A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data

Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

Immune gene regulation is associated with age and environmental adversity in a nonhuman primate

Phenotypic aging is ubiquitous across mammalian species, suggesting shared underlying mechanisms of aging. Aging is linked to molecular changes to DNA methylation and gene expression, and environmental factors, such as severe external challenges or adversities, can moderate these age-related changes. Yet, it remains unclear whether environmental adversities affect gene regulation via the same molecular pathways as chronological, or 'primary', aging. Investigating molecular aging in naturalistic animal populations can fill this gap by providing insight into shared molecular mechanisms of aging and the effects of a greater diversity of environmental adversities - particularly those that can be challenging to study in humans or laboratory organisms. Here, we characterised molecular aging - specifically, CpG methylation - in a sample of free-ranging rhesus macaques living off the coast of Puerto Rico (n samples = 571, n individuals = 499), which endured a major hurricane during our study. Age was associated with methylation at 78,661 sites (31% of all sites tested). Age-associated hypermethylation occurred more frequently in areas of active gene regulation, while hypomethylation was enriched in regions that show less activity in immune cells, suggesting these regions may become de-repressed in older individuals. Age-associated hypomethylation also co-occurred with increased chromatin accessibility while hypermethylation showed the opposite trend, hinting at a coordinated, multi-level loss of epigenetic stability during aging. We detected 32,048 CpG sites significantly associated with exposure to a hurricane, and these sites overlapped age-associated sites, most strongly in regulatory regions and most weakly in quiescent regions. Together, our results suggest that environmental adversity may contribute to aging-related molecular phenotypes in regions of active gene transcription, but that primary aging has specific signatures in non-regulatory regions.

DNA methylation signatures of early-life adversity are exposure-dependent in wild baboons

The early-life environment can profoundly shape the trajectory of an animal's life, even years or decades later. One mechanism proposed to contribute to these early-life effects is DNA methylation. However, the frequency and functional importance of DNA methylation in shaping early-life effects on adult outcomes is poorly understood, especially in natural populations. Here, we integrate prospectively collected data on fitness-associated variation in the early environment with DNA methylation estimates at 477,270 CpG sites in 256 wild baboons. We find highly heterogeneous relationships between the early-life environment and DNA methylation in adulthood: aspects of the environment linked to resource limitation (e.g., low-quality habitat, early-life drought) are associated with many more CpG sites than other types of environmental stressors (e.g., low maternal social status). Sites associated with early resource limitation are enriched in gene bodies and putative enhancers, suggesting they are functionally relevant. Indeed, by deploying a baboon-specific, massively parallel reporter assay, we show that a subset of windows containing these sites are capable of regulatory activity, and that, for 88% of early drought-associated sites in these regulatory windows, enhancer activity is DNA methylation-dependent. Together, our results support the idea that DNA methylation patterns contain a persistent signature of the early-life environment. However, they also indicate that not all environmental exposures leave an equivalent mark and suggest that socioenvironmental variation at the time of sampling is more likely to be functionally important. Thus, multiple mechanisms must converge to explain early-life effects on fitness-related traits.

Epigenetic changes to gene pathways linked to male fertility in ex situ black-footed ferrets

Environmental variation can influence the reproductive success of species managed under human care and in the wild, yet the mechanisms underlying this phenomenon remain largely mysterious. Molecular mechanisms such as epigenetic modifiers are important in mediating the timing and progression of reproduction in humans and model organisms, but few studies have linked epigenetic variation to reproductive fitness in wildlife. Here, we investigated epigenetic variation in black-footed ferrets (Mustela nigripes), an endangered North American mammal reliant on ex situ management for survival and persistence in the wild. Despite similar levels of genetic diversity in human-managed and wild-born populations, individuals in ex situ facilities exhibit reproductive problems, such as poor sperm quality. Differences across these settings suggest that an environmentally driven decline in reproductive capacity may be occurring in this species. We examined the role of DNA methylation, one well-studied epigenetic modifier, in this emergent condition. We leveraged blood, testes, and semen samples from male black-footed ferrets bred in ex situ facilities and found tissue-type specificity in DNA methylation across the genome, although 1360 Gene Ontology terms associated with male average litter size shared functions across tissues. We then constructed gene networks of differentially methylated genomic sites associated with three different reproductive phenotypes to explore the putative biological impact of variation in DNA methylation. Sperm gene networks associated with average litter size and sperm count were functionally enriched for candidate genes involved in reproduction, development, and its regulation through transcriptional repression. We propose that DNA methylation plays an important role in regulating these reproductive phenotypes, thereby impacting the fertility of male ex situ individuals. Our results provide information into how DNA methylation may function in the alteration of reproductive pathways and phenotypes in artificial environments. These findings provide early insights to conservation hurdles faced in the protection of this rare species.

