Computational Characterization of Genetic Heterogeneity

遗传异质性是许多疾病的共同特征，具有不同的因果变异，或 突变，存在于不同的疾病个体中。遗传异质性使 确定疾病的遗传基础，因为任何中等规模的研究都将包含 具有不同致病基因变异的个体。这种异质性的一个原因是， 变异存在于基因组中，这些基因在各种细胞信号传导和调节中相互作用。 途径。遗传异质性要求测试变异的组合，而不是 个体变异与疾病的关联。然而，虽然个体变体可以 尽管对关联性进行了详尽的测试，但变异体的组合不能，因为有太多的 组合进行检验，统计学显著性所需的样本数量为 天文数字我们建议开发新的计算和统计方法来识别 与疾病相关的变体的组合。与现有方法相比， 我们并不把注意力限制在先验的已知途径或基因组上。相反，我们的算法 利用基因组规模的相互作用网络和组合/统计约束， 变异的组合，并严格评估其统计学意义。此外，我们扩展 这些方法发现变异体组合与各种临床 参数，如生存时间或对治疗的反应。我们将把这些技术应用于 癌症基因组测序项目，包括癌症基因组图谱（TCGA）， 与几个生物医学研究小组合作。圆满完成拟议的 研究将促进对遗传异质性疾病的研究，特别是癌症。 - 仅使用现有DNA测序可获得的适度数量的样品 技术.

Genetic heterogeneity is a common feature of many diseases, with different causal variants, or mutations, present in different individuals with the disease. Genetic heterogeneity complicates the identification of the genetic basis of disease, as any modest sized study will contain individuals with different causal genetic variants. One reason for this heterogeneity is that causal variants are present in groups of genes that interact in various cellular signaling and regulatory pathways. Genetic heterogeneity demands the testing of combinations of variants, rather than individual variants, for association with a disease. However, while individual variants can be tested exhaustively for association, combinations of variants cannot, as there are too many combinations to test, and the number of samples required for statistical significance would be astronomical. We propose to develop new computational and statistical approaches to identify combinations of variants that are associated with a disease. In contrast to existing approaches, we do not restrict attention to known pathways or groups of genes a priori. Rather, our algorithms utilize genome-scale interaction networks and combinational/statistical constraints to identify combinations of variants and rigorously assess their statistical significance. Further, we extend these approaches to find associations between combinations of variants and various clinical parameters such as survival time or response to treatment. We will apply these techniques to cancer genome sequencing projects including The Cancer Genome Atlas (TCGA), in collaboration with several biomedical research groups. Successful completion of the proposed research will facilitate the study of genetically heterogeneous diseases - and in particular cancer - using only a modest number of samples that is attainable with present DNA sequencing technologies.