6,576 research outputs found
Inferring Gene-Gene Associations from Quantitative Association Rules
The microarray technique is able to monitor the
change in concentration of RNA in thousands of genes simultaneously.
The interest in this technique has grown exponentially
in recent years and the difficulties in analyzing data from
such experiments, which are characterized by the high number
of genes to be analyzed in relation to the low number of
experiments or samples available. Microarray experiments
are generating datasets that can help in reconstructing gene
networks. One of the most important problems in network
reconstruction is finding, for each gene in the network, which
genes can affect it and how. Association Rules are an approach
of unsupervised learning to relate attributes to each other.
In this work we use Quantitative Association Rules in order
to define interrelations between genes. These rules work with
intervals on the attributes, without discretizing the data before
and they are generated by a multi-objective evolutionary
algorithm. In most cases the extracted rules confirm the existing
knowledge about cell-cycle gene expression, while hitherto
unknown relationships can be treated as new hypotheses.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261
Discovering gene association networks by multi-objective evolutionary quantitative association rules
In the last decade, the interest in microarray technology has exponentially increased due to its
ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene
association networks from gene expression profiles is a relevant task and several statistical
techniques have been proposed to build them. The problem lies in the process to discover which
genes are more relevant and to identify the direct regulatory relationships among them. We
developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal
with this problem. We applied our methodology named GarNet to a well-known microarray data of
yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the
study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms
of quality metrics of the networks, such as accuracy and precision, which were measured using
YeastNet database as true network. Furthermore, the results were consistent with previous
biological knowledge.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752
Statistical Modeling of Epistasis and Linkage Decay using Logic Regression
Logic regression has been recognized as a tool that can identify and model non-additive genetic interactions using Boolean logic groups. Logic regression, TASSEL-GLM and SAS-GLM were compared for analytical precision using a previously characterized model system to identify the best genetic model explaining epistatic interaction for vernalization-sensitivity in barley. A genetic model containing two molecular markers identified in vernalization response in barley was selected using logic regression while both TASSEL-GLM and SAS-GLM included spurious associations in their models. The results also suggest the logic regression can be used to identify dominant/recessive relationships between epistatic alleles through its use of conjugate operators
Statistical Modeling of Epistasis and Linkage Decay using Logic Regression
Logic regression has been recognized as a tool that can identify and model non-additive genetic interactions using Boolean logic groups. Logic regression, TASSEL-GLM and SAS-GLM were compared for analytical precision using a previously characterized model system to identify the best genetic model explaining epistatic interaction of vernalization-sensitivity in barley. A genetic model containing two molecular markers identified in vernalization response in barley was selected using logic regression while both TASSEL-GLM and SAS-GLM included spurious associations in their models. The results also suggest the logic regression can be used to identify dominant/recessive relationships between epistatic alleles through its use of conjugate
operators
Using GWAS Data to Identify Copy Number Variants Contributing to Common Complex Diseases
Copy number variants (CNVs) account for more polymorphic base pairs in the
human genome than do single nucleotide polymorphisms (SNPs). CNVs encompass
genes as well as noncoding DNA, making these polymorphisms good candidates for
functional variation. Consequently, most modern genome-wide association studies
test CNVs along with SNPs, after inferring copy number status from the data
generated by high-throughput genotyping platforms. Here we give an overview of
CNV genomics in humans, highlighting patterns that inform methods for
identifying CNVs. We describe how genotyping signals are used to identify CNVs
and provide an overview of existing statistical models and methods used to
infer location and carrier status from such data, especially the most commonly
used methods exploring hybridization intensity. We compare the power of such
methods with the alternative method of using tag SNPs to identify CNV carriers.
As such methods are only powerful when applied to common CNVs, we describe two
alternative approaches that can be informative for identifying rare CNVs
contributing to disease risk. We focus particularly on methods identifying de
novo CNVs and show that such methods can be more powerful than case-control
designs. Finally we present some recommendations for identifying CNVs
contributing to common complex disorders.Comment: Published in at http://dx.doi.org/10.1214/09-STS304 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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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
Inferring a Transcriptional Regulatory Network from Gene Expression Data Using Nonlinear Manifold Embedding
Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators of transcription as well as proteins and enzymes shaping the DNA template. Such intrinsic multi-dimensionality makes uncovering connectivity patterns difficult and unreliable and it calls for adoption of methodologies commensurate with the underlying organization of the data source. Here we present a new computational method that predicts interactions between transcription factors and target genes using a compendium of microarray gene expression data and the knowledge of known interactions between genes and transcription factors. The proposed method called Kernel Embedding of REgulatory Networks (KEREN) is based on the concept of gene-regulon association and it captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct gene regulatory interactions in the model bacteria E.coli on a genome-wide scale. Our method not only yields accurate prediction of verifiable interactions, which outperforms on certain metrics comparable methodologies, but also demonstrates the utility of a geometric approach to the analysis of high-dimensional biological data. We also describe the general application of kernel embedding techniques to some other function and network discovery algorithms
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Patterns of genomic and phenomic diversity in wine and table grapes.
Grapes are one of the most economically and culturally important crops worldwide, and they have been bred for both winemaking and fresh consumption. Here we evaluate patterns of diversity across 33 phenotypes collected over a 17-year period from 580 table and wine grape accessions that belong to one of the world's largest grape gene banks, the grape germplasm collection of the United States Department of Agriculture. We find that phenological events throughout the growing season are correlated, and quantify the marked difference in size between table and wine grapes. By pairing publicly available historical phenotype data with genome-wide polymorphism data, we identify large effect loci controlling traits that have been targeted during domestication and breeding, including hermaphroditism, lighter skin pigmentation and muscat aroma. Breeding for larger berries in table grapes was traditionally concentrated in geographic regions where Islam predominates and alcohol was prohibited, whereas wine grapes retained the ancestral smaller size that is more desirable for winemaking in predominantly Christian regions. We uncover a novel locus with a suggestive association with berry size that harbors a signature of positive selection for larger berries. Our results suggest that religious rules concerning alcohol consumption have had a marked impact on patterns of phenomic and genomic diversity in grapes
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