Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains

Most variants associated with complex phenotypes in genome-wide association studies (GWAS) do not directly index coding changes affecting protein structure. Instead they are hypothesized to influence gene regulation, with common variants associated with disease being enriched in regulatory domains including enhancers and regions of open chromatin. There is interest, therefore, in using epigenomic annotation data to identify the specific regulatory mechanisms involved and prioritize risk variants. We quantified lysine H3K27 acetylation (H3K27ac) - a robust mark of active enhancers and promoters that is strongly correlated with gene expression and transcription factor binding – across the genome in entorhinal cortex samples using chromatin immunoprecipitation followed by highly parallel sequencing (ChIP-seq). H3K27ac peaks were called using high quality reads combined across all samples and formed the basis of partitioned heritability analysis using LD score regression along with publicly-available GWAS results for seven psychiatric and neurodegenerative traits. Heritability for all seven brain traits was significantly enriched in these H3K27ac peaks (enrichment ranging from 1.09–2.13) compared to regions of the genome containing other active regulatory and functional elements across multiple cell types and tissues. The strongest enrichments were for amyotrophic lateral sclerosis (ALS) (enrichment = 2.19; 95% CI = 2.12–2.27), autism (enrichment = 2.11; 95% CI = 2.05–2.16) and major depressive disorder (enrichment = 2.04; 95% CI = 1.92–2.16). Much lower enrichments were observed for 14 non-brain disorders, although we identified enrichment in cortical H3K27ac domains for body mass index (enrichment = 1.16; 95% CI = 1.13–1.19), ever smoked (enrichment = 2.07; 95% CI = 2.04–2.10), HDL (enrichment = 1.53; 95% CI = 1.45–1.62) and trigylcerides (enrichment = 1.33; 95% CI = 1.24–1.42). These results indicate that risk alleles for brain disorders are preferentially located in regions of regulatory/enhancer function in the cortex, further supporting the hypothesis that genetic variants for these phenotypes influence gene regulation in the brain

Tissue-specific patterns of allelically-skewed DNA methylation

While DNA methylation is usually thought to be symmetrical across both alleles, there are some notable exceptions. Genomic imprinting and X chromosome inactivation are two well-studied sources of allele-specific methylation (ASM), but recent research has indicated a more complex pattern in which genotypic variation can be associated with allelically-skewed DNA methylation in cis. Given the known heterogeneity of DNA methylation across tissues and cell types we explored inter- and intra-individual variation in ASM across several regions of the human brain and whole blood from multiple individuals. Consistent with previous studies, we find widespread ASM with >4% of the ~220,000 loci interrogated showing evidence of allelically-skewed DNA methylation. We identify ASM flanking known imprinted regions, and show that ASM sites are enriched in DNase I hypersensitivity sites and often located in an extended genomic context of intermediate DNA methylation. We also detect examples of genotype-driven ASM, some of which are also tissue-specific. These findings contribute to our understanding about the nature of differential DNA methylation across tissues and have important implications for genetic studies of complex disease. As a resource to the community, ASM patterns across each of the tissues studied are available in a searchable online database: http://epigenetics.essex.ac.uk/ASMBrainBlood

Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies. While this is an effective treatment, it can result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier that could be used in donor selection in HCT to reduce the incidence of aGVHD. The discovery cohort of the study consisted of 288 donors from a population receiving HLA-A, -B, -C and -DRB1 matched unrelated donor HCT with T cell replete peripheral blood stem cell grafts for treatment of acute leukaemia or myelodysplastic syndromes after myeloablative conditioning. Donors were selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD that survived at least 100 days post-HCT matched for sex, age, disease and GVHD prophylaxis. Genome-wide DNA methylation was assessed using the Infinium Methylation EPIC BeadChip (Illumina), measuring CpG methylation at >850,000 sites across the genome. Following quality control, pre-processing and exploratory analyses, we applied a machine learning algorithm (Random Forest) to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n=288) selected using the same criteria as the discovery cohort. Different attempts to validate the classifier using the independent validation cohort failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation

The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations

Epidemiological research suggests that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals indicate that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in a cross-sectional study of matched human blood and sperm from lean (discovery n = 47; replication n = 21) and obese (n = 22) males to analyse tissue covariation of DNA methylation, and identify obesity-associated methylomic signatures. We found that DNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites (~1%). At the majority of these sites, DNA methylation appears to be influenced by genetic variation. Obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity was not associated with altered covariation patterns or accelerated epigenetic ageing in the two tissues. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P = 8.95 × 10−8; 2% DNA methylation difference) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n = 5,917), spermatozoa displayed differential DNA methylation across pathways enriched in transcriptional regulation. Overall, human sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. We observed that obesity was only nominally associated with differential DNA methylation in sperm, and therefore suggest that spermatozoal DNA methylation is an unlikely mediator of intergenerational effects of metabolic traits

Genetic variation at mouse and human ribosomal DNA influences associated epigenetic states

Background: Ribosomal DNA (rDNA) displays substantial inter-individual genetic variation in human and mouse. A systematic analysis of how this variation impacts epigenetic states and expression of the rDNA has thus far not been performed. Results: Using a combination of long- and short-read sequencing, we establish that 45S rDNA units in the C57BL/6J mouse strain exist as distinct genetic haplotypes that influence the epigenetic state and transcriptional output of any given unit. DNA methylation dynamics at these haplotypes are dichotomous and life-stage specific: at one haplotype, the DNA methylation state is sensitive to the in utero environment, but refractory to post-weaning influences, whereas other haplotypes entropically gain DNA methylation during aging only. On the other hand, individual rDNA units in human show limited evidence of genetic haplotypes, and hence little discernible correlation between genetic and epigenetic states. However, in both species, adjacent units show similar epigenetic profiles, and the overall epigenetic state at rDNA is strongly positively correlated with the total rDNA copy number. Analysis of different mouse inbred strains reveals that in some strains, such as 129S1/SvImJ, the rDNA copy number is only approximately 150 copies per diploid genome and DNA methylation levels are < 5%. Conclusions: Our work demonstrates that rDNA-associated genetic variation has a considerable influence on rDNA epigenetic state and consequently rRNA expression outcomes. In the future, it will be important to consider the impact of inter-individual rDNA (epi)genetic variation on mammalian phenotypes and diseases

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine