Clark, S. — Page 4 | bioRxiv Science

bioRxiv2018

Replication timing shapes the cancer epigenome and the nature of chromosomal rearrangements

HighlightsO_LIReplication timing alterations are conserved in cancers of different cell origins\nC_LIO_LILong-range epigenetic deregulation in cancer involves altered replication timing\nC_LIO_LICancer late-replicating loci are hypomethylated and acquire facultative heterochromatin\nC_LIO_LIReplication timing status potentiates cis and trans chromosomal rearrangements\nC_LI\n\nSummaryReplication timing is known to facilitate the establishment of epigenome, however, the intimate connection between DNA replication timing and changes to the genome and epigenome in cancer remain uncharted. Here, we perform Repli-Seq and integrated epigenome analysis and show that early-replicating loci are predisposed to hypermethylation and late-replicating loci to hypomethylation, enrichment of H3K27me3 and concomitant loss of H3K9me3. We find that altered replication timing domains correspond to long-range epigenetically deregulated regions in prostate cancer, and a subset of these domains are remarkably conserved across cancers from different tissue origins. Analyses of 214 prostate and 35 breast cancer genomes reveal that late-replicating DNA is prone to cis and early-replicating DNA to trans chromosomal rearrangements. We propose that differences in epigenetic deregulation related to spatial and temporal positioning between early and late replication potentiate the landscape of chromosomal rearrangements in cancer.

genomics↗

bioRxiv2017

Estimation Of Genomic Prediction Accuracy From Reference Populations With Varying Degrees Of Relationship

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as unrelated individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Ne). Both the effective number of chromosome segments (Me) and Ne are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.

genetics↗