Genotype effects contribute to variation in longitudinal methylome patterns in older people

Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India

The peculiarity of Indian cattle lies in milk quality, resistance to diseases and stressors as well as adaptability. The investigation addressed selection signatures in Gir and Tharparkar cattle, belonging to arid ecotypes of India. Double digest restriction-site associated DNA sequencing (ddRAD-seq) yielded nearly 26 million high-quality reads from unrelated seven Gir and seven Tharparkar cows. In all, 19,127 high-quality SNPs were processed for selection signature analysis. An approach involving within-population composite likelihood ratio (CLR) statistics and between-population FST statistics was used to capture selection signatures within and between the breeds, respectively. A total of 191 selection signatures were addressed using CLR and FST approaches. Selection signatures overlapping 86 and 73 genes were detected as Gir- and Tharparkar-specific, respectively. Notably, genes related to production (CACNA1D, GHRHR), reproduction (ESR1, RBMS3), immunity (NOSTRIN, IL12B) and adaptation (ADAM22, ASL) were annotated to selection signatures. Gene pathway analysis revealed genes in insulin/IGF pathway for milk production, gonadotropin releasing hormone pathway for reproduction, Wnt signalling pathway and chemokine and cytokine signalling pathway for adaptation. This is the first study where selection signatures are identified using ddRAD-seq in indicine cattle breeds. The study shall help in conservation and leveraging genetic improvements in Gir and Tharparkar cattle.

Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia

In our work, the associations of GWAS (genome-wide associative studies) impact for sex-hormone-binding globulin (SHBG)-level SNPs with the risk of breast cancer (BC) in the cohort of Caucasian women of Russia were assessed. The work was performed on a sample of 1498 women (358 BC patients and 1140 control (non BC) subjects). SHBG correlated in previously GWAS nine polymorphisms such as rs780093 GCKR, rs17496332 PRMT6, rs3779195 BAIAP2L1, rs10454142 PPP1R21, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs440837 ZBTB10, rs12150660 SHBG, and rs8023580 NR2F2 have been genotyped. BC risk effects of allelic and non-allelic SHBG-linked gene SNPs interactions were detected by regression analysis. The risk genetic factor for BC developing is an SHBG-lowering allele variant C rs10454142 PPP1R21 ([additive genetic model] OR = 1.31; 95%CI = 1.08-1.65; pperm = 0.024; power = 85.26%), which determines 0.32% of the cancer variance. Eight of the nine studied SHBG-related SNPs have been involved in cancer susceptibility as part of nine different non-allelic gene interaction models, the greatest contribution to which is made by rs10454142 PPP1R21 (included in all nine models, 100%) and four more SNPs-rs7910927 JMJD1C (five models, 55.56%), rs17496332 PRMT6 (four models, 44.44%), rs780093 GCKR (four models, 44.44%), and rs440837 ZBTB10 (four models, 44.44%). For SHBG-related loci, pronounced functionality in the organism (including breast, liver, fibroblasts, etc.) was predicted in silico, having a direct relationship through many pathways with cancer pathophysiology. In conclusion, our results demonstrated the involvement of SHBG-correlated genes polymorphisms in BC risk in Caucasian women in Russia.

The Circassians and the Chechens in Jordan: results of a decade of epidemiological and genetic studies

Circassians and Chechens in Jordan, both with Caucasian ancestry, are genetically isolated due to high rate of endogamous marriages. Recent interest in these populations has led to studies on their genetic similarities, differences, and epidemiological differences in various diseases. Research has explored their predisposition to conditions like diabetes, hypertension, and cancer. Moreover, pharmacogenetic (PGx) studies have also investigated medication response variations within these populations, and forensic studies have further contributed to understanding these populations. In this review article, we first discuss the background of these minority groups. We then show the results of a principle component analysis (PCA) to investigate the genetic relationships between Circassian and Chechen populations living in Jordan. We here present a summary of the findings from the 10 years of research conducted on them. The review article provides a comprehensive summary of research findings that are truly valuable for understanding the unique genetic characteristics, diseases' prevalence, and medication responses among Circassians and Chechens living in Jordan. We believe that gaining deeper comprehension of the root causes of various diseases and developing effective treatment methods that benefit the society as a whole are imperative to engaging a wide range of ethnic groups in genetic research.

