Year: 2010
Type: dissertation
Abstract: Fetal alcohol spectrum disorders (FASD) describe the range of disorders that result \nfrom in utero alcohol exposure. FASD is a serious global health problem and is observed at \nexceedingly high frequencies in certain South African communities. Although in utero alcohol \nexposure is the primary trigger, there is evidence that genetic- and other susceptibility factors \ncontribute towards FASD development. To date, no genome-wide association or linkage \nstudies have been performed for any of the FASD syndromes. \nThe main objectives of this study were to develop an innovative approach to \ncomputationally identify biologically plausible candidate genes for FASD, for a future \nassociation study, and to evaluate the appropriateness and validity of this approach. Further, \nan in silico analysis of known single nucleotide polymorphisms (SNPs) within the top-ranked \ncandidate gene was performed in conjunction with de novo SNP detection, to select a subset \nof SNPs based on proposed functional impact on gene expression and protein function, for a \nprospective association study. \nA computational binary filtering technique was designed that can be employed to \nprioritize genes in a candidate list, or could be used to rank all genes in the genome in the \nabsence of such a list. 10174 FASD candidate genes were initially selected from the whole \ngenome using a previously described method. Hereafter the candidates were prioritized \nusing a binary filtering technique. The biological enrichment of the ranked genes was \nassessed by investigating the protein-protein interactions, functional enrichment and common \npromoter element binding sites of the top-ranked genes. A group of 87 genes was prioritized \nas candidates highlighting many strong candidates from the TGF-/, MAPK and Hedgehog \nsignalling pathways, which are all integral to fetal development and potential targets for \nalcohol's teratogenic effect. \nTo assess the effectiveness and accuracy of this computational approach, X-linked \nmental retardation (XLMR) was used as a test disease, considering that XLMR is a set of \nheterogeneous disorders of which some of the underlying genetics is known. This \nimplementation resulted in a prioritized gene list with a noted enrichment of known XLMR \ngenes among the top-ranked genes. Furthermore, the top-ranked list contained genes that \nwere biologically relevant to XLMR, and could potentially be as yet unknown candidate genes \nfor XLMR. Indeed, many of the top-ranked genes mapped to XLMR candidate regions, \nconfirming their status as good candidates. \nFinally, a subset of seven known and novel SNPs was selected within FGFR1 based on \nputative functional impact. Data from the HapMap project was used to identify tag SNPs for \nFGFR1 to complement the selection made based on function. \nThe main limitation of the proposed computational approach to candidate gene \nprediction is that it is primarily based on gene annotation, and that it is therefore biased \ntowards selecting better-annotated genes. However, the results obtained in this study \nsuggest that the described computational method is an effective approach that can identify \nlikely candidates that are biologically relevant to the disease of interest, and therefore \nappropriate for a candidate-gene association studies. In practice, this technique is an \nappropriate approach to select a workable set of candidate genes for a complex disease, in a \nsetting where a whole-genome association study is not a viable option.
Author: Zané Lombard
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2227236637