Jeff Spence
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jeffspence.github.io
Jeff Spence
@jeffspence.github.io
1K followers 780 following 110 posts
assistant professor at ucsf interested in genetics, statistics, etc…

jeffspence.github.io
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jeffspence.github.io
Basically, when a variant gets lucky and drifts to high frequency, it is more likely to be a hit for every trait that it affects. This makes top hits APPEAR more pleiotropic, when in fact they’re actually LESS pleiotropic on average.

18/n
Three plots showing that variation in minor allele frequency can result in the top GWAS hits being hits for more traits on average. The leftmost plot shows that minor allele frequency decreases for less significant GWAS hits in both simulations and empirically. The middle plot shows that in simulations the most significant GWAS hits are, on average, the most trait specific. The right plot shows that in simulations and empirically the top GWAS hits are significant for more traits than less significant GWAS hits.
November 7, 2025 at 12:05 AM
jeffspence.github.io
This randomness in variant frequencies also results in what seems a paradox: top GWAS hits are both more trait specific AND more likely to be hits for other traits!

17/n
A schematic showing the p-values for two traits. Specificity makes p-values more significant for one trait at the expense of the other trait. Increasing minor allele frequency makes a variant more significant for both traits.
November 7, 2025 at 12:05 AM
jeffspence.github.io
GWAS prioritize individual variants, and sometimes variants just happen to get lucky and drift to high frequencies. This plays a surprisingly large role in prioritization in simulated GWAS.

16/n
Two plots. The left shows trajectories of identical mutations that all arose at the same time in the past, and how they evolve forward in time to the present day. Despite being identical mutations, their trajectories show a wide spread with a large amount of variance in their frequencies at the present day. The right shows the "realized heritability" of simulated SNPs and how it relates to the squared effect on the study trait. There is little concordance between realized heritability and squared effect on the study trait. Instead, there is a strong relationship between realized heritability and minor allele frequency, which, in the plot is denoted using color.
November 7, 2025 at 12:05 AM
jeffspence.github.io
Specificity is not the end of the story. Burden tests and GWAS also prioritize genes based on things that have absolutely nothing to do with traits.

Burden tests aggregate signal across variants. Long genes have more variants, and so tend to get prioritized higher.

15/n
Three scatter plots showing that long genes do not have larger effects on traits on average, but they do have smaller standard errors, and as a result they have stronger squared z scores.
November 7, 2025 at 12:05 AM
jeffspence.github.io
In contrast, HHIP (hedgehog interacting protein) has tons of GWAS hits near it, and basically no burden signal. HHIP makes sense as a height hit, but it’s also been implicated in COPD, pituitary hormone deficiency, etc… It’s NOT height specific!

14/n
A zoomed-in version of the HHIP locus from a previous post. There are many genome-wide significant GWAS hits, some with p-values smaller than 10^-150. No genes in the locus show significant burden signal.
November 7, 2025 at 12:05 AM
jeffspence.github.io
NPR2 is the second most significant gene in the burden tests, but is in only the 243rd most significant GWAS locus.

Homozygous LoFs in NPR2 cause severe short stature, but don’t affect intelligence, facial features, etc… seems like NPR2 is height specific!

13/n
A zoomed in version of the NPR2 locus plot from the previous post. There are two unlinked GWAS hits that are just barely genome-wide significant, and NPR2 shows strong burden test signal.
November 7, 2025 at 12:05 AM
jeffspence.github.io
To recap:

Burden tests prioritize trait-SPECIFIC GENES.

GWAS prioritize genes near trait-SPECIFIC VARIANTS.

Looking at height gives a couple of really nice examples: NPR2 and HHIP.

12/n
A scatter plot showing p-values for GWAS loci on the x-axis and p-values for burden tests on the y-axis. There is some concordance, but a lot of scatter. Two points are circled and point to additional panels. One point is labeled NPR2 and points to plots showing a locus where there is very little GWAS signal (only two unlinked variants that are just barely genome-wide significant), but the NPR2 gene shows strong association in burden tests. Another point is labeled HHIP and points to plots showing a locus where there are many extremely significant GWAS hits, but essentially no burden signal.
November 7, 2025 at 12:05 AM
jeffspence.github.io
Both of these ways of being specific contribute to variants being ranked highly in GWAS:

1. Coding variants in specifically-expressed genes are more highly ranked

AND

2. Non-coding variants in tissue-specific ATAC peaks are more highly ranked.

11/n
Two LDSC plots showing that heritability enrichment increases with context specificity. The left plot shows that coding variants contribute more to heritability if they are in more specifically-expressed genes. The right plot shows that non-coding variants contribute more to heritability if they are in tissue-specific ATAC peaks.
November 7, 2025 at 12:05 AM
jeffspence.github.io
What about GWAS?

GWAS prioritize genes near trait-specific VARIANTS, whereas burden tests prioritize trait specific GENES.

Variants can be specific because they act on trait-specific genes, or because they act on pleiotropic genes in a context-specific way.

