Cross-platform proteomics signatures of extreme old age.

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

A Mass Spectrometry-Based Proteome Study of Twin Pairs Discordant for Incident Acute Myocardial Infarction within Three Years after Blood Sampling Suggests Novel Biomarkers.

Acute myocardial infarction (AMI) is a major cause of mortality and morbidity worldwide, yet biomarkers for AMI in the short- or medium-term are lacking. We apply the discordant twin pair design, reducing genetic and environmental confounding, by linking nationwide registry data on AMI diagnoses to a survey of 12,349 twins, thereby identifying 39 twin pairs (48-79 years) discordant for their first-ever AMI within three years after blood sampling. Mass spectrometry of blood plasma identified 715 proteins. Among 363 proteins with a call rate > 50%, imputation and stratified Cox regression analysis revealed seven significant proteins (FDR < 0.05): FGD6, MCAM, and PIK3CB reflected an increased level in AMI twins relative to their non-AMI co-twins (HR > 1), while LBP, IGHV3-15, C1RL, and APOC4 reflected a decreased level in AMI twins relative to their non-AMI co-twins (HR < 1). Additional 50 proteins were nominally significant (p < 0.05), and bioinformatics analyses of all 57 proteins revealed biology within hemostasis, coagulation cascades, the immune system, and the extracellular matrix. A protein-protein-interaction network revealed Fibronectin 1 as a central hub. Finally, technical validation confirmed MCAM, LBP, C1RL, and APOC3. We put forward novel biomarkers for incident AMI, a part of the proteome field where markers are surprisingly rare and where additional studies are highly needed.

Epigenome-wide analysis of frailty: Results from two European twin cohorts.

Epigenetics plays an important role in the aging process, but it is unclear whether epigenetic factors also influence frailty, an age-related state of physiological decline. In this study, we performed a meta-analysis of epigenome-wide association studies in four samples drawn from the Swedish Adoption/Twin Study of Aging (SATSA) and the Longitudinal Study of Aging Danish Twins (LSADT) to explore the association between DNA methylation and frailty. Frailty was defined using the frailty index (FI), and DNA methylation levels were measured in whole blood using Illumina's Infinium HumanMethylation450K and MethylationEPIC arrays. In the meta-analysis consisting of a total of 829 participants, we identified 589 CpG sites that were statistically significantly associated with either the continuous or categorical FI (false discovery rate <0.05). Many of these CpGs have previously been associated with age and age-related diseases. The identified sites were also largely directionally consistent in a longitudinal analysis using mixed-effects models in SATSA, where the participants were followed up to a maximum of 20 years. Moreover, we identified three differentially methylated regions within the MGRN1, MIR596, and TAPBP genes that have been linked to neuronal aging, tumor growth, and immune functions. Furthermore, our meta-analysis results replicated 34 of the 77 previously reported frailty-associated CpGs at p < 0.05. In conclusion, our findings demonstrate robust associations between frailty and DNA methylation levels in 589 novel CpGs, previously unidentified for frailty, and strengthen the role of neuronal/brain pathways in frailty.

A qPCR technology for direct quantification of methylation in untreated DNA.

DNA methylation is important for gene expression and alterations in DNA methylation are involved in the development and progression of cancer and other major diseases. Analysis of DNA methylation patterns has until now been dependent on either a chemical or an enzymatic pre-treatment, which are both time consuming procedures and potentially biased due to incomplete treatment. We present a qPCR technology, EpiDirect®, that allows for direct PCR quantification of DNA methylations using untreated DNA. EpiDirect® is based on the ability of Intercalating Nucleic Acids (INA®) to differentiate between methylated and unmethylated cytosines in a special primer design. With this technology, we develop an assay to analyze the methylation status of a region of the MGMT promoter used in treatment selection and prognosis of glioblastoma patients. We compare the assay to two bisulfite-relying, methyl-specific PCR assays in a study involving 42 brain tumor FFPE samples, revealing high sensitivity, specificity, and the clinical utility of the method.

