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, however it can also 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 to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm 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. Attempts to validate the classifier 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 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 biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Linking environmental risk factors with epigenetic mechanisms in Parkinson's disease.

Sporadic Parkinson's disease (PD) is a progressive neurodegenerative disease, with a complex risk structure thought to be influenced by interactions between genetic variants and environmental exposures, although the full aetiology is unknown. Environmental factors, including pesticides, have been reported to increase the risk of developing the disease. Growing evidence suggests epigenetic changes are key mechanisms by which these environmental factors act upon gene regulation, in disease-relevant cell types. We present a systematic review critically appraising and summarising the current body of evidence of the relationship between epigenetic mechanisms and environmental risk factors in PD to inform future research in this area. Epigenetic studies of relevant environmental risk factors in animal and cell models have yielded promising results, however, research in humans is just emerging. While published studies in humans are currently relatively limited, the importance of the field for the elucidation of molecular mechanisms of pathogenesis opens clear and promising avenues for the future of PD research. Carefully designed epidemiological studies carried out in PD patients hold great potential to uncover disease-relevant gene regulatory mechanisms. Therefore, to advance this burgeoning field, we recommend broadening the scope of investigations to include more environmental exposures, increasing sample sizes, focusing on disease-relevant cell types, and recruiting more diverse cohorts.

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.

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. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Harnessing the potential of machine learning and artificial intelligence for dementia research.

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

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.

The histone modification H3K4me3 is altered at the ANK1 locus in Alzheimer's disease brain.

Several epigenome-wide association studies of DNA methylation have highlighted altered DNA methylation in the ANK1 gene in Alzheimer's disease (AD) brain samples. However, no study has specifically examined ANK1 histone modifications in the disease. We use chromatin immunoprecipitation-qPCR to quantify tri-methylation at histone 3 lysine 4 (H3K4me3) and 27 (H3K27me3) in the ANK1 gene in entorhinal cortex from donors with high (n = 59) or low (n = 29) Alzheimer's disease pathology. We demonstrate decreased levels of H3K4me3, a marker of active gene transcription, with no change in H3K27me3, a marker of inactive genes. H3K4me3 is negatively correlated with DNA methylation in specific regions of the ANK1 gene. Our study suggests that the ANK1 gene shows altered epigenetic marks indicative of reduced gene activation in Alzheimer's disease.

Chd8 regulates X chromosome inactivation in mouse through fine-tuning control of Xist expression.

Female mammals achieve dosage compensation by inactivating one of their two X chromosomes during development, a process entirely dependent on Xist, an X-linked long non-coding RNA (lncRNA). At the onset of X chromosome inactivation (XCI), Xist is up-regulated and spreads along the future inactive X chromosome. Contextually, it recruits repressive histone and DNA modifiers that transcriptionally silence the X chromosome. Xist regulation is tightly coupled to differentiation and its expression is under the control of both pluripotency and epigenetic factors. Recent evidence has suggested that chromatin remodelers accumulate at the X Inactivation Center (XIC) and here we demonstrate a new role for Chd8 in Xist regulation in differentiating ES cells, linked to its control and prevention of spurious transcription factor interactions occurring within Xist regulatory regions. Our findings have a broader relevance, in the context of complex, developmentally-regulated gene expression.

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.

DNA methylation in human sperm: a systematic review.

