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Advances and Applications in Bioinformatics and Chemistry : AABC (1)
Advances and applications in bioinformatics and chemistry : AABC (1)
Knight, Jo (1)
Vine, Anna E (1)
Year of Publication
A rapid method for combined analysis of common and rare variants at the level of a region, gene, or pathway
Advances and Applications in Bioinformatics and Chemistry : AABC
Previously described methods for the combined analysis of common and rare variants have disadvantages such as requiring an arbitrary classification of variants or permutation testing to assess statistical significance. Here we propose a novel method which implements a weighting scheme based on allele frequencies observed in both cases and controls. Because the test is unbiased, scores can be analyzed with a standard t-test. To test its validity we applied it to data for common, rare, and very rare variants simulated under the null hypothesis. To test its power we applied it to simulated data in which association was present, including data using the observed allele frequencies of common and rare variants in NOD2 previously reported in cases of Crohn’s disease and controls. The method produced results that conformed well to those expected under the null hypothesis. It demonstrated more power to detect association when rare and common variants were analyzed jointly, the power further increasing when rare variants were assigned higher weights. 20,000 analyses of a gene containing 62 variants could be performed in 80 minutes on a laptop. This approach shows promise for the analysis of data currently emerging from genome wide sequencing studies.
common; rare; variant; sequence; genome; exome
A simple method for assessing the strength of evidence for association at the level of the whole gene
Vine, Anna E
Advances and applications in bioinformatics and chemistry : AABC
It is expected that different markers may show different patterns of association with different pathogenic variants within a given gene. It would be helpful to combine the evidence implicating association at the level of the whole gene rather than just for individual markers or haplotypes. Doing this is complicated by the fact that different markers do not represent independent sources of information.
We propose combining the p values from all single locus and/or multilocus analyses of different markers according to the formula of Fisher, X = ∑(−2ln(pi)), and then assessing the empirical significance of this statistic using permutation testing. We present an example application to 19 markers around the HTRA2 gene in a case-control study of Parkinson’s disease.
Applying our approach shows that, although some individual tests produce low p values, overall association at the level of the gene is not supported.
Approaches such as this should be more widely used in assimilating the overall evidence supporting involvement of a gene in a particular disease. Information can be combined from biallelic and multiallelic markers and from single markers along with multimarker analyses. Single genes can be tested or results from groups of genes involved in the same pathway could be combined in order to test biologically relevant hypotheses. The approach has been implemented in a computer program called COMBASSOC which is made available for downloading.
Fisher; significance; genetic marker
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