Identification of genetic factors associated to diseases

We can process patients’ data to map genetic variability of different phenotypes (e.g. disease patient vs control patients). Our software workflow can read SNP-microarray data from different platforms (Affymetrix, Illumina, etc.) that can normally map only a part of human variability (around 500,000 selected SNPs) and expand it to the Whole Genome. Our application will use the 1000G database to fill the missing parts of the genome through imputation. This is a complex and heavy-computation step that has two advantages: i) it expands the identification of genetic factors to the whole genome and ii) it enables multi-platform comparison.

We have developed a novel methodology to perform genome-wide association studies (GWAS), aimed at detecting associations between common genetic variants and human complex traits and diseases. This methodology is composed of several stages, including i) an initial filtering of genetic data from the control and individual cases, ii) haplotype phase inference from genotype data, iii) genotype imputation using several reference panels, iv) analysis of single SNP association, and many others.