#ASHG21 YH: Yingbo Huang.
Sex differences in the expression and genetic regulation of drug metabolism and transporter genes in human liver. @YingboHuang
#ASHG21 YH: sex differences exist in many different traits. this includes drug responses.
#ASHG21 YH: sex as a biological variable is therefore important to study. GWAS alleles can show sex-biased gene expression in tissue-dependent manner. Drug metabolism and transporter (DMT) genes impact human health, and may have sex bias.
#ASHG21 YH: Looked at DMET genes in GTEx liver. Identified several with sex biased expression. Top hits. include a UGT and SLC [editorial not to look at the ggrepel R package to avoid dot/label overlap].
#ASHG21 YH: Wanted to do sex stratified eQTL mapping. Might find sex-specific effect, i.e. one sex only. Or a sex differential effect, i.e. response in both, but more in one. Or sex opposing effects, i.e. effect in both but opposing directionality.
#ASHG21 YH: sex specific eQTLs: UGT1A3, UGT2817, ADH1C, VKORC1, PON1, ALDH2, VKORC1.
#ASHG21 YH: rs4860987 and UGT2B17. SNP behaves differently between sexes.
#ASHG21 YH: Looked at genetic heterogeneity of SNPs. Assess the difference of the genetic effect between two groups, so compared female and male. Identified 452 drug side effect traits in UKBB with GWAS summary stats.
#ASHG21 YH: 40 traits have at least one SNP with sex differences. Gout example with ABCG2. Only one significant loci in females. 2 in males. In males ABCG2, which is a urate transporter.
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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.