期刊
ANNALS OF APPLIED STATISTICS
卷 9, 期 3, 页码 1571-1600出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/15-AOAS844
关键词
Autism spectrum disorder; hidden Markov random field; neighborhood selection; network estimation; risk gene discovery
资金
- NIH [U01MH100233, R37 MH057881]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1407771] Funding Source: National Science Foundation
While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据