1. Genomic structural equation modelling (genomic SEM) is a multivariate method for analyzing the joint genetic architecture of complex traits, synthesizing genetic correlations and single-nucleotide polymorphism heritabilities inferred from genome-wide association studies (GWASs) of individual traits.
2. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores, and identify loci that cause divergence between traits.
3. Applications of genomic SEM include a joint analysis of summary statistics from five psychiatric traits, which identified 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs, and consistently outperformed polygenic scores from univariate GWASs.
The article "Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits" presents a new method for analyzing the joint genetic architecture of complex traits. The authors introduce genomic structural equation modeling (genomic SEM), which synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from genome-wide association studies (GWAS) summary statistics of individual traits from samples with varying and unknown degrees of overlap.
The article is well-written and provides a clear explanation of the method, its applications, and potential benefits. However, there are some potential biases and limitations to consider. Firstly, the study only focuses on GWAS data, which may not capture all aspects of the genetic architecture of complex traits. Other types of genetic data, such as rare variants or epigenetic modifications, may also play a role in trait development.
Secondly, while the authors claim that genomic SEM can identify loci that cause divergence between traits, they do not provide evidence for this claim. It is possible that other factors besides genetics contribute to trait divergence.
Thirdly, the study only analyzes summary statistics from five psychiatric traits. While these are important and relevant traits to study, it is unclear how well genomic SEM would perform on other types of traits or in different populations.
Finally, while the authors note that genomic SEM is flexible and open-ended, they do not discuss any potential risks or limitations associated with this approach. For example, it is possible that using multiple models to analyze genetic data could lead to overfitting or false positives.
Overall, while the article presents an interesting new method for analyzing complex trait genetics, it is important to consider its potential biases and limitations before drawing firm conclusions about its utility in research.