He Liang, Pitkäniemi Janne M
University of Helsinki, Hjelt Institute, Department of Public Health, PO Box 41, FI-00014 Helsinki, Finland.
University of Helsinki, Hjelt Institute, Department of Public Health, PO Box 41, FI-00014 Helsinki, Finland ; Finnish Cancer Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Pieni Roobertinkatu 9, FI-00130 Helsinki, Finland.
BMC Proc. 2014 Jun 17;8(Suppl 1):S37. doi: 10.1186/1753-6561-8-S1-S37. eCollection 2014.
In this study, we analyze the Genetic Analysis Workshop 18 data to identify the genes and underlying single-nucleotide polymorphisms on 11 chromosomes that exhibit significant association with systolic blood pressure. We propose a novel family-based method for rare-variant association detection based on the hierarchical Bayesian framework. The method controls spurious associations caused by population stratification, and improves the statistical power to detect not only individual rare variants, but also genes with either continuous or binary outcomes. Our method utilizes nuclear family information, and takes into account the effects of all single-nucleotide polymorphisms in a gene, using a hierarchical model. When we apply this method to the genome-wide Genetic Analysis Workshop 18 data, several genes and single-nucleotide polymorphisms are identified as potentially related to systolic blood pressure.
在本研究中，我们分析了遗传分析研讨会18的数据，以识别与收缩压表现出显著关联的11条染色体上的基因及潜在单核苷酸多态性。我们基于分层贝叶斯框架提出了一种用于罕见变异关联检测的新型家系方法。该方法控制了由群体分层导致的虚假关联，并提高了检测能力，不仅能检测单个罕见变异，还能检测具有连续或二元结果的基因。我们的方法利用核心家系信息，并使用分层模型考虑基因中所有单核苷酸多态性的影响。当我们将此方法应用于全基因组的遗传分析研讨会18数据时，识别出了几个与收缩压潜在相关的基因和单核苷酸多态性。
