Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain

Multi-omics analysis of the molecular response to glucocorticoids - insights into shared genetic risk from psychiatric to medical disorders

BACKGROUND

Alterations in the effects of glucocorticoids have been implicated in mediating some of the negative health effects associated with chronic stress, including increased risk for psychiatric disorders as well as cardiovascular and metabolic diseases. This study investigates how genetic variants influence gene expression and DNA methylation (DNAm) in response to glucocorticoid receptor (GR)-activation, and their association with disease risk.

METHODS

We measured DNAm (n=199) and gene expression (n=297) in peripheral blood before and after GR-activation with dexamethasone, with matched genotype data available for all samples. A comprehensive molecular quantitative trait locus (QTL) analysis was conducted, mapping GR-response methylation (me)QTLs, GR-response expression (e)QTLs, and GR-response expression quantitative trait methylation (eQTM). A multi-level network analysis was employed to map the complex relationships between the transcriptome, epigenome, and genetic variation.

RESULTS

We identified 3,772 GR-response meCpGs corresponding to 104,828 local GR-response meQTLs that did not strongly overlap with baseline meQTLs. eQTM and eQTL analyses revealed distinct genetic influences on gene expression and DNAm. Multi-level network analysis uncovered GR-response network trio QTLs, characterized by SNP-CpG-transcript combinations where meQTLs act as both eQTLs and eQTMs. GR-response trio variants were enriched in GWAS for psychiatric, respiratory, autoimmune and cardiovascular diseases and conferred a higher relative heritability per SNP than GR-response meQTL and baseline QTL SNP.

CONCLUSIONS

Genetic variants modulating the molecular effects of glucocorticoids are associated with psychiatric as well as medical diseases and not uncovered in baseline QTL analyses.

Genetic effects on the skin methylome in healthy older twins

Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.

Characterizing a complex CT-rich haplotype in intron 4 of SNCA using large-scale targeted amplicon long-read sequencing

Parkinson's disease (PD) is a common neurodegenerative disorder with a significant risk proportion driven by genetics. While much progress has been made, most of the heritability remains unknown. This is in-part because previous genetic studies have focused on the contribution of single nucleotide variants. More complex forms of variation, such as structural variants and tandem repeats, are already associated with several synucleinopathies. However, because more sophisticated sequencing methods are usually required to detect these regions, little is understood regarding their contribution to PD. One example is a polymorphic CT-rich region in intron 4 of the SNCA gene. This haplotype has been suggested to be associated with risk of Lewy Body (LB) pathology in Alzheimer's Disease and SNCA gene expression, but is yet to be investigated in PD. Here, we attempt to resolve this CT-rich haplotype and investigate its role in PD. We performed targeted PacBio HiFi sequencing of the region in 1375 PD cases and 959 controls. We replicate the previously reported associations and a novel association between two PD risk SNVs (rs356182 and rs5019538) and haplotype 4, the largest haplotype. Through quantitative trait locus analyzes we identify a significant haplotype 4 association with alternative CAGE transcriptional start site usage, not leading to significant differential SNCA gene expression in post-mortem frontal cortex brain tissue. Therefore, disease association in this locus might not be biologically driven by this CT-rich repeat region. Our data demonstrates the complexity of this SNCA region and highlights that further follow up functional studies are warranted.

Comprehensive analysis of genetic risk loci uncovers novel candidate genes and pathways in the comorbidity between depression and Alzheimer's disease

There is growing evidence of a shared pathogenesis between Alzheimer's disease and depression. Therefore, we aimed to further investigate their shared disease mechanisms. We made use of publicly available brain-specific eQTL data and gene co-expression networks of previously reported genetic loci associated with these highly comorbid disorders. No direct genetic overlap was observed between Alzheimer's disease and depression in our dataset, but we did detect six shared brain-specific eQTL genes: SRA1, MICA, PCDHA7, PCDHA8, PCDHA10 and PCDHA13. Several pathways were identified as shared between Alzheimer's disease and depression by conducting clustering pathway analysis on hippocampal co-expressed genes; synaptic signaling and organization, myelination, development, and the immune system. This study highlights trans-synaptic signaling and synaptoimmunology in the hippocampus as main shared pathomechanisms of Alzheimer's disease and depression.

BISCUIT: an efficient, standards-compliant tool suite for simultaneous genetic and epigenetic inference in bulk and single-cell studies

Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT's performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.

Genetic control of DNA methylation is largely shared across European and East Asian populations

DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.

