@mclapps.bsky.social
WashU Postdoc with a UT PhD. Using multivariate genetic methods to study personality, cog aging, and substance use.
Extremely excited to share the first effort of the Revived Genomics of Personality Consortium: A highly-powered, comprehensive GWAS of the Big Five personality traits in 1.14 million participants from 46 cohorts. www.biorxiv.org/content/10.1...
20.05.2025 10:14 — 👍 149 🔁 69 💬 5 📌 13
Out today, our method for comparing multivariate genetic architecture across groups of people, called Genomic Structural Invariance: www.nature.com/articles/s41...
(feat: @tuckerdrob.bsky.social @michelnivard.bsky.social @andrewgrotzinger.bsky.social @mijke.bsky.social @jorsmo.bsky.social et al.)
Genetic correlations between MDD and each neuroticism cut point. The line of best fit is shown, based on a linear regression model, with parameters estimated using generalized least squares (GLS).
Standardized results (with standard errors) for two-factor genomic structural equation model as estimated using GenomicSEM software.
Standardized results (with standard errors) for three-severity factor genomic structural equation model as estimated using GenomicSEM software.
Are mental disorders extreme manifestations of continuous traits or genetically distinct entities? We present Genomic Taxometric Analysis of Continuous and Case-Control data (GTACCC) for evaluating genetic continuity and differentiation of traits across the severity spectrum. Tweetorial coming soon.
05.02.2025 01:15 — 👍 34 🔁 13 💬 2 📌 2
Finally, I couldn’t end without mentioning that we were able to work in this fantastic representation of factor analysis from Buzz Hunt.
15.01.2024 17:22 — 👍 3 🔁 0 💬 2 📌 0Even when the factor model is correct, we observe some variants with specific effects. Variants in the APOE region are not generally associated with all cognitive tasks. Rather, they are associated with “fluid” tasks which decline with age, but not “crystallized” tasks.
15.01.2024 17:21 — 👍 1 🔁 0 💬 1 📌 0
We can see this in the results of a simple simulation. Here, although the square correlations matrices (intercorrelations among phenotypes) are indistinguishable, the rectangular matrices (associations with individual genetic variants) differ starkly.
15.01.2024 17:20 — 👍 0 🔁 0 💬 1 📌 0
This is what has been observed for the “p factor,” i.e. the general factor of psychopathology (www.nature.com/articles/s41...), and more recently for impulsivity (www.medrxiv.org/content/10.1...).
15.01.2024 17:20 — 👍 2 🔁 1 💬 1 📌 1However, if the common factor is an illusion- a statistical artifact of aggregation- then we should observe individual genetic variants that are associated with subsets of the phenotypes, but not variants that are associated with all phenotypes.
15.01.2024 17:19 — 👍 1 🔁 0 💬 1 📌 0
This is exactly what has been observed for constructs such as general intelligence (www.nature.com/articles/s41...) and externalizing psychopathology (www.nature.com/articles/s41...).
15.01.2024 17:18 — 👍 1 🔁 0 💬 1 📌 0If the common factor model is correct, then we should observe individual genetic variants that are associated with all of the phenotypes composing that factor (the SNP effects should be proportional to the factor loadings).
15.01.2024 17:18 — 👍 1 🔁 0 💬 1 📌 0Genome-wide association studies (GWAS) quantify associations between genetic variants and phenotypes. We can leverage GWAS data to resolve the factor indeterminacy problem and test the validity of latent variables.
15.01.2024 17:18 — 👍 1 🔁 0 💬 1 📌 0However, the human genome contains elementary components that, due to the shuffling process associated with sexual reproduction, come to be naturally uncorrelated.
15.01.2024 17:17 — 👍 1 🔁 0 💬 1 📌 0Reducing cognitive tasks into smaller components was unsuccessful – these narrow components still correlated with each other, leaving open the question of what model was responsible for those correlations.
15.01.2024 17:17 — 👍 1 🔁 0 💬 1 📌 0Researchers have suggested breaking down cognitive tasks into “elementary processes” to identify components that correlate with the phenotypes but are uncorrelated with one another. Such a set of variables could be used to test whether a general factor was the true causal model.
15.01.2024 17:17 — 👍 1 🔁 0 💬 1 📌 0That’s factor indeterminacy: just because a set of correlations is consistent with a common factor doesn’t mean that we can rule out other types of causal models. So what do we do about factor indeterminacy?
15.01.2024 17:15 — 👍 2 🔁 1 💬 1 📌 0
Here are correlations generated using a different model, where different combinations of 6 phenotypes share a cause, but never all 9 (so there is no common factor). It looks the same as the other matrix!
15.01.2024 17:15 — 👍 1 🔁 0 💬 1 📌 0
To make this a little more salient, let’s look at some correlation matrices. These matrices show the correlation among 9 phenotypes. Here’s one that was generated by a common factor model, where one shared cause influences all of the phenotypes in the correlation matrix.
15.01.2024 17:14 — 👍 1 🔁 0 💬 1 📌 0
Many social scientists use latent variables to capture variables that cannot be directly observed (extraversion, intelligence). However, the correlations we use to infer latent factors can result from alternative data generating mechanisms. We refer to this problem as factor indeterminacy.
15.01.2024 17:14 — 👍 1 🔁 0 💬 1 📌 0
🚨 New paper!
In this review, we explore factor indeterminacy and outline how we can use genetic data to test the validity of latent factors.
tinyurl.com/5n75446y
With @tedmond.bsky.social , @tuckerdrob.bsky.social , @kph3k.bsky.social , @andrewgrotzinger.bsky.social , and @michelnivard.bsky.social
