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Performance of a Type 1 Diabetes Genetic Risk Score in a Multi-centric Study from India and its Implications in Clinical Practice
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Summary
Background
Genetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries. Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking.
Methods
The study included 597 T1D patients and 3347 non-diabetic controls from different regions in India. Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines. Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youden’s index.
HLA-DQ
diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression.
Findings
T1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.84 (range=0·78-0·87)], supporting its comprehensive use for T1D classification in India. Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0·75 vs 0·85) and in adult-onset than in childhood-onset patients (0·74 vs 0·84). We observed a lower frequency of protective
HLA-DQ
diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D. Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off.
Interpretation
T1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific
HLA
architecture. European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians.
Funding
CDRC grant CDRC202111026 and CSIR Intramural Grant P50.
Research in context
Evidence before this study
Previous studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries. Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D). However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility. Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group. The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated.
Added value of this study
This study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool. Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective
HLA-DQ
diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity. The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential.
Implications of all the available evidence
The European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance. However, its utility is influenced by islet cell autoantibody status and the age at onset of disease. Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians. Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.
openRxiv
Alagu Sankareswaran
Dimple Lavanuru
Bhanu Teja Nalluri
Shalbha Tiwari
Rashmi Nagaraj
Nagendra Khadri
Akila Prashant
Srinivas G N S V Kandula
Vedavati Purandare
Vaibhavishree Muniswamy
Navyashree Mugur Jagadeesha
Prajwal Guruswamy
Neelaveni Kudugunti
MR Savitha
Radhe Shyam Tapadia
Basavanagowdappa Hathur
Rakesh Kumar Sahay
Ambika G Unnikrishnan
Suraj S Nongmaithem
Bipin Sethi
Giriraj R Chandak
Title: Performance of a Type 1 Diabetes Genetic Risk Score in a Multi-centric Study from India and its Implications in Clinical Practice
Description:
Summary
Background
Genetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries.
Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking.
Methods
The study included 597 T1D patients and 3347 non-diabetic controls from different regions in India.
Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines.
Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youden’s index.
HLA-DQ
diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression.
Findings
T1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.
84 (range=0·78-0·87)], supporting its comprehensive use for T1D classification in India.
Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0·75 vs 0·85) and in adult-onset than in childhood-onset patients (0·74 vs 0·84).
We observed a lower frequency of protective
HLA-DQ
diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D.
Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off.
Interpretation
T1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific
HLA
architecture.
European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians.
Funding
CDRC grant CDRC202111026 and CSIR Intramural Grant P50.
Research in context
Evidence before this study
Previous studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries.
Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D).
However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility.
Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group.
The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated.
Added value of this study
This study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool.
Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective
HLA-DQ
diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity.
The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential.
Implications of all the available evidence
The European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance.
However, its utility is influenced by islet cell autoantibody status and the age at onset of disease.
Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians.
Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.
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