Peter Michael Krawitz

The paper identifies FRMPD4 as a causal gene for X-linked non-syndromic hearing loss through genetic analysis and cross-species functional studies.

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

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

This study shows that GBA1 variants increase Parkinson's disease risk, with severe variants having higher risk than risk variants, and polygenic scores further increase this risk.

This paper introduces GestaltGAN, a generative model that creates photorealistic portraits of individuals with rare genetic disorders while preserving privacy.

The paper analyzes clinical and mechanistic data from individuals with ACTB/ACTG1 variants to identify distinct disease phenotypes and molecular mechanisms of non-muscle actinopathies.

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 paper compares ancestry-specific and multi-ancestry polygenic risk scores for LDL prediction in East Asian populations using UK Biobank and Taiwan Biobank data, finding that multi-ancestry models are more generalizable.

This study compares human and computer attention patterns when assessing images of genetic conditions using eye-tracking and deep learning saliency maps.

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 assesses the transferability of European-derived polygenic risk scores for cardiometabolic diseases in South Asians and examines their interaction with family history.

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.

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

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.

This study assesses how polygenic risk scores, high-impact monogenic variants, and family history influence breast and prostate cancer risk using UK Biobank data.

This study reports on 25 new KBG syndrome patients, identifies common clinical features, and provides recommendations for diagnosis and treatment.

The paper models how population expansion affects recessive disorder prevalence and mutation load, showing that random mating leads to a temporary decrease in disease incidence but increased mutation load.

This study examines how polygenic risk scores influence the penetrance of monogenic Parkinson's disease variants and family history in Parkinson's disease risk.

This paper introduces a CRISPR-Cas9-engineered mouse model for GPI-anchor deficiency that mirrors human phenotypes, including cognitive impairments and epilepsy, and identifies synaptic transmission deficits.

The paper introduces an AI-driven method that uses facial images to prioritize exome data, achieving 99% top-10 accuracy in identifying disease-causing genes.

This paper evaluates deep learning for syndrome recognition in metabolic disorders, finding improved accuracy with larger training sets but no significant effect from ethnicity or sex.

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.