Christian Erikstrup

This study identifies 26 risk loci for fibromyalgia through a genome-wide association analysis of 2.5 million individuals, revealing genetic links to neural functions and chronic pain disorders.

This study identifies genetic variants associated with healthcare costs across multiple systems, finding polygenic influences and large effects from specific loss-of-function variants.

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.

This paper identifies genetic variants associated with amiodarone-induced thyroid dysfunction and shows they improve risk prediction beyond general thyroid disease PRS.

This study identifies 118 new genetic loci associated with Alzheimer's disease across diverse ancestries and discovers 48 novel risk loci, including those enriched in neuronal cell 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.

The paper uses genetic data from Denmark and Britain to infer historical North Sea migrations from the Middle Ages to the Industrial Revolution, identifying distinct population patterns and migration trends.

This study identifies 51 genome-wide significant loci associated with dosage changes in 42 drug pair phenotypes, revealing distinct genetic patterns in polypharmacy.

This study identifies 80 genetic loci and 61 effector genes associated with dilated cardiomyopathy through GWAS and integrates transcriptomic data to uncover biological pathways and cellular states involved in the disease.

This study identifies seven novel genetic loci associated with chronic dry cough and ACEi-induced cough, implicating neurological genes and highlighting potential therapeutic targets.

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.

The paper develops a machine learning method to predict blood group antigens and HPA-1 from genotyping data, achieving high accuracy in multiple cohorts.

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.

This study identifies 144 genetic loci associated with abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target through Mendelian Randomization and pre-clinical validation.

This GWAS identifies 123 migraine risk loci, including subtype-specific variants and novel targets, supporting neurovascular mechanisms in migraine.

This study identifies seven new genetic loci associated with febrile seizures, linking them to fever response and neuronal excitability genes, and suggests potential clinical applications of polygenic risk scores.

This study performs a genome-wide association analysis of hemorrhoidal disease in 944,133 individuals, identifying 102 risk loci and linking them to vascular and tissue development pathways.

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.