Espin-Garcia Osvaldo, Craiu Radu V, Bull Shelley B
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Genet Epidemiol. 2018 Feb;42(1):104-116. doi: 10.1002/gepi.22099. Epub 2017 Dec 14.
We evaluate two-phase designs to follow-up findings from genome-wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation-maximization-based inference under a semiparametric maximum likelihood formulation tailored for post-GWAS inference. A GWAS-SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT-SNP-dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme-QT strata yields significant power improvements compared to marginal QT- or SNP-based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.
当对整个队列进行区域测序的成本过高时，我们评估两阶段设计以跟进全基因组关联研究（GWAS）的结果。我们在为GWAS后推断量身定制的半参数最大似然公式下，开发了基于期望最大化的新型推断方法。在推断序列变异与正态分布的数量性状（QT）之间的关联时，GWAS单核苷酸多态性（SNP）用作替代协变量。我们通过模拟评估检验有效性，并量化在替代样本分配下联合QT-SNP依赖抽样和分析的效率和功效。与基于边际QT或SNP的分配相比，在SNP基因型和极端QT分层上实现平衡的联合分配可显著提高功效。我们使用收缩压测序研究的数据说明了所提出的方法，并评估了样本分配对抽样变异的敏感性。