The inactive X chromosome accumulates widespread epigenetic variability with age

BACKGROUND

Loss of epigenetic control is a hallmark of aging. Among the most prominent roles of epigenetic mechanisms is the inactivation of one of two copies of the X chromosome in females through DNA methylation. Hence, age-related disruption of X-chromosome inactivation (XCI) may contribute to the aging process in women.

METHODS

We analyzed 9,777 CpGs on the X chromosome in whole blood samples from 2343 females and 1688 males (Illumina 450k methylation array) and replicated findings in duplicate using one whole blood and one purified monocyte data set (in total, 991/924 females/males). We used double generalized linear models to detect age-related differentially methylated CpGs (aDMCs), whose mean methylation level differs with age, and age-related variably methylated CpGs (aVMCs), whose methylation level becomes more variable with age.

RESULTS

In females, aDMCs were relatively uncommon (n = 33) and preferentially occurred in regions known to escape XCI. In contrast, many CpGs (n = 987) were found to display an increased variance with age (aVMCs). Of note, the replication rate of aVMCs was also high in purified monocytes (94%), indicating an independence of cell composition. aVMCs accumulated in CpG islands and regions subject to XCI suggesting that they stemmed from the inactive X. In males, carrying an active copy of the X chromosome only, aDMCs (n = 316) were primarily driven by cell composition, while aVMCs replicated well (95%) but were infrequent (n = 37).

CONCLUSIONS

Our results imply that age-related DNA methylation differences at the inactive X chromosome are dominated by the accumulation of variability.

Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case-control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case-control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10^-6). Telomere length was also significantly reduced (p = 6.6 × 10^-5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10^-8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

The Aryl Hydrocarbon Receptor, Epigenetics and the Aging Process

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor, classically associated with the regulation of xenobiotic metabolism in response to environmental toxins. In recent years, transgenic rodent models have implicated AhR in aging and longevity. Moreover, several AhR ligands, such as resveratrol and quercetin, are compounds proven to extend the lifespan of model organisms. In this paper, we first review AhR biology with a focus on aging and highlight several AhR ligands with potential anti-aging properties. We outline how AhR-driven expression of xenobiotic metabolism genes into old age may be a key mechanism through which moderate induction of AhR elicits positive benefits on longevity and healthspan. Furthermore, via integration of publicly available datasets, we show that liver-specific AhR target genes are enriched among genes subject to epigenetic aging. Changes to epigenetic states can profoundly affect transcription factor binding and are a hallmark of the aging process. We suggest that the interplay between AhR and epigenetic aging should be the subject of future research and outline several key gaps in the current literature. Finally, we recommend that a broad range of non-toxic AhR ligands should be investigated for their potential to promote healthspan and longevity.

Blood DNA methylation signatures of lifestyle exposures: tobacco and alcohol consumption

BACKGROUND

Smoking and alcohol consumption may compromise health by way of epigenetic modifications. Epigenetic signatures of alcohol and tobacco consumption could provide insights into the reversibility of phenotypic changes incurred with differing levels of lifestyle exposures. This study describes and validates two novel epigenetic signatures of tobacco (EpiTob) and alcohol (EpiAlc) consumption and investigates their association with disease outcomes.

METHODS

The epigenetic signatures, EpiTob and EpiAlc, were developed using data from the Swiss Kidney Project on Genes in Hypertension (SKIPOGH) (N = 689). Epigenetic and phenotypic data available from the 1921 (N = 550) and 1936 (N = 1091) Lothian Birth Cohort (LBC) studies, and two publicly available datasets on GEO Accession (GSE50660, N = 464; and GSE110043, N = 94) were used to validate the signatures. A multivariable logistic regression model, adjusting for age and sex, was used to assess the association between self-reported tobacco or alcohol consumption and the respective epigenetic signature, as well as to estimate the association between CVD and epigenetic signatures. A Cox proportional hazard model was used to estimate the risk of mortality in association with the EpiTob and EpiAlc signatures.

RESULTS

The EpiTob signature was positively associated with self-reported tobacco consumption for current or never smokers with explained variance ranging from 0.49 (LBC1921) to 0.72 (LBC1936) (pseudo-R2). In the SKIPOGH, LBC1921 and LBC1936 cohorts, the epigenetic signature for alcohol consumption explained limited variance in association with self-reported alcohol status [i.e., non-drinker, moderate drinker, and heavy drinker] (pseudo-R2 = 0.05, 0.03 and 0.03, respectively), although this improved considerably when measuring self-reported alcohol consumption with standardized units consumed per week (SKIPOGH R2 = 0.21; LBC1921 R2 = 0.31; LBC1936 R2 = 0.41). Both signatures were associated with history of CVD in SKIPOGH and LBC1936, but not in LBC1921. The EpiTob signature was associated with increased risk of all-cause and lung-cancer specific mortality in the 1936 and 1921 LBC cohorts.

