Biology subjects

Bellis, C.

Publications and source records attributed to Bellis, C..

3 recordsLinked to original sources

bioRxiv2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

bioRxiv2025

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

bioRxiv2018

Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

Asian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7x, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

genetics↗