Loïc Yengo

This study performs GWAS on medication-use data from 320,000 UK Biobank participants, identifying 505 genetic loci and linking them to 16 known therapeutic targets.

The paper uses machine learning to predict glycaemic traits from multi-omics data in longitudinal cohorts, identifying key predictors like methylation marks and metabolic variables.

The paper quantifies assortative mating on complex traits by analyzing SNP data from 400,000 individuals, finding significant evidence for height and educational attainment.

Meta-analysis of waist-to-hip ratio adjusted for BMI in 694,649 European-ancestry individuals identifies 463 signals in 346 loci, roughly one-third sexually dimorphic and mostly stronger in women.

This paper performs a meta-analysis of GWAS for height and BMI in 700,000 individuals, identifying 3,290 and 716 SNPs, with increased prediction accuracy.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper finds a genetic association between schizophrenia risk and urban living, suggesting genetic factors influence migration to densely populated areas.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

The paper extends bivariate LD score regression theory to account for population stratification, showing that the intercept can be inflated even without sample overlap.

The paper estimates the joint distribution of effect size and MAF for genetic variants in 28 complex traits and detects widespread negative selection signatures.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.