Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-14-50
DC FieldValue
dc.titleComparison of similarity-based tests and pooling strategies for rare variants
dc.contributor.authorZakharov, S.
dc.contributor.authorSalim, A.
dc.contributor.authorThalamuthu, A.
dc.date.accessioned2014-11-26T05:02:40Z
dc.date.available2014-11-26T05:02:40Z
dc.date.issued2013-01-24
dc.identifier.citationZakharov, S., Salim, A., Thalamuthu, A. (2013-01-24). Comparison of similarity-based tests and pooling strategies for rare variants. BMC Genomics 14 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2164-14-50
dc.identifier.issn14712164
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/108899
dc.description.abstractBackground: As several rare genomic variants have been shown to affect common phenotypes, rare variants association analysis has received considerable attention. Several efficient association tests using genotype and phenotype similarity measures have been proposed in the literature. The major advantages of similarity-based tests are their ability to accommodate multiple types of DNA variations within one association test, and to account for the possible interaction within a region. However, not much work has been done to compare the performance of similarity-based tests on rare variants association scenarios, especially when applied with different rare variants pooling strategies. Results: Based on the population genetics simulations and analysis of a publicly-available sequencing data set, we compared the performance of four similarity-based tests and two rare variants pooling strategies. We showed that weighting approach outperforms collapsing under the presence of strong effect from rare variants and under the presence of moderate effect from common variants, whereas collapsing of rare variants is preferable when common variants possess a strong effect. We also demonstrated that the difference in statistical power between the two pooling strategies may be substantial. The results also highlighted consistently high power of two similarity-based approaches when applied with an appropriate pooling strategy. Conclusions: Population genetics simulations and sequencing data set analysis showed high power of two similarity-based tests and a substantial difference in power between the two pooling strategies. © 2013 Zakharov et al; licensee BioMed Central Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1186/1471-2164-14-50
dc.sourceScopus
dc.subjectAssociation analysis
dc.subjectCollapsing
dc.subjectGenetics
dc.subjectMulti-locus
dc.subjectPower
dc.subjectRare variants
dc.subjectSimilarity
dc.subjectWeighting
dc.typeArticle
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1186/1471-2164-14-50
dc.description.sourcetitleBMC Genomics
dc.description.volume14
dc.description.issue1
dc.description.page-
dc.description.codenBGMEE
dc.identifier.isiut000316202700001
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