DNA methylation differences between stick insect ecotypes

Epigenetic mechanisms, such as DNA methylation, can influence gene regulation and affect phenotypic variation, raising the possibility that they contribute to ecological adaptation. Beginning to address this issue requires high-resolution sequencing studies of natural populations to pinpoint epigenetic regions of potential ecological and evolutionary significance. However, such studies are still relatively uncommon, especially in insects, and are mainly restricted to a few model organisms. Here, we characterize patterns of DNA methylation for natural populations of Timema cristinae adapted to two host plant species (i.e. ecotypes). By integrating results from sequencing of whole transcriptomes, genomes and methylomes, we investigate whether environmental, host and genetic differences of these stick insects are associated with methylation levels of cytosine nucleotides in the CpG context. We report an overall genome-wide methylation level for T. cristinae of ~14%, with methylation being enriched in gene bodies and impoverished in repetitive elements. Genome-wide DNA methylation variation was strongly positively correlated with genetic distance (relatedness), but also exhibited significant host-plant effects. Using methylome-environment association analysis, we pinpointed specific genomic regions that are differentially methylated between ecotypes, with these regions being enriched for genes with functions in membrane processes. The observed association between methylation variation and genetic relatedness, and with the ecologically important variable of host plant, suggests a potential role for epigenetic modification in T. cristinae adaptation. To substantiate such adaptive significance, future studies could test whether methylation can be transmitted across generations and the extent to which it responds to experimental manipulation in field and laboratory studies.

An ecologist's guide for studying DNA methylation variation in wild vertebrates

The field of molecular biology is advancing fast with new powerful technologies, sequencing methods and analysis software being developed constantly. Commonly used tools originally developed for research on humans and model species are now regularly used in ecological and evolutionary research. There is also a growing interest in the causes and consequences of epigenetic variation in natural populations. Studying ecological epigenetics is currently challenging, especially for vertebrate systems, because of the required technical expertise, complications with analyses and interpretation, and limitations in acquiring sufficiently high sample sizes. Importantly, neglecting the limitations of the experimental setup, technology and analyses may affect the reliability and reproducibility, and the extent to which unbiased conclusions can be drawn from these studies. Here, we provide a practical guide for researchers aiming to study DNA methylation variation in wild vertebrates. We review the technical aspects of epigenetic research, concentrating on DNA methylation using bisulfite sequencing, discuss the limitations and possible pitfalls, and how to overcome them through rigid and reproducible data analysis. This review provides a solid foundation for the proper design of epigenetic studies, a clear roadmap on the best practices for correct data analysis and a realistic view on the limitations for studying ecological epigenetics in vertebrates. This review will help researchers studying the ecological and evolutionary implications of epigenetic variation in wild populations.

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Cardiomyocyte ploidy is dynamic during postnatal development and varies across genetic backgrounds

Somatic polyploidization, an adaptation by which cells increase their DNA content to support growth, is observed in many cell types, including cardiomyocytes. Although polyploidization is believed to be beneficial, progression to a polyploid state is often accompanied by loss of proliferative capacity. Recent work suggests that genetics heavily influence cardiomyocyte ploidy. However, the developmental course by which cardiomyocytes reach their final ploidy state has only been investigated in select backgrounds. Here, we assessed cardiomyocyte number, cell cycle activity, and ploidy dynamics across two divergent mouse strains: C57BL/6J and A/J. Both strains are born and reach adulthood with comparable numbers of cardiomyocytes; however, the end composition of ploidy classes and developmental progression to reach the final state differ substantially. We expand on previous findings that identified Tnni3k as a mediator of cardiomyocyte ploidy and uncover a role for Runx1 in ploidy dynamics and cardiomyocyte cell division, in both developmental and injury contexts. These data provide novel insights into the developmental path to cardiomyocyte polyploidization and challenge the paradigm that hypertrophy is the sole mechanism for growth in the postnatal heart.

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.

Spatially aware dimension reduction for spatial transcriptomics

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.

Orang Asli Health and Lifeways Project (OA HeLP): a cross-sectional cohort study protocol

INTRODUCTION

Non-communicable disease (NCD) risk is influenced by environmental factors that are highly variable worldwide, yet prior research has focused mainly on high-income countries where most people are exposed to relatively homogeneous and static environments. Understanding the scope and complexity of environmental influences on NCD risk around the globe requires more data from people living in diverse and changing environments. Our project will investigate the prevalence and environmental causes of NCDs among the indigenous peoples of Peninsular Malaysia, known collectively as the Orang Asli, who are currently undergoing varying degrees of lifestyle and sociocultural changes that are predicted to increase vulnerability to NCDs, particularly metabolic disorders and musculoskeletal degenerative diseases.