Identifying genetic variants regulating MGMT gene expression - A study in monozygotic Danish twins.

Identifying genetic factors affecting the regulation of the O-6-Methylguanine-DNA Methyltransferase (MGMT) gene and estimating the genetic contribution of the MGMT gene through within-pair correlation in monozygotic twin pairs is of particular importance in various types of cancer such as glioblastoma. We used gene expression data in whole blood from 448 monozygotic twins from the Middle Age Danish Twins (MADT) study to investigate genetic regulation of the MGMT gene by performing a genome-wide association study (GWAS) of the variation in MGMT expression. Additionally, we estimated within-pair dependence measures of the expression values looking for the genetic influence of significant identified genes. We identified 243 single nucleotide polymorphisms (SNPs) significantly (p < 5e-8) associated with expression of MGMT, all located on chromosome 10 near the MGMT gene. Of the 243 SNPs, 7 are novel cis-eQTLs. By further looking into the suggestively significant SNPs (increasing cutoff to p = 1e-6), we identified 11 suggestive trans-eQTLs located on chromosome 17. These variants were in or proximal to a total of seven genes, which may regulate MGMT expression. The within-pair correlation of the expression of MGMT, TRIM37, and SEPT4 provided the upper bound genetic influence of these genes. Overall, identifying cis- or trans-acting genetic variations regulating the MGMT gene can pave the way for a better understanding of the MGMT gene function and ultimately in understanding the patient's sensitivity to therapeutic alkylating agents.

A metabolomic signature of the APOE2 allele.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies.

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

Clonal Hematopoiesis and Epigenetic Age Acceleration in Elderly Danish Twins.

Clonal hematopoiesis of indeterminate potential (CHIP) is common in the elderly and has been reported to associate with accelerated epigenetic age (AgeAccel), especially intrinsic (ie, cell-type independent) AgeAccel and to a lesser degree extrinsic AgeAccel, which reflects the immune-cell composition of the peripheral blood. We investigated the association between CHIP occurrence and AgeAccel in 154 Danish twin pairs aged 73-90 years (mean 79), using both individual-level and intrapair analyses, the latter to control for shared genetic and environmental factors. Of 308 individuals, 116 carried a CHIP mutation. CHIP carriers had non-significantly increased AgeAccel compared with non-carriers; the strongest association was for the Intrinsic Epigenetic Age Acceleration (IEAA) estimator (CHIP carriers 1.4 years older, P = 0.052). In intrapair analyses, the extrinsic Hannum age estimator showed the strongest association (1.6 years older, P = 0.027). In mutation-specific analyses, TET2 mutations were associated with the extrinsic Hannum age estimator in both individual-level (3.0 years older, P = 0.003) and intrapair analyses (2.8 years older, P = 0.05). DNMT3A mutations were associated with IEAA in individual-level (1.9 years older, P = 0.034) but not intrapair analysis (0.9 years, P = 0.41). Analyses of logit-transformed variant allele frequency were generally consistent with these results. Together, these observations indicate that different factors may be driving the expansion of DNMT3A and TET2 clones, respectively. Finally, CHIP carriers accelerated in both the Hannum and the GrimAge age estimators did not have an increased mortality risk in our cohort followed for 22 years (HR = 1.02, P = 0.93), hence not replicating the stratification model proposed by Nachun et al.

Integrative analysis of clinical and epigenetic biomarkers of mortality.

DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10-7 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, PMR  = 4.1 × 10-4 ) and negatively associated with longevity (Beta = -1.9, PMR  = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.

TET2 mutations are associated with hypermethylation at key regulatory enhancers in normal and malignant hematopoiesis.