CONCLUSIONS

This study found the EpiTob and EpiAlc signatures to be well-correlated with self-reported exposure status and associated with long-term health outcomes. Epigenetic signatures of lifestyle exposures may reduce measurement issues and biases and could aid in risk stratification for informing early-stage targeted interventions.

Epigenetic clock: A promising biomarker and practical tool in aging

As a complicated process, aging is characterized by various changes at the cellular, subcellular and nuclear levels, one of which is epigenetic aging. With increasing awareness of the critical role that epigenetic alternations play in aging, DNA methylation patterns have been employed as a measure of biological age, currently referred to as the epigenetic clock. This review provides a comprehensive overview of the epigenetic clock as a biomarker of aging and a useful tool to manage healthy aging. In this burgeoning scientific field, various kinds of epigenetic clocks continue to emerge, including Horvath's clock, Hannum's clock, DNA PhenoAge, and DNA GrimAge. We hereby present the most classic epigenetic clocks, as well as their differences. Correlations of epigenetic age with morbidity, mortality and other factors suggest the potential of epigenetic clocks for risk prediction and identification in the context of aging. In particular, we summarize studies on promising age-reversing interventions, with epigenetic clocks employed as a practical tool in the efficacy evaluation. We also discuss how the lack of higher-quality information poses a major challenge, and offer some suggestions to address existing obstacles. Hopefully, our review will help provide an appropriate understanding of the epigenetic clocks, thereby enabling novel insights into the aging process and how it can be manipulated to promote healthy aging.

A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens

Inflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers ('epigenetic clocks'), and two inflammatory biomarkers (methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent associations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus (β range = 0.83-1.14, p ≤ 0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus (β = 1.32, p = 5 × 10-4 ); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions (β = 0.40, p = 0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

BACKGROUND

DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown.

RESULTS

We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation.

CONCLUSIONS

Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

The immune factors driving DNA methylation variation in human blood

Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies.

Decreased Levels of DNA Methylation in the PCDHA Gene Cluster as a Risk Factor for Early-Onset High Myopia in Young Children

Purpose

High myopia (HM), an eye disorder with at least –6.0 diopters refractive error, has a complex etiology with environmental, genetic, and likely epigenetic factors involved. To complement the DNA methylation assessment in children with HM, we analyzed genes that had significantly lower DNA methylation levels.

Methods

The DNA methylation pattern was studied based on the genome-wide methylation data of 18 Polish children with HM paired with 18 controls. Genes overlapping CG dinucleotides with decreased methylation level in HM cases were assessed by enrichment analyses. From those, genes with CG dinucleotides in promoter regions were further evaluated based on exome sequencing (ES) data of 16 patients with HM from unrelated Polish families, Sanger sequencing data of the studied children, and the RNA sequencing data of human retinal ARPE-19 cells.

Results

The CG dinucleotide with the most decreased methylation level in cases was identified in a promoter region of PCDHA10 that overlaps intronic regions of PCDHA1–9 of the PCDHA gene cluster in myopia 5q31 locus. Also, two single nucleotide variants, rs200661444, detected in our ES, and rs246073, previously found as associated with a refractive error in a genome-wide association study, were revealed within this gene cluster. Additionally, genes previously linked to ocular phenotypes, myopia-related traits, or loci, including ADAM20, ZFAND6, ETS1, ABHD13, SBSPON, SORBS2, LMOD3, ATXN1, and FARP2, were found to have decreased methylation.

Conclusions

Alterations in the methylation pattern of specific CG dinucleotides may be associated with early-onset HM, so this could be used to develop noninvasive biomarkers of HM in children and adolescents.

DNA methylome-wide association study of genetic risk for depression implicates antigen processing and immune responses

BACKGROUND

Depression is a disabling and highly prevalent condition where genetic and epigenetic, such as DNA methylation (DNAm), differences contribute to disease risk. DNA methylation is influenced by genetic variation but the association between polygenic risk of depression and DNA methylation is unknown.

