Pairwise shared genomic segment analysis in high-risk pedigrees: application to Genetic Analysis Workshop 17 exome-sequencing SNP data.
Pairwise shared genomic segment analysis in high-risk pedigrees: application to Genetic Analysis Workshop 17 exome-sequencing SNP data.
复制标题
DOI：
10.1186/1753-6561-5-s9-s9
复制
发表时间：
2011-11-29
期刊：
影响因子：
--
通讯作者：
Camp, Nicola J
中科院分区：
文献类型：
--
作者：
Cai, Zheng;Knight, Stacey;Camp, Nicola J
我们将我们的双共享基因组片段（pSGS）分析方法应用于遗传分析研讨会17 （GAW17）迷你外显子组测序数据集中鉴定的高风险家系。原始的共享基因组片段方法侧重于识别谱系中所有病例受试者共享的区域；因此，它可能对散发病例敏感。我们的新方法检验了高风险谱系中所有对病例受试者之间的共享，然后使用平均共享作为检验统计量；此外，根据单核苷酸多态性的家系结构和连锁不平衡模式，对其意义进行了实证评估。使用所有GAW17重复，我们确定了18个单系高风险家系，这些家系包含过多的疾病（p < 0.01），病例受试者之间至少有15个减数分裂。18个罕见的因果变异在这组谱系中是多态的。在显著性阈值为0.001的基础上，72.2%（13/18）的家系被成功识别出至少一个包含真正因果变异的区域。鉴定的区域包括可能的18个多态性因果变异中的4个。每个谱系平均鉴定出1.1个真阳性和1.7个假阳性。总之，我们已经证明了我们的新pSGS方法在利用高风险家系和外显子组序列数据定位常见疾病中罕见疾病因果变异的潜力。
We applied our method of pairwise shared genomic segment (pSGS) analysis to high-risk pedigrees identified from the Genetic Analysis Workshop 17 (GAW17) mini-exome sequencing data set. The original shared genomic segment method focused on identifying regions shared by all case subjects in a pedigree; thus it can be sensitive to sporadic cases. Our new method examines sharing among all pairs of case subjects in a high-risk pedigree and then uses the mean sharing as the test statistic; in addition, the significance is assessed empirically based on the pedigree structure and linkage disequilibrium pattern of the single-nucleotide polymorphisms. Using all GAW17 replicates, we identified 18 unilineal high-risk pedigrees that contained excess disease (p < 0.01) and at least 15 meioses between case subjects. Eighteen rare causal variants were polymorphic in this set of pedigrees. Based on a significance threshold of 0.001, 72.2% (13/18) of these pedigrees were successfully identified with at least one region that contains a true causal variant. The regions identified included 4 of the possible 18 polymorphic causal variants. On average, 1.1 true positives and 1.7 false positives were identified per pedigree. In conclusion, we have demonstrated the potential of our new pSGS method for localizing rare disease causal variants in common disease using high-risk pedigrees and exome sequence data.