Mutations in the epigenetic modifier TET2 are frequent in myeloid malignancies and clonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of undetermined significance (CCUS). Here, we investigate associations between TET2 mutations and DNA methylation in whole blood in 305 elderly twins, 15 patients with CCUS and 18 healthy controls. We find that TET2 mutations are associated with DNA hypermethylation at enhancer sites in whole blood in CHIP and in both granulocytes and mononuclear cells in CCUS. These hypermethylated sites are associated with leukocyte function and immune response and ETS-related and C/EBP-related transcription factor motifs. While the majority of TET2-associated hypermethylation sites are shared between CHIP and in AML, we find a set of AML-specific hypermethylated loci at active enhancer elements in hematopoietic stem cells. In summary, we show that TET2 mutations is associated with hypermethylated enhancers involved in myeloid differentiation in both CHIP, CCUS and AML patients.

Toll-like receptor 4 methylation grade is linked to depressive symptom severity.

This study explores potential associations between the methylation of promoter-associated CpG sites of the toll-like receptor (TLR)-family, plasma levels of pro-inflammatory proteins and depressive symptoms in young female psychiatric patients. Ratings of depressive symptoms and blood samples were obtained from 92 young women seeking psychiatric care. Methylation of 32 promoter-associated CpG sites in TLR1 to TLR10 was analysed using the Illumina Infinium Methylation EPIC BeadChip. Expression levels of 91 inflammatory proteins were determined by proximity extension assay. Statistical correlations between depressive state, TLR1-10 methylation and inflammatory proteins were investigated. Four additional cohorts were studied to evaluate the generalizability of the findings. In the discovery cohort, methylation grade of cg05429895 (TLR4) in blood was inversely correlated with depressive symptoms score in young adults. After correction for multiple testing, plasma levels of macrophage inflammatory protein 1ß (MIP-1ß/CCL4) were associated with both TLR4 methylation and depressive symptom severity. A similar inverse association between TLR4 methylation in blood and affective symptoms score was also found in a cohort of 148 both males and females (<40 years of age) from the Danish Twin Registry. These findings were not, however, replicated in three other external cohorts; which differed from the first two cohorts by a higher age and mixed ethnicities, thus limiting the generalizability of our findings. However, TLR4 methylation inversely correlated with TLR4 mRNA expression in the Danish Twin Study indicating a functional significance of methylation at this particular CpG. Higher depression scores in young Scandinavian adults was associated with decreased methylation of TLR4 in blood.

Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging.

BACKGROUND: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. RESULTS: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. CONCLUSION: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Does diabetes modify the effect of heparin on plasma proteins? - A proteomic search for plasma protein biomarkers for diabetes-related endothelial dysfunction.

AIM: Heparin administration affects the concentrations of many plasma proteins through their displacement from the endothelial glycocalyx. A differentiated protein response in diabetes will therefore, at least partly, reflect glycocalyx changes. This study aims at identifying biomarkers of endothelial dysfunction in diabetes by statistical exploration of plasma proteome data for interactions between diabetes status and heparin treatment. METHODS: Diabetes-by-heparin interactions in relation to protein levels were inspected by regression modelling in plasma proteome data from 497 patients admitted for acute angiography. Analyses were conducted separately for all 273 proteins and as set-based analyses of 44 heparin-relevant proteins identified by gene ontology analysis and 42 heparin-influenced proteins previously reported. RESULTS: Seventy-five patients had diabetes and 361 received heparin before hospitalization. The proteome-wide analysis displayed no proteins with diabetes-heparin interaction to pass correction for multiple testing. The overall set-based analyses revealed significant association for both protein sets (p-values<2*10-4), while constraining on opposite directions of effect in diabetics and none-diabetics was insignificant (p-values = 0.11 and 0.17). CONCLUSIONS: Our plasma proteome-wide interaction approach supports that diabetes influences heparin effects on protein levels, however the direction of effects and individual proteins could not be definitively pinpointed, likely reflecting a complex protein-basis for glycocalyx dysfunction in diabetes.

Skewness of X-chromosome inactivation increases with age and varies across birth cohorts in elderly Danish women.

Mosaicism in blood varies with age, and cross-sectional studies indicate that for women, skewness of X-chromosomal mosaicism increases with age. This pattern could, however, also be due to less X-inactivation in more recent birth cohorts. Skewed X-chromosome inactivation was here measured longitudinally by the HUMARA assay in 67 septuagenarian and octogenarian women assessed at 2 time points, 10 years apart, and in 10 centenarian women assessed at 2 time points, 2-7 years apart. Skewed X-chromosome inactivation was also compared in 293 age-matched septuagenarian twins born in 1917-1923 and 1931-1937, and 212 centenarians born in 1895, 1905 and 1915. The longitudinal study of septuagenarians and octogenarians revealed that 16% (95% CI 7-29%) of the women developed skewed X-inactivation over a 10-year period. In the cross-sectional across-birth cohort study, the earlier-born septuagenarian (1917-1923) and centenarian women (1895) had a higher degree of skewness than the respective recent age-matched birth cohorts, which indicates that the women in the more recent cohorts, after the age of 70, had not only changed degree of skewness with age, they had also undergone less age-related hematopoietic sub-clone expansion. This may be a result of improved living conditions and better medical treatment in the more recent birth cohorts.

Genetic and environmental determinants of O6-methylguanine DNA-methyltransferase (MGMT) gene methylation: a 10-year longitudinal study of Danish twins.

BACKGROUND: Epigenetic inactivation of O6-methylguanine DNA-methyltransferase (MGMT) is associated with increased sensitivity to alkylating chemotherapeutic agents in glioblastoma patients. The genetic background underlying MGMT gene methylation may explain individual differences in treatment response and provide a clue to a personalized treatment strategy. Making use of the longitudinal twin design, we aimed, for the first time, to estimate the genetic contributions to MGMT methylation in a Danish twin cohort. METHODS: DNA-methylation from whole blood (18 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs) repeated 10 years apart from the Longitudinal Study of Aging Danish Twins (LSADT) were used to search for genetic and environmental contributions to DNA-methylation at 170 CpG sites of across the MGMT gene. Both univariate and bivariate twin models were applied. The intraclass correlations, performed on cross-sectional data (246 MZ twin pairs) from an independent study population, the Middle-Aged Danish Twins (MADT), were used to assess the genetic influence at each CpG site of MGMT for replication. RESULTS: Univariate twin model revealed twelve CpG sites showing significantly high heritability at intake (wave 1, h2 > 0.43), and seven CpG sites with significant heritability estimates at end of follow-up (wave 2, h2 > 0.5). There were six significant CpG sites, located at the gene body region, that overlapped among the two waves (h2 > 0.5), of which five remained significant in the bivariate twin model, which was applied to both waves. Within MZ pair correlation in these six CpGs from MADT demarks top level of genetic influence. There were 11 CpGs constantly have substantial common environmental component over the 10 years. CONCLUSIONS: We have identified 6 CpG sites linked to the MGMT gene with strong and persistent genetic control based on their DNA methylation levels. The genetic basis of MGMT gene methylation could help to explain individual differences in glioblastoma treatment response and most importantly, provide references for mapping the methylation Quantitative Trait Loci (meQTL) underlying the genetic regulation.

Novel DNA methylation marker discovery by assumption-free genome-wide association analysis of cognitive function in twins.

Privileged by rapid increase in available epigenomic data, epigenome-wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption-free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e-04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region-based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.

STEMI, Cardiogenic Shock, and Mortality in Patients Admitted for Acute Angiography: Associations and Predictions from Plasma Proteome Data.

