2025/04/14 by Austin Talbot, Alex Kotlar, Talbot, Austin +5 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · #Cancer Genomics and Diagnostics #FOS: Biological sciences #Genomics (q-bio.GN) #Genomics and Rare Diseases #Hemoglobinopathies and Related Disorders #q-bio.GN
paper · pdf · doi:10.48550/arxiv.2504.10338
openalex publication_date 2025/04/14 · arxiv published 2025/04/14 · arxiv updated 2025/04/14 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
Thalassemia, a blood disorder and one of the most prevalent hereditary genetic disorders worldwide, is often caused by copy number variations (CNVs) in the hemoglobin genes. This disorder has incredible diversity, with a large number of distinct profiles corresponding to alterations of different regions in the genes. Correctly classifying an individual's profile is critical as it impacts treatment, prognosis, and genetic counseling. However, genetic classification is challenging due to the large number of profiles worldwide, and often requires a large number of sequential tests. Targeted next generation sequencing (NGS), which characterizes segments of an individual's genome, has the potential to dramatically reduce the cost of testing and increase accuracy. In this work, we introduce a probabilistic state space model for profiling thalassemia from targeted NGS data, which naturally characterize the spatial ordering of the genes along the chromosome. We then use decision theory to choose the best profile among the different options. Due to our use of Bayesian methodology, we are also able to detect low-quality samples to be excluded from consideration, an important component of clinical screening. We evaluate our model on a dataset of 57 individuals, including both controls and cases with a variety of thalassemia profiles. Our model has a sensitivity of 0.99 and specificity of 0.93 for thalassemia detection, and accuracy of 91.5% for characterizing subtypes. Furthermore, the specificity and accuracy rise to 0.96 and 93.9% when low-quality samples are excluded using our automated quality control method. This approach outperforms alternative methods, particularly in specificity, and is broadly applicable to other disorders.