A spectral framework to map QTLs affecting joint differential networks of gene co-expression
Fig 2
Synthetic datasets in three panels have the same parameter setup. (A) Absolute genetic correlation heatmaps among the markers in real F2 hybrid three-spined stickleback data [10] and synthetic data. Markers are ordered following their positions on the genome. Genetic correlations are measured by absolute sample Pearson correlation coefficients between the genotypes of two markers. (B) Density histograms for expression counts in real stickleback and synthetic data. The parameters in synthetic data generation are carefully chosen to mimic the real data. (C) Barplots comparing the snQTL identification performances for snQTL framework and local method (F-test for regression of pairwise co-expression onto genotype) on synthetic data with varying population size from 50 to 500. We set sparsity parameter R = p in snQTL for a fair comparison with the non-sparse local method. For results labeled “at snQTL", the y-axis is the observed for tests at the single true snQTL; for results labeled “at non-snQTL", the y-axis is the averaged observed
from three tests at randomly selected non-snQTL markers. True positive (or negative) rates for the tests at snQTL (or non-snQTL) are shown above the bars. All reported numbers are averaged across 50 replications for each population size.