After 4 years, it's rather nice to finally present our work on genetic's model trait, height, in >1.4M WES/WGS samples led by @doc_locke, @Mar_ferreira17, & @gabecasis where we found 207 genes [amongst many other results].
After conditioning on common variants, we found 207 genes (P<1.75e-9) via @marchini & @covariani's gene-P method to combine burden, SBAT, SKAT-O, & ACAT-V gene-based tests into 1 p-value. Burden tests find the majority of genes, but 28 (14%) are found only via SKAT-O/ACAT-V.
With burden tests, we observed the classic tradeoff between adding increasingly common variants to boost statistical power (via more allele counts) at the cost of weaker effect sizes.
Ex: We found 17 genes via singleton pLoFs (|β|=9cm) but 76 genes from <1% pLoFs (|β|=4cm).
The most surprising result for me (obvious in hindsight🤦♂️) was finding well-known large effect, constrained, developmental genes like CHD8 (#ASD) or ANKRD11 (neurodevelopmental delay) by studying height - they do a lot more than just affect ASD or NDD!
Often ignored by papers are individual rare variants. After conditioning on GWAS loci, we found 107 rare nonsynonymous variants (P<1.75e-9) including FGFR3 & PTPN11 GoF missense variants with effects larger than singleton pLoF burdens (causing Acondroplasia & Noonan syndrome).
I found the analysis of individual rare variants to be quite insightful (shoutout to @doc_locke for suggesting it). B/c some genes' have large variance in per-variant βs, the burden β can be misleading. KMT2B burden 20x smaller than its largest missense variant (0.4cm v 7.7cm)
Inspired by one of my favorite papers by @HHeyne & @dalygene on recessive associations in FinnGen, I sadly only found 1 recessive association missed by the additive test - CFTR's delta508 mutation (recessive P=5e-10; additive P=0.14). rdcu.be/fqfiK
Lastly, my partner-in-crime, Liron Ganel, found an AMR-enriched missense variant in HHIP that lowers height by -4cm. This was even more interesting b/c it acts opposite to HHIP singleton pLoFs that increased height by +10cm.
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🤯 The black death killed 30-60% of Europe so it's reasonable to think it affected the allele frequency spectrum.
Absolutely incredible @Nature paper using ancient DNA pre-post the black death to show positive selection of immune genes due to the plague (Fig2d-g).
It's so impressive they got 206 ancient DNA samples pre-, during, and post- plague in not 1, but 2 cities (so they could replicate the findings). I'm honestly gobsmacked at how cool this paper is. Huge congrats to Jennifer Klunk, @TaurVil, @LB_Barreiro and co.🎉
One certainly has to imagine a similar situation happened with others:
Antonine Plague (165-180CE): 💀25-33% of Roman Pop
Justinian Plague (541-549CE): 💀25-60% of Byzantine Empire
Japanese smallpox epidemic (735-737CE): 💀33% of Japan
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.
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.
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.
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