基于基因组估计值的线性变换的快速上位混合模型关联分析。
A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values.
机构信息
National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Animal Biosciences, International Livestock Institute, Nairobi 00100, Kenya.
出版信息
Bioinformatics. 2018 Jun 1;34(11):1817-1825. doi: 10.1093/bioinformatics/bty017.
MOTIVATION
Epistasis provides a feasible way for probing potential genetic mechanism of complex traits. However, time-consuming computation challenges successful detection of interaction in practice, especially when linear mixed model (LMM) is used to control type I error in the presence of population structure and cryptic relatedness.
RESULTS
A rapid epistatic mixed-model association analysis (REMMA) method was developed to overcome computational limitation. This method first estimates individuals' epistatic effects by an extended genomic best linear unbiased prediction (EG-BLUP) model with additive and epistatic kinship matrix, then pairwise interaction effects are obtained by linear retransformations of individuals' epistatic effects. Simulation studies showed that REMMA could control type I error and increase statistical power in detecting epistatic QTNs in comparison with existing LMM-based FaST-LMM. We applied REMMA to two real datasets, a mouse dataset and the Wellcome Trust Case Control Consortium (WTCCC) data. Application to the mouse data further confirmed the performance of REMMA in controlling type I error. For the WTCCC data, we found most epistatic QTNs for type 1 diabetes (T1D) located in a major histocompatibility complex (MHC) region, from which a large interacting network with 12 hub genes (interacting with ten or more genes) was established.
AVAILABILITY AND IMPLEMENTATION
Our REMMA method can be freely accessed at https://github.com/chaoning/REMMA.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
上位性为探测复杂性状的潜在遗传机制提供了一种可行的方法。然而，在存在群体结构和隐性亲缘关系的情况下，使用线性混合模型（LMM）来控制 I 型错误，计算时间的消耗给相互作用的成功检测带来了挑战。
结果
为了克服计算上的限制，开发了一种快速上位性混合模型关联分析（REMMA）方法。该方法首先通过加性和上位性亲缘关系矩阵的扩展基因组最佳线性无偏预测（EG-BLUP）模型来估计个体的上位性效应，然后通过个体上位性效应的线性重转换来获得成对的相互作用效应。模拟研究表明，与现有的基于 LMM 的 FaST-LMM 相比，REMMA 可以控制 I 型错误并提高检测上位性 QTN 的统计功效。我们将 REMMA 应用于两个真实数据集，一个小鼠数据集和惠康信托基金会病例对照协会（WTCCC）数据。应用于小鼠数据进一步证实了 REMMA 控制 I 型错误的性能。对于 WTCCC 数据，我们发现大多数 1 型糖尿病（T1D）的上位性 QTN 位于主要组织相容性复合体（MHC）区域，从中建立了一个包含 12 个枢纽基因（与十个或更多基因相互作用）的大型相互作用网络。
可用性和实现
我们的 REMMA 方法可以在 https://github.com/chaoning/REMMA 上免费访问。
联系人
补充信息
补充数据可在生物信息学在线获得。
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