kauralasoo.bsky.social
Associate Professor of Bioinformatics at University of Tartu, Estonia. Project lead at eQTL Catalogue. https://kauralasoo.github.io/
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Ampler Bikes does do e-bikes with USB-C charging. As a bonus, you can also use your bike as a battery bank: amplerbikes.com/en/e-bikes
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I'm pretty sure this must be due to a underlying large-effect methylation QTL.
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I think last year there were 10k+ applicants, so approximately 15% success rate?
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Totally agree with this Kaur. I have also been playing with the data (thanks for sharing!). I found that nearly all univariate MR associations with CAD were highly diminished or disappeared in a multivariate MR including APOB. IMO this should be the baseline analysis for this kind of data.
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This is why I think the BCAA example is informative - early genome-wide MR suggested a causal link (doi.org/10.1371/jour...), but more recent studies (e.g. onlinelibrary.wiley.com/doi/10.1002/...) have attributed this to reverse causality and pleiotropy.
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I agree! One of the reasons we included this plot was to illustrate you should not treat the 249 traits as independent when performing MR (as is often done). Leaving lipid traits aside, I would still be worried about intepreting genome-wide MR that includes a bunch of anonymous trans variants.
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This work was led by brilliant PhD student Ralf Tambets. Many thanks to all of our co-authors and collaborators @urmovosa.bsky.social @adriaan-vd-graaf.bsky.social @zkutalik.bsky.social @genomicsdoge.bsky.social @jaanikakronberg.bsky.social @dzvinn.bsky.social
@kristafi.bsky.social
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I think we need to be very careful when intepreting genome-wide MR estimates that include many 'anonymous' trans-acting genetic variants as instruments (term coined by @mendelrandom.bsky.social).
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In all three gene regions we are seeing a null effect on T2D (and CAD).
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Here's the putative causal DAG. Instead of estimating the genome-wide average effect of BCAAs on T2D (which could be confounded by many factors), we are now asking if altered protein function of BCAT2/DBT/PPM1K might have a causal effect on T2D risk.
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As an example, genome-wide MR suggested that branched-chain amino acids (BCAAs) are (weakly) associated with T2D. To explore this association further, we turned to cis-MR and focussed our attention to genes that have direct effect on BCAA catabolism (BCAT2/DBT/PPM1K).
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While it might be tempting, I find it difficult convince myself that hundreds of metabolic traits have a direct causal effect on CAD and T2D, given the widespread pleiotropic effects many variants have on these traits.
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We also performed genome-wide Mendelian randomisation to explore links between metabolic traits and CAD/T2D. While this did recover known causal effects of LDL cholesterol on CAD and glucose on T2D, almost everything else appeared to be significant as well.
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This illustrates how difficult it is to saturate gene discovery in GWAS, even for realtively 'simple' traits.
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Using @genomicsengland.bsky.social imputation allowed us to test many more low-frequency variants in the UK Biobank than previous studies (95M total). As an example, in branched-chain amino acid GWAS, we detected hits for all five members of the BCKDH complex, two of which had MAF < 0.0005!
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Yes, drug pricing is another aspect of a broken healtcare system in the US. Ozempic is not coverd for weight loss in Estonia (only T2D), so I am paying the full cost of 88.67 EUR per month (~11x less than US). This is because the national health insurance negotiated that price for T2D patients.
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My recommendations
💡 add some personality or info to your letter that is not obvious from your CV
💡 use AI tools for brainstorming, drafting, and improving flow and grammar, but DO NOT let it write the entire letter
💡 remember that vacancies are about people, and hiring committees want to see you 👉
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And the newly released @opentargets.org platform now also allows you to query if a variant of interest falls inside an eQTL Catalogue credible set, eg: platform.opentargets.org/variant/22_3...
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Here's a short video of how it works:
www.youtube.com/watch?v=qS8c...
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Unfortunately I don't think it has been updated in quite some time. There are many newer eQTL datasets that are part of @eqtlcatalogue.bsky.social but not on fivex. We now have our own browser that replicates part of the functionality with an added benefit of visualising sQTLs:
elixir.ut.ee/eqtl/
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Equinox! Now I also understand why you had a deadline.
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Yes, I can see it now, 'you rate'.
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XDH (hex D H) and urate levels/gout?
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Tricky indeed as all eQTL evidence points towards TMEM128 even though the likely causal gene is OTOP1. The relevant cell type likely not present in @eqtlcatalogue.bsky.social.