使用定量多因素降维法（QMDR）识别与体重指数高度相关的基因-基因相互作用。
Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).
作者信息
De Rishika, Verma Shefali S, Drenos Fotios, Holzinger Emily R, Holmes Michael V, Hall Molly A, Crosslin David R, Carrell David S, Hakonarson Hakon, Jarvik Gail, Larson Eric, Pacheco Jennifer A, Rasmussen-Torvik Laura J, Moore Carrie B, Asselbergs Folkert W, Moore Jason H, Ritchie Marylyn D, Keating Brendan J, Gilbert-Diamond Diane
机构信息
Computational Genetics Laboratory, Department of Genetics, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, 706 Rubin Building, HB7937, One Medical Center Dr, Lebanon, NH 03756 USA.
Center for Systems Genomics, Department of Biochemistry and Molecular Biology, 512 Wartik Laboratory, The Pennsylvania State University, University Park, PA 16802 USA.
出版信息
BioData Min. 2015 Dec 14;8:41. doi: 10.1186/s13040-015-0074-0. eCollection 2015.
BACKGROUND
Despite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI.
METHODS
Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait.
RESULTS
We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study.
CONCLUSION
This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.
背景
尽管肥胖的遗传度估计在40%-70%，但到目前为止，已确定的与体重指数（BMI）相关的基因座仅解释了其不到2%的变异。上位性，即基因-基因相互作用，是解释BMI部分缺失遗传度的一个合理来源。
方法
利用来自ARIC、CARDIA、FHS、CHS、MESA这五个研究队列中18686名个体的基因型数据，我们采用两种并行方法对单核苷酸多态性（SNP）进行筛选。SNP的筛选要么基于其与BMI关联的主效应强度，要么基于在BMI背景下支持特定SNP-SNP相互作用的知识来源数量。使用定量多因素降维法（QMDR）对筛选出的SNP进行专门分析，以寻找与BMI高度相关的相互作用。QMDR是一种非参数、无遗传模型的方法，可检测与数量性状相关的非线性相互作用。
结果
我们确定了七个新的上位性模型，其经Bonferroni校正的关联p值<0.1。先前的实验证据有助于解释我们结果中突出的合理生物学相互作用及其与肥胖的关系。我们确定了参与线粒体功能障碍（POLG2）、胆固醇代谢（SOAT2）、脂质代谢（CYP11B2）、细胞黏附（EZR）、细胞增殖（MAP2K5）和胰岛素抵抗（IGF1R）的基因之间的相互作用。此外，我们发现这七个SNP-SNP相互作用解释的BMI变异增加了8.8%，超出了指数FTO SNP的主效应以及这些相互作用中的SNP所解释的范围。我们还在eMERGE研究的一个独立数据集中重复了其中一种相互作用以及代表它的58个代理SNP-SNP模型。
结论
本研究强调了一种通过将QMDR等方法与传统统计相结合来发现基因-基因相互作用的新方法。
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