Philip Awadalla

The paper uses cfMeDIP-seq to analyze cell-free placental DNA methylation in maternal plasma, identifying enriched regions associated with preterm birth and IUGR.

This study analyzes over 3.6 million single cells from 1,000 tumors across 14 cancer types to identify heterogeneous cancer cell states linked to patient survival and therapeutic vulnerabilities.

The paper examines how geography, ancestry, age, and sex influence the prevalence of somatic autosomal mosaic chromosomal alterations in blood across diverse populations.

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

This paper introduces scATOMIC, a tool for classifying single cells in cancer tumour microenvironments, showing improved accuracy over existing methods across multiple cancer types.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

This paper introduces TumE, a synthetic supervised learning method that improves accuracy and speed in inferring cancer evolution from single tumour biopsies.

This paper develops a polygenic risk score model to predict diabetic complications and their response to therapy using genome-wide association study data.

This study identifies germline genetic variants influencing somatic mutation patterns in 2,642 cancer genomes across 39 cancer types.

This study characterizes mitochondrial genomes in 2,700 cancer patients across 37 types using whole-genome sequencing and RNA-seq, revealing mutational patterns, truncating mutations, and nuclear transfer of mitochondrial DNA.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

The paper investigates how environmental factors, particularly air pollution, interact with genetic variation to influence gene expression and chronic disease traits in an urban population.

The paper examines genomic diversity in French Canadians to show that range expansion led to relaxed selection, increasing the frequency of deleterious mutations in colonizing populations.

This study examines how the X chromosome and sex influence gene regulation and disease by analyzing gene expression and regulatory variation in whole blood from 922 individuals.

The paper shows that recombination rate influences the distribution of deleterious variants across the human genome, with low recombination regions enriched for disease-associated mutations.