10/n
A schematic showing the two ways that variants can be trait specific. They can either affect a pleiotropic gene in a context-specific way, or they can affect a trait-specific gene.
November 7, 2025 at 12:05 AM
jeffspence.github.io
Using theory developed by @yuvalsim.bsky.social and @gs2747.bsky.social (journals.plos.org/plosbiology/...) we predicted that burden tests rank genes by SPECIFICITY!

This is surprising! Burden tests DO NOT rank genes by IMPORTANCE!

These predictions play out in the UKB.

8/n
Left side shows a cartoon of the derivation for why burden test rank genes in part by trait specificity. Selection decreases LoF frequencies, with frequency being inversely proportional to the selection coefficient. Selection is driven by importance across all traits, with the selection coefficient being proportional to the sum of squared effects on all traits. The right hand side shows a stratified QQ plot, where more specifically-expressed genes tend to have much stronger burden test signals.
November 7, 2025 at 12:05 AM
jeffspence.github.io
Instead, one might want to rank genes by their trait SPECIFICITY -- how much do they affect the study trait relative to their effects across all traits. A trait-specific gene might be more "core" to trait biology.

7/n
A cartoon showing the mathematical definitions of "trait importance" (the squared effect on the trait under study) and "trait specificity" (the squared effect on the trait under study divided by the sum of squared effects across all traits).
November 7, 2025 at 12:05 AM
jeffspence.github.io
We checked for ourselves, and found:

1. Most burden hits are near a GWAS hit (they converge!)

BUT

2. The ranking of hits is surprisingly discordant. E.g., the second most significant burden hit for height is ranked 243rd in GWAS!

4/n
A bar chart showing burden hits for 12 traits. Each bar represents an individual gene that was found to be significant for a given trait using LoF burden tests, and they are stacked in order of significance. They are colored by the rank of the locus that contains them (if any) in GWAS. There are many top burden hits that are not ranked highly in GWAS, with some top burden hits not even being in the top 200 or 300 most significant GWAS loci.
November 7, 2025 at 12:05 AM
jeffspence.github.io
GWAS and burden tests both regress trait values against genetic variation.

In line with this conceptual similarity, previous work (link.springer.com/article/10.1... , www.biorxiv.org/content/10.1...) suggested these tests “converge” on the same genes.

2/n
Schematic showing how GWAS and burden tests work. Both regress phenotypes against genetic variation. In GWAS "genetic variation" is genotypes at individual variants. In burden tests individuals are grouped together depending on whether or not they have any LoF within a given gene.
November 7, 2025 at 12:05 AM
jeffspence.github.io
Overall, many of our models did a pretty job of ranking which guides would drive expression the most, but almost all of that performance came across genes.
When trying to predict which guide would have the largest effect on the expression of a particular gene, our results were more mixed.
A scatter plot of the predicted fold-change in expression of different genes for different guide RNAs compared to the actual fold-change measured by qPCR. There is generally a good concordance between the two (Spearman correlation of 0.7956), but the points are colored by which gene they came from, and it is clear that much of the predictive power comes from cross-gene accuracy, whereas accuracy for predicting the effects of different guides acting on the same gene is lower.
May 30, 2025 at 2:45 AM
jeffspence.github.io
And we considered a lot of different models of what dCas9-p300 actually does to chromatin tracks (How far away does p300 acetylate? How strongly would it increase signal in a ChIP-seq track? Does this interact with nucleosome occupancy? Do nucleosomes interfere with guide binding?)
Schematic of models of how we model the effects of dCas9-p300 on local chromatin. Panel A shows a dCas9-p300 preferentially binding to a region with lower nucleosome occupancy (as measured by MNase-seq). Panel B shows dCas9-p300 depositing H3K27ac marks with some intensity lambda, and spread over a distance sigma. Panel C shows the resulting perturbed H3K27ac track.
May 30, 2025 at 2:45 AM
jeffspence.github.io
On the experimental side, we can use dCas9 fused to a chromatin modifier to locally alter chromatin structure. We can then read out how those modifications affect gene expression.
This lets us directly test the “causal understanding” of our deep learning models!
Cartoon showing an overview of the study. The top row shows a flow chart of how epigenetic data is used to train neural networks to predict either endogenous expression or predict the effects of how epigenome editing affects expression. The middle row shows that the neural networks take histone modification epigenetic tracks as input and then predict expression levels. The bottom row shows how perturbed epigenetic tracks get used to make predictions that can then be compared to experimental results obtained using dCas9-p300.
May 30, 2025 at 2:45 AM
jeffspence.github.io
E.g., for height we also don't find a lot of loci that have significant burden hits but lack a GWAS hit. But we do find the opposite quite a bit.

