{"title":"Association rule mining for genome-wide association studies through Gibbs sampling","authors":"Guoqi Qian, Pei-Yun Sun","doi":"10.1007/s41060-023-00456-y","DOIUrl":null,"url":null,"abstract":"Abstract Finding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.","PeriodicalId":45667,"journal":{"name":"International Journal of Data Science and Analytics","volume":"225 1","pages":"0"},"PeriodicalIF":3.4000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Science and Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41060-023-00456-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Finding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.
期刊介绍:
Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics