Xavier Estivill

This paper identifies 39,749 autosomal cis-eQTMs in children's blood using HELIX data, showing most are near genes and many have genetic variants associated with methylation and expression.

The paper introduces BATI, a Bayesian rare variant association test that outperforms existing methods in detecting rare variants associated with complex diseases, especially when genetic variation explains little phenotypic variance.

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

The paper estimates genetic correlations between eating disorder and substance-use-related phenotypes using GWAS data, finding both positive and negative associations.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

The paper identifies novel long non-coding RNAs and circular RNAs in hematopoietic cells using BLUEPRINT consortium data, highlighting their cell type specificity.

This study identifies genetic effects on birth weight and their impact on cardio-metabolic traits, distinguishing fetal and maternal contributions.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

This study identifies maternal genetic variants associated with offspring birth weight using GWAS and SEM, showing independent effects from fetal genetics.

This study identifies genetic loci associated with early childhood growth phenotypes and links them to later adiposity traits, suggesting potential targets for obesity prevention.

This study introduces a Bayesian model to identify cancer driver genes by integrating evolutionary signatures, revealing novel gene-tumor type associations and highlighting chromatin modifiers as key targets.