Javier de la Fuente

The paper introduces an ultra-fast analytic solution for multivariate GWAS in Genomic SEM, reducing computation time by over 800 times and enabling efficient analysis on standard hardware.

This study identifies 1,294 genomic regions and 961 effector genes associated with externalizing disorders, revealing neural mechanisms and polygenic predictors of variance in these conditions.

The paper introduces an effect size index and automated functions to assess specific genetic associations between external traits and common factors, applied to cognitive ability and Alzheimer's disease indicators.

This paper evaluates methods to estimate lifetime brain atrophy from a single MRI scan and identifies genetic correlates, finding that the residual method best captures brain shrinkage and has strong heritability.

This study identifies 1,257 genetic variants associated with human personality traits using large-scale GWAS and finds widespread genetic correlations with behaviors and life outcomes.

The paper uses deep learning on DXA images to identify genetic variants affecting skeletal proportions and links these to evolutionary changes and disease risk.

This study shows noncognitive skills become more predictive of academic achievement over time and their genetic contribution increases with age.

This study uses genomic PCA to identify shared genetic architecture between brain morphometry, ageing, and cognitive ability using UK Biobank data.

The paper combines GWAS and GWAX data using a multivariate method to estimate SNP heritability of Alzheimer's Disease, finding higher heritability than previous SNP-based estimates.

The paper introduces T-SEM to analyze gene expression's role in cognitive traits, identifying 184 genes linked to general cognitive function and enrichments in specific genomic categories.

The paper identifies a genetic general intelligence factor (g) that explains 58.4% of the genetic variance in cognitive traits using genome-wide data from seven cognitive tests.