Next up in the #ASHG21 plenary session is Jonathan Sebat (@sebatlab) covering WGS of #Autism combining common and rare variants.
#ASHG21 @sebatlab: Found more de novo variants in cases than controls, rare inherited variants overtransmitted to cases, polygenic scores overtransmitted to cases. As such, all 3 categories are associated with #Autism risk.
#ASHG21 @sebatlab: created rare variant and common variant risk scores and both were associated with #autism status.
#ASHG21 @sebatlab: Found females with autism have a higher polygenic score (both common and rare) than males with autism. However, this same pattern was also observed with controls.

Found inverse correlation between rare and common variant risk.
#ASHG21 @sebatlab: Both de novo variants and polygenic scores are associated with social deficits and repetitive behavior.
Also DNVs strongly associated with motor function but not polygenic risk.
#ASHG21 @sebatlab: All genetic factors associated with parental age.

PGS of educational attainment positively correlated with parental age. But educational attainment was negatively correlated with effects on social deficits.
#ASHG21 @sebatlab: Found ASD genes enriched in developing cortex and the prenatal brain.
#ASHG21 @sebatlab: excellent use of polygenic TDT by @danweiner92, @EmilieWigdor, @elisebrobinson in this presentation : )

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More from @jakphd

20 Oct
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.
#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.
Read 8 tweets
20 Oct
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.
Read 9 tweets
20 Oct
#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
#ASHG21 Lu: Group 491 ICD diseases into 41 domains.
Example: LDLR and ADH1B each have multiple gene associations across different domains
Read 7 tweets
19 Oct
Really impressive talk on noncoding #constraint in #gnomAD genomes from Siwei Chen (@konradjk's lab).

#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
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
Read 8 tweets
19 Oct
Next up is my @RegeneronDNA colleague, Julie Horowitz, presenting "Common and rare variant analysis of 21K psoriasis cases and 623K controls identifies novel, protective associations in several genes in the type 1 interferon #ASHG21
Horowitz: Previous GWAS of psoriasis have identified >60 loci, but no large scale sequencing of psoriasis has been performed to identify 1) very rare variants and 2) burden tests.

#ASHG21
Horowitz: Leveraging data from >5 cohorts and 4 ancestries (EUR, AFR, SAS, AMR), they performed a trans-ancestry meta-analysis across a total of 21k cases and 623k controls.

#ASHG21
Read 7 tweets
19 Oct
Suyash Shringarpure (@suyashss) from @23andMeResearch presented a fantastic #raredisease study: "Novel genetic associations for rare diseases with GWAS and trans-ethnic analysis of self-reported medical data"

#ASHG21

medrxiv.org/content/10.110…
.@suyashss: It's well known that self-reported data works very well for common diseases, but what about rare diseases? The assumption is that it wouldn't work.

The other common assumption is that rare disease requires sequencing to find rare causal variants.
#ASHG21
.@suyashss: They launched a survey to evaluate these two commonly held assumptions about genetics in #raredisease.

#ASHG21
Read 10 tweets

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