The Impact of Apolipoprotein E (APOE) Epigenetics on Aging and Sporadic Alzheimer's Disease

Sporadic Alzheimer's disease (AD) derives from an interplay among environmental factors and genetic variants, while epigenetic modifications have been expected to affect the onset and progression of its complex etiopathology. Carriers of one copy of the apolipoprotein E gene (APOE) ε4 allele have a 4-fold increased AD risk, while APOE ε4/ε4-carriers have a 12-fold increased risk of developing AD in comparison with the APOE ε3-carriers. The main longevity factor is the homozygous APOE ε3/ε3 genotype. In the present narrative review article, we summarized and described the role of APOE epigenetics in aging and AD pathophysiology. It is not fully understood how APOE variants may increase or decrease AD risk, but this gene may affect tau- and amyloid-mediated neurodegeneration directly or indirectly, also by affecting lipid metabolism and inflammation. For sporadic AD, epigenetic regulatory mechanisms may control and influence APOE expression in response to external insults. Diet, a major environmental factor, has been significantly associated with physical exercise, cognitive function, and the methylation level of several cytosine-phosphate-guanine (CpG) dinucleotide sites of APOE.

Somatic mutation as an explanation for epigenetic aging

DNA methylation marks have recently been used to build models known as "epigenetic clocks" which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In analysis of multimodal data from 9,331 human individuals, we find that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but also with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping enables mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging faster or slower than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

Role of the redox state of the Pirin-bound cofactor on interaction with the master regulators of inflammation and other pathways

Persistent cellular stress induced perpetuation and uncontrolled amplification of inflammatory response results in a shift from tissue repair toward collateral damage, significant alterations of tissue functions, and derangements of homeostasis which in turn can lead to a large number of acute and chronic pathological conditions, such as chronic heart failure, atherosclerosis, myocardial infarction, neurodegenerative diseases, diabetes, rheumatoid arthritis, and cancer. Keeping the vital role of balanced inflammation in maintaining tissue integrity in mind, the way to combating inflammatory diseases may be through identification and characterization of mediators of inflammation that can be targeted without hampering normal body function. Pirin (PIR) is a non-heme iron containing protein having two different conformations depending on the oxidation state of the iron. Through exploration of the Pirin interactome and using molecular docking approaches, we identified that the Fe2+-bound Pirin directly interacts with BCL3, NFKBIA, NFIX and SMAD9 with more resemblance to the native binding pose and higher affinity than the Fe3+-bound form. In addition, Pirin appears to have a function in the regulation of inflammation, the transition between the canonical and non-canonical NF-κB pathways, and the remodeling of the actin cytoskeleton. Moreover, Pirin signaling appears to have a critical role in tumor invasion and metastasis, as well as metabolic and neuro-pathological complications. There are regulatory variants in PIR that can influence expression of not only PIR but also other genes, including VEGFD and ACE2. Disparity exists between South Asian and European populations in the frequencies of variant alleles at some of these regulatory loci that may lead to differential occurrence of Pirin-mediated pathogenic conditions.

Assessment of the Genetic Characteristics of a Generation Born during a Long-Term Socioeconomic Crisis

BACKGROUND

A socioeconomic crisis in Russia lasted from 1991 to 1998 and was accompanied by a sharp drop in the birth rate. The main factor that influenced the refusal to have children during this period is thought to be prolonged social stress.

METHODS

comparing frequencies of common gene variants associated with stress-induced diseases among generations born before, after, and during this crisis may show which genes may be preferred under the pressure of natural selection during periods of increased social stress in urban populations.

RESULTS

In the "crisis" group, a statistically significant difference from the other two groups was found in rs6557168 frequency (p = 0.001); rs4522666 was not in the Hardy-Weinberg equilibrium in this group, although its frequency did not show a significant difference from the other groups (p = 0.118). Frequencies of VNTRs in SLC6A3 and MAOA as well as common variants rs17689918 in CRHR1, rs1360780 in FKBP5, rs53576 in OXTR, rs12720071 and rs806377 in CNR1, rs4311 in ACE, rs1800497 in ANKK1, and rs7412 and rs429358 in APOE did not differ among the groups.

CONCLUSIONS

a generation born during a period of prolonged destructive events may differ from the rest of the gene pool of the population in some variants associated with personality traits or stress-related disorders.

Age-related promoter-switch regulates Runx1 expression in adult rat hearts

BACKGROUND

Runt-related transcription factor-1 (RUNX1), a key member of the core-binding factor family of transcription factors, has emerged as a novel therapeutic target for cardiovascular disease. There is an urgent need to fully understand the expression pattern of Runx1 in the heart and the mechanisms by which it is controlled under normal conditions and in response to disease. The expression of Runx1 is regulated at the transcriptional level by two promoters designated P1 and P2. Alternative usage of these two promoters creates differential mRNA transcripts diversified in distribution and translational potential. While the significance of P1/P2 promoter-switch in the transcriptional control of Runx1 has been highlighted in the embryogenic process, very little is known about the level of P1- and P2-specific transcripts in adult hearts, and the underlying mechanisms controlling the promoter-switch.

METHODS

To amplify P1/P2 specific sequences in the heart, we used two different sense primers complementary to either P1 or P2 5'-regions to monitor the expression of P1/P2 transcripts. DNA methylation levels were assessed at the Runx1 promoter regions. Rats were grouped by age.

