Some extremely interesting results were shown today by @joelhirschhorn on behalf of GIANT consortium (here I use the word GIANT as both noun and an adjective ;))
Veera Rajagopal 
 @doctorveera
21 Oct 2021
"Computational and functional gene prioritization from a saturated GWAS of adult height in 5 million people" 5 MILLION! Looking forward to this talk by @joelhirschhorn today at #ASHG2021
1
28
7
73
Previous GWAS of height (to the best of my knowledge) is this one by Yengo et al in a sample size of 700,000. ncbi.nlm.nih.gov/pmc/article…

Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 indivi...

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide...

ncbi.nlm.nih.gov
2
4
Yengo et el reported that the SNP based heritability (SNP-h2) of height is ~50% and the ~3300 genome-wide independent signals explain 1/4th of the SNP-h2, leaving 3/4th unexplained. Note, twin h2 is ~80%.
1
3
The current study made a huge jump in the sample size from 700k to 5 million. And it looks like that is all it takes to explain the full SNP-h2 (which is 50%).

Oct 21, 2021 · 4:23 PM UTC

2
5
1
17
So, the study seems to have reached it saturation phase where the ~12000 genome-wide signals explained the full SNP-h2 of height, which is 50% (note the rest of 30% of the twin h2 is likely explained by rare variants)
2
1
1
5
The most interesting and also important finding is this ~12000 genome-wide signals represent only 20% of the genome and not the entire genome.
1
1
9
So, the finding goes against the expectation of omigenic hypothesis. I'd love to hear @jkpritch 's view on this observation.
Veera Rajagopal 
 @doctorveera
22 Apr 2020
J Pritchard group demonstrates that even for biologically simpler traits such as urate, IGF1 and testosterone, the associated variants are not restricted to only relevant genes, but are spread over the entire genome suggesting an omnigenic architecture. bit.ly/3atEFHx
1
1
1
5
The second exciting finding shown today is the allelic series. Now we have seen so many examples that as the sample size grows, we capture more and more independent signals from known GWAS regions.
Veera Rajagopal 
 @doctorveera
13 Jun 2021
Replying to @doctorveera
Last week, we've seen that as the sample size grows, we'll discover not only novel variants in novel genes, but also novel (and often large effect, rare) variants in known genes. E.g. LPA variants in the 1 million CAD GWAS
1
2
4
@joelhirschhorn showed a beautiful Brisbane plot that displayed the density of the independent signals around each of the ~12000 GWAS regions.
2
5
Note the highest peak is at ACAN locus that has the largest effect size of all. Because it's tagging a VNTR. This is one of my favorite stories that I'll be never get tired of repeating.
Veera Rajagopal 
 @doctorveera
16 Dec 2020
Replying to @doctorveera
The GWAS from GIANT consortium published in 2010 reported 180 loci associated with height. Our locus of interest here is the one at chromosome 15 (red square) nature.com/articles/nature09…
1
1
7
GWAS signals with strong biology will show allelic series. Such signals will lead to discovery of new drug targets. We've seen a beautiful illustration of this just one day ago.
Veera Rajagopal 
 @doctorveera
19 Oct 2021
I attended a #ASHG2021 talk by @rplenge on how genetics findings can aid in drug discovery. I think it's one of the best talks on this topic I've ever listened to. Here I list some snippets of this talk
1
1
3
Impressive achievement by the GIANT consortium. Can't wait to read the full paper.
1
Correction: Previous GWAS of height report that genome-wide SNPs explained 50% of the SNP-h2 (not 1/4th). Sorry for the confusion.
1
2
@arbelharpak raised an important point in relation to my comment about the omnigenic model. Quoting here for the benefit of the readers.
Arbel Harpak @arbelharpak
21 Oct 2021
Thanks for the live reporting, @doctorveera! Do you really see the estimate of 20% of the genome as defying the omnigenic model? As I understand the prediction, it relates to all regions of the genome that are accessible in the relevant cell types, not all of the genome.
1
1