Supplemental Material for Glassberg et al., 2018
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Table S1 summarizes data used to demonstrate challenges in interpreting eQTL effect size estimates. Figure S1 compares eQTL effect sizes in ascertainment and validation datasets. Figure S2 compares candidate eQTLs around high- and low-pLI genes. Figure S3 shows correlations between predictors for estimating effect sizes of regulatory variants in ASE. Figure S4 compares effect size estimates from ASE data to those from data permuted across individuals within a gene. Figure S5 shows effect sizes estimated using variants within 50kb of the TSS. Figure S6 shows examples of effect sizes estimated for individual tissues. Figure S7 summarizes sample sizes across tissues. Figure S8 summarizes read depth of the ASE data across tissues and gene sets. Figure S9 summarizes allele frequencies of variants within 10kb of the TSS. Figure S10 shows cumulative distributions of eQTL-estimated genetic variance by pLI class (GTEx data).