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Sökning: onr:"swepub:oai:lup.lub.lu.se:4c8c59b9-3301-47ac-8605-38393af7ff9f" > Analysis of case-co...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003656naa a2200481 4500
001oai:lup.lub.lu.se:4c8c59b9-3301-47ac-8605-38393af7ff9f
003SwePub
008160401s2012 | |||||||||||000 ||eng|
0247 a https://lup.lub.lu.se/record/30014272 URI
0247 a https://doi.org/10.1093/bioinformatics/bts2592 DOI
040 a (SwePub)lu
041 a engb eng
042 9 SwePub
072 7a art2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Zaitlen, Noah4 aut
2451 0a Analysis of case-control association studies with known risk variants
264 c 2012-05-03
264 1b Oxford University Press (OUP),c 2012
520 a Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.
650 7a NATURVETENSKAPx Biologix Bioinformatik och systembiologi0 (SwePub)106102 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciencesx Bioinformatics and Systems Biology0 (SwePub)106102 hsv//eng
7001 a Pasaniuc, Bogdan4 aut
7001 a Patterson, Nick4 aut
7001 a Pollack, Samuela4 aut
7001 a Voight, Benjamin4 aut
7001 a Groop, Leifu Lund University,Lunds universitet,Genomik, diabetes och endokrinologi,Forskargrupper vid Lunds universitet,Genomics, Diabetes and Endocrinology,Lund University Research Groups4 aut0 (Swepub:lu)endo-lgr
7001 a Altshuler, David4 aut
7001 a Henderson, Brian E.4 aut
7001 a Kolonel, Laurence N.4 aut
7001 a Le Marchand, Loic4 aut
7001 a Waters, Kevin4 aut
7001 a Haiman, Christopher A.4 aut
7001 a Stranger, Barbara E.4 aut
7001 a Dermitzakis, Emmanouil T.4 aut
7001 a Kraft, Peter4 aut
7001 a Price, Alkes L.4 aut
7102 a Genomik, diabetes och endokrinologib Forskargrupper vid Lunds universitet4 org
7730 t Bioinformaticsd : Oxford University Press (OUP)g 28:13, s. 1729-1737q 28:13<1729-1737x 1367-4803x 1367-4811
8564 u http://dx.doi.org/10.1093/bioinformatics/bts259y FULLTEXT
8564 u https://academic.oup.com/bioinformatics/article-pdf/28/13/1729/16905052/bts259.pdf
8564 8u https://lup.lub.lu.se/record/3001427
8564 8u https://doi.org/10.1093/bioinformatics/bts259

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