Abstract
Background: Congenital heart defects (CHD) are the most common type of birth defect, affecting approximately 8 in 1,000 newborns. Hundreds of genes have been reported as CHD candidate genes. Nevertheless, each patient/patient group may demonstrate unique etiologic characteristics requiring personalized treatment.Methods: We proposed a sparse representation-based variable selection (SRVS) approach to select disease-related genetic markers from a huge disease candidate gene pool acquired from ResNet relation database. The proposed approach was used to evaluate 167 CHD candidate genes and was followed by validation on a microarray expression data set. Pathway enrichment analysis (PEA), sub-network enrichment analysis (SNEA), and network connectivity analysis (NCA) were conducted to study the functional profile of the variables selected by SRVS and compare them with previous reported genetic markers.Results: A significantly high disease prediction accuracy of 81.40 % was obtained (permutation p-value < 0.0002) using the top 24 SRVS-selected genes, which had been enriched within multiple pathways and sub-networks that had been previously implicated with CHD. Using the most frequently reported genes out of the 167 CHD candidate genes, the highest accuracy of 69.77 % was obtained with a permutation p-value = 0.017. Enrichment analysis and NCA showed that the top genes selected by the proposed SRVS approach were strongly related to the frequently reported CHD genes, although functional differences were present.Conclusion: Our study suggests that SRVS is an effective method in data driven variable selection for CHD and that frequently reported CHD candidate genes may not be the best biomarkers for a specific CHD patient/ patient group.
Highlights
Congenital heart defects (CHD) are heart and large blood vessel anatomical abnormalities occurring during embryonic development [1]
Our study suggests that sparse representation-based variable selection (SRVS) is an effective method
MED ONE 2016,1(4);3 | Email:mo@qingres.com in data driven variable selection for CHD and that frequently reported CHD candidate genes may not be the best biomarkers for a specific CHD patient/ patient group
Summary
Congenital heart defects (CHD) are heart and large blood vessel anatomical abnormalities occurring during embryonic development [1]. There have been an increased number of articles reporting hundreds of genes/proteins related to CHD, many of which were suggested as candidate genes for the disease every patient/patient group has unique human genome variations that requires treatment based on their predicted response or risk of disease [4]. In the cases of large variable and small sample number applications, specific modulation is required to fulfil the variable selected task. Congenital heart defects (CHD) are the most common type of birth defect, affecting approximately 8 in 1,000 newborns. Hundreds of genes have been reported as CHD candidate genes. Each patient/patient group may demonstrate unique etiologic characteristics requiring personalized treatment
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