Wouter J. Peyrot

The paper recalculates CNV penetrance for intellectual disability, showing lower estimates due to methodological differences, and recommends phenotype-specific risk reporting.

The paper identifies biologically distinct subtypes of MDD based on energy-related symptoms and finds genetic and metabolic differences between them.

This study identifies shared genetic risk between major depression and atherosclerotic diseases and links causal gene expression changes in blood, brain, and heart tissues to depression etiology.

The paper introduces the BPC approach to improve polygenic score calibration for binary disorders, achieving better calibration than existing methods in simulated and empirical data.

The paper identifies five genomic factors explaining most genetic variance across 14 psychiatric disorders and finds shared and disorder-specific genetic signals with distinct biological functions.

This study shows that interpreting TWAS results as evidence of genetic relationships between gene expression and phenotypes leads to inflated type 1 errors and unreliable conclusions.

This paper shows that using the same genetic variants to create a polygenic risk score (PRS) and then to perform a GWAS on the PRS-derived phenotype leads to inflated false positive rates.

This study uses machine learning to predict 2-year MDD chronicity by integrating proteomics with clinical data, showing improved accuracy over psychiatrists' predictions.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

The paper introduces CC-GWAS, a method to detect allele frequency differences among cases of psychiatric disorders using summary statistics, identifying 196 loci distinguishing eight disorders.

This study uses genomics to investigate whether vitamin D, omega-3 fatty acids, or their genetic links are causally or genetically associated with major depression, finding no strong evidence for such relationships.

This study investigates the genetic and environmental links between major depressive disorder and traumatic experiences using UK Biobank data, finding shared genetic factors and distinct patterns in trauma-exposed individuals.