RESULTS

The expression levels of both P1- and P2-derived Runx1 transcripts were decreased in older rats when compared with that in young adults, paralleled with an age-dependent decline in Runx1 protein level. Furthermore, older rats demonstrated a higher degree of DNA methylation at Runx1 promoter regions. Alternative promoter usage was observed in hearts with increased age, as reflected by altered P1:P2 mRNA ratio.

CONCLUSION

Our data demonstrate that the expression of Runx1 in the heart is age-dependent and underscore the importance of gene methylation in the promoter-mediated transcriptional control of Runx1, thereby providing new insights to the role of epigenetic regulation in the heart.

Large-scale rare variant burden testing in Parkinson's disease

Parkinson's disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson's disease. To address this gap, we investigated the rare genetic component of Parkinson's disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson's disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2, have variants that have been previously implicated as risk factors for Parkinson's disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson's disease. To date, this is the largest analysis of rare genetic variants in Parkinson's disease.

Metabolic syndrome: Nutri-epigenetic cause or consequence?

Metabolic syndrome is a cluster of conditions that results from the interplay of genetic and environmental factors, which increase the comorbidity risk of obesity, hyperglycemia, dyslipidemia, arterial hypertension, stroke, and cardiovascular disease. In this article, we review various high-impact studies which link epigenetics with metabolic syndrome by comparing each study population, methylation effects, and strengths and weaknesses of each research. We also discuss world statistical data on metabolic syndrome incidence in developing countries where the metabolic syndrome is common condition that has significant public health implications.

Effects of Melatonin on Liver of D-Galactose-Induced Aged Mouse Model

Melatonin, a hormone secreted by the pineal gland of vertebrates, regulates sleep, blood pressure, and circadian and seasonal rhythms, and acts as an antioxidant and anti-inflammatory agent. We investigated the protective effects of melatonin against markers of D-galactose (D-Gal)-induced hepatocellular aging, including liver inflammation, hepatocyte structural damage, and non-alcoholic fatty liver. Mice were divided into four groups: phosphate-buffered saline (PBS, control), D-Gal (200 mg/kg/day), melatonin (20 mg/kg), and D-Gal (200 mg/kg) and melatonin (20 mg) cotreatment. The treatments were administered once daily for eight consecutive weeks. Melatonin treatment alleviated D-Gal-induced hepatocyte impairment. The AST level was significantly increased in the D-Gal-treated groups compared to that in the control group, while the ALT level was decreased compared to the melatonin and D-Gal cotreated group. Inflammatory genes, such as IL1-β, NF-κB, IL-6, TNFα, and iNOS, were significantly increased in the D-Gal aging model, whereas the expression levels of these genes were low in the D-Gal and melatonin cotreated group. Interestingly, the expression levels of hepatic steatosis-related genes, such as LXRα, C/EBPα, PPARα, ACC, ACOX1, and CPT-1, were markedly decreased in the D-Gal and melatonin cotreated group. These results suggest that melatonin suppresses hepatic steatosis and inflammation in a mouse model of D-Gal-induced aging.

Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer's and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.

Integration of methylation quantitative trait loci (mQTL) on dietary intake on DNA methylation levels: an example of n-3 PUFA and ABCA1 gene

BACKGROUND

Epigenetic studies have reported relationships between dietary nutrient intake and methylation levels. However, genetic variants that may affect DNA methylation (DNAm) pattern, called methylation quantitative loci (mQTL), are usually overlooked in these analyses. We investigated whether mQTL change the relationship between dietary nutrient intake and leukocyte DNAm levels with an example of estimated fatty acid intake and ATP-binding cassette transporter A1 (ABCA1).

METHODS

A cross-sectional study on 231 participants (108 men, mean age: 62.7 y) without clinical history of cancer and no prescriptions for dyslipidemia. We measured leukocyte DNAm levels of 8 CpG sites within ABCA1 gene by pyrosequencing method and used mean methylation levels for statistical analysis. TaqMan assay was used for genotyping a genetic variant of ABCA1 (rs1800976). Dietary fatty acid intake was estimated with a validated food frequency questionnaire and adjusted for total energy intake by using residual methods.

RESULTS

Mean ABCA1 DNAm levels were 5% lower with the number of minor alleles in rs1800976 (CC, 40.6%; CG, 35.9%; GG, 30.6%). Higher dietary n-3 PUFA intake was associated with lower ABCA1 DNAm levels (1st (ref) vs. 4th, β [95% CI]: -2.52 [-4.77, -0.28]). After controlling for rs180076, the association between dietary n-3 PUFA intake and ABCA1 DNAm levels was attenuated, but still showed an independent association (1st (ref) vs. 4th, β [95% CI]: -2.00 [-3.84, -0.18]). The interaction of mQTL and dietary n-3 PUFA intake on DNAm levels was not significant.

