Biology subjects

Jamuar, S. S.

Publications and source records attributed to Jamuar, S. S..

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↗

bioRxiv2024

StarPhase: Comprehensive Phase-Aware Pharmacogenomic Diplotyper for Long-Read Sequencing Data

Pharmacogenomics is central to precision medicine, informing medication safety and efficacy. Phar-macogenomic diplotyping of complex genes requires full-length DNA sequences and detection of structural rearrangements. We introduce StarPhase, a tool that leverages PacBio HiFi sequence data to diplotype 21 CPIC Level A pharmacogenes and provides detailed haplotypes and supporting visualizations for HLA-A, HLA-B, and CYP2D6. StarPhase diplotypes have high concordance with benchmarks where 99.5% are either exact matches or minor discrepancies. Manual inspection of the 0.5% mismatches indicates they were correctly called by StarPhase. With StarPhase, we update or correct 26.2% of GeT-RM pharmacogenomic diplotypes. Population distributions from StarPhase mostly reflect those of the All of Us cohort, while also highlighting gaps in existing pharmacogenomic databases that long-read sequencing can fill. With a single HiFi whole genome sequencing assay, StarPhase enables robust PGx diplotyping even as additional pharmacogenes and haplotypes are discovered.

bioinformatics↗

bioRxiv2020

Family History Assessment Significantly Enhances Delivery of Precision Medicine in the Genomics Era

BackgroundFamily history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs, with less emphasis on family history assessment. We evaluated the utility of family history for genomic sequencing selection. MethodsWe analysed whole genome sequences of 1750 healthy research participants, with and without preselection based on standardised family history collection, screening 95 cancer genes. ResultsThe frequency of likely pathogenic/ pathogenic (LP/P) variants in 884 participants with no family history available (FH not available group) (2%) versus 866 participants with family history available (FH available group) (3.1%) was not significant (p=0.158). However, within the FH available group, amongst 73 participants with an increased family history cancer risk (increased FH risk), 1 in 7 participants carried a LP/P variant inferring a six-fold increase compared with 1 in 47 participants assessed at average family history cancer risk (average FH risk) and a seven-fold increase compared to the FH not available group. The enrichment was further pronounced (up to 18-fold) when assessing the 25 cancer genes in the ACMG 59-gene panel. Furthermore, 63 participants had an increased family history cancer risk in absence of an apparent LP/P variant. ConclusionOur findings show that systematic family history collection remains critical for health risk assessment, providing important actionable data and augmenting the yield from genomic data. Family history also highlights the potential impact of additional hereditary, environmental and behavioural influences not reflected by genomic sequencing.

genomics↗