BACKGROUND: Studies in non-human mammals suggest that environmental factors can influence spermatozoal DNA methylation, and some research suggests that spermatozoal DNA methylation is also implicated in conditions such as subfertility and imprinting disorders in the offspring. Together with an increased availability of cost-effective methods of interrogating DNA methylation, this premise has led to an increasing number of studies investigating the DNA methylation landscape of human spermatozoa. However, how the human spermatozoal DNA methylome is influenced by environmental factors is still unclear, as is the role of human spermatozoal DNA methylation in subfertility and in influencing offspring health. OBJECTIVE AND RATIONALE: The aim of this systematic review was to critically appraise the quality of the current body of literature on DNA methylation in human spermatozoa, summarize current knowledge and generate recommendations for future research. SEARCH METHODS: A comprehensive literature search of the PubMed, Web of Science and Cochrane Library databases was conducted using the search terms 'semen' OR 'sperm' AND 'DNA methylation'. Publications from 1 January 2003 to 2 March 2020 that studied human sperm and were written in English were included. Studies that used sperm DNA methylation to develop methodologies or forensically identify semen were excluded, as were reviews, commentaries, meta-analyses or editorial texts. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria were used to objectively evaluate quality of evidence in each included publication. OUTCOMES: The search identified 446 records, of which 135 were included in the systematic review. These 135 studies were divided into three groups according to area of research; 56 studies investigated the influence of spermatozoal DNA methylation on male fertility and abnormal semen parameters, 20 studies investigated spermatozoal DNA methylation in pregnancy outcomes including offspring health and 59 studies assessed the influence of environmental factors on spermatozoal DNA methylation. Findings from studies that scored as 'high' and 'moderate' quality of evidence according to GRADE criteria were summarized. We found that male subfertility and abnormal semen parameters, in particular oligozoospermia, appear to be associated with abnormal spermatozoal DNA methylation of imprinted regions. However, no specific DNA methylation signature of either subfertility or abnormal semen parameters has been convincingly replicated in genome-scale, unbiased analyses. Furthermore, although findings require independent replication, current evidence suggests that the spermatozoal DNA methylome is influenced by cigarette smoking, advanced age and environmental pollutants. Importantly however, from a clinical point of view, there is no convincing evidence that changes in spermatozoal DNA methylation influence pregnancy outcomes or offspring health. WIDER IMPLICATIONS: Although it appears that the human sperm DNA methylome can be influenced by certain environmental and physiological traits, no findings have been robustly replicated between studies. We have generated a set of recommendations that would enhance the reliability and robustness of findings of future analyses of the human sperm methylome. Such studies will likely require multicentre collaborations to reach appropriate sample sizes, and should incorporate phenotype data in more complex statistical models.

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.

Epigenetics in eating disorders: a systematic review.

Eating disorders are complex heritable conditions influenced by both genetic and environmental factors. Given the progress of genomic discovery in anorexia nervosa, with the identification of the first genome-wide significant locus, as well as animated discussion of epigenetic mechanisms in linking environmental factors with disease onset, our goal was to conduct a systematic review of the current body of evidence on epigenetic factors in eating disorders to inform future directions in this area. Following PRISMA guidelines, two independent authors conducted a search within PubMed and Web of Science and identified 18 journal articles and conference abstracts addressing anorexia nervosa (n = 13), bulimia nervosa (n = 6), and binge-eating disorder (n = 1), published between January 2003 and October 2017. We reviewed all articles and included a critical discussion of field-specific methodological considerations. The majority of epigenetic analyses of eating disorders investigated methylation at candidate genes (n = 13), focusing on anorexia and bulimia nervosa in very small samples with considerable sample overlap across published studies. Three studies used microarray-based technologies to examine DNA methylation across the genome of anorexia nervosa and binge-eating disorder patients. Overall, results were inconclusive and were primarily exploratory in nature. The field of epigenetics in eating disorders remains in its infancy. We encourage the scientific community to apply methodologically sound approaches using genome-wide designs including epigenome-wide association studies (EWAS), to increase sample sizes, and to broaden the focus to include all eating disorder types.

A histone acetylome-wide association study of Alzheimer's disease identifies disease-associated H3K27ac differences in the entorhinal cortex.

We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer's disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-ß and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.

Early life diet conditions the molecular response to post-weaning protein restriction in the mouse.

BACKGROUND: Environmental influences fluctuate throughout the life course of an organism. It is therefore important to understand how the timing of exposure impacts molecular responses. Herein, we examine the responses of two key molecular markers of dietary stress, namely variant-specific methylation at ribosomal DNA (rDNA) and small RNA distribution, including tRNA fragments, in a mouse model of protein restriction (PR) with exposure at pre- and/or post-weaning. RESULTS: We first confirm that pre-weaning PR exposure modulates the methylation state of rDNA in a genotype-dependent manner, whereas post-weaning PR exposure has no such effect. Conversely, post-weaning PR induces a shift in small RNA distribution, but there is no effect in the pre-weaning PR model. Intriguingly, mice exposed to PR throughout their lives show neither of these two dietary stress markers, similar to controls. CONCLUSIONS: The results show that the timing of the insult affects the nature of the molecular response but also, critically, that 'matching' diet exposure either side of weaning eliminates the stress response at the level of rDNA methylation and small RNA in sperm.

Analysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood.

OBJECTIVE: DNA methylation has been proposed as an epigenetic mechanism by which early-life experiences become "embedded" in the genome and alter transcriptional processes to compromise health. The authors sought to investigate whether early-life victimization stress is associated with genome-wide DNA methylation. METHOD: The authors tested the hypothesis that victimization is associated with DNA methylation in the Environmental Risk (E-Risk) Longitudinal Study, a nationally representative 1994-1995 birth cohort of 2,232 twins born in England and Wales and assessed at ages 5, 7, 10, 12, and 18 years. Multiple forms of victimization were ascertained in childhood and adolescence (including physical, sexual, and emotional abuse; neglect; exposure to intimate-partner violence; bullying; cyber-victimization; and crime). RESULTS: Epigenome-wide analyses of polyvictimization across childhood and adolescence revealed few significant associations with DNA methylation in peripheral blood at age 18, but these analyses were confounded by tobacco smoking and/or did not survive co-twin control tests. Secondary analyses of specific forms of victimization revealed sparse associations with DNA methylation that did not replicate across different operationalizations of the same putative victimization experience. Hypothesis-driven analyses of six candidate genes in the stress response (NR3C1, FKBP5, BDNF, AVP, CRHR1, SLC6A4) did not reveal predicted associations with DNA methylation in probes annotated to these genes. CONCLUSIONS: Findings from this epidemiological analysis of the epigenetic effects of early-life stress do not support the hypothesis of robust changes in DNA methylation in victimized young people. We need to come to terms with the possibility that epigenetic epidemiology is not yet well matched to experimental, nonhuman models in uncovering the biological embedding of stress.

Genetic polymorphisms and their association with brain and behavioural measures in heterogeneous stock mice.

Although the search for quantitative trait loci for behaviour remains a considerable challenge, the complicated genetic architecture of quantitative traits is beginning to be understood. The current project utilised heterogeneous stock (HS) male mice (n = 580) to investigate the genetic basis for brain weights, activity, anxiety and cognitive phenotypes. We identified 126 single nucleotide polymorphisms (SNPs) in genes involved in regulation of neurotransmitter systems, nerve growth/death and gene expression, and subsequently investigated their associations with changes in behaviour and/or brain weights in our sample. We found significant associations between four SNP-phenotype pairs, after controlling for multiple testing. Specificity protein 2 (Sp2, rs3708840), tryptophan hydroxylase 1 (Tph1, rs262731280) and serotonin receptor 3A (Htr3a, rs50670893) were associated with activity/anxiety behaviours, and microtubule-associated protein 2 (Map2, rs13475902) was associated with cognitive performance. All these genes except for Tph1 were expressed in the brain above the array median, and remained significantly associated with relevant behaviours after controlling for the family structure. Additionally, we found evidence for a correlation between Htr3a expression and activity. We discuss our findings in the light of the advantages and limitations of currently available mouse genetic tools, suggesting further directions for association studies in rodents.

Iterative Fragmentation Improves the Detection of ChIP-seq Peaks for Inactive Histone Marks.

As chromatin immunoprecipitation (ChIP) sequencing is becoming the dominant technique for studying chromatin modifications, new protocols surface to improve the method. Bioinformatics is also essential to analyze and understand the results, and precise analysis helps us to identify the effects of protocol optimizations. We applied iterative sonication - sending the fragmented DNA after ChIP through additional round(s) of shearing - to a number of samples, testing the effects on different histone marks, aiming to uncover potential benefits of inactive histone marks specifically. We developed an analysis pipeline that utilizes our unique, enrichment-type specific approach to peak calling. With the help of this pipeline, we managed to accurately describe the advantages and disadvantages of the iterative refragmentation technique, and we successfully identified possible fields for its applications, where it enhances the results greatly. In addition to the resonication protocol description, we provide guidelines for peak calling optimization and a freely implementable pipeline for data analysis.