Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing

Abstract

Genome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

Graphical abstract

Figure 1.

eQTL analysis pipeline. Samples were processed and analyzed separately by ethnicity (African American [AA] and European American [EA]) and tissue type (megakaryocytes [MKs] and platelets). Reads of each ethnicity/sample were aligned and assembled using the standard steps of the Tuxedo suite and the results were loaded into R using the Ballgown package. Only autosomal genes with median FPKM across samples larger than 1 (0.3 for platelets) were kept and logarithmically transformed. SNPs with less than 2 samples per genotype were excluded as were nonautosomal SNPs. Differences in gene expression between genotypes were assessed with linear models adjusting for clinical covariates, batch information, and principal components derived from gene-expression and genotype data. Meta-analysis was performed to combine results from AAs and EAs for MKs and platelets, respectively. Finally, significance of “peak” SNPs per gene were calculated by eigenMT software. Shared eQTLs between MKs and platelets were detected using these results, and the comparison with eQTLs reported at the GTEx portal was performed as well.

Figure 2.

Comparison of eQTL effect sizes between MKs and platelets. (A) Effect sizes of detected eQTLs in MKs with corresponding matches in platelets. Scatterplot of effect sizes among detected eQTLs in MKs with q-values < 0.05 and their corresponding direct matches in platelets. Among the 948 eQTLs detected in MKs, there are 446 direct gene-SNP matches in platelets and among these 446 pairs, 380 (85.2%) have the same direction of the estimated effect sizes. (B) Effect sizes of detected eQTLs in platelets with corresponding matches in MKs. Scatterplot of effect sizes among detected eQTLs in platelets with q-values < 0.05 and their corresponding direct matches in MKs. Among the 1830 eQTLs detected in platelets, there are 1448 direct gene-SNP matches in MKs and among these 1448 pairs 1152 (79.6%) have the same direction of the estimated effect sizes.

Figure 3.

Number of shared eQTLs in MKs and platelets. Barplots represent the number of shared eQTLs in MKs and platelets for direct matches (0 kb) as well as 5-kb, 100-kb, and 1-Mb replication windows. There is greater overlap with the distance-based approach compared with the direct overlap in discovery signal. The overlap is higher when allowing for a larger overlap window.

Figure 4.

Enrichment of overlap of eigenMT eQTLs and relevant annotation tracks. Scatter plots show odds ratios (x-axis) vs -log₁₀ (P value) from Fisher’s exact test assessing the overlap of significant eQTLs from our eigenMT analysis with the indicated annotation tracks, including 2 control tracks of no biological relevance to MKs or platelets. HMEC, human mammary epithelial cells.