CONCLUSIONS

This result suggested that dietary n-3 PUFA intake would be an independent predictor of DNAm levels in ABCA1 gene after adjusting for individual genetic background. Considering mQTL need to broaden into other genes and nutrients for deeper understanding of DNA methylation, which can contribute to personalized nutritional intervention.

Analysis of subcellular RNA fractions demonstrates significant genetic regulation of gene expression in human brain post-transcriptionally

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

How Does Nutrition Affect the Epigenetic Changes in Dairy Cows?

Dairy cows require a balanced diet that provides enough nutrients to support milk production, growth, and reproduction. Inadequate nutrition can lead to metabolic disorders, impaired fertility, and reduced milk yield. Recent studies have shown that nutrition can affect epigenetic modifications in dairy cows, which can impact gene expression and affect the cows' health and productivity. One of the most important epigenetic modifications in dairy cows is DNA methylation, which involves the addition of a methyl group to the DNA molecule. Studies have shown that the methylation status of certain genes in dairy cows can be influenced by dietary factors such as the level of methionine, lysine, choline, and folate in the diet. Other important epigenetic modifications in dairy cows are histone modification and microRNAs as regulators of gene expression. Overall, these findings suggest that nutrition can have a significant impact on the epigenetic regulation of gene expression in dairy cows. By optimizing the diet of dairy cows, it may be possible to improve their health and productivity by promoting beneficial epigenetic modifications. This paper reviews the main nutrients that can cause epigenetic changes in dairy cattle by analyzing the effect of diet on milk production and its composition.

Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms

Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00100-6.

Genome-Wide Analysis of Structural Variants in Parkinson Disease

OBJECTIVE

Identification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD.

METHODS

We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data.

RESULTS

We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2 kb intronic deletion within the gene LRRN4.

INTERPRETATION

We identified three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. ANN NEUROL 2023;93:1012-1022.

Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses

Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.

Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation

Long-read sequencing technologies substantially overcome the limitations of short-reads but to date have not been considered as feasible replacement at scale due to a combination of being too expensive, not scalable enough, or too error-prone. Here, we develop an efficient and scalable wet lab and computational protocol for Oxford Nanopore Technologies (ONT) long-read sequencing that seeks to provide a genuine alternative to short-reads for large-scale genomics projects. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the NIH Center for Alzheimer's and Related Dementias (CARD). Using a single PromethION flow cell, we can detect SNPs with F1-score better than Illumina short-read sequencing. Small indel calling remains to be difficult inside homopolymers and tandem repeats, but is comparable to Illumina calls elsewhere. Further, we can discover structural variants with F1-score comparable to state-of the-art methods involving Pacific Biosciences HiFi sequencing and trio information (but at a lower cost and greater throughput). Using ONT based phasing, we can then combine and phase small and structural variants at megabase scales. Our protocol also produces highly accurate, haplotype-specific methylation calls. Overall, this makes large-scale long-read sequencing projects feasible; the protocol is currently being used to sequence thousands of brain-based genomes as a part of the NIH CARD initiative. We provide the protocol and software as open-source integrated pipelines for generating phased variant calls and assemblies.

Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders

Increasing evidence supports the role of placenta in neurodevelopment and potentially, in the later onset of neuropsychiatric disorders. Recently, methylation quantitative trait loci (mQTL) and interaction QTL (iQTL) maps have proven useful to understand SNP-genome wide association study (GWAS) relationships, otherwise missed by conventional expression QTLs. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation (DNAm). We constructed the first public placental cis-mQTL database including nearly eight million mQTLs calculated in 368 fetal placenta DNA samples from the INMA project, ran cell type- and gestational age-imQTL models and combined those data with the summary statistics of the largest GWAS on 10 neuropsychiatric disorders using Summary-based Mendelian Randomization (SMR) and colocalization. Finally, we evaluated the influence of the DNAm sites identified on placental gene expression in the RICHS cohort. We found that placental cis-mQTLs are highly enriched in placenta-specific active chromatin regions, and useful to map the etiology of neuropsychiatric disorders at prenatal stages. Specifically, part of the genetic burden for schizophrenia, bipolar disorder and major depressive disorder confers risk through placental DNAm. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, regional pleiotropic methylation signals associated to the same disorder, and cell type-imQTLs, additionally associated to the expression levels of relevant immune genes in placenta. In conclusion, the genetic risk of several neuropsychiatric disorders could operate, at least in part, through DNAm and associated gene expression in placenta.

