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Assessing the impact of a combined analysis of four common low-risk genetic variants on autism risk

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Author(s): Carayol Jerome | Schellenberg Gerard D | Tores Frederic | Hager Jörg | Ziegler Andreas | Dawson Geraldine

Journal: Molecular Autism
ISSN 2040-2392

Volume: 1;
Issue: 1;
Start page: 4;
Date: 2010;
Original page

ABSTRACT
Abstract Background Autism is a complex disorder characterized by deficits involving communication, social interaction, and repetitive and restrictive patterns of behavior. Twin studies have shown that autism is strongly heritable, suggesting a strong genetic component. In other disease states with a complex etiology, such as type 2 diabetes, cancer and cardiovascular disease, combined analysis of multiple genetic variants in a genetic score has helped to identify individuals at high risk of disease. Genetic scores are designed to test for association of genetic markers with disease. Method The accumulation of multiple risk alleles markedly increases the risk of being affected, and compared with studying polymorphisms individually, it improves the identification of subgroups of individuals at greater risk. In the present study, we show that this approach can be applied to autism by specifically looking at a high-risk population of children who have siblings with autism. A two-sample study design and the generation of a genetic score using multiple independent genes were used to assess the risk of autism in a high-risk population. Results In both samples, odds ratios (ORs) increased significantly as a function of the number of risk alleles, with a genetic score of 8 being associated with an OR of 5.54 (95% confidence interval [CI] 2.45 to 12.49). The sensitivities and specificities for each genetic score were similar in both analyses, and the resultant area under the receiver operating characteristic curves were identical (0.59). Conclusions These results suggest that the accumulation of multiple risk alleles in a genetic score is a useful strategy for assessing the risk of autism in siblings of affected individuals, and may be better than studying single polymorphisms for identifying subgroups of individuals with significantly greater risk.
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