Dataset Information

Anorectal atresia and variants at predicted regulatory sites in candidate genes.

ABSTRACT: Anorectal atresia is a serious birth defect of largely unknown etiology but candidate genes have been identified in animal studies and human syndromes. Because alterations in the activity of these genes might lead to anorectal atresia, we selected 71 common variants predicted to be in transcription factor binding sites, CpG windows, splice sites, and miRNA target sites of 25 candidate genes, and tested for their association with anorectal atresia. The study population comprised 150 anorectal atresia cases and 623 control infants without major malformations. Variants predicted to affect transcription factor binding, splicing, and DNA methylation in WNT3A, PCSK5, TCF4, MKKS, GLI2, HOXD12, and BMP4 were associated with anorectal atresia based on a nominal P value < 0.05. The GLI2 and BMP4 variants are reported to be moderately associated with gene expression changes (Spearman's rank correlation coefficients between -0.260 and 0.226). We did not find evidence for interaction between maternal pre-pregnancy obesity and variants in MKKS, a gene previously associated with obesity, on the risk of anorectal atresia. Our results for MKKS support previously suggested associations with anorectal malformations. Our findings suggest that more research is needed to determine whether altered GLI2 and BMP4 expression is important in anorectal atresia in humans.

SUBMITTER: Carter TC

PROVIDER: S-EPMC3535506 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Anorectal atresia and variants at predicted regulatory sites in candidate genes.

Carter Tonia C TC Kay Denise M DM Browne Marilyn L ML Liu Aiyi A Romitti Paul A PA Kuehn Devon D Conley Mary R MR Caggana Michele M Druschel Charlotte M CM Brody Lawrence C LC Mills James L JL

Annals of human genetics 20121106 1

Anorectal atresia is a serious birth defect of largely unknown etiology but candidate genes have been identified in animal studies and human syndromes. Because alterations in the activity of these genes might lead to anorectal atresia, we selected 71 common variants predicted to be in transcription factor binding sites, CpG windows, splice sites, and miRNA target sites of 25 candidate genes, and tested for their association with anorectal atresia. The study population comprised 150 anorectal atr ...[more]

PMID: 23127126

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Project description:With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Project description:BACKGROUND:Deafness is a highly heterogenous disorder with over 100 genes known to underlie human non-syndromic hearing impairment. However, many more remain undiscovered, particularly those involved in the most common form of deafness: adult-onset progressive hearing loss. Despite several genome-wide association studies of adult hearing status, it remains unclear whether the genetic architecture of this common sensory loss consists of multiple rare variants each with large effect size or many common susceptibility variants each with small to medium effects. As next generation sequencing is now being utilised in clinical diagnosis, our aim was to explore the viability of diagnosing the genetic cause of hearing loss using whole exome sequencing in individual subjects as in a clinical setting. METHODS:We performed exome sequencing of thirty patients selected for distinct phenotypic sub-types from well-characterised cohorts of 1479 people with adult-onset hearing loss. RESULTS:Every individual carried predicted pathogenic variants in at least ten deafness-associated genes; similar findings were obtained from an analysis of the 1000 Genomes Project data unselected for hearing status. We have identified putative causal variants in known deafness genes and several novel candidate genes, including NEDD4 and NEFH that were mutated in multiple individuals. CONCLUSIONS:The high frequency of predicted-pathogenic variants detected in known deafness-associated genes was unexpected and has significant implications for current diagnostic sequencing in deafness. Our findings suggest that in a clinic setting, efforts should be made to a) confirm key sequence results by Sanger sequencing, b) assess segregations of variants and phenotypes within the family if at all possible, and c) use caution in applying current pathogenicity prediction algorithms for diagnostic purposes. We conclude that there may be a high number of pathogenic variants affecting hearing in the ageing population, including many in known deafness-associated genes. Our findings of frequent predicted-pathogenic variants in both our hearing-impaired sample and in the larger 1000 Genomes Project sample unselected for auditory function suggests that the reference population for interpreting variants for this very common disorder should be a population of people with good hearing for their age rather than an unselected population.

Dataset Information

Anorectal atresia and variants at predicted regulatory sites in candidate genes.

Publications

Anorectal atresia and variants at predicted regulatory sites in candidate genes.

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