Dataset Information

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test.

ABSTRACT:

Motivation

For samples of unrelated individuals, we propose a general analysis framework in which hundred thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multilocus analysis, which has focused on the dimension reduction of the data, our multilocus association-clustering test profits from the availability of large numbers of genetic loci by detecting clusters of loci that are associated with the phenotype.

Results

The approach is computationally fast and powerful, enabling the simultaneous association testing of large genomic regions. Even the entire genome or certain chromosomes can be tested simultaneously. Using simulation studies, the properties of the approach are evaluated. In an application to a genome-wide association study for chronic obstructive pulmonary disease, we illustrate the practical relevance of the proposed method by simultaneously testing all genotyped loci of the genome-wide association study and by testing each chromosome individually. Our findings suggest that statistical methodology that incorporates spatial-clustering information will be especially useful in whole-genome sequencing studies in which millions or billions of base pairs are recorded and grouped by genomic regions or genes, and are tested jointly for association.

Availability and implementation

Implementation of the approach is available upon request.

SUBMITTER: Qiao D

PROVIDER: S-EPMC3892690 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Publications

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test.

Qiao Dandi D Cho Michael H MH Fier Heide H Bakke Per S PS Gulsvik Amund A Silverman Edwin K EK Lange Christoph C

Bioinformatics (Oxford, England) 20131120 2

<h4>Motivation</h4>For samples of unrelated individuals, we propose a general analysis framework in which hundred thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multilocus analysis, which has focused on the dimension reduction of the data, our m ...[more]

PMID: 24262215

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Project description:BackgroundDNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated.ResultsUsing simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data.ConclusionsWe propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

Project description:Background and aimsLarge indels are commonly identified in patients but are not detectable by routine Sanger sequencing and panel sequencing. We specially designed a multi-gene panel that could simultaneously test known large indels in addition to ordinary variants, and reported the diagnostic yield in patients with intrahepatic cholestasis.MethodsThe panel contains 61 genes associated with cholestasis and 25 known recurrent large indels. The amplicon library was sequenced on Ion PGM system. Sequencing data were analyzed using a routine data analysis protocol and an internal program encoded for large indels test simultaneously. The validation phase was performed using 54 patients with known genetic diagnosis, including 5 with large insertions. At implement phase, 141 patients with intrahepatic cholestasis were evaluated.ResultsAt validation phase, 99.6% of the variations identified by Sanger sequencing could be detected by panel sequencing. Following the routine protocol, 99.8% of false positives could be filtered and 98.8% of retained variations were true positives. Large insertions in the 5 patients with known genetic diagnosis could be correctly detected using the internal program. At implementation phase, 96.9% of the retained variations, following the routine protocol, were confirmed to be true. Twenty-nine patients received a potential genetic diagnosis when panel sequencing data were analyzed using the routine protocol. Two additional patients, who were found to harbor large insertions in SLC25A13, obtained a potential genetic diagnosis when sequencing data were further analyzed using the internal program. A total of 31 (22.0%) patients obtained a potential genetic diagnosis. Nine different genetic disorders were diagnosed, and citrin deficiency was the commonest.ConclusionSpecially designed multi-gene panel can correctly detect large indels simultaneously. By using it, we assigned a potential genetic diagnosis to 22.0% of patients with intrahepatic cholestasis.

Dataset Information

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test.

Motivation

Results

Availability and implementation

Publications

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test.

Similar Datasets

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