OTTERS: a powerful TWAS framework leveraging summary-level reference data

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

An overview of DNA methylation-derived trait score methods and applications

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

Genome-wide contribution of common short-tandem repeats to Parkinson's disease genetic risk

Parkinson's disease is a complex neurodegenerative disorder with a strong genetic component, for which most known disease-associated variants are single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels). DNA repetitive elements account for >50% of the human genome; however, little is known of their contribution to Parkinson's disease aetiology. While select short tandem repeats (STRs) within candidate genes have been studied in Parkinson's disease, their genome-wide contribution remains unknown. Here we present the first genome-wide association study of STRs in Parkinson's disease. Through a meta-analysis of 16 imputed genome-wide association study cohorts from the International Parkinson's Disease Genomic Consortium (IPDGC), totalling 39 087 individuals (16 642 cases and 22 445 controls of European ancestry), we identified 34 genome-wide significant STR loci (P < 5.34 × 10-6), with the strongest signal located in KANSL1 [chr17:44 205 351:[T]11, P = 3 × 10-39, odds ratio = 1.31 (95% confidence interval = 1.26-1.36)]. Conditional-joint analyses suggested that four significant STRs mapping nearby NDUFAF2, TRIML2, MIRNA-129-1 and NCOR1 were independent from known risk SNPs. Including STRs in heritability estimates increased the variance explained by SNPs alone. Gene expression analysis of STRs (eSTRs) in RNA sequencing data from 13 brain regions identified significant associations of STRs influencing the expression of multiple genes, including known Parkinson's disease genes. Further functional annotation of candidate STRs revealed that significant eSTRs within NUDFAF2 and ZSWIM7 overlap with regulatory features and are associated with change in the expression levels of nearby genes. Here, we show that STRs at known and novel candidate loci contribute to Parkinson's disease risk and have functional effects in disease-relevant tissues and pathways, supporting previously reported disease-associated genes and giving further evidence for their functional prioritization. These data represent a valuable resource for researchers currently dissecting Parkinson's disease risk loci.

Sex, Age, and Regional Differences in CHRM1 and CHRM3 Genes Expression Levels in the Human Brain Biopsies: Potential Targets for Alzheimer's Disease-related Sleep Disturbances

BACKGROUND

Cholinergic hypofunction and sleep disturbance are hallmarks of Alzheimer's disease (AD), a progressive disorder leading to neuronal deterioration. Muscarinic acetylcholine receptors (M1-5 or mAChRs), expressed in hippocampus and cerebral cortex, play a pivotal role in the aberrant alterations of cognitive processing, memory, and learning, observed in AD. Recent evidence shows that two mAChRs, M1 and M3, encoded by CHRM1 and CHRM3 genes, respectively, are involved in sleep functions and, peculiarly, in rapid eye movement (REM) sleep.

METHODS

We used twenty microarray datasets extrapolated from post-mortem brain tissue of nondemented healthy controls (NDHC) and AD patients to examine the expression profile of CHRM1 and CHRM3 genes. Samples were from eight brain regions and stratified according to age and sex.

RESULTS

CHRM1 and CHRM3 expression levels were significantly reduced in AD compared with ageand sex-matched NDHC brains. A negative correlation with age emerged for both CHRM1 and CHRM3 in NDHC but not in AD brains. Notably, a marked positive correlation was also revealed between the neurogranin (NRGN) and both CHRM1 and CHRM3 genes. These associations were modulated by sex. Accordingly, in the temporal and occipital regions of NDHC subjects, males expressed higher levels of CHRM1 and CHRM3, respectively, than females. In AD patients, males expressed higher levels of CHRM1 and CHRM3 in the temporal and frontal regions, respectively, than females.

CONCLUSION

Thus, substantial differences, all strictly linked to the brain region analyzed, age, and sex, exist in CHRM1 and CHRM3 brain levels both in NDHC subjects and in AD patients.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

Severe COVID-19 is associated with molecular signatures of aging in the human brain

As coronavirus disease 2019 (COVID-19) and aging are both accompanied by cognitive decline, we hypothesized that COVID-19 might lead to molecular signatures similar to aging. We performed whole-transcriptome analysis of the frontal cortex, a critical area for cognitive function, in individuals with COVID-19, age-matched and sex-matched uninfected controls, and uninfected individuals with intensive care unit/ventilator treatment. Our findings indicate that COVID-19 is associated with molecular signatures of brain aging and emphasize the value of neurological follow-up in recovered individuals.

Analysis of Y chromosome haplogroups in Parkinson's disease

Parkinson's disease is a complex neurodegenerative disorder that is about 1.5 times more prevalent in males than females. Extensive work has been done to identify the genetic risk factors behind Parkinson's disease on autosomes and more recently on Chromosome X, but work remains to be done on the male-specific Y chromosome. In an effort to explore the role of the Y chromosome in Parkinson's disease, we analysed whole-genome sequencing data from the Accelerating Medicines Partnership-Parkinson's disease initiative (1466 cases and 1664 controls), genotype data from NeuroX (3491 cases and 3232 controls) and genotype data from UKBiobank (182 517 controls, 1892 cases and 3783 proxy cases), all consisting of male European ancestry samples. We classified sample Y chromosomes by haplogroup using three different tools for comparison (Snappy, Yhaplo and Y-LineageTracker) and meta-analysed this data to identify haplogroups associated with Parkinson's disease. This was followed up with a Y-chromosome association study to identify specific variants associated with disease. We also analysed blood-based RNASeq data obtained from the Accelerating Medicines Partnership-Parkinson's disease initiative (1020 samples) and RNASeq data obtained from the North American Brain Expression Consortium (171 samples) to identify Y-chromosome genes differentially expressed in cases, controls, specific haplogroups and specific tissues. RNASeq analyses suggest Y-chromosome gene expression differs between brain and blood tissues but does not differ significantly in cases, controls or specific haplogroups. Overall, we did not find any strong associations between Y-chromosome genetics and Parkinson's disease, suggesting the explanation for the increased prevalence in males may lie elsewhere.

Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives

Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.

Association of a common genetic variant with Parkinson's disease is mediated by microglia

Studies of multiple neurodegenerative disorders have identified many genetic variants that are associated with risk of disease throughout a lifetime. For example, Parkinson's disease (PD) risk is attributed in part to both coding mutations in the leucine-rich repeat kinase 2 (LRRK2) gene and to a common noncoding variation in the 5' region of the LRRK2 locus, as identified by genome-wide association studies (GWAS). However, the mechanisms linking GWAS variants to pathogenicity are largely unknown. Here, we found that the influence of PD-associated noncoding variation on LRRK2 expression is specifically propagated through microglia and not by other cell types that express LRRK2 in the human brain. We find microglia-specific regulatory chromatin regions that modulate the LRRK2 expression in human frontal cortex and substantia nigra and confirm these results in a human-induced pluripotent stem cell-derived microglia model. We showed, using a large-scale clustered regularly interspaced short palindromic repeats interference (CRISPRi) screen, that a regulatory DNA element containing the single-nucleotide variant rs6581593 influences the LRRK2 expression in microglia. Our study demonstrates that cell type should be considered when evaluating the role of noncoding variation in disease pathogenesis and sheds light on the mechanism underlying the association of the 5' region of LRRK2 with PD risk.

DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk

Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.

Evaluating the challenges and reproducibility of studies investigating DNA methylation signatures of psychological stress

Psychological stress can increase the risk of a wide range of negative health outcomes. Studies have been completed to determine if DNA methylation changes occur in the human brain because of stress and are associated with long-term effects and disease, but results have been inconsistent. Human candidate gene studies (150) and epigenome-wide association studies (67) were systematically evaluated to assess how DNA methylation is impacted by stress during the prenatal period, early childhood and adulthood. The association between DNA methylation of NR3C1 exon 1F and child maltreatment and early life adversity was well demonstrated, but other genes did not exhibit a clear association. The reproducibility of individual CpG sites in epigenome-wide association studies was also poor. However, biological pathways, including stress response, brain development and immunity, have been consistently identified across different stressors throughout the life span. Future studies would benefit from the increased sample size, longitudinal design, standardized methodology, optimal quality control, and improved statistical procedures.

Identification of genetic variants influencing methylation in brain with pleiotropic effects on psychiatric disorders

Psychiatric disorders affect 29% of the global population at least once in the lifespan, and genetic studies have proved a shared genetic basis among them, although the underlying molecular mechanisms remain largely unknown. DNA methylation plays an important role in complex disorders and, remarkably, enrichment of common genetic variants influencing allele-specific methylation (ASM) has been reported among variants associated with specific psychiatric disorders. In the present study we assessed the contribution of ASM to a set of eight psychiatric disorders by combining genetic, epigenetic and expression data. We interrogated a list of 3896 ASM tagSNPs in the brain in the summary statistics of a cross-disorder GWAS meta-analysis of eight psychiatric disorders from the Psychiatric Genomics Consortium, including more than 162,000 cases and 276,000 controls. We identified 80 SNPs with pleiotropic effects on psychiatric disorders that show an opposite directional effect on methylation and gene expression. These SNPs converge on eight candidate genes: ZSCAN29, ZSCAN31, BTN3A2, DDAH2, HAPLN4, ARTN, FAM109B and NAGA. ZSCAN29 shows the broadest pleiotropic effects, showing associations with five out of eight psychiatric disorders considered, followed by ZSCAN31 and BTN3A2, associated with three disorders. All these genes overlap with CNVs related to cognitive phenotypes and psychiatric traits, they are expressed in the brain, and seven of them have previously been associated with specific psychiatric disorders, supporting our results. To sum up, our integrative functional genomics analysis identified eight psychiatric disease risk genes that impact a broad list of disorders and highlight an etiologic role of SNPs that influence DNA methylation and gene expression in the brain.

Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics

Background

Type 2 diabetes mellitus (T2DM) is caused by a combination of environmental, genetic, and epigenetic factors including, fasting blood glucose (FBG), genetic variant rs841853, and cg19693031 methylation. We evaluated the interaction between rs841853 and cg19693031 on the FBG levels of non-diabetic Taiwanese adults.

