A hybrid feature selection method for complex diseases SNPs

Machine learning techniques have the potential to revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability; thus, they have been implicated in several human diseases. To separate the affected samples from the normal ones, various techniques have been applied on SNPs. Achieving high classification accuracy in such a high-dimensional space is crucial for successful diagnosis and treatment. In this work, we propose an accurate hybrid feature selection method for detecting the most informative SNPs and selecting an optimal SNP subset. The proposed method is based on the fusion of a filter and a wrapper method, i.e., the Conditional Mutual Information Maximization (CMIM) method and the support vector machine-recursive feature elimination, respectively. The performance of the proposed method was evaluated against four state-of-The-Art feature selection methods, minimum redundancy maximum relevancy, fast correlation-based feature selection, CMIM, and ReliefF, using four classifiers, support vector machine, naive Bayes, linear discriminant analysis, and k nearest neighbors on five different SNP data sets obtained from the National Center for Biotechnology Information gene expression omnibus genomics data repository. The experimental results demonstrate the efficiency of the adopted feature selection approach outperforming all of the compared feature selection algorithms and achieving up to 96% classification accuracy for the used data set. In general, from these results we conclude that SNPs of the whole genome can be efficiently employed to distinguish affected individuals with complex diseases from the healthy ones. 1 2013 IEEE.Scopu

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

Complex genome evolution in Anopheles coluzzii associated with increased insecticide usage in Mali.

In certain cases, a species may have access to important genetic variation present in a related species via adaptive introgression. These novel alleles may interact with their new genetic background, resulting in unexpected phenotypes. In this study, we describe a selective sweep on standing variation on the X chromosome in the mosquito Anopheles coluzzii, a principal malaria vector in West Africa. This event may have been influenced by the recent adaptive introgression of the insecticide resistance gene known as kdr from the sister species Anopheles gambiae. Individuals carrying both kdr and a nearly fixed X-linked haplotype, encompassing at least four genes including the P450 gene CYP9K1 and the cuticular protein CPR125, have rapidly increased in relative frequency. In parallel, a reproductively isolated insecticide-susceptible A. gambiae population (Bamako form) has been driven to local extinction, likely due to strong selection from increased insecticide-treated bed net usage

Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases

Recent advances of information technology in biomedical sciences and other applied areas have created numerous large diverse data sets with a high dimensional feature space, which provide us a tremendous amount of information and new opportunities for improving the quality of human life. Meanwhile, great challenges are also created driven by the continuous arrival of new data that requires researchers to convert these raw data into scientific knowledge in order to benefit from it. Association studies of complex diseases using SNP data have become more and more popular in biomedical research in recent years. In this paper, we present a review of recent statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic association studies for complex diseases. The review includes both general feature reduction approaches for high dimensional correlated data and more specific approaches for SNPs data, which include unsupervised haplotype mapping, tag SNP selection, and supervised SNPs selection using statistical testing/scoring, statistical modeling and machine learning methods with an emphasis on how to identify interacting loci.Comment: Published in at http://dx.doi.org/10.1214/07-SS026 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org

Applied Computational Techniques on Schizophrenia Using Genetic Mutations

[Abstract] Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; Ref. 2009/5

Gene expression in Leishmania is regulated predominantly by gene dosage

ABSTRACT Leishmania tropica, a unicellular eukaryotic parasite present in North and East Africa, the Middle East, and the Indian subcontinent, has been linked to large outbreaks of cutaneous leishmaniasis in displaced populations in Iraq, Jordan, and Syria. Here, we report the genome sequence of this pathogen and 7,863 identified protein-coding genes, and we show that the majority of clinical isolates possess high levels of allelic diversity, genetic admixture, heterozygosity, and extensive aneuploidy. By utilizing paired genome-wide high-throughput DNA sequencing (DNA-seq) with RNA-seq, we found that gene dosage, at the level of individual genes or chromosomal “somy” (a general term covering disomy, trisomy, tetrasomy, etc.), accounted for greater than 85% of total gene expression variation in genes with a 2-fold or greater change in expression. High gene copy number variation (CNV) among membrane-bound transporters, a class of proteins previously implicated in drug resistance, was found for the most highly differentially expressed genes. Our results suggest that gene dosage is an adaptive trait that confers phenotypic plasticity among natural Leishmania populations by rapid down- or upregulation of transporter proteins to limit the effects of environmental stresses, such as drug selection. IMPORTANCE Leishmania is a genus of unicellular eukaryotic parasites that is responsible for a spectrum of human diseases that range from cutaneous leishmaniasis (CL) and mucocutaneous leishmaniasis (MCL) to life-threatening visceral leishmaniasis (VL). Developmental and strain-specific gene expression is largely thought to be due to mRNA message stability or posttranscriptional regulatory networks for this species, whose genome is organized into polycistronic gene clusters in the absence of promoter-mediated regulation of transcription initiation of nuclear genes. Genetic hybridization has been demonstrated to yield dramatic structural genomic variation, but whether such changes in gene dosage impact gene expression has not been formally investigated. Here we show that the predominant mechanism determining transcript abundance differences (>85%) in Leishmania tropica is that of gene dosage at the level of individual genes or chromosomal somy

A genetic ensemble approach for gene-gene interaction identification

<p>Abstract</p> <p>Background</p> <p>It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.</p> <p>Methods</p> <p>In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA) and an ensemble of classifiers (called genetic ensemble). Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP) subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise <it>double fault </it>is designed to quantify the degree of complementarity.</p> <p>Conclusions</p> <p>Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR) and is slightly better than Polymorphism Interaction Analysis (PIA), which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of combining identification results from different algorithms.</p