#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 BMG: exome seq of 615 RMS cases. 9,963 adult controls. Mainly from the ARIC and VIVA studies.
#ASHG21 BMG: 63 cancer predisposition genes. 24 RMS genes. 39 additional cancer predisposition gene. Germline variants in a cancer predisposition genes 7.3% in RMS, 1.5% in controls.
#ASHG21 BMG: odds-ratios significant for variants in all cancer genes, RMS genes only, and other genes only.
#ASHG21 BMG: largest number of variants in TP53 followed by RAS pathways. Other variants in DICER.
#ASHG21 BMG: 10% of ERMS have a germline variant. 3% of ARMS had a germline variant.
#ASHG21 BMG: next study has 580 cases with new RMS with exome data. Representative of population data. Male predominance. Mostly diagnosed < 10yo.
#ASHG21 BMG: Used Kaplna-Meier and cox proportional hazards. Adjusted for age at diagnosis, histology, age, PCs from exome.
#ASHG21 BMG: Compare carriers of cancer gene germline variants. For RMS cancer genes if you carry a germline variant it decreases survival and event free survival.
#ASHG21 BMG: Decreased survival in ERMS without fusions if they have germline variants in cancer genes.
#ASHG21 BMG:
[ technical difficulties kicked me out of the talk ]
#ASHG21 BMG: hazard of death greater for carriers of TP53 and HRAS germline variants.
#ASHG21 [ taking some questions on her study now and I'm getting repeatedly kicked out, so that is a wrap up for late breaking abstract session. ]
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#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.