In case you missed it, I presented results on behalf of @Regeneron from a trans-ancestry #COVID19 meta-analysis of common and rare variants + gene burden tests in >883k imputed samples and >592k exomes. #ASHG20
Using REGENIE developed by @joellembatchou and @marchini (SAIGE gave us some bizarre results with rare variants) to run our common and rare variant GWASes, we found 2 loci associated with susceptibility and 3 loci with hospitalization. #ASHG20
Curiously, despite losing ~75% of the cases, we found more loci with hospitalization than using all COVID19 positive individuals - @covid19_hgi sees the same pattern.
One might suspect a severe COVID19 GWAS would be even more powerful at the same sample size #ASHG20
Unlike our common var GWAS, our rare variant analysis was flat across the board - most likely because we only have 1108 hospitalized cases in the exome data compared to 3539 cases in the GWAS data. Similar situation with burden tests. #ASHG20
However, we are looking forward to collaborating with @covid19_hgi to run WES/WGS meta-analyses and hopefully identify some rare variants and genes in the future! #ASHG20
Given ~2x the cases and >17x the controls, we should be well-powered to replicate this biologically attractive hypothesis!
Sadly, our #replication failed despite testing 1) just pLoFs or pLoFs+missense, 2) 3 different AF thresholds, or 3) focusing on only severe COVID19 #ASHG20
In the spirit of #openscience, we've made the #COVID19 exome summary statistics for the UKB 450k freeze publicly available (rgc-covid19.regeneron.com) and hope to release the rest of the exome summary stats soon :) #ASHG20
Also, for those #phd students out there, don't let someone tell you that you'll never get an ASHG plenary talk if you leave academia. People can be mean and I was a fool to believe them when I was younger.
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Up next in the first #ASHG20 plenary session is my former Daly lab colleague, @HHeyne, presenting work from @FinnGen_FI on "Recessive effects of 82,516 coding variants in 176,899 Finns."
Bottleneck in Finland makes the Finnish population and @FinnGen_FI ideal for understanding ultra-rare variants that stochastically rose in frequency #ASHG20
Using SAIGE, ran GWAS using 1) a recessive model and 2) additive model on all coding variants in @FinnGen_FI.
For the majority of coding variants, additive model performs better. However, the recessive model performed 2x better for some variants. #ASHG20
1st Plenary #ASHG20 session: Meredith Course identified a human-specific 69bp repeat expansion in the last exon of WDR7.
This repeat was associated with ALS (repeats in ALS cases are on average longer in ALS cases than controls). Longest repeat observed was 86bp. #ASHG20
Observed periodicity in the WDR7 repeat and found that it expands in multiples of 2 in the 3' - 5' direction. #ASHG20
Initial analysis was done in a EUR cohort, so repeated the analysis in non-EUR cohorts, finding population specific repeats in Han Chinese descent and AFR ancestry #ASHG20
Up next is @joellembatchou from @Regeneron presenting REGENIE - a computationally efficient whole genome regression for quantitative and binary traits #ASHG20
Both BOLT and SAIGE are fantastic LMMs for quantitative and binary traits respectively, but have high memory requirements and long computational times. #ASHG20
Noticed inflation in Betas as MAF decreases in SAIGE and becomes worse with larger case/control imbalance - meaning for rare variants, SAIGE produces nonsensical results. REGENIE resolves this issue #ASHG20