| Group website | https://schulzlab.github.io |
| GitHub account | https://github.com/SchulzLab |
RT @SFB1531
📢 We are very much looking forward to the next Joint Research Meeting Heart | Kidney | Vessel on May, 26!
Don't miss these three exciting talks!
#JRMHeartKidneyVessel @TheMarcelSchulz @DimmelerLab @BrandesLab @m_leistner @IVS_FFM
RT @sinabooeshaghi
I am excited to share another preprint with @lpachter and Fan Gao. We explain a method (called snATAK) for preprocessing single nuclei ATAC seq data.
Background Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.
RT @tomaspueyo
Starship is about to change the world, but ppl haven't realized yet
@SpaceX and @elonmusk's rocket will drop transportation costs to space
And in the past, every drop in transportation costs has revolutionized the world.
Here's what's going to happen:
RT @minouye271
The strongest polygenic risk scores I've seen are for autoimmune diseases, e.g. type 1 diabetes and ankylosing spongylitis (cuz HLA of course)
Anyone seen another disease with PRS AUC~0.92-93 (or even better)?
OBJECTIVE. Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, a
RT @MuenchLab
📣We profiled host cell responses after infection with different #SARSCoV2 variants. Omicron showed a strikingly delayed immune response!
https://doi.org/10.1016/j.mcpro.2023.100537
Great teamwork by @TharyanGeorge and Melinda from @CiesekSandra lab et al.
@IBC2_GU,@dfg_public,@EnableFrankfurt