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

BMC Proceedings

Proceedings

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

[1] Laboratory of Genetic and Molecular Cardiology, Heart Institute, University of Sao Paulo Medical School, Av. Dr. Eneas C Aguiar, 44-10 andar, 05403-000, São Paulo, Brazil;National Laboratory of Biosciences, Campinas, Caixa Postal 6192, CEP 13083-970, São Paulo, Brazil;

关键词: Rare Variant;    Causal Gene;    Trait Heritability;    Nonsynonymous Variant;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-5-S9-S78

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Alfonso de Almeida et al; licensee BioMed Central Ltd. 2011