#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.
#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 ML: Identify profiles of cells from mouse femurs (bone cells) that overlap with monogenic skeletal disease causing genes [i.e. try to flag potential bone effector cells from their profiles].
#ASHG21 ML: Cells from 3 compartments in mouse femurs. 133,942 single cells. Cluster cells based on similarity of profiles. 34 distinct clusters. Each cluster defined by distinct DE genes.
#ASHG21 ML: ~420 known monogenic disease causing genes. Hypergeometric test for enrichment relative to human/mouse orthologs. 3 robustly enriched cell types. Subgroup analysis show enrichment in high bone mass and bone growth disorders.
#ASHG21 ML: Looked at enrichment for polygenetic skeletal traits, such as height. MAGMA gene-based tests for association with each of ~20k genes. Mapped to mouse orthologs. Gene set analysis - is the set of genes that define a cell type more related to the trait genes than chance
#ASHG21 ML: Looked for mouse knockouts that have bone mineral density changes. Genes associated with these changes in mice are enriched in genes that define cell types in their profiles.
#ASHG21 ML: Genes that have an effect on BMD are often genes with restricted expression and help define the cell type they are expressed in.
#ASHG21 ML: SLC9A3R2 is an example that has restricted expression and is a potential effector.
#ASHG21 ML: SLC9A3R2 knockout mice have altered bone marrow density. So the method can potentially identify novel effectors that can be confirmed in mouse, cell line, and other models.

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More from @thatdnaguy

20 Oct
#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.
Read 15 tweets
20 Oct
#ASHG21 EDF: Elisa De Franco.
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.
Read 14 tweets
20 Oct
#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.
Read 11 tweets
20 Oct
#ASHG21 WL: Wenhan Lu.
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
#ASHG21 WL: Interested in pleiotropy. Look at variant-level pleiotropy. 349k vars. 5.4k with > 1 phenotype. 152 associated with > 20 phenotypes.
Read 12 tweets
20 Oct
#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.
Read 7 tweets
20 Oct
#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.
Read 14 tweets

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