Arni Ankit M, Fraser Diane P, Sharp Seth A, Oram Richard A, Johnson Matthew B, Weedon Michael N, Patel Kashyap A
Department of Clinical and Biomedical Sciences, RILD Building, Royal Devon and Exeter Hospital, University of Exeter, Barrack Road, Exeter, EX2 5DW, UK.
Department of Pediatrics, Stanford University, Stanford, CA, 94305, USA.
Sci Rep. 2024 Dec 28;14(1):31044. doi: 10.1038/s41598-024-82278-x.
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), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10). 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) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 ). 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) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10). Notably, when applying the European ancestry-derived 90 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.
1型糖尿病遗传风险评分（T1DGRS）有助于1型糖尿病（T1D）的诊断和预测。传统上，T1DGRS是从推算的阵列基因型得出的，而全基因组测序（WGS）提供了一种更直接的方法，现在越来越多地用于临床和研究。我们使用WGS、TOPMed推算基因型和千人基因组计划推算的阵列基因型，在149,265名英国生物银行参与者中，研究了基于WGS和基于阵列的T1DGRS在不同遗传血统中的一致性。在整个队列中，基于WGS的T1DGRS与基于TOPMed推算的阵列评分显示出强相关性（r = 0.996，基于WGS的平均评分低0.0028个标准差（SD），p < 10），而与基于千人基因组计划推算的阵列评分相关性较低（r = 0.981，WGS中低0.043 SD，p < 10）。基于WGS和基于TOPMed推算的阵列评分之间的血统分层分析显示，与欧洲血统的相关性最高（r = 0.996，WGS中低0.044 SD，p < 10），其次是非洲血统（r = 0.989，WGS中低0.0193 SD，p < 10）和南亚血统（r = 0.986，WGS中低0.0129 SD，p < 10）。当将基于WGS的评分与基于千人基因组计划推算的阵列评分进行比较时，这些差异更为明显（欧洲、南亚、非洲血统的r分别为0.982、0.975、0.957）。使用基于WGS的T1DGRS进行的人群水平分析显示，存在显著的基于血统的分层，欧洲血统个体的评分最高，其次是南亚血统个体（平均比欧洲人低0.28 SD，p < 10）和非洲血统个体（平均比欧洲人低0.89 SD，p < 10）。值得注意的是，当应用源自欧洲血统的第90百分位风险阈值时，只有0.71%（95% CI 0.41 - 1.13）的非洲血统个体和6.4%（95% CI 5.6 - 7.2）的南亚个体被确定为高风险，大大低于预期的10%。总之，虽然WGS可用于生成T1DGRS，TOPMed推算基因型提供了一种经济有效的替代方法，但即使使用全基因组测序，T1DGRS分布中基于血统的差异仍然存在，这强调了在临床实践中需要特定血统或泛血统标准。
