File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Others: KS-Burden: Assessing distributional differences of rare variants in dichotomous traits

TitleKS-Burden: Assessing distributional differences of rare variants in dichotomous traits
Authors
Issue Date2018
Citation
Porsch, RM, Mak, SHT, Tang, C & Sham, PC. KS-Burden: Assessing distributional differences of rare variants in dichotomous traits (July 5, 2018). Retrieved from bioRxiV: https://www.biorxiv.org/content/10.1101/368696v1 How to Cite?
AbstractA number of rare variant tests have been developed to explore the effect of low frequency genetic variations on complex phenotypes. However, an often neglected aspect in these tests is the position of genetic variations. Here we are proposing a way to assess the differences in spatial organization of rare variants by assessing their distributional differences between affected and unaffected subjects. To do so, we have formulated an adaptation of the well know Kolmogorov-Smirnov (KS) test, combining both KS and a simple gene burden approach, called KS-Burden. The performance of our test was evaluated under a comprehensive simulations framework using real data and various scenarios. Our results show that the KS-Burden test is able to outperform the commonly used SKAT-O test, as well as others, in the presents of clusters of causal variants within a genomic region. Furthermore, our test is able to maintain competitive statistical power in scenarios unfavorable to its original assumptions. Hence, the KS-Burden test is a valuable alternative to existing tests and provides better statistical power in the presents of causal clusters within a gene.
Persistent Identifierhttp://hdl.handle.net/10722/269238

 

DC FieldValueLanguage
dc.contributor.authorPorsch, RM-
dc.contributor.authorMak, SHT-
dc.contributor.authorTang, C-
dc.contributor.authorSham, PC-
dc.date.accessioned2019-04-17T03:47:27Z-
dc.date.available2019-04-17T03:47:27Z-
dc.date.issued2018-
dc.identifier.citationPorsch, RM, Mak, SHT, Tang, C & Sham, PC. KS-Burden: Assessing distributional differences of rare variants in dichotomous traits (July 5, 2018). Retrieved from bioRxiV: https://www.biorxiv.org/content/10.1101/368696v1-
dc.identifier.urihttp://hdl.handle.net/10722/269238-
dc.description.abstractA number of rare variant tests have been developed to explore the effect of low frequency genetic variations on complex phenotypes. However, an often neglected aspect in these tests is the position of genetic variations. Here we are proposing a way to assess the differences in spatial organization of rare variants by assessing their distributional differences between affected and unaffected subjects. To do so, we have formulated an adaptation of the well know Kolmogorov-Smirnov (KS) test, combining both KS and a simple gene burden approach, called KS-Burden. The performance of our test was evaluated under a comprehensive simulations framework using real data and various scenarios. Our results show that the KS-Burden test is able to outperform the commonly used SKAT-O test, as well as others, in the presents of clusters of causal variants within a genomic region. Furthermore, our test is able to maintain competitive statistical power in scenarios unfavorable to its original assumptions. Hence, the KS-Burden test is a valuable alternative to existing tests and provides better statistical power in the presents of causal clusters within a gene.-
dc.languageeng-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleKS-Burden: Assessing distributional differences of rare variants in dichotomous traits-
dc.typeOthers-
dc.identifier.emailPorsch, RM: rmporsch@hku.hk-
dc.identifier.emailMak, SHT: tshmak@hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authoritySham, PC=rp00459-
dc.description.naturepreprint-
dc.identifier.doi10.1101/368696-
dc.identifier.hkuros292621-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats