Computational prediction of gene targets for fetal alcohol spectrum disorders

s related to XLMR were obtained from the PubMed scientific literature database, using the following query: “(mental retardation, X-linked [MH])” Limits: only items with abstracts, English. The online literature mining tools DDE and DTFAM were used to extract the relevant ontology terms from the body of literature, whereafter gene lists were extracted from the Ensembl database, as described before, for binary evaluation purposes. Animal model homology Animal models offer major contributions to the understanding of human disease. Although many different animal models for XLMR have been developed, the mouse model is the model most often used (Watase and Zoghbi, 2003). Three areas of homology were chosen as criteria in this category: Genes associated with phenotypes affected by the disease Genes expressed at different developmental stages Genes expressed in the developing brain MGD was accessed to select genes associated with the above-mentioned categories (Blake et al., 2006; Eppig et al., 2005). Human homologues were identified and used to populate these criteria lists. 3.2. 3 Binary filtering and prioritization of genes on the X chromosome For each of the genes on the X chromosome a binary score was calculated simply by summing all binary scores for each of the criteria lists used. This was achieved by comparing the criteria-specific gene lists generated to the candidate gene list to create a binary matrix. The binary evaluation was performed as follows: A gene in the candidate gene list was assigned a 1 when that gene was also present in the criteria gene list that it was evaluated against. If the gene was absent it was assigned a 0. All genes were then ranked based on this score, with those having higher scores being higher in the rank list. Genes in the candidate list that were present in most criteria lists (obtaining the most 1-scores in the binary matrix) received the highest rank as candidates. Chapter 3 – Validation of the computational candidate gene prioritization method – XLMR 67 3.2. 4 Evaluation of biological significance of prioritized genes for XLMR It was expected that the described computational method will not be 100% accurate (i.e. detect all known XLMR genes), but that the top-ranked part of the candidate gene list will be significantly enriched for known and presently unknown XLMR causative genes. It is therefore possible that some of the top-ranked genes that are not currently known XLMR genes may be good candidates for XLMR syndromes for which the genetic cause has not been identified. This anticipated link between XLMR and the top-ranked genes was evaluated by assessing protein-protein interactions and by functional enrichment analysis. It is possible that top-ranked genes that are not known XLMR genes are false-positives. To evaluate the likelihood of the non-XLMR top-ranked genes being XLMR candidates, functional annotation (GO terms) of the non-XLMR top-ranked genes was compared to that of known XLMR genes. Protein-protein interactions The biological significance of the prioritized genes was evaluated by using STRING v7.1 to assess protein-protein interaction among the top-ranked genes (Von Mering et al., 2007). The STRING database provides a comprehensive source of protein-protein association evidence under a common framework. The top-ranked genes and randomly selected lowranked genes were used as input and a medium confidence score for evidence was selected (50%). Functional enrichment analysis The online tool DAVID 2007 (Dennis et al., 2003) was used to evaluate if significant functional enrichment is present among the top-ranked gene list. DAVID is an online tool that integrates genomic functional annotations to reveal biologically relevant enrichment in a gene list. DAVID promotes functional discovery through exploration of biochemical pathway maps, functional classification using GO terms and conserved protein domain architecture. Data from various sources are integrated into DAVID, including GenBank, UniGene, RefSeq, Locuslink, KEGG, OMIM and GO. The top-ranked genes were submitted as a list to DAVID 2007 (January 2007), which was then compared to a background gene list to assess functional enrichment within the list. The background list is user-defined – for this analysis two runs with different background lists were performed. Chapter 3 – Validation of the computational candidate gene prioritization method – XLMR 68 Firstly, all genes on the X chromosome were used as background, and secondly the Homo sapiens default background list from the DAVID website was used as background. For illustrative purposes, only genes ranked within the top 10 criteria matched categories will be used for evaluation of biological significance. This is an arbitrary cut-off used to select an appropriately sized group of prioritized genes to investigate, which could typically also be suitable for a candidate gene association study. A negative control set was generated to assess the validity of the ranking method, by randomly selecting a set of genes of the subset matching to no criteria. 3.2. 5 Identifying candidate genes for XLMR with unknown genetic aetiology An assessment was made of whether genes from the regions shown to be linked to XLMR (Table 3.2 and Figure 3.1) were among the top-ranked genes. If genes from these linked regions are prioritized by the binary filtering method, they become putative candidates for the forms of XLMR for which the genetic cause has not yet been uncovered.