Search bioRxiv⌕ Search

bioRxiv · 10.1101/849620

Integrative genomic analysis unifying epigenetic inheritance in adaptation and canalization

Abstract

Epigenetic inheritance, especially its biomedical and evolutionary significance, is an immensely interesting but highly controversial subject. Notably, a recent analysis of existing multi-omics has supported the mechanistic plausibility of epigenetic inheritance and its implications in disease and evolution. The evolutionary support stemmed from the specific finding that genes associated with cold induced inheritance and with latitudinal adaptation in mice are exceptionally common. Here, a similar gene set overlap analysis is presented that integrates cold induced inheritance with evolutionary adaptation and genetic canalization in cold environment in Drosophila. Genes showing differential expression in inheritance specifically overrepresent gene sets associated with differential and allele specific expression, though not with genome-wide genetic differentiation, in adaptation. On the other hand, the differentiated outliers uniquely overrepresent genes dysregulated by radicicol, a decanalization inducer. Both gene sets in turn exclusively show enrichment of genes that accumulate, in intended experimental lines, de novo mutations, a potential source of canalization. Successively, the three gene sets distinctively overrepresent genes exhibiting, between mutation accumulation lines, invariable expression, a potential signal for canalization. Sequentially, the four gene sets solely display enrichment of genes grouped in gene ontology under transcription factor activity, a signature of regulatory canalization. Cumulatively, the analysis suggests that epigenetic inheritance possibly contributes to evolutionary adaptation in the form of cis regulatory variations, with trans variations arising in the course of genetic canalization.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sharma, A.. 2019-11-23. Integrative genomic analysis unifying epigenetic inheritance in adaptation and canalization. https://doi.org/10.1101/849620

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

High-throughput sequencing of unmethylated DNA (UMR-seq) reveals extensive epigenetic variation in the hypomethylome of diverse plant species

The unmethylated regions (UMRs) in a genome can reveal the location of genes and gene regulatory loci. However, discovering UMR variation across and between entire genomes and disentangling the contribution of genetics and epigenetics to traits are challenging because they require separate and costly whole-DNA methylome and whole-genome genetic analyses. In many species this is prohibitively expensive for even a small number of samples, curtailing analysis on a population or breeding scale. Here we introduce UMR-seq, a method for the specific capture of UMRs from a genome and simultaneous sequencing of genetic variants in a single economical and scalable assay. UMR-seq charts the hypomethylome, revealing that it is highly enriched with functionally important regulatory loci. While UMRs are highly stable between vegetative tissues in plants, we find they are dynamic in the endosperm and vary substantially between accessions. Adaptable to diverse species, using this method we uncover a positive correlation between gene number in a species and UMR content, reflecting gene regulatory space in a genome. At 1/50th of the conventional cost of methylome sequencing in wheat, UMR-seq enables simultaneous population scale analysis of genetic and epigenetic variation in the functional genomic space. We reveal that in a panel of 187 elite wheat cultivars, profiling UMR variation improved genomic prediction across population groups, and UMR-based association mapping identified trait associations that were largely distinct from those detected with SNPs, providing new opportunities to understand trait biology and to enhance predictive breeding.

genomics↗

Predicting single-molecule chromatin distribution from DNA sequence

Genome regulation unfolds through dynamic and stochastic single-molecule events. Deep learning models that read gene regulatory activities from DNA sequences have markedly advanced our ability to predict cell-population-level averages. However, they cannot capture the full spectrum of heterogeneity and dynamics at the single-molecule level, which directly reflect the underlying mechanisms of genome regulation. Here, we introduce a sequence-to-distribution (S2d) framework that directly models the entire probabilistic distribution of single-molecule chromatin states from DNA sequence alone. We used this framework to train Mola, a model of single-molecule chromatin accessibility. We harness Mola to investigate sequence-based regulatory code and mechanisms for multiple aspects of chromatin regulation at the single-molecule level. By quantifying sequence-driven nucleosome regulation, we reveal distinct modes of transcription factor engagement with nucleosomes, including a novel nucleosome-trapping factor, ZNF146. We characterized the sequence determinants governing the +1 nucleosome after transcription start sites and identified YY1 as the strongest +1 nucleosome-positioning factor. Leveraging long-context single-molecule modeling, we further discovered a shared sequence basis for the co-accessibility of regulatory elements and the co-footprinting of transcription factors. Joint prediction of DNA methylation and accessibility reveals a sequence-driven bimodal state mediated by methylation-sensitive transcription factors. Beyond static single-molecule snapshots, the S2d framework can generate pseudo-dynamic trajectories of chromatin states and propose a roadmap toward genome-wide chromatin dynamics. Moreover, we showed that Mola can predict and interpret the effects of genetic variants at the single-molecule level, revealing how sequence changes reshape distal regulatory coordination. Together, this sequence-to-distribution framework lays the foundation for understanding how genomic sequence encodes stochastic and dynamic genome regulation, and how genetic variation changes it, one molecule at a time.

genomics↗

GPBench: A benchmark framework for genomic prediction

Genomic prediction (GP) has emerged as a powerful data mining strategy for disease risk assessment and molecular breeding. GP leverages genome-wide genetic variations and phenotypic information through a computational model to estimate genetic potential or disease risk. However, the field of GP still faces several major challenges: (i) dataset bias and insufficient representativeness; (ii) the lack of standardized evaluation protocols and a systematic benchmarking framework; (iii) heavy reliance on empirical expertise for experimental result interpretation, which hinders large-scale and systematic analysis. These issues make it difficult to conduct systematic cross-species evaluation and comparison of GP methods, and their authentic applications. To address these challenges, we established a standardized and extensible GP benchmark framework-GPBench. We first collected and harmonized public datasets from 16 species, covering animals, plants, and microorganisms, comprising 57 traits, 40,821 samples, and 493,821 single-nucleotide polymorphisms. Then, using the standardized datasets, the framework evaluates 21 GP models and provides a unified assessment across four dimensions: effectiveness, sensitivity, robustness, and scalability. Furthermore, we developed a large language model-based intelligent analysis system to facilitate the interpretation of GP experiments and model recommendations. GPBench provides the community with a fair, unified, and scalable platform (https://www.sdu-idea.cn/GPBench/) and an open-source library for comparing and optimizing GP methods, ultimately accelerating the application of GP in life sciences and biomedical research.

genomics↗