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Comparing family-based rare variant association tests for dichotomous phenotypes

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Authors
Wang, Longfei; Choi, Sungkyoung; Lee, Sungyoung; Park, Taesung; Won, Sungho
Issue Date
2016-10-18
Publisher
BioMed Central
Citation
BMC Proceedings, 10(Suppl 7):25
Description
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Abstract
Abstract

Background
It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.


Methods
In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).


Results
The results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.


Conclusions
Considering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.
Language
English
URI
https://hdl.handle.net/10371/100403
DOI
https://doi.org/10.1186/s12919-016-0027-8
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