Discover and read the best of Twitter Threads about #ashg21
Most recents (24)
Last in this #ASHG21 late breaking plenary session is Bailey Martin-Giacalone presenting on germline variants in cancer predisposition genes predict survival for children with rhabdomyosarcoma
#ASHG21 Martin-Giacalone: Want to look at germline (not somatic) variants associated with rhabdomyosarcoma (RMS ).
Exome-sequenced 615 RMS cases and 9963 adult controls.
Exome-sequenced 615 RMS cases and 9963 adult controls.
#ASHG21 Martin-Giacalone: Examined 63 cancer predisposition genes. Found 7.3% RMS cases had variants (not sure what type??) compared to 1.5% of controls. TP53, NF1, HRAS had the largest excess.
#ASHG21 BMG: Bailey Martin-Giacalone.
Germline variants in cancer predisposition genes predict survival for children with rhabdomyosarcoma (RMS)
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.
Next up in the #ASHG21 late breaking plenary session is Elisa De Franco (@Elisa_EDF) presenting loss of primate-specific gene ZNF808.
#ASHG21 @Elisa_EDF: studying mice can provide insights into human biology, but there are differences. Mice have 2 genes for insulin (Ins1, Ins2), humans have 1 (INS).
#ASHG21 @Elisa_EDF: looked at 2877 neonatal diabetes patients from 111 countries and want Identify genes with pancreatic genesis.
#ASHG21 EDF: Elisa De Franco.
loss of the primate specific gene ZNF808 causes pancreatic disease.
loss of the primate specific gene ZNF808 causes pancreatic disease.
#ASHG21 EDF: studying human development to better understand common and rare diseases. specifically interested in diabetes.
#ASHG21 EDF: Mice can offer important insights, but there are fundamental differences between humans and mice.
Next up in the #ASHG21 plenary session is Jonathan Sebat (@sebatlab) covering WGS of #Autism combining common and rare variants.
#ASHG21 JS: Jonathan Sebat.
a phenotypic spectrum of autisim attributable to... [ too fast I missed it ]
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 first up in the late breaking plenary session is Wenhan Lu looking at pleiotropy in the UKB exomes available at genebass.org
#ASHG21 Lu: observe large-scale pleiotropy across individual variants and genes.
24 genes have >10 independent phenotypic associations
24 genes have >10 independent phenotypic associations
#ASHG21 Lu: Group 491 ICD diseases into 41 domains.
Example: LDLR and ADH1B each have multiple gene associations across different domains
Example: LDLR and ADH1B each have multiple gene associations across different domains
#ASHG21 WL: Wenhan Lu.
quantifying the extent of pleiotropy using rare variant association data in 281k human exomes
quantifying the extent of pleiotropy using rare variant association data in 281k human exomes
#ASHG21 WL: have large scale rare variant association in UKBB exomes.
Preprint - medrxiv.org/content/10.110…
Explorer at - genebass.org
Preprint - medrxiv.org/content/10.110…
Explorer at - genebass.org
#ASHG21 WL: Interested in pleiotropy. Look at variant-level pleiotropy. 349k vars. 5.4k with > 1 phenotype. 152 associated with > 20 phenotypes.
#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…
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.
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.
#ASHG21 NC: Nicholas Cheng.
Early detection of cancers using plasma cell-free DNA methylomes up to 7 years prior to clinical diagnosis.
Early detection of cancers using plasma cell-free DNA methylomes up to 7 years prior to clinical diagnosis.
#ASHG21 NC: cancer development is a mostly evolutionary process involving sequenctial somatic mutation and methylation / accessibility changes.
#ASHG21 NC: When can you detect changes? The pathogenic transitions can occur earlier than the onset of disease.
#ASHG21 ALP: Alkes L Price.
Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics.
Preprint - biorxiv.org/content/10.110…
Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics.
Preprint - biorxiv.org/content/10.110…
#ASHG21 ALP: Cellular dysfunction leads to disease. We can use GWAS data to find the critical cell types.
#ASHG21 ALP: They wanted to use scRNA-Seq to help understand GWAS. Use the sc-linker program. [ I see no GitHub link ]
#ASHG21 YH: Yingbo Huang.
Sex differences in the expression and genetic regulation of drug metabolism and transporter genes in human liver.
@YingboHuang
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 AN: Aparna Nathan.
Modeling eQTLs at single-cell resolution identifies 2000 eQTLs with differential effects across continuous T cell states.
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 FW: Frank Wessely.
Understanding cell-type-specific drug effects while controlling for genotype.
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 AT: Adelaide Tovar.
A modular massively parallel reporter assay uncovers context-specific allelic activity of GWAS variants.
A modular massively parallel reporter assay uncovers context-specific allelic activity of GWAS variants.
#ASHG21 AT: GWAS hits often not in coding sequence [though plenty are in gene bodies] and suggests that the promoter / enhancer / other non-coding sites may affect gene regulation and therefore disease risk.
