#ASHG21 AN: Aparna Nathan.
Modeling eQTLs at single-cell resolution identifies 2000 eQTLs with differential effects across continuous T cell states.
#ASHG21 AN: cell-state dependent eQTLs means that the same genetic variant may have an effect or no effect depending on cell type and activation. How do you find state-dependent QTLs?
#ASHG21 AN: Think about cell states as continuum rather than a fixed, binarized cell type. Use a poisson mixed effect model in individual cells. Thought to be more robust of single cell modeling than things like linear mixed models of normalized counts.
#ASHG21 AN: Random effects by donor and batch. Shown in their preprint. Modeled in a data from ~500k T cells (Nathan 2021 Nat Imm).
#ASHG21 AN: Use canonical correlation analysis (CCA). Cell given a continuous score across 7 dimensions from the 7 CVs they found. That's how you test for state dependence.
#ASHG21 AN: ~6500 memory t cell eQTLs from pseudo-bulk analysis. 2/3rds had a cell-state dependent analysis.
#ASHG21 AN: Can have opposing state effects at the same locus, i.e. degree of cytotoxicity function can have opposite effects for individual variants.
#ASHG21 AN: The eQTL effect in each cell is a sum of individual CV effects. Can calculate a sum of the betas to get the overall effects.
#ASHG21 AN: Find some new eQTLs that aren't found in pseudo-bulk analysis. Particularly apparent for rare cell types.
#ASHG21 AN: Where are these effects? Enriched in promoters. The non-promoter t-cell regions you see cell-state interacting eQTLs over non state interacting.
#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.