Methods

We used Taiwan Biobank (TWB) data collected between 2008 and 2016. The TWB data source contains information on basic demographics, personal lifestyles, medical history, methylation, and genotype. The study participants included 1300 people with DNA methylation data. The association of cg19693031 methylation (stratified into quartiles) with rs841853 and FBG was determined using multiple linear regression analysis. The beta-coefficients (β) and p-values were estimated.

Results

The mean ± standard deviation (SD) of FBG in rs841853-CC individuals (92.07 ± 7.78) did not differ significantly from that in the CA + AA individuals (91.62 ± 7.14). However, the cg19693031 methylation levels were significantly different in the two groups (0.7716 ± 0.05 in CC individuals and 0.7631 ± 0.05 in CA + AA individuals (p = 0.002). The cg19693031 methylation levels according to quartiles were β < 0.738592 (< Q1), 0.738592 ≤ 0.769992 (Q1–Q2), 0.769992 ≤ 0.800918 (Q2–Q3), and β ≥ 0.800918 (≥ Q3). FBG increased with decreasing cg19693031 methylation levels in a dose–response manner (p_trend = 0.005). The β-coefficient was − 0.0236 (p = 0.965) for Q2–Q3, 1.0317 (p = 0.058) for Q1–Q2, and 1.3336 (p = 0.019 for < Q1 compared to the reference quartile (≥ Q3). The genetic variant rs841853 was not significantly associated with FBG. However, its interaction with cg19693031 methylation was significant (p-value = 0.036). Based on stratification by rs841853 genotypes, only the CC group retained the inverse and dose–response association between FBG and cg19693031 methylation. The β (p-value) was 0.8082 (0.255) for Q2–Q3, 1.6930 (0.022) for Q1–Q2, and 2.2190 (0.004) for < Q1 compared to the reference quartile (≥ Q3). The p_trend was 0.002.

Conclusion

Summarily, methylation at cg19693031 was inversely associated with fasting blood glucose in a dose-dependent manner. The inverse association was more prominent in rs841853-CC individuals, suggesting that rs841853 could modulate the association between cg19693031 methylation and FBG. Our results suggest that genetic variants may be involved in epigenetic mechanisms associated with FBG, a hallmark of diabetes. Therefore, integrating genetic and epigenetic data may provide more insight into the early-onset of diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03269-y.

The relationship between ageing and changes in the human blood and brain methylomes

Changes in DNA methylation have been found to be strongly correlated with age, enabling the creation of 'epigenetic clocks'. Previously, studies on the relationship between ageing and DNA methylation have assumed a linear relationship. Here, we show that several markers show a non-linear behaviour. In particular, we observe a tendency for saturation with age, especially in the cerebellum. Further, we show that the relationships between significant methylation changes and ageing are different in different tissues. We suggest a straightforward method of assessing all methylation-age relationships and cluster them according to their relative fold change. Our fold change selection outperforms the most common epigenetic clocks in predicting age for the cerebellum, but not for Blood or the Frontal Cortex. Further, we find that the saturation of methylation observed at older ages for the cerebellum explains why epigenetic clocks consistently underestimate the age there. The findings imply that assuming linear correlations might cause biologically important markers to be missed.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

Genome-Wide Analysis of Disordered Eating Behavior in the Mexican Population

Alterations in eating behavior characterized eating disorders (ED). The genetic factors shared between ED diagnoses have been underexplored. The present study performed a genome-wide association study in individuals with disordered eating behaviors in the Mexican population, blood methylation quantitative trait loci (blood-meQTL), summary data-based Mendelian randomization (SMR) analysis, and in silico function prediction by different algorithms. The analysis included a total of 1803 individuals. We performed a genome-wide association study and blood-meQTL analysis by logistic and linear regression. In addition, we analyzed in silico functional variant prediction, phenome-wide, and multi-tissue expression quantitative trait loci. The genome-wide association study identified 44 single-nucleotide polymorphisms (SNP) associated at a nominal value and seven blood-meQTL at a genome-wide threshold. The SNPs show enrichment in genome-wide associations of the metabolic and immunologic domains. In the in silico analysis, the SNP rs10419198 (p-value = 4.85 × 10-5) located on an enhancer mark could change the expression of PRR12 in blood, adipocytes, and brain areas that regulate food intake. Additionally, we found an association of DNA methylation levels of SETBP1 (p-value = 6.76 × 10-4) and SEMG1 (p-value = 5.73 × 10-4) by SMR analysis. The present study supports the previous associations of genetic variation in the metabolic domain with ED.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10-14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71

BACKGROUND

This research aimed to explore the association between the RIG-I-like receptor (RIG-I and MDA5 encoded by DDX58 and IFIH1, respectively) pathways and the risk or severity of hand, foot, and mouth disease caused by enterovirus 71 (EV71-HFMD). In this context, we explored the influence of gene methylation and polymorphism on EV71-HFMD.

