Variant detection in 3 dimensional pooled samples

Recent studies have implicated rare genetic variants in a great number of complex traits and diseases. These studies base themselves upon the Rare Variant, Common Disease hypothesis which states that multiple low-frequency disease-causing alleles cumulatively explain a large portion of disease susceptibility in the human population. Identifying these variants therefore represents an important step in personalized medicine. As the frequencies of these rare variants are low though, identifying them requires genotyping large populations of individuals. Such an undertaking can either be done sequentially, genotyping multiple samples or by pooling these samples and genotyping them as one. Next generation sequencing technology is, due to its massively parallel nature ideally suited for pooled genotyping. Such a strategy could therefore be used to identify rare variants in targeted regions of the genome in large populations, whilst minimizing both cost and time of such a project. Keeping all this in mind, this dissertation aims at determining the presence of rare variants in DNA sequences for a selected gene set, on a large number of tumors. More specifically, we wish to scan for rare variants in selected target regions of 3-dimensionally pooled neuroblastoma tumor samples. For this purpose, we have developed a hierarchal statistical model usable for data analysis, that incorporates an increasing complexity inherent to the data gathering process and the 3-dimensional design of this study. Our model bases itself on an assumed normal distribution for the expected number of copies of base pair positions after amplification and a Poisson distribution of the observed number of copies around their mean. The assumptions allow estimation of the parameters defining this normal distribution (and their characteristics), relying on the Method of Moments. With these two parameters estimated for our simulation studies, we were able to compute the necessary probabilities of encountering a specific number of variants present in a pool, at a specific base pair position. These simulation probabilities were computed in various ways: ignoring the existence of sequencing errors, incorporating such errors, accounting for only one or accounting for both complementary strands of DNA and/or taking some of the 3-dimensional structure of our pooled data into account. Comparing the efficiency of all these different probability equations, we found that the best estimates for the number of variants -in the presence of sequencing errorswere obtained by considering but the product of the two individual strand probabilities, whilst relying on either of the two estimation methods (the ’maximum probability’ and ’median of distribution’ methods for estimation). The estimators that were derived from the information at hand in the test data set showed strange yet ordered patterns, so we decided to limit our analysis to one such subgroup of base pair positions. When we naively estimated the number of variants present in this subgroup (by considering but one of both DNA strands and choosing to neglect the restrictions of the 3-dimensional data structure), we saw that estimation based on the