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However, previous studies may have over\u2010simplified the complex differences in dose\u2010response profiles between genotypes, resulting in a loss of power.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome\u2010wide association studies. This manuscript builds upon a previous study, where differences in dose\u2010response curves between genotypes were constructed using the hill slope equation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Overall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user\u2010friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1756-0381-5-21","type":"journal-article","created":{"date-parts":[[2012,12,12]],"date-time":"2012-12-12T09:14:01Z","timestamp":1355303641000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multivariate methods and software for association mapping in dose\u2010response genome\u2010wide association studies"],"prefix":"10.1186","volume":"5","author":[{"given":"Chad C","family":"Brown","sequence":"first","affiliation":[]},{"given":"Tammy M","family":"Havener","sequence":"additional","affiliation":[]},{"given":"Marisa Wong","family":"Medina","sequence":"additional","affiliation":[]},{"given":"Ronald M","family":"Krauss","sequence":"additional","affiliation":[]},{"given":"Howard L","family":"McLeod","sequence":"additional","affiliation":[]},{"given":"Alison A","family":"Motsinger\u2010Reif","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,12,12]]},"reference":[{"key":"86_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.2217\/pgs.11.121","volume":"13","author":"H Wheeler","year":"2012","unstructured":"Wheeler H, Dolan M: Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation. 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