From GWAS to PheWAS: Scanning the EMR phenome for gene-disease associations

描述（由申请人提供）：基因组医学为改进诊断方法和更有效的患者特异性治疗提供了希望。全基因组相关研究（GWAS）阐明了遗传标记，提高了对许多疾病的风险和原因的理解，并可在患者特定的基础上指导诊断和治疗。本项目将采用另一种方法来确定基因-疾病关联：进行“反向GWAS”或全表型关联研究（PheWAS），以确定哪些表型与给定的基因型相关。该项目由一个大型DNA生物库与一份去识别的电子病历副本相结合来实现。该项目有四个具体目标。首先，该项目将开发和验证一种标准化方法，从EMR记录中提取疾病表型，整合临床疾病的国家标准术语和与每种疾病的治疗和诊断相关的描述符，以创建一个可共享的知识库。该项目将使用自然语言处理、结构化数据查询以及启发式和机器学习方法来准确识别每种疾病的患者和相应的对照。第二个目的是使用现有的基因型数据进行PheWAS分析。为了验证该方法，该项目将使用PheWAS来“重新发现”具有已知疾病关联的SNP。该项目还将研究大规模多重假设检验的统计方法，以发现新的表型关联。第三个目标是将PheWAS算法应用于具有EMR连接的DNA生物库的其他四个站点并比较结果。在第四个目标中，该项目将验证通过PheWAS发现的新表型-基因型关联，并在以前未测试的人群中进行新的基因分型。该项目产生的工具不仅使PheWAS成为可能，而且还将广泛支持临床研究和随后的遗传研究。

DESCRIPTION (provided by applicant): Genomic medicine offers hope for improved diagnostic methods and for more effective, patient-specific therapies. Genome-wide associated studies (GWAS) elucidate genetic markers that improve understanding of risks and causes for many diseases, and may guide diagnosis and therapy on a patient-specific basis. This project will take another approach to identify gene-disease associations: perform "reverse GWAS," or phenome- wide association study (PheWAS), to determine which phenotypes are associated with a given genotype. The project is enabled by a large DNA biobank coupled to a de- identified copy of the electronic medical record. This project has four specific aims. First, the project will develop and validate a standardized approach to extract disease phenotypes from EMR records, integrating national standard terminologies of clinical disorders and descriptors relating to treatment and diagnosis of each disease to create a sharable knowledge base. The project will use natural language processing, structured data queries, and heuristic and machine learning methods to accurately identify patients with each disease and corresponding controls. The second aim is to perform PheWAS analyses using existing genotype data. To validate the method, the project will use PheWAS to "rediscover" SNPs with known disease associations. The project will also investigate statistical methods for large-scale multiple hypothesis testing to discover novel phenotype associations. The third aim is to apply the PheWAS algorithms in four other sites with EMR-linked DNA biobanks and compare results. In the fourth aim, the project will validate novel phenotype-genotype associations discovered through PheWAS with new genotyping in a previously untested population. The tools generated from this project will not only make PheWAS possible, but will also broadly enable clinical research and subsequent genetic studies.