ASSESSING THE IMPACT OF GLOBAL VERSUS LOCAL ANCESTRY IN ASSOCIATION STUDIES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 为了解释群体分层，通常使用跨基因组的单核苷酸多态性（SNP）进行主成分分析。 我们使用Frachial GAW 16数据来比较本地血统的表现，通过从本地染色体区域的SNP估计的主成分（PC）来测量，与全球血统，通过从全基因组SNP估计的PC来测量，以解决潜在的人群分层。从纵向数据中对来自Fraidian后代队列的无关成年人的标准化身高残差进行平均。 计算SNP基因型数据的PC以代表个体的血统，1）整体使用基因组上的所有SNP或2）局部使用每条染色体内相邻20 Mbp区域中的SNP。 我们评估了身高相关性研究中的差异程度，这取决于是否将全球、当地或两者的PC作为协变量。当地和全球PC之间的相关性较低（r < 0.12），表明当地和全球祖先估计之间存在变异性。 来自全基因组关联检验的p值的Q-Q图表明，在不调整血统的情况下，I型错误率膨胀，但可以通过调整当地血统、全球血统或两者来合理控制。伪协会是观察到的，在这个欧洲-美国人口和人口分层的关联分析的影响，可以通过调整与当地或全球的祖先PC。 人群分层是这一看似同质的Fragrance人群中偏倚的潜在来源。 然而，来自SNPs的本地和全球PC似乎提供了足够的祖先信息。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. To account for population stratification, principal components analysis is often performed using single nucleotide polymorphisms (SNPs) across the genome. We used Framingham GAW16 data to compare the performance of local ancestry, measured by principal components (PCs) estimated from SNPs in a local chromosomal region, with global ancestry, measured by PCs estimated from genome-wide SNPs, to address underlying population stratification. Standardized height residuals from unrelated adults from the Framingham Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20 Mbp regions within each chromosome. We assessed the extent to which there are differences in association studies of height depending on whether PCs for global, local or both are included as covariates.The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Q-Q plots of the p values from genome-wide association tests indicate inflated type I error rate without adjusting for ancestry, but can be reasonably controlled by adjusting for local ancestry, global ancestry, or both. Spurious associations are observable in this European-American population and the effect of population stratification in association analysis can be controlled by adjustment with local or global ancestry PCs. Population stratification is a potential source of bias in this seemingly homogenous Framingham population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.