Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches.

Autor: Arni AM; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK., Fraser DP; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK., Sharp SA; Department of Pediatrics, Stanford University, Stanford, CA, 94305, USA., Oram RA; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK., Johnson MB; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK., Weedon MN; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK., Patel KA; Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK. K.A.Patel@exeter.ac.uk.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Dec 28; Vol. 14 (1), pp. 31044. Date of Electronic Publication: 2024 Dec 28.
DOI: 10.1038/s41598-024-82278-x
Abstrakt: A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10 - 31 ), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10 - 300 ). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10 - 300 ) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10 - 14 ) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10  - 6 ). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10 - 58 ) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10 - 300 ). Notably, when applying the European ancestry-derived 90 th centile risk threshold, only 0.71% (95% CI 0.41-1.13) of African ancestry individuals and 6.4% (95% CI 5.6-7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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