Simpson Claire L, Justice Cristina M, Krishnan Mera, Wojciechowski Robert, Sung Heejong, Cai Jerry, Green Tiffany, Lewis Deyana, Behneman Dana, Wilson Alexander F, Bailey-Wilson Joan E
Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 31 Center Drive, 333 Cassell Drive Suite 1200, Baltimore, MD 21224, USA.
Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 31 Center Drive, 333 Cassell Drive Suite 1200, Baltimore, MD 21224, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S83. doi: 10.1186/1753-6561-5-S9-S83.
Family-based study designs are again becoming popular as new next-generation sequencing technologies make whole-exome and whole-genome sequencing projects economically and temporally feasible. Here we evaluate the statistical properties of linkage analyses and family-based tests of association for the Genetic Analysis Workshop 17 mini-exome sequence data. Based on our results, the linkage methods using relative pairs or nuclear families had low power, with the best results coming from variance components linkage analysis in nuclear families and Elston-Stewart model-based linkage analysis in extended pedigrees. For family-based tests of association, both ASSOC and ROMP performed well for genes with large effects, but ROMP had the advantage of not requiring parental genotypes in the analysis. For the linkage analyses we conclude that genome-wide significance levels appear to control type I error well but that "suggestive" significance levels do not. Methods that make use of the extended pedigrees are well powered to detect major loci segregating in the families even when there is substantial genetic heterogeneity and the trait is mainly polygenic. However, large numbers of such pedigrees will be necessary to detect all major loci. The family-based tests of association found the same major loci as the linkage analyses and detected low-frequency loci with moderate effect sizes, but control of type I error was not as stringent.
随着新一代测序技术使全外显子组和全基因组测序项目在经济和时间上变得可行，基于家系的研究设计再次受到欢迎。在此，我们针对遗传分析研讨会17的小型外显子组序列数据评估连锁分析和基于家系的关联检验的统计特性。基于我们的结果，使用相对对或核心家系的连锁方法效能较低，最佳结果来自核心家系中的方差成分连锁分析以及扩展家系中基于埃尔斯顿 - 斯图尔特模型的连锁分析。对于基于家系的关联检验，ASSOC和ROMP对于具有大效应的基因表现良好，但ROMP的优势在于分析中不需要亲代基因型。对于连锁分析，我们得出结论，全基因组显著性水平似乎能很好地控制I型错误，但“提示性”显著性水平则不然。即使存在大量遗传异质性且性状主要为多基因性状，利用扩展家系的方法仍有足够效能检测在家系中分离的主要基因座。然而，需要大量这样的家系才能检测到所有主要基因座。基于家系的关联检验发现了与连锁分析相同的主要基因座，并检测到了具有中等效应大小的低频基因座，但对I型错误的控制不那么严格。
