A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression
A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression
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DOI：
10.1038/s41598-019-56494-9
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发表时间：
2019-12
影响因子：
4.6
通讯作者：
Fan Song;Yu Tao;Yue Sun;D. Saffen
中科院分区：
文献类型：
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作者：
Fan Song;Yu Tao;Yue Sun;D. Saffen
在这项研究中，我们提出了一种新的，多决定系数（R2 M）为基础的方法，用于解析SNP位于染色体附近的基因到半独立的家庭，其中每个对应于一个或多个功能的变异，调节基因的转录。具体而言，我们的方法利用矩阵方程框架来计算染色体感兴趣区域（ROI）内SNP的R2 M值，该R2 M值基于作为潜在调节变体的代理的1-4个“索引”SNP（iSNP）的选择。对1-4个候选iSNP的集合进行穷举测试，确定了最能解释来自mRNA表达与单个SNP基因型之间相关性的单变量线性回归分析的估计R2值的iSNP模型。随后基于基因型估计每个iSNP和其他ROI SNP之间的成对r2连锁不平衡（LD）系数，允许将SNP解析为半独立家族。从基因表达综合数据库（GEO）和基因型和表型数据库（dbGAP）下载的mRNA表达和基因型数据的分析证明了该方法用于解析基于实验数据的SNP的有用性。我们相信，这种方法将广泛适用于分析mRNA表达的遗传基础，并可视化多个遗传变异对单个基因调控的贡献。
In this study, we present a novel, multiple coefficient of determination (R2M)-based method for parsing SNPs located within the chromosomal neighborhood of a gene into semi-independent families, each of which corresponds to one or more functional variants that regulate transcription of the gene. Specifically, our method utilizes a matrix equation framework to calculate R2Mvalues for SNPs within a chromosome region of interest (ROI) based upon the choices of 1-4 “index” SNPs (iSNPs) that serve as proxies for underlying regulatory variants. Exhaustive testing of sets of 1–4 candidate iSNPs identifies iSNP models that best account for estimated R2values derived from single-variable linear regression analysis of correlations between mRNA expression and genotypes of individual SNPs. Subsequent genotype-based estimation of pairwise r2linkage disequilibrium (LD) coefficients between each iSNP and the other ROI SNPs allows the SNPs to be parsed into semi-independent families. Analysis of mRNA expression and genotypes data downloaded from Gene Expression Omnibus (GEO) and database for Genotypes and Phenotypes (dbGAP) demonstrates the usefulness of this method for parsing SNPs based on experimental data. We believe that this method will be widely applicable for the analysis of the genetic basis of mRNA expression and visualizing the contributions of multiple genetic variants to the regulation of individual genes.