#ASHG21 FW: Frank Wessely.
Understanding cell-type-specific drug effects while controlling for genotype.
#ASHG21 FW: there are databases of gene expression responses to perturbation. Such as Cmap and CLUE.
#ASHG21 FW: drug perturbation in cellular context. 1,680 bulk RNA-Seq samples from 7 hour single dose of 1/12 compounds. 14 cell types. Used 3' Quant-Seq.
#ASHG21 FW: Cell type main driver of variance across the study. Further some separation by layer and level of differentiation.
#ASHG21 FW: Cell line identity, i.e. genotype, drives transcriptional variability. [hmm. Are these same cell types derived from different individuals? I'm not sure]
#ASHG21 FW: Half of tested compounds induce weak response of <50 DE genes. Observed variability similar to what is observed in connectivity map (CMap2) for the same compound.
#ASHG21 FW: Highly variable response to the same compound amongst different cells.
#ASHG21 FW: Response to treatment varies between cells. MCF cancer cell line showed the least responsiveness. Progenitor cells showed response to all compounds (though some are small). Undifferentiated cells share common response with differentiated counterparts.
#ASHG21 FW: majority of compounds share a small core set of perturbed genes between multiple cell types.
#ASHG21 FW: The core set can be as small as a single gene, which may be the target gene for the compound. Directionality of shared gene changes is usually the same in all cells that share it.
#ASHG21 FW: Conclusion is that the responses are variable generally among cell types, but often have a core change of a few genes that are shared with all. Progenitors often show similar responses to the differentiated cells derived from them.
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#ASHG21 BMG: Bailey Martin-Giacalone.
Germline variants in cancer predisposition genes predict survival for children with rhabdomyosarcoma (RMS)
#ASHG21 BMG: RMS most common soft tissue cancer of childhood. Usually two subtypes. alveolar RMS (ARMS) ~80% have a PAX3/7-FOX01 fusion. Embryonal RMS (ERMS) have heterogeneous somatic mutation landscape.
#ASHG21 BMG: risk factors for survival include histology, primary site, fusion status, metastasis, age at diagnosis. tumor size. ERMS cases have better outcome. In fusion negative RMS with TP53 mutation have worse survival.
#ASHG21 JS: Jonathan Sebat.
a phenotypic spectrum of autisim attributable to... [ too fast I missed it ]
#ASHG21 JS: combined 3 cohorts. Two WGS cohorts and the SPARK study. 11k families. 37,375 individuals. Called with GATK best practices. Imputed genotypes from SPARK combined with PLINK. Calculated polygenic scores.
#ASHG21 JS: autism enriched in de novo mutations, rare inherited variants, and increased PRS scores.
#ASHG21 RB: Richard Border.
Widespread evidence of systematic bias in estimates of genetic correlation due to assortative mating.
[ I think this is the preprint. Don't hold me to it. ] biorxiv.org/content/10.110…
#ASHG21 RB: interested in characterizing what extent two traits share similarity across traits.
#ASHG21 RB: Can compute the polygenic scores for each trait and see how they compare. Or correlation of effect correlations. Many ways to look at genetic correlations.
#ASHG21 MO: Meritxell Oliva.
Genetic regulation of DNA methylation across tissues reveals thousands of molecular links to complex traits.
#ASHG21 MO: GTEx looks at gene expression, regulation, and its relationship to genetic variation. The v8 set has eQTL data. 15,201 RNA-Seq samples from 838 post-mortem donors. 49 tissues.
#ASHG21 MO: A mQTLs are variants associated with DNA methylation. GWAS-QTL colocalization can help prioritize the causal genes at a GWAS locus.