Mette Nyegaard

This study evaluates polygenic risk scores (PRS) for colorectal cancer (CRC) screening, showing they stratify risk independently of ancestry and sex but offer limited additional predictive value when combined with FIT.

This paper introduces DoBSeq, a low-cost framework for detecting rare variants in population-scale genetic screening, achieving 95% variant detection and recovering all clinically relevant pathogenic variants in ACMG genes.

This paper uses a polygenic risk score to show that genetic liability for endometriosis is linked to pain, inflammatory, and metabolic traits in both sexes.

The paper compares the clinical impact of missense variants in CALM1, CALM2, and CALM3 genes by analyzing expression levels and translation efficiency in cardiac tissue.

This study investigates how calmodulin mutations affect voltage-gated calcium channel regulation in neurons, linking these effects to neurological symptoms in calmodulinopathies.

DoBSeqWF is a pipeline for analyzing pooled sequencing data to detect rare genetic variants, improving sensitivity with machine learning filters.

This study identifies 20 GWAS risk genes in smooth muscle cells linked to coronary artery disease and reveals shared disease mechanisms across different gene types.

This study identifies 21 infertility loci and over 400 reproductive hormone loci through GWAS across multiple cohorts, finding limited genetic correlation between reproductive hormones and infertility.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

This study identifies 40 genetic loci associated with placental weight using GWAS in 179,025 children and parents, revealing shared and distinct genetic influences with fetal growth.

The paper identifies five genetic loci associated with postpartum hemorrhage through a meta-analysis and links them to progesterone receptor binding sites and related traits.

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

This study identifies genetic loci associated with gestational duration and preterm delivery, showing maternal and fetal genetic effects and antagonistic pleiotropy with birth weight.

The paper analyzes whole genome sequences of 150,119 UK Biobank individuals, identifying variants and associations with traits, and provides a haplotype reference panel for imputation.

The paper identifies rare variants in the ASTN2 locus associated with migraine, independent of GWAS findings, and proposes a role for Polycomb Response Elements in migraine etiology.

This study benchmarks small variant detection using ONT long-read sequencing, showing high performance in challenging genomic regions compared to short-read sequencing.

This study performs a GWAS on medication-use data from 335,744 UK Biobank individuals, identifying 59 genetic loci and showing that medication-use is a highly polygenic trait with 18% heritability from common variants.

This study identifies 29 risk variants for problematic alcohol use through a large meta-analysis and explores its genetic relationships with other traits.

This study identifies five genome-wide significant loci and seven shared with other traits in a large ASD GWAS, highlighting polygenic heterogeneity and the value of large-scale GWAS in ASD.

This paper identifies a genome-wide significant risk locus for cannabis use disorder, implicating CHRNA2 and linking it to cognitive performance.

This study identifies 13 novel genetic loci associated with endometriosis through a large GWAS meta-analysis and finds genetic links to other pain-related conditions.

This study investigates the role of BRD1 in sex-biased nuclear receptor signaling and its implications in mental disorders using mouse models and gene expression analysis.