PPRS-ID: Indonesian-Adjusted Partitioned PRS for Type 2 Diabetes using Obesity PRS Integration and West Javanese Population LD Mapping

Polygenic risk scores (PRS) for type 2 diabetes (T2D) often lose accuracy when applied outside the populations in which they were developed. Partitioned PRS (PPRS) mitigate this by decomposing T2D risk into biologically interpretable pathways (e.g., obesity, body fat), but have not been adapted to Indonesians. We present PPRS-ID, an Indonesian-adjusted PPRS that integrates a locally derived obesity PRS with a population-specific linkage disequilibrium resource. We analyzed 2,936 Indonesian participants to construct an Indonesian obesity PRS. To localize the T2D partitions, we harmonized SNPs with the published T2D PPRS and addressed limited direct overlap via LD proxy mapping. For this, we built a merged GRCh38 LD reference by liftover of West Javanese whole-genome sequences (n=227), performing per-chromosome QC and imputation against 1000 Genomes, and then merging the imputed West Java genomes with 1000 Genomes to form a combined panel. Signed LD correlations (r) were computed within 1 Mb of PPRS loci, enabling projection of T2D effects onto Indonesian obesity SNPs. Partition-specific hybrid weights were then formed by blending projected T2D betas with Indonesian obesity betas using biologically informed parameters. An ancestry analysis confirmed that Indonesian samples cluster distinctly from other 1000 Genomes groups, supporting the need for population-aware LD reference. Direct overlap between T2D PPRS and our obesity PRS comprised a single SNP; LD mapping recovered 10 additional proxies with r2 > 0.4. The evaluation on the UK Biobank Asian subset, PPRS-ID achieved an AUC of 0.633 for T2D discrimination. In a head-to-head test of the obesity pathway within Indonesians, the Indonesian obesity PRS outperformed the original obesity partition (AUC 0.594 vs. AUC 0.465). PPRS-ID demonstrates a feasible path to population-tailored, pathway-aware T2D risk prediction in Indonesians. Ongoing work focuses on larger Southeast Asian validation, refined partition weighting, and assessment of clinical utility.