METHODS AND ANALYSIS

Biospecimen sampling and screening for a suite of NCDs (eg, cardiovascular disease, type II diabetes, osteoarthritis and osteoporosis), combined with detailed ethnographic work to assess key lifestyle and sociocultural variables (eg, diet, physical activity and wealth), will take place in Orang Asli communities spanning a gradient from remote, traditional villages to acculturated, market-integrated urban areas. Analyses will first test for relationships between environmental variables, NCD risk factors and NCD occurrence to investigate how environmental changes are affecting NCD susceptibility among the Orang Asli. Second, we will examine potential molecular and physiological mechanisms (eg, epigenetics and systemic inflammation) that mediate environmental effects on health. Third, we will identify intrinsic (eg, age and sex) and extrinsic (eg, early-life experiences) factors that predispose certain people to NCDs in the face of environmental change to better understand which Orang Asli are at greatest risk of NCDs.

ETHICS AND DISSEMINATION

Approval was obtained from multiple ethical review boards including the Malaysian Ministry of Health. This study follows established principles for ethical biomedical research among vulnerable indigenous communities, including fostering collaboration, building cultural competency, enhancing transparency, supporting capacity building and disseminating research findings.

Loss of key endosymbiont genes may facilitate early host control of the chromatophore in Paulinella

The primary plastid endosymbiosis (∼124 Mya) that occurred in the heterotrophic amoeba lineage, Paulinella, is at an earlier stage of evolution than in Archaeplastida, and provides an excellent model for studying organelle integration. Using genomic data from photosynthetic Paulinella, we identified a plausible mechanism for the evolution of host control of endosymbiont (termed the chromatophore) biosynthetic pathways and functions. Specifically, random gene loss from the chromatophore and compensation by nuclear-encoded gene copies enables host control of key pathways through a minimal number of evolutionary innovations. These gene losses impact critical enzymatic steps in nucleotide biosynthesis and the more peripheral components of multi-protein DNA replication complexes. Gene retention in the chromatophore likely reflects the need to maintain a specific stoichiometric balance of the encoded products (e.g., involved in DNA replication) rather than redox state, as in the highly reduced plastid genomes of algae and plants.

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Endosymbiont DNA replication cannot be completed without several key host proteins

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Endosymbiont nucleotide biosynthesis is completed by import of host proteins

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Limited gene loss allowed the host to gain control of endosymbiont division

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Paulinella regulates chromatophore function using the stringent response pathway

Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples

DNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2% of human CpG sites. To detect such associations outside these regions, we chose the bisulphite sequencing approach. We collected and curated clinical data on 200 newborn infants; whose umbilical cord blood samples were analysed with the reduced representation bisulphite sequencing (RRBS) method. A generalized linear mixed-effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables, such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models.

Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling

Mendelian randomization (MR) is a common tool for identifying causal risk factors underlying diseases. Here, we present a method, MR with automated instrument determination (MRAID), for effective MR analysis. MRAID borrows ideas from fine-mapping analysis to model an initial set of candidate single-nucleotide polymorphisms that are in potentially high linkage disequilibrium with each other and automatically selects among them the suitable instruments for causal inference. MRAID also explicitly models both uncorrelated and correlated horizontal pleiotropic effects that are widespread for complex trait analysis. MRAID achieves both tasks through a joint likelihood framework and relies on a scalable sampling-based algorithm to compute calibrated P values. Comprehensive and realistic simulations show that MRAID can provide calibrated type I error control and reduce false positives while being more powerful than existing approaches. We illustrate the benefits of MRAID for an MR screening analysis across 645 trait pairs in U.K. Biobank, identifying multiple lifestyle causal risk factors of cardiovascular disease-related traits.

Effective and scalable single-cell data alignment with non-linear canonical correlation analysis

Data alignment is one of the first key steps in single cell analysis for integrating multiple datasets and performing joint analysis across studies. Data alignment is challenging in extremely large datasets, however, as the major of the current single cell data alignment methods are not computationally efficient. Here, we present VIPCCA, a computational framework based on non-linear canonical correlation analysis for effective and scalable single cell data alignment. VIPCCA leverages both deep learning for effective single cell data modeling and variational inference for scalable computation, thus enabling powerful data alignment across multiple samples, multiple data platforms, and multiple data types. VIPCCA is accurate for a range of alignment tasks including alignment between single cell RNAseq and ATACseq datasets and can easily accommodate millions of cells, thereby providing researchers unique opportunities to tackle challenges emerging from large-scale single-cell atlas.

