Valentina Giansanti, D. D'Agostino, C. Maj, S. Beretta, I. Merelli
{"title":"计算经验p值估计基因-基因相互作用在全基因组关联研究:并行计算方法","authors":"Valentina Giansanti, D. D'Agostino, C. Maj, S. Beretta, I. Merelli","doi":"10.1109/PDP2018.2018.00071","DOIUrl":null,"url":null,"abstract":"In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.","PeriodicalId":333367,"journal":{"name":"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computing Empirical P-Values for Estimating Gene-Gene Interactions in Genome-Wide Association Studies: A Parallel Computing Approach\",\"authors\":\"Valentina Giansanti, D. D'Agostino, C. Maj, S. Beretta, I. Merelli\",\"doi\":\"10.1109/PDP2018.2018.00071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.\",\"PeriodicalId\":333367,\"journal\":{\"name\":\"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDP2018.2018.00071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDP2018.2018.00071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing Empirical P-Values for Estimating Gene-Gene Interactions in Genome-Wide Association Studies: A Parallel Computing Approach
In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.