Share this page!

Eric Fauman Profile picture
Twitter logo
31 Jan, 6 tweets, 4 min read
Today's GWAS of urolithiasis, kidney stones and other stones of the urinary tract, provides a wonderful window into calcium, phosphate and vitamin D metabolism.
One nice thing about putting my GWAS interpretations here in Twitter is I can always quickly find what I may have written about a gene or a trait before.

Here's my write up on urolithiasis from 2 years ago in a completely different cohort, biobank japan

Do GWAS demonstrate good reproducibility?

On the left is the top hits from @finngen; on the right the top hits from Biobank Japan.
5 of 6 loci from FinnGen also found in BBJ.
Note some of the lead SNPs may differ, but the causal genes line up.

meta-analysis anyone?(@masakanai?)
Genes contributing to kidney stones via calcium and phosphate regulation show genetic associations with a large set of inter-related phenotypes including:
levels of parathyroid hormone, FGF23, vitamin D, phosphate, phosphorus, calcium and eGFR.

pubmed.ncbi.nlm.nih.gov/23045255/
Ok, mystery #1 - there are many genes with clear biological and genetic links to circulating vitamin D levels.

Why is it that only CYP24A1 also influences kidney stone formation?

(figure illustrates max beta for each VitD gene, data from: pubmed.ncbi.nlm.nih.gov/32059762/)
Mystery #2 - what is the causal gene near RSPH14
genetics.opentargets.org/variant/22_230…

Well reproduced in UKB.

Clearly metabolism related as the locus also harbors associations with parathyroid hormone, bone mineral density, vitD, potassium levels.

Thoughts?

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Eric Fauman

Eric Fauman Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @Eric_Fauman

Eric Fauman Profile picture
30 Jan
Welcome all! We've added several hundred followers over the past few weeks, so as a quick intro, I use this account mainly to explore interesting issues related to the biological interpretation of GWAS
@SbotGwa from @andganna provides me with a steady diet of interesting material
@SbotGwa alternates between GWAS from @uk_biobank and @FinnGen_FI.
Yesterday's Manhattan plot from FinnGen yielded a single hit for the trait "other and unspecified corneal deformities and disorders"
Let's dive in,
I often say it's good to take the most significant association at a locus as we start to interpret it.
@FinnGen_FI uses a modified PheWeb server to show results.

Here is the PheWAS for this SNP.

Top association is Keratitis, inflammation of the cornea.

r4.finngen.fi/variant/2-2272…
Read 11 tweets
Eric Fauman Profile picture
2 Jan
Thanks for the shout out, and welcome any new followers.

I like looking at GWAS and trying to decipher the causal biology behind the hits.
I use this account to highlight interesting results and provide links to the tools and approaches I find most useful.
One theme I come back to is that because the closest gene is usually the correct causal gene any analysis of a new GWAS should start there.
Here's a story from almost a year ago, a great study on heart trabeculae that initially ignored the closest genes

Fortunately this paper was on biorxiv. After I tweeted about the omissions and before it was published in Nature, the paper was amended to include 2 of the prominent heart structure genes: pubmed.ncbi.nlm.nih.gov/32814899/ Image
Read 5 tweets
Eric Fauman Profile picture
2 Jan
Image
I love the idea of this GWAS. The authors estimated the abundance of mtDNA in the blood of @uk_biobank participants by using the intensities of probes mapping to the mito genome

@HaggSara
Juulia Jylhävä
Yunzhang Wang
Kamila Czene & 
Felix Grassmann

pubmed.ncbi.nlm.nih.gov/33385171/
There are many analyses in this paper; I'm (naturally) only focusing on the GWAS which identified 66 lead SNPs in 50 loci.

I think I found 4 likely causal genes not mentioned in the paper including an explanation for the strongest hit and largest beta out there on Chr 19. Image
Read 18 tweets
Eric Fauman Profile picture
24 Dec 20
So this is fun - what connects these images:
Well the image on the right is supposed to represent yesterday's @SbotGwa @uk_biobank Manhattan plot for "Leg Fat Free Mass (Left):
I count 1,199 lead SNPs in this Manhattan plot! Nothing specifically special about leg fat free mass; this trait is highly correlated with other body size traits:
ukbb-rg.hail.is/rg_summary_231…
Read 9 tweets
Eric Fauman Profile picture
24 May 20
I do love this figure from the new PR interval paper.

Interestingly 16/25 highlighted genes are in fact the closest gene to the lead SNP.

64%, very similar to eQTLs, pQTLs and metabolite QTLs.
About half the genes in the diagram (the ones with a 7) are also involved in closely related monogenic diseases. This is generally a reliable way to identify a true causal gene.

I looked across all the loci at all genes involved in "rare cardiac diseases"
orpha.net/consor/cgi-bin…
First up are genes involved in depolarization and repolarization of the heart. These are all previously known loci, but fall into that nice category of closest gene and also rare disease gene that makes them highly likely to be causal (ok: SCN5A/SCN10A is a special case)
Read 8 tweets
Eric Fauman Profile picture
19 Feb 20
Here's how I see the SNP->gene gold standard issue.

This map separates the problem of identifying the causal transcript for a disease from the issue of identifying which transcripts are altered by a SNP.

As we know from, eg, lactase, many mRNAs are altered but only 1 is causal.
The map acknowledges that a GWAS association is probably acting through a functional variant that impacts a transcript that (usually) impacts a protein that may alter a biomarker or intermediate phenotype which manifests as a change in disease risk or complex phenotype.
From left to right:
cis-eQTLs and splicing-QTLs reveal mechanisms by which a DNA variant can impact mRNA abundance. It's good to model and predict these.
At a particular locus these may or may not translate into elucidation of the causal transcript for the disease phenotype.
Read 10 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

Like this author's thread?

Get emails when @Eric_Fauman has new unrolls!

Check out other threads from @Eric_Fauman!

Read all threads by @Eric_Fauman