Biology subjects

Khng, A. J.

Publications and source records attributed to Khng, A. J..

2 recordsLinked to original sources

bioRxiv2025

Polygenic Risk Scores Across Genomic Platforms for Reliable Breast Cancer Risk Stratification

PurposeWe evaluated differences in a 313-variant breast cancer polygenic risk score (PRS313) across genomic platforms and their impact on risk stratification. MethodsWe compared PRS313 derived from genotyping arrays (Global Screening Array [GSA], OncoArray-500K [OncoArray], Global Diversity Array [GDA], custom Axiom_PrecipV1 array [ThermoFisher]) and low-coverage genome sequencing (lc-WGS) in 2 cell lines and 92 individuals. Probes were designed for all variants on ThermoFisher (success rate: 259/313). Sanger sequencing was performed to profile indels. Concordance of high-risk classification (PRSscore>0.6) across platforms was assessed using Kappa statistics. ResultsPRS313-lc-WGS was identical in the 4 cell line repeats. In saliva samples, indel concordance with Sanger sequencing varied widely (Kappa: 0.007-1.000). PRS313-ThermoFisher was predictable from other platforms using linear models, despite systematic differences. Greater agreement was observed between arrays with high imputation overlap (e.g., GDA[~]GSA slope=0.986). Pre-calibration agreement in high-risk classification was moderate (Fleiss Kappa=0.552) and improved post-calibration (Kappa=0.650). Arrays with similar designs showed higher pre-calibration agreement (Kappa=0.745). Calibration narrowed high-risk proportions from 4-45% to 15-21% -28% were high-risk by any platform, while 8% were high-risk across all five. ConclusionPlatform-specific biases affect PRS interpretation. Calibration enhances consistency in identifying high-risk individuals. STATEMENT OF SIGNIFICANCEThis study compares the performance of a validated 313-variant breast cancer polygenic risk score across platforms, revealing systematic biases in risk stratification and raising concerns about including inconsistent indels in the model.

genetics↗

bioRxiv2022

16S rRNA sequencing for profiling microbial communities in idiopathic granulomatous mastitis

BackgroundIdiopathic granulomatous mastitis (IGM) is a rare, chronic, and benign inflammatory breast disease with ambiguous aetiology. Contrastingly, lactational mastitis (LM) is commonly diagnosed in breastfeeding women. To investigate IGM aetiology, we profiled the microbial flora of pus and skin in IGM and LM patients. MethodsIGM patients were recruited from 2018 to 2020; LM patients were recruited as controls. 16S rRNA sequencing libraries were constructed from 16S rRNA gene amplified from total DNA extracted from pus and skin swabs. Metagenomic analysis was conducted using modified microbiome abundance analysis-suite customised R-resource for paired pus and skin samples. Microbiome multivariable association analyses were performed using linear models. Results21 IGM and 3 LM paired pus and skin samples underwent metagenomic analysis. Bray-Curtis ecological dissimilarity distance showed dissimilarity across four sample types (IGM-pus, IGM-skin, LM-pus, LM-skin; PERMANOVA, p<0.001). No characteristic dominant genus was observed across IGM samples. IGM pus samples were more diverse than corresponding IGM skin samples (Shannon and Simpson index; Wilcoxon paired signed-rank tests, p=0.022 and p=0.07). Corynebacterium kroppenstedtii, reportedly associated with IGM in literature, was higher in IGM pus samples than paired skin sample (Wilcoxon, p=0.022). Three other species and nineteen genera were statistically significant in paired IGM pus-skin comparison after antibiotic treatment adjustment, and multiple comparisons correction. ConclusionsMicrobial profiles are unique between IGM and LM patients. Inter-patient variability and polymicrobial IGM pus samples cannot implicate specific genus or species as an infectious cause for IGM.

microbiology↗