.png){kind=icon}
返回
A probabilistic method for identifying rare variants underlying complex traits
.png){kind=icon}
OA
AI
DOI：10.1186/1471-2164-14-S1-S11.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Identifying the genetic variants that contribute to disease susceptibilities is important both for developing methodologies and for studying complex diseases in molecular biology. It has been demonstrated that the spectrum of minor allelic frequencies (MAFs) of risk genetic variants ranges from common to rare. Although association studies are shifting to incorporate rare variants (RVs) affecting complex traits, existing approaches do not show a high degree of success, and more efforts should be considered. Results: In this article, we focus on detecting associations between multiple rare variants and traits. Similar to RareCover, a widely used approach, we assume that variants located close to each other tend to have similar impacts on traits. Therefore, we introduce elevated regions and background regions, where the elevated regions are considered to have a higher chance of harboring causal variants. We propose a hidden Markov random field (HMRF) model to select a set of rare variants that potentially underlie the phenotype, and then, a statistical test is applied. Thus, the association analysis can be achieved without pre-selection by experts. In our model, each variant has two hidden states that represent the causal/non-causal status and the region status. In addition, two Bayesian processes are used to compare and estimate the genotype, phenotype and model parameters. We compare our approach to the three current methods using different types of datasets, and though these are simulation experiments, our approach has higher statistical power than the other methods. The software package, RareProb and the simulation datasets are available at: http://www.engr.uconn.edu/similar to jiw09003.
Keyword:
COMMON DISEASES
CHOLESTEROL
RISK
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
1.9W
被引数:
5.2W
机构
引用论文
SIFT: predicting amino acid changes that affect protein functionSIFT: 预测影响蛋白质功能的氨基酸变化.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏A Groupwise Association Test for Rare Mutations Using a Weighted Sum StatisticPLOS GENETICSOAAI
PLOS GENETICSIF3.7
OAAI分享收藏Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9NATURE GENETICS
NATURE GENETICSIF31.8
PREAI
分享收藏Observation of a many-body dynamical phase transition with a 53-qubit quantum simulatorNatureOAAI
NatureIF0
OAAI分享收藏
查看更多内容