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Unlabelled: The influence of genetic ancestry on genomics in T-cell acute lymphoblastic leukemia (T-ALL) has not been fully explored. We examined the impact of genetic ancestry on multiomic alterations, survival outcomes, and risk stratification. Among 1,309 children and young adults with T-ALL treated on the Children's Oncology Group trial AALL0434, the prognostic value of five commonly altered T-ALL genes varied by ancestry-including NOTCH1, which was associated with superior overall survival for patients of European ancestry but was nonprognostic among patients of African ancestry. Integrating genetic ancestry with published T-ALL risk classifiers, we identified that an X01 penalized Cox regression classifier stratified patients regardless of ancestry, whereas a European multigene classifier misclassified patients of certain ancestries. Overall, 80% of patients harbored a genomic alteration in at least one gene with differential prognostic impact in an ancestry-specific manner. These data demonstrate the importance of incorporating genetic ancestry into genomic risk classification.
Significance: There is a lack of studies examining the prognostic significance of genomic features by genetic ancestry in T-ALL, especially in non-European ancestral groups. In this study, we demonstrate how the prognostic value of individual alterations differs by genetic ancestry, warranting future studies to identify germline alleles affecting these associations. See related commentary by de Smith, p. xxx.
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http://dx.doi.org/10.1158/2643-3230.BCD-25-0049 | DOI Listing |
Genet Med Open
July 2025
Faculty of Biology Medicine and Health, University of Manchester, United Kingdom.
Purpose: Familial chylomicronemia syndrome (FCS) is a rare autosomal recessive disorder. This study aimed to analyze the genotype distribution of FCS-causing genes in the United Kingdom.
Methods: Data were anonymously collated from 2 genetic testing laboratories providing national genetic diagnosis services for severe hypertriglyceridemia in the United Kingdom.
Am J Prev Cardiol
September 2025
Department of Cardiology, Albert Einstein College of Medicine/ Montefiore Medical Center, Bronx, NY, USA.
Background: Hispanics/Latinos are a heterogenous population with no validated atherosclerotic cardiovascular disease (ASCVD) risk estimation tool. We examined performance of the pooled cohort equation (PCE) across Hispanic/Latino background groups and quantiles of African, Amerindian, and European genetic ancestry.
Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) was used to evaluate the performance of the non-Hispanic Black (NHB) and non-Hispanic White (NHW) PCE defined by predicted to observed (P/O) ratios of 10-year ASCVD events.
NAR Genom Bioinform
September 2025
Centre for Integrative Biology and Systems Medicine (IBSE), Wadhwani School of Data Science and AI, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Genome graphs provide a powerful reference structure for representing genetic diversity. Their structure emphasizes the polymorphic regions in a collection of genomes, enabling network-based comparisons of population-level variation. However, current tools are limited in their ability to quantify and compare structural features across large genome graphs.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
September 2025
Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou City, Jiangxi Province, People's Republic of China.
Background: Chronic obstructive pulmonary disease (COPD) frequently co-occurs with autoimmune diseases (ADs), yet their shared genetic basis remains incompletely understood. This study aimed to evaluate genetic correlations between COPD and seven ADs and identify shared genetic risk loci underlying this comorbidity.
Methods: We integrated summary statistics from large-scale genome-wide association studies (GWAS) of COPD and seven ADs in European populations.
Stat Biosci
August 2024
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of "gold standard" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions.
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