56º Congresso Brasileiro de Genética
Resumos do 56º Congresso Brasileiro de Genética • 14 a 17 de setembro de 2010
Casa Grande Hotel Resort • Guarujá • SP • Brasil
www.sbg.org.br - ISBN 978-85-89109-06-2
412
Strategies for genetic model specification in the
screening of genome-wide meta-analysis signals for
further replication
Pereira, TV1,2,3; Patsopoulos, NA4
Laboratório de Genética e Cardiologia Molevular, Instituto do Coração, Universidade de São Paulo, Brazil
Departamento de Bioquímica e Biologia Molecular, Universidade Federal de São Paulo, Brazil
3
Deparatmento de Genética e Biologia Evolutiva, Universidade de São Paulo, Brazil
4
Division of Genetics, Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, USA
[email protected]
1
2
Keywords: meta-analysis, genome-wide, association, polymorphism, complex diseases
Background: Meta-analysis is increasingly being employed as a screening procedure in large-scale association
studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require
the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce
model mis-specifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which
augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication
studies. We used simulated meta-analyses of large genetic association studies to investigate naïve strategies of
genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication.
Methods: Different methods of meta-analysis, effect sizes, meta-analytical models and strategies for genetic model
specification were compared in terms of power and type-I error. Simulations were carried out for a binary trait in
a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and
2
between-study heterogeneity ( � ). We further applied the proposed approaches in real data sets by performing
a meta-analysis of three genome-wide association studies in Alzheimer disease encompassing >310,000 single
nucleotide polymorphisms (n = 1411 subjects). Results: Among the investigated strategies, a simple Bonferronicorrected approach that fits both multiplicative and recessive models was found to be optimal in most examined
scenarios, reducing false discoveries and enhancing power in scenarios with small MAFs either in the presence or
2
absence of heterogeneity. Nonetheless, this strategy is sensitive to � whenever the susceptibility allele is common
(MAF>30%), resulting in an increased number of false-positive associations compared to an analysis that considers
only the multiplicative model, regardless of the meta-analytical methods applied. In the meta-analysis of three
real genome-wide studies, inverse-variance fixed-effects analyses identified one to six loci displaying genomewide association with Alzheimer’s disease depending on the genome-wide threshold chosen (e.g. P<10-5, P<10-6,
P<10-7 or P<10-8). In all analyses, the proposed approaches showed highly comparable results compared to the
specification of a multiplicative model alone. Conclusions: Invoking a simple Bonferroni adjustment and testing for
both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings.
However, care must be taken when examined variants are common, where specification of a multiplicative model
alone may preferable.