Fernando S. Goes

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

The paper identifies novel CNV loci and dose-dependent gene effects across six psychiatric disorders using genome-wide analysis.

The paper identifies shared and divergent genetic mechanisms across psychiatric disorders using gene-set burden analysis of copy number variants.

The paper develops pathway-specific polygenic scores to predict lithium treatment response in bipolar disorder and finds associations with specific biological pathways.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

The paper reports exome sequencing findings in bipolar disorder, identifying AKAP11 as a shared risk gene with schizophrenia through ultra-rare protein-truncating variants.

This study identifies a genome-wide significant variant for OC traits and shows shared genetic risk between OC traits and OCD.

The paper introduces lncRNAKB, a comprehensive knowledgebase of 77,199 human lncRNAs with expression, conservation, and cis-eQTL data from GTEx.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

This study investigates whether genetic variation in the Major Histocompatibility Complex (MHC) is associated with depression risk, finding no significant associations at a conservative threshold.

The paper examines how genetic and environmental factors influence DNA methylation at variably methylated regions in neonatal blood, finding that combined effects best explain methylation variability.

The paper shows that polygenic scores for depression traits predict lithium response in bipolar disorder patients, with lower genetic load linked to better treatment outcomes.

The paper integrates brain DNA methylation data with GWAS to identify psychiatric risk genes, finding heritable CpG sites and methylation-trait associations linked to schizophrenia and bipolar disorder.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

Genome-wide association meta-analysis of 130,664 major depression cases and 330,470 controls identifies 44 independent risk loci enriched in cortical neurons, conserved mammalian regions, splicing networks, and antidepressant targets.

This study finds that a higher genetic risk for schizophrenia is linked to poorer lithium response in bipolar disorder patients, suggesting potential for personalized treatment strategies.