Wild epigenetics: insights from epigenetic studies on natural populations

Epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNAs are increasingly targeted in studies of natural populations. Here, I review some of the insights gained from this research, examine some of the methods currently in use and discuss some of the challenges that researchers working on natural populations are likely to face when probing epigenetic mechanisms. While studies supporting the involvement of epigenetic mechanisms in generating phenotypic variation in natural populations are amassing, many of these studies are currently correlative in nature. Thus, while empirical data point to widespread contributions of epigenetic mechanisms in generating phenotypic variation, there are still concerns as to whether epigenetic variation is instead ultimately controlled by genetic variation. Disentangling these two sources of variation will be a key to resolving the debate about the importance of epigenetic mechanisms, and studies on natural populations that partition the relative contribution of genetic and epigenetic factors to phenotypic variation can play an important role in this debate.

Accurate Measurement of DNA Methylation: Challenges and Bias Correction

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility is still not fully understood. As the cost of genome sequencing technologies continues to drop, it will soon become commonplace to perform genome-wide quantification of DNA methylation at a single base-pair resolution. However, the demand for its accurate quantification might vary across studies. When the scope of the analysis is to detect regions of the genome with different methylation patterns between two or more conditions, e.g., case vs control; treatments vs placebo, accuracy is not crucial. This is the case in epigenome-wide association studies used as genome-wide screening of methylation changes to detect new candidate genes and regions associated with a specific disease or condition. If the aim of the analysis is to use DNA methylation measurements as a biomarker for diseases diagnosis and treatment (Laird, Nat Rev Cancer 3:253-266, 2003; Bock, Epigenomics 1:99-110, 2009), it is instead recommended to produce accurate methylation measurements. Furthermore, if the objective is the detection of DNA methylation in subclonal tumor cell populations or in circulating tumor DNA or in any case of mosaicism, the importance of accuracy becomes critical. The aim of this chapter is to describe the factors that could affect the precise measurement of methylation levels and a recent Bayesian statistical method called MethylCal and its extension that have been proposed to minimize this problem.

Role of Phenolic Compounds in Human Disease: Current Knowledge and Future Prospects

Inflammation is a natural protective mechanism that occurs when the body's tissue homeostatic mechanisms are disrupted by biotic, physical, or chemical agents. The immune response generates pro-inflammatory mediators, but excessive output, such as chronic inflammation, contributes to many persistent diseases. Some phenolic compounds work in tandem with nonsteroidal anti-inflammatory drugs (NSAIDs) to inhibit pro-inflammatory mediators' activity or gene expression, including cyclooxygenase (COX). Various phenolic compounds can also act on transcription factors, such as nuclear factor-κB (NF-κB) or nuclear factor-erythroid factor 2-related factor 2 (Nrf-2), to up-or downregulate elements within the antioxidant response pathways. Phenolic compounds can inhibit enzymes associated with the development of human diseases and have been used to treat various common human ailments, including hypertension, metabolic problems, incendiary infections, and neurodegenerative diseases. The inhibition of the angiotensin-converting enzyme (ACE) by phenolic compounds has been used to treat hypertension. The inhibition of carbohydrate hydrolyzing enzyme represents a type 2 diabetes mellitus therapy, and cholinesterase inhibition has been applied to treat Alzheimer's disease (AD). Phenolic compounds have also demonstrated anti-inflammatory properties to treat skin diseases, rheumatoid arthritis, and inflammatory bowel disease. Plant extracts and phenolic compounds exert protective effects against oxidative stress and inflammation caused by airborne particulate matter, in addition to a range of anti-inflammatory, anticancer, anti-aging, antibacterial, and antiviral activities. Dietary polyphenols have been used to prevent and treat allergy-related diseases. The chemical and biological contributions of phenolic compounds to cardiovascular disease have also been described. This review summarizes the recent progress delineating the multifunctional roles of phenolic compounds, including their anti-inflammatory properties and the molecular pathways through which they exert anti-inflammatory effects on metabolic disorders. This study also discusses current issues and potential prospects for the therapeutic application of phenolic compounds to various human diseases.

Rapid Macrosatellite Evolution Promotes X-Linked Hybrid Male Sterility in a Feline Interspecies Cross

The sterility or inviability of hybrid offspring produced from an interspecific mating result from incompatibilities between parental genotypes that are thought to result from divergence of loci involved in epistatic interactions. However, attributes contributing to the rapid evolution of these regions also complicates their assembly, thus discovery of candidate hybrid sterility loci is difficult and has been restricted to a small number of model systems. Here we reported rapid interspecific divergence at the DXZ4 macrosatellite locus in an interspecific cross between two closely related mammalian species: the domestic cat (Felis silvestris catus) and the Jungle cat (Felis chaus). DXZ4 is an interesting candidate due to its structural complexity, copy number variability, and described role in the critical yet complex biological process of X-chromosome inactivation. However, the full structure of DXZ4 was absent or incomplete in nearly every available mammalian genome assembly given its repetitive complexity. We compared highly continuous genomes for three cat species, each containing a complete DXZ4 locus, and discovered that the felid DXZ4 locus differs substantially from the human ortholog, and that it varies in copy number between cat species. Additionally, we reported expression, methylation, and structural conformation profiles of DXZ4 and the X chromosome during stages of spermatogenesis that have been previously associated with hybrid male sterility. Collectively, these findings suggest a new role for DXZ4 in male meiosis and a mechanism for feline interspecific incompatibility through rapid satellite divergence.

