Enhancing the Diagnosis of Rare Diseases

Recent key publications

Smart medical report: efficient detection of common and rare diseases on common blood tests. 
Front Digit Health 2024; 6:1505483. Németh Á, Tóth G, Fülöp P et al. 

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=39703757

The integration of artificial intelligence (AI) into healthcare has the potential to revolutionise medical diagnostics by enabling earlier and more accurate disease detection. This study presents an AI-assisted diagnostic support tool that leverages routinely ordered blood tests to predict both common and rare diseases, validated on a dataset comprising over one million patients. The model demonstrates high diagnostic performance, with a mean ROC-AUC of 0.9293 and the ability to detect diseases up to 1–9 months earlier than traditional clinical diagnosis. By utilising ensemble learning and tracking changes in blood values over a 15-year patient history, this tool offers a robust, cost-effective solution for enhancing clinical practice and improving patient outcomes.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. 
Nat Genet 2025; 57:193-205. Scherer N, Fässler D, Borisov O et al.

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=39747595

This study presents a comprehensive integration of metabolomics and exome sequencing to elucidate the impact of rare, damaging heterozygous variants on gene function and human traits. By analysing 1,294 plasma and 1,396 urine metabolites from 4,737 participants, the research identifies 235 gene-metabolite associations, many previously unreported. The findings demonstrate that heterozygous variants can provide insights comparable to those from inborn errors of metabolism, revealing graded effects on metabolic pathways and associated traits. This integrative approach not only uncovers novel players in human metabolism but also highlights metabolic readouts with implications for understanding complex diseases and traits.

Genome Sequencing for Diagnosing Rare Diseases. 
N Engl J Med 2024; 390:1985-1997. Wojcik MH, Lemire G, Berger E et al. 

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=38838312

Genome sequencing has transformed rare disease diagnosis by detecting pathogenic variants missed by conventional testing, its utility after inconclusive exome sequencing remains understudied. This study analyses over 800 families with suspected monogenic disorders, demonstrating that genome sequencing identifies clinically relevant variants in 8% of previously undiagnosed cases, including complex structural changes and deep intronic mutations. The findings establish genome sequencing as an essential diagnostic tool for elusive genetic conditions, highlighting its critical role in advancing precision medicine.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease. 
Am J Hum Genet 2024; 111:863-876. Lemire G, Sanchis-Juan A, Russell K et al. 

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=38565148 

Although copy-number variants (CNV) significantly influence rare disease genetics, their detection and classification remain challenging in clinical genomics. This study analyses exome sequencing data from 6,633 families, identifying and classifying CNVs through an advanced analytical framework that increases diagnostic yield by 2.6%. The findings demonstrate that CNV detection in exome sequencing offers a cost-effective alternative to genome sequencing while enhancing genetic diagnosis and expanding understanding of rare disease mechanisms.

Reanalysis of genomic data in rare disease: current practice and attitudes among Australian clinical and laboratory genetics services. 
Eur J Hum Genet 2024; 32:1428-1435. Best S, Fehlberg Z, Richards C et al.

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=38796577

Genomic data reanalysis has become crucial for improving rare disease diagnosis; however, its implementation varies widely across clinical settings. This study explored Australian genetic services' practices and attitudes toward automated reanalysis, revealing significant workforce limitations and policy inconsistencies through comprehensive audit data and healthcare professional surveys. The findings demonstrate strong support for automation while highlighting the need for leadership, standardised policies, and technological advancement to optimise genomic reanalysis in clinical practice.

Inconsistencies between prenatal diagnostic and genetic testing laboratories on variant validation of rare monogenic diseases. 
Prenat Diagn 2024; 44:1053-1061. Lin L, Zhang Y, Pan H et al.

Link to full article: http://www.ncbi.nlm.nih.gov/pubmed/?term=38898598

Despite advances in next-generation sequencing for rare disease diagnosis, variant validation inconsistencies between prenatal diagnostic and genetic testing laboratories present ongoing challenges. This study analyses 286 families to identify factors contributing to diagnostic discordance, revealing that 6% of cases show inconsistencies, primarily from variant interpretation errors and false-positive results in pseudogenes and GC-rich regions. The findings underscore the importance of rigorous variant validation and early prenatal diagnostic centre referral for accurate genetic counselling and reproductive decision-making.