Hannah Klinkhammer

The paper reclassifies a variant of uncertain significance as likely pathogenic in Mowat-Wilson syndrome using next-generation phenotyping and multimodal data integration.

This paper investigates how polygenic risk scores (PRS) transfer between populations by simulating demographic changes and dominance coefficients, finding that recessive traits are more conserved.

The paper uses next-generation phenotyping to identify Cornelia de Lange Syndrome in Nigerian children through phenotypic similarity and exome sequencing.

This study finds that paternal exposure to ionizing radiation increases clustered de novo mutations in offspring, suggesting cDNMs as a transgenerational biomarker.

This study evaluates the ability of a polygenic risk score to refine colorectal cancer risk in Lynch syndrome individuals, finding weak associations in specific subgroups.

The paper reports a cohort of 35 individuals with CUL3 variants causing a syndromic neurodevelopmental disorder, showing loss-of-function variants disrupt protein homeostasis by impairing proteasomal degradation of CUL3 substrates.

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

The paper introduces AlphScore, a pathogenicity prediction score for missense variants using AlphaFold2-derived features, showing improved performance when combined with existing scores.

The paper evaluates a structured diagnostic framework for ultra-rare diseases, showing improved genetic diagnoses and novel gene-disease associations using exome sequencing and AI tools.

This paper introduces an efficient boosting algorithm for polygenic risk scores, showing improved computational efficiency and prediction accuracy compared to existing methods.

The paper expands the phenotypic spectrum of NAA10 and NAA15-related neurodevelopmental syndromes by analyzing clinical data from 106 NAA10 and 66 NAA15 variant carriers, revealing variable severity and multi-system involvement.

The paper applies statistical learning methods to develop sparser polygenic models for LDL-cholesterol prediction using UK Biobank data, showing competitive performance compared to classical approaches.

GestaltMatcher improves diagnosis of ultra-rare genetic disorders by matching facial phenotypes to molecular diagnoses using a deep learning model trained on a large dataset of patient photographs.