#ASHG21 AT: MPRA assay activity depends on context (genomic context, episomes versus integrated, promoter/enhancer compatibility)
#ASHG21 ML: Mischa Lundberg.
Integrative analysis of human genetic association studies, single cell transcriptomics, and knockout mouse models identifies cell types and genes involved in skeletal disease.
Integrative analysis of human genetic association studies, single cell transcriptomics, and knockout mouse models identifies cell types and genes involved in skeletal disease.
#ASHG21 ML: Lack of 'omics data for skeletal disorders. Use murine 'omics to try and fill this gap since the data aren't always available for human samples.
#ASHG21 ML: Calls sets of genes expressed in cell types a profile, and that profiles are conserved between species in many cases.
#ASHG21 EP: Evin Padhi.
Computational and functional characterization of the hs737 enhancer in autism
Computational and functional characterization of the hs737 enhancer in autism
#ASHG21 EP: Noncoding de novo variants are enriched in individuals with autism. How does that work? hs737 enhancer reaches significance in discovery and replication cohorts.
#ASHG21 EP: Individuals with de novo in hs737 have autism but also motor issues / hypotonia. All variants affect enhancer activity in luciferase activity in mouse neuronal cell line.
#ASHG21 SK: Samuel Kim.
Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types.
biorxiv.org/content/10.110…
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.
Really impressive talk on noncoding #constraint in #gnomAD genomes from Siwei Chen (@konradjk's lab).
#ASHG21
#ASHG21
Chen: Calculated constraint on 1kb windows using Z scores.
How do known non-coding elements (like enhancers) look when viewed through the lens on constraint?
#ASHG21
How do known non-coding elements (like enhancers) look when viewed through the lens on constraint?
#ASHG21
Chen: When looking at largest constraint Z-scores (top Z-score was 4 on the figures)
- Super enhancers ~3x enriched
- ENCODE cCRE enhancers ~2.25x enriched
- FANTOM enhancers ~1.75x enriched
#ASHG21
- Super enhancers ~3x enriched
- ENCODE cCRE enhancers ~2.25x enriched
- FANTOM enhancers ~1.75x enriched
#ASHG21
#ASHG21 OEG: Omar El Garwany.
Genetic determinants of alternative splicing in stimulated iPSC-derived macrophages enhance the understanding of immune-mediated disease risk.
@oelgarwany
Genetic determinants of alternative splicing in stimulated iPSC-derived macrophages enhance the understanding of immune-mediated disease risk.
@oelgarwany
@oelgarwany #ASHG21 OEG: macrophages respond quickly to environmental stimuli, and these stimuli may cause substantial changes to their gene expression.
#ASHG21 OEG: QTLs are context-specific. May require not just the right cell type (macrophages), but the right stimulation (environmental condition).
#ASHG21 KG: Kiran Girdhar.
Acetylated chromatin domains link chromosomal organization to cell and circuit level dysfunction in schizophrenia and bipolar disorder.
Acetylated chromatin domains link chromosomal organization to cell and circuit level dysfunction in schizophrenia and bipolar disorder.
#ASHG21 KG: Generated cell specific histone modified regions from controls postmortem brain samples. Enrichment of neuron specific histone peaks.
#ASHG21 KG: hyper acetylated peaks are enriched for schizophrenia and bipolar risk loci. Hypothesized that coordinated structure of histone peaks may be important in disease.
#ASHG21 BJS: Benjamin J Strober.
Uncovering context-specific genetic regulation of gene expression from single-cell RNA-sequencing using latent-factor models.
Uncovering context-specific genetic regulation of gene expression from single-cell RNA-sequencing using latent-factor models.
#ASHG21 BJS: Many contexts modulate gene expression. Such as cell type, developmental stage, environmental stimulus.
One way to study is to look at eQTLs. Especially context-specific eQTLs. Example might be an eQTL in B cells that isn't an eQTL in T cells.
One way to study is to look at eQTLs. Especially context-specific eQTLs. Example might be an eQTL in B cells that isn't an eQTL in T cells.
#ASHG21 BJS: Developed SURGE method. Single cell Unsupervised Regulation of Gene Expression. Want to find context-specific eQTLs without guidance. Mainly intended for single-cell eQTL data since you don't have to specify context.
#ASHG21 TA: Tiffany Amariuta.
Modeling tissue co-regulation to quantify tissue-specific contributions to disease heritability. [ dropping in the middle of it ]
Modeling tissue co-regulation to quantify tissue-specific contributions to disease heritability. [ dropping in the middle of it ]
#ASHG21 TA: TCSC applied to real traits. 25 GTEx v8 tissues. 350-550 meta tissues and singular tissues. analyze 82 GWAS summary statistics. TCSC identifies 67 tissue trait pairs with non-zero heritability at 10% FDR.
#ASHG21 TA: TCSC reduces the number of trait-associated tissues within a tissue category by 67% because it accounts for tissue co-regulation.
