An exploration of BMSF algorithm in genome-wide association mapping
dc.contributor.author | Jiang, Dayou | |
dc.date.accessioned | 2013-04-15T20:01:57Z | |
dc.date.available | 2013-04-15T20:01:57Z | |
dc.date.graduationmonth | May | |
dc.date.issued | 2013-04-15 | |
dc.date.published | 2013 | |
dc.description.abstract | Motivation: Genome-wide association studies (GWAS) provide an important avenue for investigating many common genetic variants in different individuals to see if any variant is associated with a trait. GWAS is a great tool to identify genetic factors that influence health and disease. However, the high dimensionality of the gene expression dataset makes GWAS challenging. Although a lot of promising machine learning methods, such as Support Vector Machine (SVM), have been investigated in GWAS, the question of how to improve the accuracy of the result has drawn increased attention of many researchers A lot of the studies did not apply feature selection to select a parsimonious set of relevant genes. For those that performed gene selections, they often failed to consider the possible interactions among genes. Here we modify a gene selection algorithm BMSF originally developed by Zhang et al. (2012) for improving the accuracy of cancer classification with binary responses. A continuous response version of BMSF algorithm is provided in this report so that it can be applied to perform gene selection for continuous gene expression dataset. The algorithm dramatically reduces the dimension of the gene markers under concern, thus increases the efficiency and accuracy of GWAS. Results: We applied the continuous response version of BMSF on the wheat phenotypes dataset to predict two quantitative traits based on the genotype marker data. This wheat dataset was previously studied in Long et al. (2009) for the same purpose but used only direct application of SVM regression methods. By applying our gene selection method, we filtered out a large portion of genes which are less relevant and achieved a better prediction result for the test data by building SVM regression model using only selected genes on the training data. We also applied our algorithm on simulated datasets which was generated following the setting of an example in Fan et al. (2011). The continuous response version of BMSF showed good ability to identify active variables hidden among high dimensional irrelevant variables. In comparison to the smoothing based methods in Fan et al. (2011), our method has the advantage of no ambiguity due to difference choices of the smoothing parameter. | |
dc.description.advisor | Haiyan Wang | |
dc.description.degree | Master of Science | |
dc.description.department | Department of Statistics | |
dc.description.level | Masters | |
dc.identifier.uri | http://hdl.handle.net/2097/15505 | |
dc.language.iso | en_US | |
dc.publisher | Kansas State University | |
dc.rights | © the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | BMSF alogorithm | |
dc.subject | Genome-wide association mapping | |
dc.subject | Feature selection | |
dc.subject.umi | Computer Science (0984) | |
dc.title | An exploration of BMSF algorithm in genome-wide association mapping | |
dc.type | Report |