Biology subjects

Cheung, W. A.

Publications and source records attributed to Cheung, W. A..

4 recordsLinked to original sources

bioRxiv2026

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

genomics↗

bioRxiv2024

Expanded methylome and quantitative trait loci detection by long-read profiling of personal DNA

Structural variants (SVs) are omnipresent in human DNA, yet their genotype and methylation status is rarely characterized due to previous limitations in genome assembly and detection of modified nucleotides. Because of this, the extent to which these regions act as quantitative-trait loci is also largely unknown. Here, we generated a pangenome graph summarizing the SVs in 782 de novo assembled genomes obtained from the Genomic Answers for Kids rare disease cohort, that captures 14.6 million CpGs in DNA segments that are absent from the CHM13v2 assembly (SV-CpGs), expanding their number by 43.6%. Next, using 435 methylomes from the same samples, we genotyped a total of 7.99 million SV-CpGs, of which 5.18 million (64.8%) were found to be methylated (SV-5mCpGs) in at least one sample. To understand the provenance and impact of these novel SV-CpGs, we noted that non-repeat sequences were the leading contributor of SV-CpGs (3.3 x 106), followed by centromeric satellites (1.58 x 106), simple repeats (1.19 x 106), Alus (0.67 x 106), satellites (0.39 x 106), L1s (0.27 x 106), and SVAs (0.19 x 106). Meanwhile, the methylation rate of SV-CpGs was the highest in repeat sequences. Moreover, in contrast to Alus and L1s, centromeric satellites, simple repeats and SVA sequences were overrepresented in SV-5mCpGs compared to reference CpGs. Similarly, we established that non-reference CpGs were more than twice (37% vs. 15%) as likely to be variable, showing intermediate methylation levels in the population. Lastly, to explore if SVs detected in this pangenome are potentially causal for functional variation in population we measured methylation quantitative trait loci (SV-mQTLs) using CHM13v2 as a backbone. This revealed over 230,464 methylation bins within 100 kbp of a common SV (>5% MAF) showing significant association (at 5% FDR) with methylation variation. Finally, we assessed how many of these SVs-mQTLs were the leading QTL variant compared to SNVs and identified 65,659 methylation bins (28.5%) where the leading variant was an SV. In conclusion, our results demonstrate that graph genome references providing full SV structures in combination with the associated methylation variation reveal tens-of-thousands of QTLs that are more accurately mapped in personal genomes, underscoring the importance of assembly-based analyses of human traits.

bioinformatics↗

bioRxiv2023

Resolving the unsolved: Comprehensive assessment of tandem repeats at scale

Tandem repeat (TR) variation is associated with gene expression changes and over 50 rare monogenic diseases. Recent advances in sequencing have enabled accurate, long reads that can characterize the full-length sequence and methylation profile of TRs. However, despite these advances in sequencing technology, computational methods to fully profile tandem repeats across the genome do not exist. To address this gap, we introduce tools for tandem repeat genotyping (TRGT), visualization and an accompanying TR database. TRGT accurately resolves the length and sequence composition of TR regions in the human genome. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 99.56%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all repeat expansions while also identifying methylation signals, mosaicism, and providing finer resolution of repeat length. Additionally, we release a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes.

genomics↗

bioRxiv2019

GSDMA drives the most replicated association with asthma in naïve CD4+ T cells

BackgroundThe 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals.\n\nObjectiveThis study takes benefit of samples from naive CD4+ T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus.\n\nMethods and ResultsAfter isolation of naive CD4+ T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naive CD4+ T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels, showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts.\n\nConclusionAvailability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naive CD4+ T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

genetics↗