Modeling Genome-wide by Environment Interactions through Omnigenic Interactome Networks by Wang et al. 2021, Cell Reports

Phenotyping Imaging: Experiment of Euphrates poplar rooting capacity in transparent tubes via tissue culture. Root images were monitored repeatedly once every 5 days until the 78th day when growing roots fill tubes. Related to Figure 1. FunMap: Simulation result about functional mapping (FunMap). (A) Logistic genotypic curves for three genotypes at a locus used to simulate growth data with 10 time points under three scenarios, i.e., “Parallel” in which three curves are assumed to be parallel, “Non-parallel” in which three curves neither parallel to each other nor cross over, and “Crossing-over” in which three curves cross over at a certain point. Residual covariance matrix is assumed to follow the SAD(1) structure. (B) – (D) Estimates of genetic effect curves (dotted line), in comparison with true genetic effects curves (solid line), under different simulation scenarios, heritabilities (H2), and sample sizes (n). (E) Empirical power of QTL detection by conventional static mapping for individual time points and FunMap under different simulation scenarios, heritabilities, and sample sizes. The empirical power (given at the upper row) is estimated as the proportion of simulation replicates that are tested to be significant over a total of 1000 simulation replicates, and this “power” becomes false positive rate (FPR) (type I error rate) (given at the lower row) if three simulated genotype curves entirely overlap (i.e., there actually is only one curve specified by a single set of growth parameters). Note that FPR was estimated under different residual variances corresponding to a given heritability level used to calculate the power. The critical threshold at the  = 0.05 significance level for each simulation replicate is determined from 1000 permutation tests. Related to Figures 2 – 6.表型成像（Phenotyping Imaging）：基于透明试管组织培养的胡杨生根能力试验。每5天重复采集根图像进行监测，直至第78天根系完全充满试管。相关内容见图1。 功能作图（FunMap）：功能作图仿真结果。 (A) 针对某基因座的3种基因型的Logistic生长曲线，用于模拟3种场景下含10个时间点的生长数据：①"平行场景"：假设3条曲线完全平行；②"非平行场景"：3条曲线既不相互平行也不相交；③"交叉场景"：3条曲线在某一特定点相交。残差协方差矩阵服从SAD(1)结构。 (B)~(D) 在不同仿真场景、遗传力（H²）与样本量（n）下，遗传效应曲线的估计值（虚线）与真实遗传效应曲线（实线）的对比。 (E) 不同仿真场景、遗传力与样本量下，传统静态作图法（针对单个时间点）与FunMap的数量性状基因座（QTL）检测经验功效。其中，上行所示的经验功效为总计1000次仿真重复中，经检验显著的重复次数占比；当3条仿真基因型曲线完全重合时（即实际仅存在一条由单一组生长参数定义的曲线），该"功效"将转变为假阳性率（FPR，即I类错误率），如下行所示。需注意：假阳性率是在与计算功效时给定遗传力水平对应的不同残差方差下进行估计的。每次仿真重复的α=0.05显著性水平临界阈值通过1000次置换检验确定。相关内容见图2~6。