Bull Shelley B, John Sally, Briollais Laurent
Samuel Lunenfeld Research Institute of Mount Sinai Hospital and Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada.
Genet Epidemiol. 2005;29 Suppl 1:S48-58. doi: 10.1002/gepi.20110.
This report summarizes the Genetic Analysis Workshop 14 contributions related to fine-mapping strategies, in which examining smaller regions by association with single-nucleotide polymorphisms (SNPs) can yield savings in genotyping and multiple-testing penalties. The aim of the analyses conducted in Group 7 contributions was to localize disease susceptibility loci from either the simulated or the Collaborative Study on the Genetics of Alcoholism (COGA) data within identified regions of linkage. Among the 10 contributions, most groups analyzed the simulated data, one group analyzed the COGA data only, and one group analyzed both data sets. The research questions included evaluation of new methods of analysis, as well as comparisons among alternative methods, analytic strategies, and study designs. Methods of interest included an algorithm for SNP marker ordering, a locally weighted transmission disequilibrium test statistic, a likelihood-ratio test statistic for family-based association in nuclear families, a robust test statistic for case-control association studies, and Bayesian spatial modeling methods for haplotype clustering and association. Evaluations included comparisons among confidence intervals for loci detected via linkage, effects of multiple testing adjustments and trade-offs between type I error and power, comparisons among haplotype-based (multilocus) and genotype-based (multilocus and single-locus) association analyses, and design of fine-mapping and replication studies. While several promising new approaches were identified, further development and evaluation of methods for multiple testing, regression modeling of association with multiple markers and haplotypes, and combined treatment of linkage and association data are necessary if we are to identify many of the genes that contribute to complex traits.
本报告总结了与精细定位策略相关的遗传分析研讨会14的成果，其中通过与单核苷酸多态性（SNP）关联来检查较小区域可节省基因分型和多重检验惩罚。第7组贡献中所进行分析的目的是在已确定的连锁区域内，从模拟数据或酒精中毒遗传学合作研究（COGA）数据中定位疾病易感基因座。在这10项贡献中，大多数组分析了模拟数据，一组仅分析了COGA数据，还有一组分析了这两个数据集。研究问题包括对新分析方法的评估，以及不同方法、分析策略和研究设计之间的比较。感兴趣的方法包括一种SNP标记排序算法、一种局部加权传递不平衡检验统计量、一种用于核心家庭中基于家系关联的似然比检验统计量、一种用于病例对照关联研究的稳健检验统计量，以及用于单倍型聚类和关联的贝叶斯空间建模方法。评估包括通过连锁检测到的基因座的置信区间之间的比较、多重检验调整的效果以及I型错误和检验效能之间的权衡、基于单倍型（多位点）和基于基因型（多位点和单一位点）的关联分析之间的比较，以及精细定位和重复研究的设计。虽然确定了几种有前景的新方法，但如果我们要识别许多导致复杂性状的基因，就需要对多重检验方法、与多个标记和单倍型关联的回归建模以及连锁和关联数据的联合处理进行进一步的开发和评估。
