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On Rare-Variant Analysis in Population-Based Designs: Decomposing the Likelihood to Two Informative Components
Author(s) -
Sungho Won,
Youngdoe Kim,
Christoph Lange
Publication year - 2013
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000357643
Subject(s) - statistic , computer science , construct (python library) , computational biology , test statistic , population , data mining , genetics , biology , statistical hypothesis testing , statistics , mathematics , medicine , environmental health , programming language
Various analytical approaches have been suggested for the characterization of rare variants. One main approach is to collapse the genetic information of rare variants in a region and to construct an overall test statistic. Here, we proposed a new approach based on collapsed genotype scores. By utilizing the information of the association signal that is ignored in collapsing methods, i.e. the configuration of rare alleles, we constructed a more powerful test and compared it with existing rare-variant approaches. With extensive simulation studies, we showed that our method performs better than existing approaches, and we applied our method to a sequencing study of nonsyndromic cleft lip illustrating the practical advantages of the proposed method.

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