We were also struck by how discordant the rankings are, and arguably when you have this many significant loci, some kind of ranking is necessary. 5/6
Scatter plot showing minimum p-values in LD blocks for GWAS and burden tests. Very few blocks are genome-wide significant in the burden test but not genome-wide significant in the GWAS, whereas many blocks are genome-wide significant in GWAS but not in burden tests.
March 28, 2025 at 1:22 AM
jeffspence.github.io
😭😭😭
A screenshot of a google AI overview confidently saying that the reciprocal of the sample mean is an unbiased estimate of the reciprocal of the population mean.
January 8, 2025 at 6:00 PM
jeffspence.github.io
Basically, when a variant gets lucky and drifts to high frequency, it is more likely to be a hit for every trait that it affects. This makes top hits APPEAR more pleiotropic, when in fact they’re actually LESS pleiotropic on average. 16/n
The plots. In all plots the x-axis is "p-value rank". Left: The y-axis is "Mean MAF relative to overall mean MAF" and the plot shows both simulations and empirical results. The simulations and empirical results show good agreement, and both show that the most significant GWAS hits are are higher than average MAF. Center: The y-axis is "Mean specificity" and the plot only shows simulations. The most significant GWAS hits are the most specific. Right: The y-axis is "Mean number of significant traits per hit". The plot shows both simulations and empirical results and they are in good agreement. Both show that the top GWAS hits are hits for more than 2 traits on average, while weaker GWAS hits are only hits for between 1.5 and 1.75 traits on average.
December 17, 2024 at 7:05 AM
jeffspence.github.io
This randomness in variant frequencies also results in what seems a paradox: top GWAS hits are both more trait specific AND more likely to be hits for other traits! 15/n
A schematic showing two traits. Increasing trait specificity for the first trait increases power for that trait but decreases power for the other trait. Increasing MAF increases power for both traits.
December 17, 2024 at 7:05 AM
jeffspence.github.io
GWAS prioritize individual variants, and sometimes variants just happen to get lucky and drift to high frequencies. This plays a surprisingly large role in prioritization in simulated GWAS 14/n
A scatter plot titled "In GWAS, sufficiently strong variants are mainly prioritized by frequency, no effect size". The y-axis is "realized heritability", a measure of how highly a variant would be prioritized, and the x-axis is squared effect size on the study trait. Individual points are simulated SNPs. The most highly ranked SNPs do no necessarily have the highest squared effect sizes, but do seem to have the highest MAF.
December 17, 2024 at 7:05 AM
jeffspence.github.io
Specificity is not the end of the story. Burden tests and GWAS also prioritize genes based on things that have absolutely nothing to do with traits.

Burden tests aggregate signal across variants. Long genes have more variants, and so tend to get prioritized higher. 13/n
Three plots. Left: a scatter plot of an unbiased estimate of mean squared effect size across traits within a bin (y-axis) plotted against the mean expected LoFs in the bin (x-axis). An arrow indicates that the x-axis also corresponds to genes with longer CDS. There is essentially no trend between the two. Center: a scatter plot with the same x-axis, but the y-axis is now the mean standard error of LoF burden estimates of the effect size. There is a strong negative trend with longer genes having smaller standard errors. Right: a scatter plot with the same x-axis, but the y-axis is now the mean squared z-score across traits. There is a strong positive relationship with longer genes having more burden signal.
December 17, 2024 at 7:05 AM
jeffspence.github.io
To recap so far: burden tests prioritize trait-specific genes; GWAS prioritize genes near trait-specific variants.

Height GWAS and burden tests give a couple of really nice examples: NPR2 and HHIP. 10/n
Three plots: Top: The same scatter plot as before showing how burden tests and GWAS prioritize different loci for height. Two genes, HHIP and NPR 2 are highlighted. Bottom left: a closer look at the NPR2 locus. There are 2 weak unlinked GWAS hits in the locus, but tons of burden signal for the NPR2 gene, and not much burden signal at other genes in the locus. Bottom right: a closer look at the HHIP locus. There are tons of very strong unlinked GWAS hits, but none of the genes in the locus show much if any burden signal.
December 17, 2024 at 7:05 AM
jeffspence.github.io
We find that both of these ways of being specific contribute to variants being ranked highly in GWAS:

1) Coding variants in specifically-expressed genes are more highly ranked
2) Non-coding variants in tissue-specific ATAC peaks are more highly ranked

9/n
Two plots. Left side: a plot showing LDSC's tau plotted against expression specificity bins for 9 trait-tissue pairs. Across all traits, and on average, tau increases with increasing tissue specificity. Right side: a similar plot showing LDSC's tau plotted against ATAC-seq peak specificity for non-coding variants. Across all traits, and on average, tau increases with increasing ATAC peak specificity.
December 17, 2024 at 7:05 AM
jeffspence.github.io
What about GWAS?
GWAS prioritize genes near trait-specific VARIANTS. This is profoundly different from prioritizing trait-specific GENES. Variants can be specific because they act on trait-specific genes, or because they act on pleiotropic genes in a context-specific way. 8/n
A schematic titled "Variants can be trait specific in two ways". The left hand side shows contours of variant specificity in a plot with the x-axis being the trait specificity of the gene through which the variant acts and the y-axis being how specific the variant is relative to that gene. Three points are labeled, and presented with separate cartoons. Points 1 and 2 show variants acting through a highly pleiotropic gene. Variant 2 is coding and hence also pleiotropic and not trait specific. Variant 1 is non-coding and only affects a specific context resulting in it being quite trait specific. Variant 3 is a coding variant but in a gene that is itself trait specific, making the variant also trait specific.
December 17, 2024 at 7:05 AM