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Identifying highly conserved and highly differentiated gene ontology categories in human populations.

ABSTRACT: Detecting and interpreting certain system-level characteristics associated with human population genetic differences is a challenge for human geneticists. In this study, we conducted a population genetic study using the HapMap genotype data to identify certain special Gene Ontology (GO) categories associated with high/low genetic difference among 11 Hapmap populations. Initially, the genetic differences in each gene region among these populations were measured using allele frequency, linkage disequilibrium (LD) pattern, and transferability of tagSNPs. The associations between each GO term and these genetic differences were then identified. The results showed that cellular process, catalytic activity, binding, and some of their sub-terms were associated with high levels of genetic difference, and genes involved in these functional categories displayed, on average, high genetic diversity among different populations. By contrast, multicellular organismal processes, molecular transducer activity, and some of their sub-terms were associated with low levels of genetic difference. In particular, the neurological system process under the multicellular organismal process category had low levels of genetic difference; the neurological function also showed high evolutionary conservation between species in some previous studies. These results may provide a new insight into the understanding of human evolutionary history at the system-level.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC3227580 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Identifying highly conserved and highly differentiated gene ontology categories in human populations.

Jiang Yongshuai Y Zhang Ruijie R Sun Peng P Tang Guoping G Zhang Xuehong X Wang Xing X Guo Xiaodan X Wang Qiuyu Q Li Xia X

PloS one 20111130 11

Detecting and interpreting certain system-level characteristics associated with human population genetic differences is a challenge for human geneticists. In this study, we conducted a population genetic study using the HapMap genotype data to identify certain special Gene Ontology (GO) categories associated with high/low genetic difference among 11 Hapmap populations. Initially, the genetic differences in each gene region among these populations were measured using allele frequency, linkage dis ...[more]

PMID: 22140477

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Project description:The Gene Ontology (GO) vocabulary has been extensively explored to analyze the functions of coexpressed genes. However, despite its extended use in Biology and Medical Sciences, there are still high levels of uncertainty about which ontology (i.e. Molecular Process, Cellular Component or Molecular Function) should be used, and at which level of specificity. Moreover, the GO database can contain incomplete information resulting from human annotations, or highly influenced by the available knowledge about a specific branch in an ontology. In spite of these drawbacks, there is a trend to ignore these problems and even use GO terms to conduct searches of gene expression profiles (i.e. expression + GO) instead of more cautious approaches that just consider them as an independent source of validation (i.e. expression versus GO). Consequently, propagating the uncertainty and producing biased analysis of the required gene grouping hypotheses. We proposed a web tool, Onto-CC, as an automatic method specially suited for independent explanation/validation of gene grouping hypotheses (e.g. coexpressed genes) based on GO clusters (i.e. expression versus GO). Onto-CC approach reduces the uncertainty of the queries by identifying optimal conceptual clusters that combine terms from different ontologies simultaneously, as well as terms defined at different levels of specificity in the GO hierarchy. To do so, we implemented the EMO-CC methodology to find clusters in structural databases [GO Directed acyclic Graph (DAG) tree], inspired on Conceptual Clustering algorithms. This approach allows the management of optimal cluster sets as potential parallel hypotheses, guided by multiobjective/multimodal optimization techniques. Therefore, we can generate alternative and, still, optimal explanations of queries that can provide new insights for a given problem. Onto-CC has been successfully used to test different medical and biological hypotheses including the explanation and prediction of gene expression profiles resulting from the host response to injuries in the inflammatory problem. Onto-CC provides two versions: Ready2GO, a precalculated EMO-CC for several genomes and an Advanced Onto-CC for custom annotation files (http://gps-tools2.wustl.edu/onto-cc/index.html).

Project description:Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

Dataset Information

Identifying highly conserved and highly differentiated gene ontology categories in human populations.

Publications

Identifying highly conserved and highly differentiated gene ontology categories in human populations.

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