CONFIDENT INFERENCE FOR SNP EFFECTS ON TREATMENT EFFICACY

• 成果类型: Article
• 署名作者: Ding, Ying; Li, Ying Grace; Liu, Yushi; Ruberg, Stephen J.; Hsu, Jason C.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Eli Lilly; Lilly Research Laboratories; University System of Ohio; Ohio State University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/17-AOAS1128
• 发表日期: 2018
• 页码: 1727-1748
• 关键词: genetic model; association; hla-b-asterisk-5701; hypersensitivity; POWER

摘要

Our research is for finding SNPs that are predictive of treatment efficacy, to decide which subgroup (with enhanced treatment efficacy) to target in drug development. Testing SNPs for lack of association with treatment outcome is inherently challenging, because any linkage disequilibrium between a noncausal SNP with a causal SNP, however small, makes the zero-null (no association) hypothesis technically false. Control of Type I error rate in testing such null hypotheses are therefore difficult to interpret. We propose a completely different formulation to address this problem. For each SNP, we provide simultaneous confidence intervals directed toward detecting possible dominant, recessive, or additive effects. Across the SNPs, we control the expected number of SNPs with at least one false confidence interval coverage. Since our confidence intervals are constructed based on pivotal statistics, the false coverage control is guaranteed to be exact and unaffected by the true values of test quantities (whether zero or nonzero). Our method is applicable to the therapeutic areas of Diabetes and Alzheimer's diseases, and perhaps more, as a step toward confidently targeting a patient subgroup in a tailored drug development process.