Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches
Abstract 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 inv...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82278-x |
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| author | Ankit M. Arni Diane P. Fraser Seth A. Sharp Richard A. Oram Matthew B. Johnson Michael N. Weedon Kashyap A. Patel |
| author_facet | Ankit M. Arni Diane P. Fraser Seth A. Sharp Richard A. Oram Matthew B. Johnson Michael N. Weedon Kashyap A. Patel |
| author_sort | Ankit M. Arni |
| collection | DOAJ |
| description | Abstract 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 90th 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. |
| format | Article |
| id | doaj-art-cbc6294777ee44d5ad0efa40e0fc8ea7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-cbc6294777ee44d5ad0efa40e0fc8ea72024-12-29T12:27:44ZengNature PortfolioScientific Reports2045-23222024-12-0114111010.1038/s41598-024-82278-xType 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approachesAnkit M. Arni0Diane P. Fraser1Seth A. Sharp2Richard A. Oram3Matthew B. Johnson4Michael N. Weedon5Kashyap A. Patel6Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterDepartment of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterDepartment of Pediatrics, Stanford UniversityDepartment of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterDepartment of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterDepartment of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterDepartment of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of ExeterAbstract 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 90th 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.https://doi.org/10.1038/s41598-024-82278-x |
| spellingShingle | Ankit M. Arni Diane P. Fraser Seth A. Sharp Richard A. Oram Matthew B. Johnson Michael N. Weedon Kashyap A. Patel Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches Scientific Reports |
| title | Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches |
| title_full | Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches |
| title_fullStr | Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches |
| title_full_unstemmed | Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches |
| title_short | Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches |
| title_sort | type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array based approaches |
| url | https://doi.org/10.1038/s41598-024-82278-x |
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