Bertha Hidalgo

LANTERN improves rare-variant association testing in admixed populations by incorporating local ancestry information, identifying ancestry-specific associations in a cohort of African Americans.

This study identifies a PTER inhibitor that reduces food intake and body weight in mice, linking PTER function to obesity treatment.

This study identifies twelve novel blood pressure loci and shows smoking modifies genetic effects on BP through immune-related pathways in over 1 million individuals.

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

This study identifies gene-environment interactions between excessive daytime sleepiness and genetic variants linked to obstructive sleep apnea severity, revealing novel therapeutic targets.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

The paper performs whole genome sequence analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper uses bidirectional Mendelian randomization to show LTL lengthening increases CHIP risk, while CHIP accelerates LTL shortening, with modest mediation between CHIP and CAD via LTL.

The paper develops a methylation risk score model for metabolic syndrome using data from two ethnic groups, showing its predictive power across populations.

This study identifies 59 sentinel variants and two novel genes associated with telomere length using whole genome sequences from 109,122 trans-ethnic individuals.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.

This study identifies nine novel DNA methylation sites associated with fasting glucose and insulin levels by integrating cross-omics data from multiple cohorts.