Metrics for evaluating differentially methylated region sets predicted from BS-seq data

Investigating differentially methylated regions (DMRs) presented in different tissues or cell types can help to reveal the mechanisms behind the tissue-specific gene expression. The identified tissue-/disease-specific DMRs also can be used as feature markers for spotting the tissues-of-origins of cell-free DNA (cfDNA) in noninvasive diagnosis. In recent years, many methods have been proposed to detect DMRs. However, due to the lack of benchmark DMRs, it is difficult for researchers to choose proper methods and select desirable DMR sets for downstream studies. The application of DMRs, used as feature markers, can be benefited by the longer length of DMRs containing more CpG sites when a threshold is given for the methylation differences of DMRs. According to this, two metrics ($Qn$ and $Ql$), in which the CpG numbers and lengths of DMRs with different methylation differences are weighted differently, are proposed in this paper to evaluate the DMR sets predicted by different methods on BS-seq data. DMR sets predicted by eight methods on both simulated datasets and real BS-seq datasets are evaluated by the proposed metrics, the benchmark-based metrics, and the enrichment analysis of biological data, including genomic features, transcription factors and histones. The rank correlation analysis shows that the $Qn$ and $Ql$ are highly correlated to the benchmark metrics for simulated datasets and the biological data enrichment analysis for real BS-seq data. Therefore, with no need for additional biological data, the proposed metrics can help researchers selecting a more suitable DMR set on a certain BS-seq dataset.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Early-life social experience affects offspring DNA methylation and later life stress phenotype

Studies in rodents and captive primates suggest that the early-life social environment affects future phenotype, potentially through alterations to DNA methylation. Little is known of these associations in wild animals. In a wild population of spotted hyenas, we test the hypothesis that maternal care during the first year of life and social connectedness during two periods of early development leads to differences in DNA methylation and fecal glucocorticoid metabolites (fGCMs) later in life. Here we report that although maternal care and social connectedness during the den-dependent life stage are not associated with fGCMs, greater social connectedness during the subadult den-independent life stage is associated with lower adult fGCMs. Additionally, more maternal care and social connectedness after den independence correspond with higher global (%CCGG) DNA methylation. We also note differential DNA methylation near 5 genes involved in inflammation, immune response, and aging that may link maternal care with stress phenotype.

Investigating Different DNA Methylation Patterns at the Resolution of Methylation Haplotypes

Different DNA methylation patterns presented on different tissues or cell types are considered as one of the main reasons accounting for the tissue-specific gene expressions. In recent years, many methods have been proposed to identify differentially methylated regions (DMRs) based on the mixture of methylation signals from homologous chromosomes. To investigate the possible influence of homologous chromosomes on methylation analysis, this paper proposed a method (MHap) to construct methylation haplotypes for homologous chromosomes in CpG dense regions. Through comparing the methylation consistency between homologous chromosomes in different cell types, it can be found that majority of paired methylation haplotypes derived from homologous chromosomes are consistent, while a lower methylation consistency was observed in the breast cancer sample. It also can be observed that the hypomethylation consistency of differentiated cells is higher than that of the corresponding undifferentiated stem cells. Furthermore, based on the methylation haplotypes constructed on homologous chromosomes, a method (MHap_DMR) is developed to identify DMRs between differentiated cells and the corresponding undifferentiated stem cells, or between the breast cancer sample and the normal breast sample. Through comparing the methylation haplotype modes of DMRs in two cell types, the DNA methylation changing directions of homologous chromosomes in cell differentiation and cancerization can be revealed. The code is available at: https://github.com/xqpeng/MHap_DMR.

SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges

Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

High social status males experience accelerated epigenetic aging in wild baboons

Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting 'epigenetic clock' closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons - the best predictor of reproductive success - imposes costs consistent with a 'live fast, die young' life-history strategy.

