Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

Groza, Cristian; Chen, X; Pacis, Alain; Maguire, Simon; Pramatarova, Albéna; Aracena, Katherine A.; Pastinen, Tomi; Barreiro, Luis B.; Bourque, Guillaume

doi:10.1016/j.xgen.2023.100294

Cited by 6 publications

(5 citation statements)

References 73 publications

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“…In conclusion, our observations underscore the importance of assessing genomes for the entirety of sequence space not only for structural but also for functional variation (Groza et al 2023). We also confirm the properties of SVs linking to QTLs impacting at greater distances and at a higher frequency.…”

Section: Discussionsupporting

confidence: 79%

“…The publication of the draft human pangenome reference also facilitates the study of SVs and their features at scale in a range of datasets (Liao et al 2023; Groza et al 2024). Indeed, such developments allow mapping epigenomic data directly to SVs and exploring the epigenetic status of regions that were not included in the reference genome (Groza et al 2023; 2020). For example, it would be interesting to explore the link between SVs and 5mC base modifications, given the well known connection between DNA methylation and gene expression (Razin A and Cedar H 1991; Breiling and Lyko 2015; Dhar et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, mapping methylation data to pangenomes to correct reference bias and recover more signal (Wulfridge et al 2019) and then correlating the resulting methylation features with SVs is a promising approach that is enabled by pangenomes. Therefore, the tools necessary to answer long-standing questions regarding the epigenetic status of SVs (Daron and Slotkin 2017; Groza et al 2023; Sun et al 2023) and their associations with other quantitative traits are increasingly accessible.…”

Section: Introductionmentioning

confidence: 99%

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

Groza,

Ge,

Cheung

et al. 2024

Preprint

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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 × 106), followed by centromeric satellites (1.58 × 106), simple repeats (1.19 × 106), Alus (0.67 × 106), satellites (0.39 × 106), L1s (0.27 × 106), and SVAs (0.19 × 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.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Groza,

Ge,

Cheung

et al. 2024

Preprint

Self Cite

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“…Analyzing the multi-omics data, MATES unexpectedly reveals that certain TEs are uniquely discernible in specific cell sub-populations and exclusive to a particular modality (Fig.5 mn, Supplementary Fig.4 e). TEs with elevated gene expression often exhibit increased chromatin accessibility (represented by red dots, such as AluYa5, have been reported [49]). Conversely, transposons with enhanced chromatin accessibility do not always indicate high expression levels (marked as blue dots).…”

Section: Resultsmentioning

confidence: 99%

“…4 e). TEs with elevated gene expression often exhibit increased chromatin accessibility (represented by red dots, such as AluYa5, have been reported [49]). Conversely, transposons with enhanced chromatin accessibility do not always indicate high expression levels (marked as blue dots).…”

Section: Mates Empowers In Multi-omics Te Quantification and Analysismentioning

confidence: 99%

MATES: A Deep Learning-Based Model for Locus-specific Quantification of Transposable Elements in Single Cell

Wang,

Zheng,

Zhang

et al. 2024

Preprint

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Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either `best-mapped' or `random-mapped' locations and categorize them at sub-family levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a novel deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development enables exploring single-cell heterogeneity and gene regulation through the lens of TEs, offering a transformative tool for the single-cell genomics community.

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Epigenetics and genotypic variation

Merenciano,

Larue,

Groza

et al. 2024

On Epigenetics and Evolution

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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

Cited by 6 publications

References 73 publications

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

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

MATES: A Deep Learning-Based Model for Locus-specific Quantification of Transposable Elements in Single Cell

Epigenetics and genotypic variation

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