Frederik Heymann Lassen

The paper investigates Menin's role in enhancer-promoter interactions in MLL-rearranged and NPM1c leukemia models, finding context-dependent effects on transcriptional regulation and enhancer function.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

The paper identifies transcriptional and epigenomic differences in Brodmann Area 22 in autism, linking them to RFX3-dependent networks and verbal ability.

The paper performs whole exome sequencing on 44,028 British South Asians to identify gene-phenotype associations and knockout genes, finding novel links and implications for drug development.

The paper identifies rare damaging variants in genes associated with misaligned phenotypes using polygenic scores and enrichment analysis in the UK Biobank.

The paper identifies cell-type-specific molecular and regulatory changes in D1 striosome medium spiny neurons and astrocytes in ASD using single-nucleus multiomic profiling in human putamen.

The paper identifies genes associated with obesity and fat distribution using genetic and functional data, then validates their roles through CRISPR knockdown in cell lines.

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 identifies compound heterozygous variants in UK Biobank data and shows their phenotypic impact on common diseases using statistical phasing and mixed models.

The paper examines 5' untranslated regions (UTRs) to show their role in translational regulation of dosage-sensitive genes and introduces a web tool for analyzing 5UTR composition.

The paper shows KRAS mutations disrupt its interaction with LZTR1, affecting protein stability via the CUL3 ubiquitin ligase complex, contributing to oncogenesis.

Genoppi integrates proteomic and genetic data to identify cell-type-specific interactions and their role in disease.