METHODS

We investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N = 8898, mean age = 49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in the Avon Longitudinal Study of Parents and Children (ALSPAC) (Ncombined = 2049, mean age = 79.1, 69.6 and 47.2 years, respectively). We also conducted a replication MWAS in the ALSPAC children (N = 423, mean age = 17.1 years). Gene ontology analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with depression PRS, followed by Mendelian randomisation (MR) analysis to infer the causal relationship between depression and DNAm.

RESULTS

Widespread associations (NCpG = 71, pBonferroni < 0.05, p < 6.3 × 10-8) were found between PRS constructed using genetic risk variants for depression and DNAm in CpG probes that localised to genes involved in immune responses and neural development. The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r = 0.79) and in adolescents (r = 0.82). Gene Ontology analysis showed that significant CpG probes are enriched in immunological processes in the human leukocyte antigen system. Additional MWAS was conducted for each lead genetic risk variant. Over 47.9% of the independent genetic risk variants included in the PRS showed associations with DNAm in CpG probes located in both the same (cis) and distal (trans) locations to the genetic loci (pBonferroni < 0.045). Subsequent MR analysis showed that there are a greater number of causal effects found from DNAm to depression than vice versa (DNAm to depression: pFDR ranged from 0.024 to 7.45 × 10-30; depression to DNAm: pFDR ranged from 0.028 to 0.003).

CONCLUSIONS

PRS for depression, especially those constructed from genome-wide significant genetic risk variants, showed methylome-wide differences associated with immune responses. Findings from MR analysis provided evidence for causal effect of DNAm to depression.

Accounting for age of onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

An epigenetic predictor of death captures multi-modal measures of brain health

Individuals of the same chronological age exhibit disparate rates of biological ageing. Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm). A novel epigenetic clock, termed 'DNAm GrimAge' has outperformed its predecessors in predicting the risk of mortality as well as many age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n = 709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over the eighth decade (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 × 10-16). Higher DNAm GrimAge was associated with lower age 11 IQ (β = -0.11), lower age 73 general cognitive ability (β = -0.18), decreased brain volume (β = -0.25) and increased brain white matter hyperintensities (β = 0.17). There was tentative evidence for a longitudinal association between DNAm GrimAge and cognitive decline from age 70 to 79. Sixty-nine of 137 health- and brain-related phenotypes tested were significantly associated with GrimAge. Adjusting all models for childhood intelligence attenuated to non-significance a small number of associations (12/69 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge associates with lower cognitive ability and brain vascular lesions in older age, independently of early-life cognitive ability. This epigenetic predictor of mortality associates with different measures of brain health and may aid in the prediction of age-related cognitive decline.

Epigenetic predictors of lifestyle traits applied to the blood and brain

Modifiable lifestyle factors influence the risk of developing many neurological diseases. These factors have been extensively linked with blood-based genome-wide DNA methylation, but it is unclear if the signatures from blood translate to the target tissue of interest-the brain. To investigate this, we apply blood-derived epigenetic predictors of four lifestyle traits to genome-wide DNA methylation from five post-mortem brain regions and the last blood sample prior to death in 14 individuals in the Lothian Birth Cohort 1936. Using these matched samples, we found that correlations between blood and brain DNA methylation scores for smoking, high-density lipoprotein cholesterol, alcohol and body mass index were highly variable across brain regions. Smoking scores in the dorsolateral prefrontal cortex had the strongest correlations with smoking scores in blood (r = 0.5, n = 14, P = 0.07) and smoking behaviour (r = 0.56, n = 9, P = 0.12). This was also the brain region which exhibited the largest correlations for DNA methylation at site cg05575921 - the single strongest correlate of smoking in blood-in relation to blood (r = 0.61, n = 14, P = 0.02) and smoking behaviour (r = -0.65, n = 9, P = 0.06). This suggested a particular vulnerability to smoking-related differential methylation in this region. Our work contributes to understanding how lifestyle factors affect the brain and suggest that lifestyle-related DNA methylation is likely to be both brain region dependent and in many cases poorly proxied for by blood. Though these pilot data provide a rarely-available opportunity for the comparison of methylation patterns across multiple brain regions and the blood, due to the limited sample size available our results must be considered as preliminary and should therefore be used as a basis for further investigation.

Creating and validating a DNA methylation-based proxy for interleukin-6

Background

Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals’ persisting levels of inflammation. DNA methylation (DNAm) has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation.

