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One of the promises of precision medicine is to understand and act on inter-individual genetic differences in drug responses. SNPdrug3D contains the complete genomic landscape of missense single nucleotide variants (SNV) across the human proteome and at population-wide level that could affect drug binding. In the Singapore SG10K Health and global gnomAD cohorts, comprising variations in over 80,000 individuals, we identified ~ 1.17 million variants mapped to residues near bound drug molecules in protein-drug complexes relative to ~ 6000 drug molecules and experimentally verified effects of selected SNVs, including previously uncharacterized variants, on drug binding in relevant proteins ranging from kinases to cytochrome P450s (CYPs). The latter led to a specific predictor for interpreting variants in the CYP family that vastly outperforms existing tools in the prediction of pharmacogenetic effects. By placing variants and drugs in structural context, SNPdrug3D aids drug development by early flagging of resistance potential through population-specific variability.

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a “five-safes” framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

We examined the construct validity of 2 self-reported frailty questionnaires, the Frailty Phenotype Questionnaire (FPQ) and FRAIL, against the Cardiovascular Health Study frailty phenotype (CHS-FP).Cross-sectional data analysis of longitudinal prospective cohort study.We included data from 230 older adults (mean age: 67.2 ± 7.4 years) from the "Longitudinal Assessment of Biomarkers for characterization of early Sarcopenia and Osteosarcopenic Obesity in predicting frailty and functional decline in community-dwelling Asian older adults Study" (GeriLABS 2) recruited between December 2017 and March 2019.We compared area under receiver operating characteristic curves (AUC), agreement, correlation, and predictive validity against outcome measures [Short Physical Performance Battery, 5 times repeat chair stand (RCS-5), Frenchay activities index, International Physical Activity Questionnaire, life-space assessment, Social Functioning Scale 8 (SFS-8), EuroQol-5 dimensions (utility value)] using logistic regression adjusted for age, gender, and vascular risk factors. We examined concurrent validity across robust versus prefrail/frail for inflammatory blood biomarkers [tumor necrosis factor receptor 1 and C-reactive protein (CRP)] and dual-energy x-ray absorptiometry body composition [bone mineral density (BMD); appendicular lean mass index (ALMI), and fat mass index (FMI)].Prevalence of prefrail/frail was 25.7%, 14.8%, and 48.3% for FPQ, FRAIL, and CHS-FP, respectively. Compared with FRAIL, FPQ had better diagnostic performance (AUC = 0.617 vs 0.531, P = .002; sensitivity = 37.8% vs 18.0%; specificity = 85.6% vs 88.2%) and agreement (AC1-Stat = 0.303 vs 0.197). FPQ showed good predictive validity [RCS-5: odds ratio (OR) 2.38; 95% CI: 1.17-4.86; International Physical Activity Questionnaire: OR 3.62; 95% CI:1.78-7.34; SFS-8: OR 2.11; 95% CI: 1.64-5.89 vs FRAIL: all P gt; .05]. Only FRAIL showed concurrent validity for CRP, compared with both FPQ and FRAIL for TNF-R1. FRAIL showed better concurrent validity for BMD, FMI, and possibly ALMI, unlike FPQ (all Pgt; .05).Our results support complementary validity of FPQ and FRAIL in independent community-dwelling older adults. FPQ has increased case detection sensitivity with good predictive validity, whereas FRAIL demonstrates concurrent validity for inflammation and body composition. With better diagnostic performance and validity for blood biomarkers and clinical outcomes, FPQ has utility for early frailty detection in the community setting.