Integrated Analysis for Genetic Association and Prediction

遗传关联与预测的综合分析

• 批准号: 9768384
• 负责人: Juan Pablo Lewinger
• 金额: $ 38.98万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9768384
• 关键词: Address; Algorithms; Biological; Cancer Prognosis; Clinical; Clinical Research; Colorectal Cancer; Complex; Copy Number Polymorphism; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Development; Dimensions; Disease; Etiology; Gene Expression; Genes; Genome; Genomics; Genotype; Goals; Grouping; Hand; Health; Individual; Light; Malignant Neoplasms; Measurement; Measures; Methods; Methylation; MicroRNAs; Modeling; Molecular; Multiomic Data; Oncogenes; Ontology; Operative Surgical Procedures; Outcome; Patients; Pattern; Predisposition; Probability; Property; Recurrence; Regulation; Series; Statistical Data Interpretation; Statistical Methods; Structure; Supervision; Technology; The Cancer Genome Atlas; Uncertainty; Variant; Work; analytical tool; anticancer research; base; cancer genetics; cancer genomics; cancer recurrence; cohort; colon cancer patients; design; disease phenotype; epidemiology study; epigenome; flexibility; genetic analysis; genetic association; genome wide association study; genome-wide; genomic data; genomic platform; genomic profiles; high dimensionality; improved; insight; novel; novel marker; oncology; outcome forecast; personalized medicine; platform-independent; predictive modeling; risk variant; statistics; tool; translational study; treatment response; tumor; tumor progression

ABSTRACT The overall goal of this project is to develop novel statistical methods for integrative analysis of genomic data in cancer research. We propose to develop analytical tools that can integrate data from multiple genomic platforms and incorporate external omic information from publically available databases. These tools will be applicable to both etiological studies geared toward causal discovery and to clinical and translational studies geared toward predictive modeling. Advances in high-throughput molecular technologies have enabled large-scale omic projects (e.g. Encode, The Cancer Genome Atlas, Epigenome Roadmap) to generate vast amounts of information on the structure, function and regulation of the genome. In addition to this publically available data, individual studies are increasingly generating multiplatform genomic profiles (e.g. genotypes, gene expression, methylation copy number variation, miRNA) to elucidate the complex mechanisms of cancer development and progression, and investigate determinants and predictors of health and clinical outcomes. Integration across these multiple genomic “dimensions” and incorporation of the available external information can increase the ability to discovery causal relationships (e.g. Cancer-SNP associations), enhance prediction and prognosis modeling (e.g. cancer aggressiveness), and provide insights into biological mechanisms. We propose two analytic approaches aimed at addressing the challenges to effective integration across multiplatform genomic data and incorporation of external information from omic projects. The first approach (Aim 1) is a Bayesian regression and feature selection method that can integrate prior omic information in a very flexible manner allowing the data to `speak for itself' to determine which pieces of external information are relevant for the problem at hand. The method works with individual-level data and also with meta-analytic summaries, making it well suited for analyzing data from large multi-study consortia. The second approach (Aim 2) is a regularized regression and feature selection method for integrating multiplatform genomic features measured on the same set of individuals. The method is designed to scale to the very large numbers of features typical of genomewide platforms, to account for the different properties of each genomic data type, and to incorporate relevant external information to increase efficiency. Both approaches can be applied for causal discovery and for developing predictive and prognostic models. We will apply our methods to search for novel risk variants in the CORECT consortium of genome association studies, and to construct a prognostic model of CRC recurrence based on genomewide expression methylation data in the ColoCare consortium cohort of CRC patients. This work will provide new tools for analyzing high-dimensional multi-platform genomic that can take advantage of available external information.

摘要