Method

We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNAm-based predictor. The predictor was tested in an independent cohort (Generation Scotland; N = 7028 [417 with measured IL-6], mean age: 51 years).

Results

A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R² = 4.4%, p = 2.1 × 10⁻⁵). In the independent test cohort, both measured IL-6 and the DNAm proxy increased with age (serum IL-6: n = 417, β = 0.02, SE = 0.004, p = 1.3 × 10⁻⁷; DNAm IL-6 score: N = 7028, β = 0.02, SE = 0.0009, p < 2 × 10⁻¹⁶). Serum IL-6 did not associate with cognitive ability (n = 417, β = −0.06, SE = 0.05, p = .19); however, an inverse association was identified between the DNAm score and cognitive functioning (N = 7028, β = −0.16, SE = 0.02, p_FDR < 2 × 10⁻¹⁶).

Conclusions

These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.

Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data

Background

Greying of the hair is an obvious sign of human aging. In addition to age, sex- and ancestry-specific patterns of hair greying are also observed and the progression of greying may be affected by environmental factors. However, little is known about the genetic control of this process. This study aimed to assess the potential of genetic data to predict hair greying in a population of nearly 1000 individuals from Poland.

Results

The study involved whole-exome sequencing followed by targeted analysis of 378 exome-wide and literature-based selected SNPs. For the selection of predictors, the minimum redundancy maximum relevance (mRMRe) method was used, and then two prediction models were developed. The models included age, sex and 13 unique SNPs. Two SNPs of the highest mRMRe score included whole-exome identified KIF1A rs59733750 and previously linked with hair loss FGF5 rs7680591. The model for greying vs. no greying prediction achieved accuracy of cross-validated AUC = 0.873. In the 3-grade classification cross-validated AUC equalled 0.864 for no greying, 0.791 for mild greying and 0.875 for severe greying. Although these values present fairly accurate prediction, most of the prediction information was brought by age alone. Genetic variants explained < 10% of hair greying variation and the impact of particular SNPs on prediction accuracy was found to be small.

Conclusions

The rate of changes in human progressive traits shows inter-individual variation, therefore they are perceived as biomarkers of the biological age of the organism. The knowledge on the mechanisms underlying phenotypic aging can be of special interest to the medicine, cosmetics industry and forensics. Our study improves the knowledge on the genetics underlying hair greying processes, presents prototype models for prediction and proves hair greying being genetically a very complex trait. Finally, we propose a four-step approach based on genetic and epigenetic data analysis allowing for i) sex determination; ii) genetic ancestry inference; iii) greying-associated SNPs assignment and iv) epigenetic age estimation, all needed for a final prediction of greying.

DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan

The number of age predictors based on DNA methylation (DNAm) profile is rising due to their potential in predicting healthspan and application in age-related illnesses, such as neurodegenerative diseases. The cumulative assessment of DNAm levels at age-related CpGs (DNAm clock) may reflect biological aging. Such DNAm clocks have been developed using various training models and could mirror different aspects of disease/aging mechanisms. Hence, evaluating several DNAm clocks together may be the most effective strategy in capturing the complexity of the aging process. However, various confounders may influence the outcome of these age predictors, including genetic and environmental factors, as well as technical differences in the selected DNAm arrays. These factors should be taken into consideration when interpreting DNAm clock predictions. In the current review, we discuss 15 reported DNAm clocks with consideration for their utility in investigating neurodegenerative diseases and suggest research directions towards developing a more optimal measure for biological aging.

Interactions between season of birth, chronological age and genetic polymorphisms in determining later-life chronotype

Human chronotype, the temporal pattern of daily behaviors, is influenced by postnatal seasonal programming and ageing. The aim of this study was to investigate genetic variants that are associated with season of birth programming and longitudinal chronotype change. Longitudinal sleep timing and genotype data from 1449 participants were collected for up to 27 years. Gene-environment interaction analysis was performed for 445 candidate single nucleotide polymorphisms that have previously been associated with chronotype. Associations were tested using linear mixed model. We identified 67 suggestively significant genomic loci that have genotype-ageing interaction and 25 genomic loci that may have genotype-season of birth interaction in determining chronotype. We attempted to replicate the results using longitudinal data of the UK Biobank from approximately 30,000 participants. Biological functions of these genes suggest that epigenetic regulation of gene expression and neural development may have roles in these processes. The strongest associated gene for sleep trajectories was ALKBH5, which has functions of DNA repair and epigenetic regulation. A potential candidate gene for postnatal seasonal programming was SIRT1, which has previously been implicated in postnatal programming, ageing and longevity. Genetic diversity may explain the heterogeneity in ageing-related change of sleep timing and postnatal environmental programming of later-life chronotype.

