Using Protein Interactome Networks to Understand the Functional Role of Coding DN

描述（由申请人提供）：最近的全外显子组和基因组测序研究，包括我们自己的研究，表明健康个体和受遗传疾病影响的个体都携带许多罕见的DNA序列变异。他们正确地将它们归类为遗传病的真正因果突变，或者仅仅是良性多态性。显然需要新的方法来筛选在全外显子组/基因组研究中确定的候选序列变异，以确定因果突变并评估它们在人类遗传疾病，特别是复杂性状中的epistatic相互作用。基于pi的经验，我们提出了一种创新的混合计算实验系统。高通量3DIP策略可以将编码遗传变异分类为致病突变或良性多态性，并评估可广泛应用于人类遗传疾病（包括复杂性状）的上位性相互作用。我们的总体目标是开发一种高通量的方法，可以准确地验证编码遗传变异是因果突变还是仅仅是良性多态性，以提高外显子组研究的成功率。我们的长期目标是应用这种方法来改善在遗传疾病风险基因中携带不确定意义变异的患者及其家庭成员的医疗结果。

DESCRIPTION (provided by applicant): Recent whole exome and genome sequencing studies, including our own, have revealed that both healthy individuals and those affected by genetic diseases carry many rare DNA sequence variants. They classify them correctly as either bona fide causal mutations for genetic diseases, or merely benign polymorphisms. New approaches are clearly needed to filter through candidate sequence variants identified in whole exome/genome studies to identify causal mutations and evaluate their epistatic interactions in human genetic diseases, particularly complex traits. Building upon the experience of the PIs, we propose an innovative hybrid computational-experimental systems.The high-throughput 3DIP strategy can classify coding genetic variants as causative mutations or benign polymorphisms and evaluate epistatic interactions that can be broadly applied to human genetic diseases, including complex traits. Our overall goal is to develop a high-throughput approach that can accurately validate coding genetic variants as causal mutations or merely benign polymorphisms to improve exome study success rates. Our long-term goal is to apply this approach to improve the medical outcomes of patients and their family members who carry variants of uncertain significance in genetic disease risk genes.