. Robinson | Zendy; Stefan Mundlos | Zendy; Tom Kamphans | Zendy; Peter Krawitz | Zendy

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Strategies to improve the performance of rare variant association studies by optimizing the selection of controls

Author(s) -

Na Zhu,

Verena Heinrich,

Thorsten Dickhaus,

Jochen Hecht,

Peter N. Robinson,

Stefan Mundlos,

Tom Kamphans,

Peter Krawitz

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv457

Subject(s) - computer science , spurious relationship , data mining , exome , selection (genetic algorithm) , weighting , genetic association , population , statistics , medicine , exome sequencing , machine learning , biology , genetics , single nucleotide polymorphism , mutation , mathematics , gene , environmental health , radiology , genotype

When analyzing a case group of patients with ultra-rare disorders the ethnicities are often diverse and the data quality might vary. The population substructure in the case group as well as the heterogeneous data quality can cause substantial inflation of test statistics and result in spurious associations in case-control studies if not properly adjusted for. Existing techniques to correct for confounding effects were especially developed for common variants and are not applicable to rare variants.

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