DNA methylation outlier burden, health, and ageing in Generation Scotland and the Lothian Birth Cohorts of 1921 and 1936

BACKGROUND

DNA methylation outlier burden has been suggested as a potential marker of biological age. An outlier is typically defined as DNA methylation levels at any one CpG site that are three times beyond the inter-quartile range from the 25th or 75th percentiles compared to the rest of the population. DNA methylation outlier burden (the number of such outlier sites per individual) increases exponentially with age. However, these findings have been observed in small samples.

RESULTS

Here, we showed an association between age and log10-transformed DNA methylation outlier burden in a large cross-sectional cohort, the Generation Scotland Family Health Study (N = 7010, β = 0.0091, p < 2 × 10-16), and in two longitudinal cohort studies, the Lothian Birth Cohorts of 1921 (N = 430, β = 0.033, p = 7.9 × 10-4) and 1936 (N = 898, β = 0.0079, p = 0.074). Significant confounders of both cross-sectional and longitudinal associations between outlier burden and age included white blood cell proportions, body mass index (BMI), smoking, and batch effects. In Generation Scotland, the increase in epigenetic outlier burden with age was not purely an artefact of an increase in DNA methylation level variability with age (epigenetic drift). Log10-transformed DNA methylation outlier burden in Generation Scotland was not related to self-reported, or family history of, age-related diseases, and it was not heritable (SNP-based heritability of 4.4%, p = 0.18). Finally, DNA methylation outlier burden was not significantly related to survival in either of the Lothian Birth Cohorts individually or in the meta-analysis after correction for multiple testing (HRmeta = 1.12; 95% CImeta = [1.02; 1.21]; pmeta = 0.021).

CONCLUSIONS

These findings suggest that, while it does not associate with ageing-related health outcomes, DNA methylation outlier burden does track chronological ageing and may also relate to survival. DNA methylation outlier burden may thus be useful as a marker of biological ageing.

DNA methylation-based measures of accelerated biological ageing and the risk of dementia in the oldest-old: a study of the Lothian Birth Cohort 1921

BACKGROUND

Previous studies have demonstrated an association between DNA methylation-based measures of accelerated ageing and age-related health outcomes and mortality. As a disease closely associated with advancing age, we hypothesized that DNA methylation-based measures of accelerated ageing might be associated with risk for dementia. This study therefore aimed to examine the association between four recognised measures of age acceleration and subsequent dementia.

METHODS

Study subjects (n = 488) were members of the Lothian Birth Cohort 1921. Dementia case ascertainment used data from death certificates, electronic hospital records, and clinical reviews. Venous blood samples were taken at baseline, at age 79 years. DNA methylation and measures of epigenetic age were calculated in accordance with Horvath's epigenetic clock tutorial, using the online calculator (https://dnamage.genetics.ucla.edu/). From these values, four measures of accelerated ageing were calculated: extrinsic epigenetic age acceleration (EEAA), intrinsic epigenetic age acceleration (IEAA), AgeAccelPheno and AgeAccelGrim. Competing risk regression models - with death as a competing risk - were performed to examine the association between each measure of accelerated ageing and incident dementia. APOE ɛ4 status, sex, age, smoking status, history of cardiovascular disease, cerebrovascular disease, hypertension, and diabetes were included as covariates.

RESULTS

None of the multivariate models revealed a positive association between increased epigenetic age acceleration and dementia risk. Across all included models, never-smoking increased risk for dementia (HR 1.69 [1.06, 2.71], p = 0.03), and having no APOE ɛ4 alleles reduced risk for dementia (HR 0.44 [0.29, 0.67], p < 0.001).

CONCLUSIONS

The present study did not demonstrate any consistent association between DNA methylation-based measures of accelerated ageing and dementia in subjects aged over 79 years. Further, larger studies - including separate analyses of dementia subtypes - are required to further investigate the potential association between DNA methylation-based measures of accelerated ageing and dementia.

DNA methylation aging clocks: challenges and recommendations

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

BACKGROUND

DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association.

METHODS

In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues.

RESULTS

We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor.

CONCLUSIONS

This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.