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Sasha Gusev @sashagusevposts.bsky.social · 18d

Yeah I don't really get it. The hard part is getting the genetic estimates so running this across many other 23andme traits (even restricting to non-controversial traits) would have been very informative. Perhaps they're going to follow up with more detailed linkage analyses?

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Sasha Gusev @sashagusevposts.bsky.social · 18d

Massive sibling regression study finds that human traits are largely environmentally driven (average heritability = 0.3)

Within-family heritability estimates for behavioural and disease phenotypes from 500,000 sibling pairs of diverse ancestries

Quantification of the direct effect of genetic variation on human behavioural traits is important for understanding between-individual variation in socio-economic and health outcomes but estimates of ...

www.medrxiv.org

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Sasha Gusev @sashagusevposts.bsky.social · 21d

You can estimate the total genetic difference between populations without having to know the underlying causal variants. You don't get the mechanism and you still have to make strong assumptions on GxE and ascertainment, but IMO an effective way to prioritize traits for follow-up.

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Sasha Gusev @sashagusevposts.bsky.social · 27d

Interesting evidence for genetics explaining disparities in T2D. Previous work identified a Neanderthal introgressed haplotype common to Native American ancestry with a large effect on T2D (pmc.ncbi.nlm.nih.gov/articles/PMC...). Wonder if there's power to look at archaic admixture here.

Shai Carmi @shaicarmi.bsky.social · 27d

Brilliant paper by Visscher et al.

Populations differ in traits/disease burden. Are these differences due to genetics?

Comparing single variants or polygenic scores between populations is biased due to environmental confounders correlated with the variants.

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www.medrxiv.org/content/10.1...

Direct effect of genetic ancestry on complex traits in a Mexican population

Human populations differ in disease prevalences and in average values of phenotypes, but the extent to which differences are caused by genetic or environmental factors is unknown for most complex trai...

www.medrxiv.org

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Reposted by Sasha Gusev

Shai Carmi @shaicarmi.bsky.social · 27d

Brilliant paper by Visscher et al.

Populations differ in traits/disease burden. Are these differences due to genetics?

Comparing single variants or polygenic scores between populations is biased due to environmental confounders correlated with the variants.

1/3

www.medrxiv.org/content/10.1...

Direct effect of genetic ancestry on complex traits in a Mexican population

Human populations differ in disease prevalences and in average values of phenotypes, but the extent to which differences are caused by genetic or environmental factors is unknown for most complex trai...

www.medrxiv.org

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Reposted by Sasha Gusev

Lino Ferreira @linoafferreira.bsky.social · 28d

Delighted to see this paper with @anaignatieva.bsky.social now published in Genetics!
academic.oup.com/genetics/adv...

We tackle a thorny issue arising in statistical tests for genetic interactions (epistasis) using ancestral recombination graphs (ARGs)... 🧵

Phantom epistasis through the lens of genealogies

Abstract. Phantom epistasis arises when, in the course of testing for gene-by-gene interactions, the omission of a causal variant with a purely additive ef

academic.oup.com

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Sasha Gusev @sashagusevposts.bsky.social · 29d

Yes, exactly, the idea was to overload the cells with many perturbations and then estimate components of higher-order interactions GREML-like. But we couldn't get enough cells with multiple edits and only had power to look at module interactions.

www.nature.com/articles/s41...

Scalable genetic screening for regulatory circuits using compressed Perturb-seq - Nature Biotechnology

Compressed Perturb-seq incorporates compressed sensing to genetic screening for scalable discovery of genetic interactions.

www.nature.com

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Sasha Gusev @sashagusevposts.bsky.social · 29d

We were hoping to look at this in perturb-seq data, but very hard to get the number of combinatorial effects needed for statistical power.

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Reposted by Sasha Gusev

Arun Durvasula @arundurvasula.bsky.social · Sep 2

Excited to share our latest manuscript, "Exposure accumulation drives age-dependent disease architectures and polygenic risk scores," led by Xilin Jiang: www.medrxiv.org/content/10.1...

I am attempting an explainer thread for the first time here:
(I am usually too exhausted to post one)

Exposure accumulation drives age-dependent disease architectures and polygenic risk scores

Our understanding of the dependence of the genetic and environmental architecture of common diseases on age is incomplete. Here, we use longitudinal data to quantify age-dependent genetic and environm...

www.medrxiv.org

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Sasha Gusev @sashagusevposts.bsky.social · Sep 3

I think about this every time I shave:

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Reposted by Sasha Gusev

Michel Nivard @michelnivard.bsky.social · Sep 2

I wrote about a rare genetic variant (a CNV) that appears to reduce the risk of schizophrenia diagnosis by ~5x, and why that's not as unambigously amazing as it sounds, but potentially still a pretty big deal.