DNA methylomic homogeneity and heterogeneity in muscles and testes throughout pig adulthood

DNA methylome pattern is significantly different among tissues, ages, breeds, and genders. We assessed 20 methylome and transcriptome data in longissimus dorsi (LD) or testicles from Bamaxiang (BMX) and Large White pigs (LW) by deep sequencing technology. We identified ~55.7M CpGs and 5.30M, 0.20M, 1.20M, and 0.16M differential CpGs (P<0.01) between tissues, ages, breeds, and genders, respectively. Interestingly, 7.54% of differentially methylated regions (DMRs) are co-localized with promoters, which potentially regulate gene expression. RNA-seq analysis revealed that 23.42% CpGs are significantly correlated with gene expression (mean |r|=0.58, P<0.01), most of which are enriched in tissue-specific functions. Specially, we also found that the methylation levels in promoters of 655 genes were strongly associated with their expression levels (mean |r|=0.66, P<0.01). In addition, differentially methylated CpGs (DMCpGs) between breeds in HOXC gene cluster imply important regulatory roles in myocytes hypertrophy and intermuscular fat (IMF) deposition. Dramatically, higher similarity of methylation pattern was observed within pedigree than across pedigrees, which indicates the existence of heritable methylation regions. In summary, a part of CpGs in promoter can change its methylation pattern and play a marked regulatory function in different physiological or natural environments.

Rhesus macaques as a tractable physiological model of human ageing

Research in the basic biology of ageing is increasingly identifying mechanisms and modifiers of ageing in short-lived organisms such as worms and mice. The ultimate goal of such work is to improve human health, particularly in the growing segment of the population surviving into old age. Thus far, few interventions have robustly transcended species boundaries in the laboratory, suggesting that changes in approach are needed to avoid costly failures in translational human research. In this review, we discuss both well-established and alternative model organisms for ageing research and outline how research in nonhuman primates is sorely needed, first, to translate findings from short-lived organisms to humans, and second, to understand key aspects of ageing that are unique to primate biology. We focus on rhesus macaques as a particularly promising model organism for ageing research owing to their social and physiological similarity to humans as well as the existence of key resources that have been developed for this species. As a case study, we compare gene regulatory signatures of ageing in the peripheral immune system between humans and rhesus macaques from a free-ranging study population in Cayo Santiago. We show that both mRNA expression and DNA methylation signatures of immune ageing are broadly shared between macaques and humans, indicating strong conservation of the trajectory of ageing in the immune system. We conclude with a review of key issues in the biology of ageing for which macaques and other nonhuman primates may uniquely contribute valuable insights, including the effects of social gradients on health and ageing. We anticipate that continuing research in rhesus macaques and other nonhuman primates will play a critical role in conjunction with the model organism and human biodemographic research in ultimately improving translational outcomes and extending health and longevity in our ageing population. This article is part of the theme issue 'Evolution of the primate ageing process'.

pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions

MOTIVATION

DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs.

RESULTS

We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods.

AVAILABILITY AND IMPLEMENTATION

pWGBSSimla is available at https://omicssimla.sourceforge.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Optimization of Extrusion and Ultrasound-Assisted Extraction of Phenolic Compounds from Jizi439 Black Wheat Bran

Jizi439, a newly developed black wheat breeding line, was reported to effectively regulate blood glucose, which may potentially be associated with its intrinsic high level of phenolic compounds (PCs). To maximize the PCs yield and thereby enhance their antioxidant activity, orthogonal experiments were designed in sequence for extrusion of Jizi439 black wheat bran (BWB) powder and followed by the extraction of PCs assisted with ultrasound technique. White wheat bran was used as a control. The optimum condition for extrusion was 110 °C, 25% feed water content, 140 rpm screw speed; meanwhile, 50 °C, 40 min, 35 kHz ultrasonic frequency, 300 W ultrasonic power for ultrasound-assisted extraction (UAE). Total phenolic content (TPC) as determined by Folin–Ciocalteu method was 2856.3 ± 57.7 μg gallic acid equivalents (GAE) per gram of dry weight (DW) of phenolic extract; meanwhile, antioxidant activity (AA) in terms of DPPH radical scavenging ratio was 85.5% ± 1.1% under optimized conditions, which were both significantly higher than the control. Phenolic acids except for gallic acid, as well as flavonoids, including luteolin and apigenin were increased by extrusion and ultrasound, as suggested by HPLC results. In conclusion, our study would provide a valuable reference for processing Jizi439 BWB before making or commercially utilize it into health-related food products.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

Age influences domestic dog cognitive performance independent of average breed lifespan

Across mammals, increased body size is positively associated with lifespan. However, within species, this relationship is inverted. This is well illustrated in dogs (Canis familiaris), where larger dogs exhibit accelerated life trajectories: growing faster and dying younger than smaller dogs. Similarly, some age-associated traits (e.g., growth rate and physiological pace of aging) exhibit accelerated trajectories in larger breeds. Yet, it is unknown whether cognitive performance also demonstrates an accelerated life course trajectory in larger dogs. Here, we measured cognitive development and aging in a cross-sectional study of over 4000 dogs from 66 breeds using nine memory and decision-making tasks performed by citizen scientists as part of the Dognition project. Specifically, we tested whether cognitive traits follow a compressed (accelerated) trajectory in larger dogs, or the same trajectory for all breeds, which would result in limited cognitive decline in larger breeds. We found that all breeds, regardless of size or lifespan, tended to follow the same quadratic trajectory of cognitive aging-with a period of cognitive development in early life and decline in later life. Taken together, our results suggest that cognitive performance follows similar age-related trajectories across dog breeds, despite remarkable variation in developmental rates and lifespan.

Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

Epimutations in Developmental Genes Underlie the Onset of Domestication in Farmed European Sea Bass

Domestication of wild animals induces a set of phenotypic characteristics collectively known as the domestication syndrome. However, how this syndrome emerges is still not clear. Recently, the neural crest cell deficit hypothesis proposed that it is generated by a mildly disrupted neural crest cell developmental program, but clear support is lacking due to the difficulties of distinguishing pure domestication effects from preexisting genetic differences between farmed and wild mammals and birds. Here, we use a farmed fish as model to investigate the role of persistent changes in DNA methylation (epimutations) in the process of domestication. We show that early domesticates of sea bass, with no genetic differences with wild counterparts, contain epimutations in tissues with different embryonic origins. About one fifth of epimutations that persist into adulthood are established by the time of gastrulation and affect genes involved in developmental processes that are expressed in embryonic structures, including the neural crest. Some of these genes are differentially expressed in sea bass with lower jaw malformations, a key feature of domestication syndrome. Interestingly, these epimutations significantly overlap with cytosine-to-thymine polymorphisms after 25 years of selective breeding. Furthermore, epimutated genes coincide with genes under positive selection in other domesticates. We argue that the initial stages of domestication include dynamic alterations in DNA methylation of developmental genes that affect the neural crest. Our results indicate a role for epimutations during the beginning of domestication that could be fixed as genetic variants and suggest a conserved molecular process to explain Darwin’s domestication syndrome across vertebrates.

Approaches to Whole-Genome Methylome Analysis in Plants

Cytosine methylation as a reversible chromatin mark has been investigated extensively for its influence on gene silencing and the regulation of its dynamic association-disassociation at specific sites within a eukaryotic genome. With the remarkable reductions in cost and time associated with whole-genome DNA sequence analysis, coupled with the high fidelity of bisulfite-treated DNA sequencing, single nucleotide resolution of cytosine methylation repatterning within even very large genomes is increasingly achievable. What remains a challenge is the analysis of genome-wide methylome datasets and, consequently, a clear understanding of the overall influence of methylation repatterning on gene expression or vice versa. Reported data have sometimes been subject to stringent data filtering methods that can serve to skew downstream biological interpretation. These complications derive from methylome analysis procedures that vary widely in method and parameter setting. DNA methylation as a chromatin feature that influences DNA stability can be dynamic and rapidly responsive to environmental change. Consequently, methods to discriminate background "noise" of the system from biological signal in response to specific perturbation is essential in some types of experiments. We describe numerous aspects of whole-genome bisulfite sequence data that must be contemplated as well as the various steps of methylome data analysis which impact the biological interpretation of the final output.

IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

Identifying genetic variants that are associated with methylation variation-an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping-is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions

Background

The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.

Results

We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.

Conclusion

HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2845-y) contains supplementary material, which is available to authorized users.

Temporal Dynamics of DNA Methylation Patterns in Response to Rearing Juvenile Steelhead (Oncorhynchus mykiss) in a Hatchery versus Simulated Stream Environment

Genetic selection is often implicated as the underlying cause of heritable phenotypic differences between hatchery and wild populations of steelhead trout (Oncorhynchus mykiss) that also differ in lifetime fitness. Developmental plasticity, which can also affect fitness, may be mediated by epigenetic mechanisms such as DNA methylation. Our previous study identified significant differences in DNA methylation between adult hatchery- and natural-origin steelhead from the same population that could not be distinguished by DNA sequence variation. In the current study, we tested whether hatchery-rearing conditions can influence patterns of DNA methylation in steelhead with known genetic backgrounds, and assessed the stability of these changes over time. Eyed-embryos from 22 families of Methow River steelhead were split across traditional hatchery tanks or a simulated stream-rearing environment for 8 months, followed by a second year in a common hatchery tank environment. Family assignments were made using a genetic parentage analysis to account for relatedness among individuals. DNA methylation patterns were examined in the liver, a relatively homogeneous organ that regulates metabolic processes and somatic growth, of juveniles at two time points: after eight months of rearing in either a tank or stream environment and after a subsequent year of rearing in a common tank environment. Further, we analyzed DNA methylation in the sperm of mature 2-year-old males from the earlier described treatments to assess the potential of environmentally-induced changes to be passed to offspring. Hepatic DNA methylation changes in response to hatchery versus stream-rearing in yearling fish were substantial, but few persisted after a second year in the tank environment. However, the early rearing environment appeared to affect how fish responded to developmental and environmental signals during the second year since novel DNA methylation differences were identified in the livers of hatchery versus stream-reared fish after a year of common tank rearing. Furthermore, we found profound differences in DNA methylation due to age, irrespective of rearing treatment. This could be due to smoltification associated changes in liver physiology after the second year of rearing. Although few rearing-treatment effects were observed in the sperm methylome, strong family effects were observed. These data suggest limited potential for intergenerational changes, but highlight the importance of understanding the effects of kinship among studied individuals in order to properly analyze and interpret DNA methylation data in natural populations. Our work is the first to study family effects and temporal dynamics of DNA methylation patterns in response to hatchery-rearing.

