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Comparative analysis of vertebrate dystrophin loci indicate intron gigantism as a common feature.

ABSTRACT: The human DMD gene is the largest known to date, spanning > 2000 kb on the X chromosome. The gene size is mainly accounted for by huge intronic regions. We sequenced 190 kb of Fugu rubripes (pufferfish) genomic DNA corresponding to the complete dystrophin gene (FrDMD) and provide the first report of gene structure and sequence comparison among dystrophin genomic sequences from different vertebrate organisms. Almost all intron positions and phases are conserved between FrDMD and its mammalian counterparts, and the predicted protein product of the Fugu gene displays 55% identity and 71% similarity to human dystrophin. In analogy to the human gene, FrDMD presents several-fold longer than average intronic regions. Analysis of intron sequences of the human and murine genes revealed that they are extremely conserved in size and that a similar fraction of total intron length is represented by repetitive elements; moreover, our data indicate that intron expansion through repeat accumulation in the two orthologs is the result of independent insertional events. The hypothesis that intron length might be functionally relevant to the DMD gene regulation is proposed and substantiated by the finding that dystrophin intron gigantism is common to the three vertebrate genes.

SUBMITTER: Pozzoli U

PROVIDER: S-EPMC430921 | biostudies-literature | 2003 May

REPOSITORIES: biostudies-literature

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Comparative analysis of vertebrate dystrophin loci indicate intron gigantism as a common feature.

Pozzoli Uberto U Elgar Greg G Cagliani Rachele R Riva Laura L Comi Giacomo P GP Bresolin Nereo N Bardoni Alessandra A Sironi Manuela M

Genome research 20030501 5

The human DMD gene is the largest known to date, spanning > 2000 kb on the X chromosome. The gene size is mainly accounted for by huge intronic regions. We sequenced 190 kb of Fugu rubripes (pufferfish) genomic DNA corresponding to the complete dystrophin gene (FrDMD) and provide the first report of gene structure and sequence comparison among dystrophin genomic sequences from different vertebrate organisms. Almost all intron positions and phases are conserved between FrDMD and its mammalian cou ...[more]

PMID: 12727896

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Project description:BackgroundInteractions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.ResultsWe report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.ConclusionThis comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.

Dataset Information

Comparative analysis of vertebrate dystrophin loci indicate intron gigantism as a common feature.

Publications

Comparative analysis of vertebrate dystrophin loci indicate intron gigantism as a common feature.

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