METHODOLOGY/PRINCIPAL FINDINGS

60 healthy controls and 120 EV71-HFMD patients, including 60 mild EV71-HFMD and 60 severe EV71-HFMD patients, were enrolled. First, MiSeq was performed to explore the methylation of CpG islands in the DDX58 and IFIH1 promoter regions. Then, DDX58 and IFIH1 expression were detected in PBMCs using RT-qPCR. Finally, imLDR was used to detect DDX58 and IFIH1 single-nucleotide polymorphism (SNP) genotypes. Severe EV71-HFMD patients exhibited higher DDX58 promoter methylation levels than healthy controls and mild EV71-HFMD patients. DDX58 promoter methylation was significantly associated with severe HFMD, sex, vomiting, high fever, neutrophil abundance, and lymphocyte abundance. DDX58 expression levels were significantly lower in mild patients than in healthy controls and lower in severe patients than in mild patients. Binary logistic regression analysis revealed statistically significant differences in the genotype frequencies of DDX58 rs3739674 between the mild and severe groups. GeneMANIA revealed that 19 proteins displayed correlations with DDX58, including DHX58, HERC5, MAVS, RAI14, WRNIP1 and ISG15, and 19 proteins displayed correlations with IFIH1, including TKFC, IDE, MAVS, DHX58, NLRC5, TSPAN6, USP3 and DDX58.

CONCLUSIONS/SIGNIFICANCE

DDX58 expression and promoter methylation were associated with EV71 infection progression, especially in severe EV71-HFMD patients. The effect of DDX58 in EV71-HFMD is worth further attention.

Investigation of the genetic effect of 56 tobacco-smoking susceptibility genes on DNA methylation and RNA expression in human brain

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10^-7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02-0.62; P = 4.47 × 10^-3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Deciphering DNA Methylation in HIV Infection

With approximately 38 million people living with HIV/AIDS globally, and a further 1.5 million new global infections per year, it is imperative that we advance our understanding of all factors contributing to HIV infection. While most studies have focused on the influence of host genetic factors on HIV pathogenesis, epigenetic factors are gaining attention. Epigenetics involves alterations in gene expression without altering the DNA sequence. DNA methylation is a critical epigenetic mechanism that influences both viral and host factors. This review has five focal points, which examines (i) fluctuations in the expression of methylation modifying factors upon HIV infection (ii) the effect of DNA methylation on HIV viral genes and (iii) host genome (iv) inferences from other infectious and non-communicable diseases, we provide a list of HIV-associated host genes that are regulated by methylation in other disease models (v) the potential of DNA methylation as an epi-therapeutic strategy and biomarker. DNA methylation has also been shown to serve as a robust therapeutic strategy and precision medicine biomarker against diseases such as cancer and autoimmune conditions. Despite new drugs being discovered for HIV, drug resistance is a problem in high disease burden settings such as Sub-Saharan Africa. Furthermore, genetic therapies that are under investigation are irreversible and may have off target effects. Alternative therapies that are nongenetic are essential. In this review, we discuss the potential role of DNA methylation as a novel therapeutic intervention against HIV.

Integrative Analysis of Genome-Wide Association Studies and DNA Methylation Profile Identified Genetic Control Genes of DNA Methylation for Kashin-Beck Disease

OBJECTIVE

Epigenetic modifications of DNA are regarded as a crucial factor for understanding the molecular basis of complex phenotypes. This study aims to uncover insight into the epigenetic modifications for Kashin-Beck disease (KBD) by integrating genome-wide association studies (GWAS), methylation quantitative trait loci (meQTLs), and DNA methylation profiles data.

DESIGN

The knee articular cartilages of 5 KBD patients and 5 healthy controls were collected for DNA methylation profiling, using Illumina Infinium HumanMethylation450 BeadChip. Mass spectrograph validation of identified differently methylated genes was conducted using independent samples of 4 KBD patients and 3 healthy controls, together with a previous sample of 2743 Han Chinese individuals of GWAS study for KBD and a study of 697 normal subjects for meQTLs annotation datasets. KBD GWAS single nucleotide polymorphisms (SNPs) and normal meQTLs SNPs were integrated with DNA methylation profiles of KBD articular cartilage to identify genetic control (GC) genes of DNA methylation for KBD. Quantitative polymerase chain reaction (qPCR) was performed to validate the mRNA expression of several identified candidate genes.

RESULTS

A total of 162 CpG sites, 253 SNPs, and 123 GC genes for KBD were identified. Enrichment analysis detected 642 marked GO terms and 19 KEGG pathways (P < 0.05). Six potential key GC genes were conducted for qPCR experiment (ERG, MN1, MITF, WISP1, TRIO, and NOSTRIN).

CONCLUSIONS

The results suggest that GC genes of DNA methylation may lead to the erosion of cartilage in KBD, which may help us in understanding the epigenetic alteration of KBD.