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Chunk #28 — Results — MetaBrain allows for the identification of trans-eQTLs

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Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.
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We observed trans-eQTLs from multiple independent genomic loci for seven genes, suggesting convergent trans-eQTL effects (ARRDC4, HBG2, POP1, COX7A1, RFPL2, ZNF311 and ZNF404; Supplementary Table 17). This includes a convergent trans-eQTL on hemoglobin subunit ɣ-2 (HBG2; 11p15.4) that was previously identified in blood. HBG2 was affected in trans by two independent variants (rs1427407 on 2p16.1 and rs4895441 on 6q23.3; Fig. 6b), which have previously been associated with fetal hemoglobin levels45–47. We also found converging effects that were not identified in blood. For example, the ZNF311 gene (6p22.1) was affected by the rs1150668 variant in cis and the rs8106871 variant in trans (19q13.2), both of which have been previously associated with smoking48 and risk tolerance49. For both associations, the risk allele also increased ZNF311 expression. Furthermore, the risk allele rs1150668-G increased the expression of S100A5 in trans, and rs8106871-T decreased the expression of POU2F2 and increased expression of DEDD2 in cis (Fig. 6b). ZNF311 has been suggested to be a tumor-suppressor gene50 potentially involved in gliomas51, S100A5 is used as a biomarker for astrocytomas52 and POU2F2 has previously been associated with glioblastoma53. This example shows how multiple variants associated with smoking may alter multiple genes involved in cancer.