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A Strategy analysis for genetic association studies with known inbreeding

Cabras, Stefano and Castellanos, Maria Eugenia and Biino, Ginevra and Persico, Ivana and Sassu, Alessandro and Casula, Laura and Del Giacco, Stefano and Bertolino, Francesco and Pirastu, Mario and Pirastu, Nicola (2011) A Strategy analysis for genetic association studies with known inbreeding. BMC Genetics, Vol. 12:63 (1), p. 1-12. eISSN 1471-2156. Article.

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DOI: 10.1186/1471-2156-12-63

Abstract

Background: Association studies consist in identifying the genetic variants which are related to a specific disease through the use of statistical multiple hypothesis testing or segregation analysis in pedigrees. This type of studies has been very successful in the case of Mendelian monogenic disorders while it has been less successful in identifying genetic variants related to complex diseases where the insurgence depends on the interactions between different genes and the environment. The current technology allows to genotype more than a million of markers and this number has been rapidly increasing in the last years with the imputation based on templates sets and whole genome sequencing. This type of data introduces a great amount of noise in the statistical analysis and usually requires a great number of samples. Current methods seldom take into account gene-gene and gene-environment interactions which are fundamental especially in complex diseases. In this paper we propose to use a non-parametric additive model to detect the genetic variants related to diseases which accounts for interactions of unknown order. Although this is not new to the current literature, we show that in an isolated population, where the most related subjects share also most of their genetic code, the use of additive models may be improved if the available genealogical tree is taken into account. Specifically, we form a sample of cases and controls with the highest inbreeding by means of the Hungarian method, and estimate the set of genes/environmental variables, associated with the disease, by means of Random Forest.
Results: We have evidence, from statistical theory, simulations and two applications, that we build a suitable procedure to eliminate stratification between cases and controls and that it also has enough precision in identifying genetic variants responsible for a disease. This procedure has been successfully used for the betathalassemia, which is a well known Mendelian disease, and also to the common asthma where we have identified candidate genes that underlie to the susceptibility of the asthma. Some of such candidate genes have been also found related to common asthma in the current literature.
Conclusions: The data analysis approach, based on selecting the most related cases and controls along with the Random Forest model, is a powerful tool for detecting genetic variants associated to a disease in isolated populations. Moreover, this method provides also a prediction model that has accuracy in estimating the unknown disease status and that can be generally used to build kit tests for a wide class of Mendelian diseases.

Item Type:Article
ID Code:6388
Status:Published
Refereed:Yes
Uncontrolled Keywords:Mendelian diseases, Beta-thalassemia, Asthma, Random Forest model
Subjects:Area 06 - Scienze mediche > MED/03 Genetica medica
Publisher:BioMed Central
eISSN:1471-2156
Copyright Holders:© 2011 Cabras et al; licensee BioMed Central Ltd.
Deposited On:23 Aug 2011 17:42

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