Catarina Gomes

Background: Whole-Genome Sequencing (WGS) and Whole-Transcriptome Sequencing (WTS) have emerged as transformative tools in the diagnosis of rare diseases with complex phenotypes. These technologies enable deep analysis of the genome and RNA expression, uncovering structural, intronic, non-coding, and mitochondrial variants that traditional methods might miss, thus facilitating the understanding of gene function and regulation. Methods: We enrolled 8966 patients with suspected rare diseases or hereditary cancer risk syndromes from 21 centers throughout Brazil. Their genomes were sequenced with short, paired-end reads, and diagnostic reports were provided for 7614 of these patients. Results: The overall diagnostic yield was 35.8%, with an additional 5.6% of all Positive reports including gains from WGS compared to other diagnostic tests. In patients with pathogenic/likely pathogenic copy number or structural variants, WGS provided a 10.2% increase in diagnostic yield. Almost 1900 variant/phenotype interpretations were submitted to ClinVar. Conclusion: WGS and WTS are proving to be invaluable resources for shortening the diagnostic odyssey of patients with rare diseases, providing crucial genomic diagnostics, and enriching genetic databases with variant interpretations from underrepresented populations. These technologies have the potential to significantly enhance the precision of healthcare in genetically diverse populations. Key Points for NEJM Editors and Reviewers: Scope : Largest Brazilian rare disease–focused sequencing project to date. Findings : A 35.8% diagnostic yield with notable additional detection of structural, deep intronic, non-coding, and mitochondrial variants. Impact : Shortens diagnostic odyssey for patients, enriches ClinVar with almost 1900 variant interpretations from underrepresented populations, and demonstrates potential cost-effectiveness of integrating WGS into public healthcare. These interim results underscore how national WGS programs can facilitate precision medicine, especially in genetically diverse and previously underrepresented populations.

Polygenic risk scores (PRS) for breast cancer (BC) have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium (LD) and differences in variant frequency (AF-diff). Thus, locally-sourced population-based phenotypic and genomic datasets are essential to assess the validity of PRS derived from signals detected across populations. Here, assess the transferability of a BC PRS composed of 313 risk variants (313-PRS) in two Brazilian tri-hybrid admixed ancestries (European, African and Native American) whole-genome sequenced cohorts. We computed 313-PRS in both cohorts (n=753 and n=853) versus the UK Biobank (UKBB, n=264,307) as reference. We show that although the Brazilian cohorts have a high European (EA) component, with AF-diff and to a lesser extent LD patterns like those found in EA populations, the 313-PRS distribution is inflated when compared to that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. Interestingly, we find that case-controls lead to equivalent predictive power when compared to UKBB-EA samples with AUROC values of 0.66-0.62 compared to 0.63 for UKBB.

Approaches to deal and understand Autism Spectrum Disorder (ASD) phenotypic heterogeneity, quantitatively and multidimensionally, are in need. Being able to access a specific individual relative to a normative reference ASD sample would provide a severity estimate that takes into account the spectrum variance. We propose such an approach analyzing the principal components of variance observable in a clinical reference sample. Using phenotypic data available in a comprehensive reference sample, the Simons Simplex Collection ( n =2744 individuals), we performed Principal Component Analysis (PCA). The PCA considered ASD core-symptoms (accessed by ADI-R), important clinical features (accessed by VABS and CBCL) and IQ. PCA-projected dimensions supported a normative modeling where a multivariate normal distribution was used to calculate percentiles. An additional phenotypically homogeneous sample (ASD, IQ0.50) with clinical features as: Social Functionality (39%), Behavioral Disturbance (18%) and Communication Problems (15%). A Multidimensional Severity Score (MSS) to evaluate new prospective single subjects was developed based on percentiles. Additionally, the disequilibrium among PCA-projected dimensions gave rise to an individualized Imbalance Score (ImS). The approach, named TEAplot, is implemented in user-friendly free software and was illustrated in a homogenous independent sample. Our approach proposes a basis for patient monitoring in clinical practice, guides research sample selection and pushes the field towards personalized precision medicine. Lay Summary: Most families or clinicians already heard the now adage: “If you’ve met one person with autism, you’ve met one person with autism”. The phenotypic heterogeneity presented by the Autism Spectrum Disorders (ASD) is a challenge to research and clinical practice. Here in this work we summon established mathematical tools from the Machine Learning field to help one to organize the principal components of such variability. These mathematical tools were applied to a comprehensive database of autistic individuals’ mensurable profiles (cognitive, emotional, behavioural, and so on) maintained by the Simons Foundation Autism Research Initiative (SFARI). Using this normative model one can quantitatively estimate how a given individual person fits into the whole, as pediatricians often do by evaluating growth charts, a tool we named TEAplot. We made freely available Excel/Libreoffice spreadsheets that calculate our proposed Multidimensional Severity Score in order to effectively engage the research and clinical communities. The TEAplot model is a step towards a personalized precision medicine approach for ASD.