一种在亲本基因型缺失时推断外显子变异的框架增强了自闭症的关联研究。
A framework to infer exonic variants when parental genotypes are missing enhances association studies of autism.
作者信息
Moon Haeun, Sloofman Laura, Avila Marina Natividad, Klei Lambertus, Devlin Bernie, Buxbaum Joseph D, Roeder Kathryn
机构信息
Department of Statistics, Seoul National University, Seoul, South Korea.
School of Transdisciplinary Innovations, Seoul National University, Seoul, South Korea.
出版信息
bioRxiv. 2025 Jul 24:2025.07.24.666675. doi: 10.1101/2025.07.24.666675.
MOTIVATION
Gene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer status and use this inference for association studies.
RESULTS
We use data from autism spectrum disorder to illustrate and evaluate methods. Separating from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate.
AVAILABILITY AND IMPLEMENTATION
The implementation code and publicly available data are provided at: https://github.com/HaeunM/TADA-RD.
动机
基因损伤突变对于旨在发现发育障碍潜在基因的研究具有很高的信息价值。传统上，这些变异是通过评估来自受影响后代和父母的高质量DNA序列来识别的。然而，当父母的序列不可用时，就需要方法来推断状态并将这种推断用于关联研究。
结果
我们使用自闭症谱系障碍的数据来说明和评估方法。将罕见的遗传变异分离出来具有挑战性，因为后者更为常见。使用不平衡数据分类器和已知遗传类别的变异，我们构建了一个遗传模型，然后在缺少父母数据时为变异生成一个分数。接下来，我们提出了一种新的随机抽取（RD）模型，以使用这个分数进行基因发现。RD模型内置于现有的推理框架中，产生了一个更强大的基于基因的关联测试，并控制了错误发现率。
可用性和实现方式
实现代码和公开可用的数据可在以下网址获取：https://github.com/HaeunM/TADA-RD。
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