Classification of Missense Mutations of Disease Genes
疾病基因错义突变的分类AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要属于遗传疾病关联分类；校正入样偏倚本身不足以将其归为所列因果推断方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
疾病基因错义突变的分类
- 研究问题
- 在样本较小且病例收集偏向高家族风险人群时，如何依据家系资料判断多种错义突变与疾病风险的关联？
- 方法与思路
- 作者提出贝叶斯层级模型，同时刻画多种突变，并用同一诊所的已知致病突变阳性与检测阴性家系校准分类。
- 材料陈述的贡献
- 摘要称该模型可在高风险样本中分类突变的疾病关联，并控制潜在查明偏倚，展示乳腺癌与卵巢癌易感基因应用。
- 阅读关注点
- 应核对家系入样机制、参照家系的可比性、突变间共享信息方式，以及分类标准和查明偏倚控制的具体条件。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；摘要未提供分类准确率、风险估计或偏倚控制条件，不能据此作临床判断或推断刊本变化。
原文摘要
Clinical management of individuals found to harbor a mutation at a known disease-susceptibility gene depends on accurate assessment of mutation-specific disease risk. For missense mutations (MMs)-mutations that lead to a single amino acid change in the protein coded by the gene-this poses a particularly challenging problem. Because it is not possible to predict the structural and functional changes to the protein product for a given amino acid substitution, and because functional assays are often not available, disease association must be inferred from data on individuals with the mutation. Inference is complicated by small sample sizes and by sampling mechanisms that bias toward individuals at high familial risk of disease. We propose a Bayesian hierarchical model to classify the disease association of MMs given pedigree data collected in the high-risk setting. The model's structure allows simultaneous characterization of multiple MMs. It uses a group of pedigrees identified through probands tested positive for known disease associated mutations and a group of test-negative pedigrees, both obtained from the same clinic, to calibrate classification and control for potential ascertainment bias. We apply this model to study MMs of breast-ovarian susceptibility genes BRCA1 and BRCA2, using data collected at the Duke University Medical Center in Durham, North Carolina.