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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Marcio Augusto Alfonso de Almeida1*, Andrea Roseli Vançan Russo Horimoto1, Paulo Sérgio Lopes de Oliveira2, José Eduardo Krieger1 and Alexandre da Costa Pereira1

Author Affiliations

1 Laboratory of Genetic and Molecular Cardiology, Heart Institute, University of Sao Paulo Medical School, Av. Dr. Eneas C Aguiar, 44-10 andar, São Paulo 05403-000, Brazil

2 National Laboratory of Biosciences, Campinas, Caixa Postal 6192, São Paulo, CEP 13083-970, Brazil

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BMC Proceedings 2011, 5(Suppl 9):S78  doi:10.1186/1753-6561-5-S9-S78

Published: 29 November 2011

Abstract

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait’s heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.