A survey of the approaches for identifying differential methylation using bisulfite sequencing data

DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.

Evolution of DNA Methylation in Papio Baboons

Changes in gene regulation have long been thought to play an important role in primate evolution. However, although a number of studies have compared genome-wide gene expression patterns across primate species, fewer have investigated the gene regulatory mechanisms that underlie such patterns, or the relative contribution of drift versus selection. Here, we profiled genome-scale DNA methylation levels in blood samples from five of the six extant species of the baboon genus Papio (4-14 individuals per species). This radiation presents the opportunity to investigate DNA methylation divergence at both shallow and deeper timescales (0.380-1.4 My). In contrast to studies in human populations, but similar to studies in great apes, DNA methylation profiles clearly mirror genetic and geographic structure. Divergence in DNA methylation proceeds fastest in unannotated regions of the genome and slowest in regions of the genome that are likely more constrained at the sequence level (e.g., gene exons). Both heuristic approaches and Ornstein-Uhlenbeck models suggest that DNA methylation levels at a small set of sites have been affected by positive selection, and that this class is enriched in functionally relevant contexts, including promoters, enhancers, and CpG islands. Our results thus indicate that the rate and distribution of DNA methylation changes across the genome largely mirror genetic structure. However, at some CpG sites, DNA methylation levels themselves may have been a target of positive selection, pointing to loci that could be important in connecting sequence variation to fitness-related traits.

Fast and flexible linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.

Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies

Motivation

Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.

Results

Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.

Availability and implementation

PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Social affiliation predicts mitochondrial DNA copy number in female rhesus macaques

In many social mammals, social adversity predicts compromised health and reduced fitness. These effects are thought to be driven in part by chronic social stress, but their molecular underpinnings are not well understood. Recent work suggests that chronic stress can affect mitochondrial copy number, heteroplasmy rates and function. Here, we tested the first two possibilities for the first time in non-human primates. We manipulated dominance rank in captive female rhesus macaques ( n = 45), where low rank induces chronic social stress, and measured mitochondrial DNA (mtDNA) copy number and heteroplasmy in five peripheral blood mononuclear cell types from each study subject. We found no effect of dominance rank on either mtDNA copy number or heteroplasmy rates. However, grooming rate, a measure of affiliative social behaviour predicted by high social status, was positively associated with mtDNA copy number in B cells, cytotoxic T cells and monocytes. Our results suggest that social interactions can influence mtDNA regulation in immune cells. Further, they indicate the importance of considering both affiliative and competitive interactions in investigating this relationship.

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

Association testing of bisulfite-sequencing methylation data via a Laplace approximation

Motivation

Epigenome-wide association studies can provide novel insights into the regulation of genes involved in traits and diseases. The rapid emergence of bisulfite-sequencing technologies enables performing such genome-wide studies at the resolution of single nucleotides. However, analysis of data produced by bisulfite-sequencing poses statistical challenges owing to low and uneven sequencing depth, as well as the presence of confounding factors. The recently introduced Mixed model Association for Count data via data AUgmentation (MACAU) can address these challenges via a generalized linear mixed model when confounding can be encoded via a single variance component. However, MACAU cannot be used in the presence of multiple variance components. Additionally, MACAU uses a computationally expensive Markov Chain Monte Carlo (MCMC) procedure, which cannot directly approximate the model likelihood.

Results

We present a new method, Mixed model Association via a Laplace ApproXimation (MALAX), that is more computationally efficient than MACAU and allows to model multiple variance components. MALAX uses a Laplace approximation rather than MCMC based approximations, which enables to directly approximate the model likelihood. Through an extensive analysis of simulated and real data, we demonstrate that MALAX successfully addresses statistical challenges introduced by bisulfite-sequencing while controlling for complex sources of confounding, and can be over 50% faster than the state of the art.

Availability and Implementation

The full source code of MALAX is available at https://github.com/omerwe/MALAX.

Supplementary information

Supplementary data are available at Bioinformatics online.

Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is a crucial step for accurate downstream analysis. Here, we present a novel statistical method, which we refer to as scPLS (single cell partial least squares), for robust and accurate inference of confounding effects. scPLS takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.