**3. Integrative approaches for post-genomic analysis**

Over the years, thousands of genetic associations have been discovered using genetic approach, known as *genome-wide association studies* (GWAS). GWAS approaches are mostly based on a single-marker association test model that leverages thousands of genomes of cases and controls (sick and healthy individuals) in order to elucidate variants or single-nucleotide polymorphisms (SNPs) with unusual significant differences in frequency throughout genomes [48]. This indicates that GWAS approaches are based on machine learning techniques, which mostly take SNP profiles of cases and controls as inputs, and predict a SNP carrying disease risk. Note that these approaches have been successful [49] and several GWAS results have helped elucidating genetic determinants of susceptibility to several diseases, including complex diseases, such as cancer, and monogenic diseases, such as sickle cell

disease. In fact, in case of the breast cancer disease, a genetic testing tool has been implemented [50] based on specific genetic variants in breast cancer type 1 (BRCA1) and 2 (BRCA2) susceptibility genes in chromosomes 17 (17q21.31) and 13 (13q13.1) [51], respectively. It is widely known that the outcome of a disease, in particular a complex disease, or a response to a drug is influenced by multiple genes and significant contribution from the environment. This strongly argues that using only genomic analysis will not be sufficient to entirely embed phenotypic variation and heritability, suggesting that genomic analysis alone is not sufficient to elucidate the complex structure of the disease [52]. Thus, there is a significant need of integrating information derived from environmental studies and other heterogeneous datasets into genomic analysis to enhance the predictive power of genomic analysis.

As indicated above, even though genomic information is critical, it is not sufficient to completely elucidate disease outcome and progression, which involve gene-gene and gene-environment interactions. In this context, the post-genomic analysis may provide a new paradigm to genomic analysis and may enable further functional characterization of genetic susceptibility to a disease and correlate disease-associated (candidate) genes by combining association signals from genomic analysis and available knowledge, including functional, environmental, epidemiological, and clinical information. This integrative approach increases the likelihood of effectively identifying suitable candidate genes [53] and biological pathways that may be critical in the etiology and pathogenesis of the disease, and in the drug response. The next goal is to integrate large-scale datasets from heterogeneous sources [2, 54] to move beyond a single genomic approach and foster a whole genome-based integrative approach to achieve global view [55]. A biological network, which is a network modeling a biological system as an entity composed of sub-units connected as a whole, has become a useful tool enabling the integration of heterogeneous datasets into a single framework [26].
