Unravelling the genetics of complex diseases through random forest: application to genome wide Association of asthma in a genetic isolate of Ogliastra

As of today, the association studies of genetic variants and common complex diseases requires the examination of a huge number of samples in order to identify variants with limited weight in the insurgence of the disease. This has had limited success if we consider what was expected. One of the problems could be the lack of adequate statistical methods apt to reveal biological mechanisms complexities, their interaction with the environment and with different life styles. In our work we propose a procedure based on ensemble methods which solves some problems linked to association studies such as: false associations due to LD, multiple testing, elicitation of a genetic mode of inheritance, the detection and definition of variant/variant and variant/environment interactions. Moreover, we apply this procedure to a complex disease (Asthma) in a small village located in Ogliastra a secluded area of Sardinia. First we identified, through a large screening on the whole village of Talana, 57 Asthma cases. The clinical study was carried out by specialized physicians according to international guidelines based on: ECRHS short screening questionnaire, spirometry, nitric oxide breath test, skin tests with allergens, and measurements of IgE in serum. As potential controls we chose people who were negative to all the parameters used for Asthma diagnosis and that were not under anti-asthmatic therapy. We identified 191 such controls. One of the main problems related to association studies in genetic isolates is the non-independence of the subjects included in the analysis which is a prerequisite for the application of most statistical techniques. This may lead to "population stratification" effects caused by differences in relatedness between the group of cases and controls. Different approaches have been proposed to avoind this problem. However these techniques are either too conservative (Genomic Control [3] ) or have limited application to some kind of statistical tests. For this reason we selected for each case the most related control, this way we should both avoid population stratification and also reduce the number of false positive variants. In fact the presence of IBD regions between each case and each control not linked to the disease should prevent false association due to chance. In order to find the solution that maximizes the kinship of all of the subjects we used the Hungarian method [4] which is commonly used to find the best solution in assignment problems. This way we selected 57 control samples matched to the 57 cases identified through the screening. All of the subjects had been previously been genotyped with the Affymetix GeneChip Human Mapping 500K Array. We also included non genetic variables possibly related to asthma such as sex , smoking and physical activity. In order to verify if this approach succeed in avoiding population stratification in our sample, we performed fisher test of dependency among markers and desease status. We then calculated the lambda coefficient which measures the compatibility of uniformity assumptions on p-values according to the genomic control method [3]. The calculated lambda was equal to 0.3, indicating that not only there was no population stratification between cases and controls, but also that genome wide the p-values were higher than expected. Therefore, we can assume that the p-values are less significant in loci which are not related to the disease while they should be unaffected in loci associated to Asthma. The identification of genes involved in complex disease is composed essentially of two main concerns: variable selection and model elicitation. Ensemble methods based on classification trees solve both these problems at the same time, in fact they provide a measure of importance fo