Its probably nothing, but if it is...

A rare copy number variant appears to significantly reduce the risk of schizophrenia.

open.substack.com

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Sasha Gusev @sashagusevposts.bsky.social · Aug 31

very relevant, thank you!

Sasha Gusev @sashagusevposts.bsky.social · Aug 29

That's a great idea, and looks something like the below. I'm working on the size scaling a bit more to convey the point more clearly

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Sasha Gusev @sashagusevposts.bsky.social · Aug 29

Yeah I was thinking about this initially and unable to find any examples in the literature where epistasis tagged by additivity versus "pure" additivity mattered. I think the response really is just about narrow-sense h2 (as in the Breeder's Equation).

Sasha Gusev @sashagusevposts.bsky.social · Aug 29

Great mini thread here on lessons for GxG and GxE from non-human organisms:

Ewan Birney @ewanbirney.bsky.social · Aug 28

Nice blog and good to see this also from the twins/shared environment side. We (with my colleagues in @wittbrodtlab.bsky.social) have tried to tackle the non-additive in experimental settings (in medaka fish) which we can map to human (as the medaka fish are "wild") www.biorxiv.org/content/10.1...

Discovery and characterisation of gene by environment and epistatic genetic effects in a vertebrate model

Phenotypic variation arises from the interplay between genetic and environmental factors. However, disentangling these interactions for complex traits remains challenging in observational cohorts such...

www.biorxiv.org

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Reposted by Sasha Gusev

Ewan Birney @ewanbirney.bsky.social · Aug 28

Nice blog and good to see this also from the twins/shared environment side. We (with my colleagues in @wittbrodtlab.bsky.social) have tried to tackle the non-additive in experimental settings (in medaka fish) which we can map to human (as the medaka fish are "wild") www.biorxiv.org/content/10.1...

Discovery and characterisation of gene by environment and epistatic genetic effects in a vertebrate model

Phenotypic variation arises from the interplay between genetic and environmental factors. However, disentangling these interactions for complex traits remains challenging in observational cohorts such...

www.biorxiv.org

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Sasha Gusev @sashagusevposts.bsky.social · Aug 29

Yeah, uncentered effects make more sense to me biologically but of course you could design a statistical generative model where the interaction is purely non-additive.

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Sasha Gusev @sashagusevposts.bsky.social · Aug 28

great commentary article (and thanks for the thread!)

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Reposted by Sasha Gusev

Giacomo Bignardi @giacomobignardi.bsky.social · Aug 28

🧵 on cool choices of image headers (and titles) all from @sashagusevposts.bsky.social's Substack
🔗 at the end

'Beneath the surface of the sum'

Genetic interactions may look like the thing they deviate from

Interaction (1964) by Julian Stanczak

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Sasha Gusev @sashagusevposts.bsky.social · Aug 28

Yeah, in general multi component models will only take the part of the interaction that is not captured by additivity. The twin ADE model partitions them correctly but ONLY if there's no shared environment (and other assumptions hold).

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Sasha Gusev @sashagusevposts.bsky.social · Aug 28

Sure thing, see this gist. Note that this is always the affect allele frequency and not the minor allele frequency and there's no genotype scaling. Let me know if you think there's a better presentation.

simple epistasis model

simple epistasis model. GitHub Gist: instantly share code, notes, and snippets.

gist.github.com

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Sasha Gusev @sashagusevposts.bsky.social · Aug 27

Everyone is free to make their own choices, but I personally think what platform you use has negligible impact on the influence of wealthy and powerful people in the US, and far far less than being heard on important topics.

Sasha Gusev @sashagusevposts.bsky.social · Aug 27

:) I still post on twitter as well

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Sasha Gusev @sashagusevposts.bsky.social · Aug 27

So the mystery remains. It is tempting to conclude that biological epistasis is widespread but mostly gets mapped to statistical additivity. But that does not explain the deviations from additivity often observed in twins. /x

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Sasha Gusev @sashagusevposts.bsky.social · Aug 27

There is no statistical power to identify individual rare var interactions. But interactions between rare variants and common polygenic scores can be tested and show ... nothing. This is genuinely surprising: large deleterious effects simply add up with common polygenic burden.

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