Brennan Jennifer S, He Yunxiao, Calixte Rose, Nyirabahizi Epiphanie, Jiang Yuan, Zhang Heping
Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S100. doi: 10.1186/1753-6561-5-S9-S100.
Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into "composite" markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes, SIRT1 and PDGFD.
具有罕见变异的遗传标记在基因组中分布广泛，这使得在基因关联研究中考虑它们既必要又困难。因此，明智地将罕见变异组合成“复合”标记可能有助于进行有意义的分析。在本文中，我们提出了一种通过纳入最小绝对收缩和选择算子技术来分析罕见变异数据的新方法。我们将此方法应用于遗传分析研讨会17的数据，结果表明这种新方法很有前景。此外，我们利用有200个表型重复的优势，通过重复分类树分析评估了我们方法的性能。我们的方法和分析是在不知道潜在模拟模型的情况下进行的。我们的方法识别出38个与“患病”表型显著相关标记（存在于65个基因中），并正确识别出两个因果基因，即SIRT1和PDGFD。
