Ferreira Manuel A R, Visscher Peter M, Martin Nicholas G, Duffy David L
Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Australia.
Eur J Hum Genet. 2006 Aug;14(8):953-62. doi: 10.1038/sj.ejhg.5201646. Epub 2006 May 24.
Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.
单变量连锁分析通常用于定位人类复杂性状的基因。通常会对许多性状进行分析，但对于每个性状的连锁显著性并未针对多重性状检验进行校正，这会增加实验性I型错误率。此外，单变量分析无法充分利用多变量数据集所提供的全部效能。多变量连锁是理想的解决方案，但计算量很大，因此全基因组分析和经验显著性评估往往难以实现。我们描述了两种简单的方法，通过合并多个单变量连锁分析的P值来有效缓解这些问题。第一种方法在对多个性状进行检验时，估计一个性状与一个标记之间的经验逐点显著性和全基因组显著性。它与适当的Bonferroni校正一样稳健，优点是无需对所执行的独立检验数量做任何假设。第二种方法估计多个性状与一个标记之间的连锁显著性，因此可用于定位含有多效性数量性状基因座（QTL）的区域。我们表明，在不同情况下，该方法比单个单变量分析检测多效性QTL的效能更高。此外，当性状中度相关且QTL影响所有性状时，它的表现可能优于正式的多变量VC分析。对于任何数量的性状，这种方法在计算上都是可行的，并且不受性状之间残余相关性的影响。我们通过对以双胞胎先证者家庭为样本测量的三种哮喘性状进行全基因组扫描，来说明我们方法的实用性。
