Inês Barroso

The paper estimates whole-genome heritability of complex traits across diverse populations, finding rare and non-coding variants significantly contribute to heritability.

The paper integrates multiomic data from human brain nuclei with GWAS statistics to prioritize neural cell types associated with brain-related traits.

This study uses MR-Clust to identify three MDD genetic clusters with opposing effects on T2D and cardiometabolic risk, linking them to distinct biological pathways and depression subtypes.

This paper combines multi-trait fine-mapping and functional annotation to improve the identification of causal variants at glycaemic trait loci, reducing the number of likely causal variants significantly.

The paper identifies ancestry-specific T2D risk variants in South Asian populations through exome-wide association study and functional validation.

The paper investigates the association between neurodevelopmental copy number variants (ND-CNVs) and increased risk of internalising and cardiometabolic multimorbidity using UK Biobank data.

This paper introduces MGflashfm and MGfm, methods that improve fine-mapping resolution by integrating multiple traits and population groups using summary statistics.

The paper defines a standard format for GWAS summary statistics to improve data sharing and analysis.

This study identifies rare loss of function variants in four genes associated with beneficial changes in body fat distribution using genome-wide and exome-sequence data from UK Biobank.

Flashfm improves fine-mapping of multiple quantitative traits by sharing information between traits, reducing potential causal variants by 20% in a Ugandan cohort.

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

This study identifies 48 genetic variants associated with handedness using a large GWAS, highlighting polygenic nature and genetic links to neuropsychiatric traits.

The paper identifies novel gene-trait associations and pathways involving rare variants in metabolic biomarkers linked to cardiovascular disease risk.

The paper identifies novel BMI genotype-environment interactions using a conditional false discovery rate approach on UK Biobank data, finding 140 candidate loci with GxE effects.

The paper identifies 9 novel genetic loci, including low-frequency variants in TP53, associated with head circumference and intracranial volume in up to 46,000 individuals.

This paper introduces StructLMM, a linear mixed model for analyzing multivariate gene-environment interactions, demonstrating its effectiveness in detecting GxE signals in BMI and eQTL data.

The paper performs deep whole genome sequencing on 1,457 individuals to investigate rare variant contributions to cardiometabolic traits, identifying new regulatory variants and gene-trait associations.

ProxECAT is a new method that uses external control allele frequencies to test for genetic associations in case-control studies, increasing power while maintaining type I error rates.

This study identifies rare variant associations with fat distribution and cardio-metabolic risk, implicating intracellular lipolysis genes in these traits.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.