#ASHG21 SK: Samuel Kim.
Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types. biorxiv.org/content/10.110…
#ASHG21 SK: need to go from disease-critical cell type identification to disease mechanism.
#ASHG21 SK: goals include to identify disease critical cell types from scATAC and impact on different cells types and different stages.
#ASHG21 SK: 28 brain traits. 6 non-brain related control traits. 4 single-cell atlases of the brain. Fetal and adult. scRNA-Seq and scATAC-Seq.
#ASHG21 SK: Use stratified LD score regression (S-LDSC). find two things. Enrichment and standardized effect.
scATAC union of per dataset open chromatin regions
[ missed the scRNA data ]
#ASHG21 SK: identified more significant disease-cell type pairs from scATAC-Seq. 13 cells types appeared in both data.
#ASHG21 SK: confirmed associations of schizophrenia, MDD and ADHD genetic associations with excitatory neuron cell types.
#ASHG21 SK: insomnia associated with photoreceptor cell type [ that's interesting ]
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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.