AIM: Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity, and cardiogenic shock (CS) a major cause of hospital mortality after AMI. Especially for ST elevation myocardial infarction (STEMI) patients, fast intervention is essential.Few proteins have proven clinically applicable for AMI. Most proposed biomarkers are based on a priori hypothesis-driven studies of single proteins, not enabling identification of novel candidates. For clinical use, the ability to predict AMI is important; however, studies of proteins in prediction models are surprisingly scarce.Consequently, we applied proteome data for identifying proteins associated with definitive STEMI, CS, and all-cause mortality after admission, and examined the ability of the proteins to predict these outcomes. METHODS AND RESULTS: Proteome-wide data of 497 patients with suspected STEMI were investigated; 381 patients were diagnosed with STEMI, 35 with CS, and 51 died during the first year. Data analysis was conducted by logistic and Cox regression modeling for association analysis, and by multivariable LASSO regression models for prediction modeling.Association studies identified 4 and 29 proteins associated with definitive STEMI or mortality, respectively. Prediction models for CS and mortality (holding two and five proteins, respectively) improved the prediction ability as compared with protein-free prediction models; AUC of 0.92 and 0.89, respectively. CONCLUSION: The association analyses propose individual proteins as putative protein biomarkers for definitive STEMI and survival after suspected STEMI, while the prediction models put forward sets of proteins with putative predicting ability of CS and survival. These proteins may be verified as biomarkers of potential clinical relevance.

A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins.

Monozygotic twins are genetically identical but rarely phenotypically identical. Epigenetic and transcriptional variation could influence this phenotypic discordance. Investigation of intra-pair differences in molecular markers and a given phenotype in monozygotic twins controls most of the genetic contribution, enabling studies of the molecular features of the phenotype. This study aimed to identify genes associated with cognition in later life using integrated enrichment analyses of the results of blood-derived intra-pair epigenome-wide and transcriptome-wide association analyses of cognition in 452 middle-aged and old-aged monozygotic twins (56-80 years). Integrated analyses were performed with an unsupervised approach using KeyPathwayMiner, and a supervised approach using the KEGG and Reactome databases. The supervised approach identified several enriched gene sets, including "neuroactive ligand receptor interaction" (p-value = 1.62∗10-2), "Neurotrophin signaling" (p-value = 2.52∗10-3), "Alzheimer's disease" (p-value = 1.20∗10-2), and "long-term depression" (p-value = 1.62∗10-2). The unsupervised approach resulted in a 238 gene network, including the Alzheimer's disease gene APP (Amyloid Beta Precursor Protein) as an exception node, and several novel candidate genes. The strength of the unsupervised method is that it can reveal previously uncharacterized sub-pathways and detect interplay between biological processes, which remain undetected by the current supervised methods. In conclusion, this study identified several previously reported cognition genes and pathways and, additionally, puts forward novel candidates for further verification and validation.

Cohort Differences in the Associations of Selected Candidate Genes With Risk of All-Cause Mortality at Advanced Ages.

Considerable efforts have been made to identify the genetic basis of human longevity, with only limited progress. One important drawback of current genetic studies is the limited knowledge of gene-environment interaction. Using 2 cohorts of long-lived individuals born in 1905 and 1915 in Denmark, we performed survival analysis to estimate risk of mortality for major candidate genes of aging and longevity and their cohort effects. Through statistical modeling that combines individual genetic and survival information with cohort-specific survival data, we estimated the relative risks of mortality from ages 95 to 103 years associated with genetic variants in apolipoprotein E (APOE), forkhead box class O3a, clusterin, and phosphatidylinositol binding clathrin assembly protein. Our analysis estimated a decreased risk of carrying the APOE$\varepsilon $4 allele (change in risk = -0.403, 95% confidence interval (CI): -0.831, 0.021; P = 0.040) in men of the later cohort, although the allele itself was harmful to survival across sexes (relative risk = 1.161, 95% CI: 1.027, 1.345; P = 0.026). We also estimated a cohort effect of increased risk for the minor allele of rs3851179 in phosphatidylinositol binding clathrin assembly protein with borderline significance (change in risk = 0.165, 95% CI: -0.010, 0.331; P = 0.052) in women. Our estimated significant cohort effect on APOE$\varepsilon $4 is indicative of the interplay between the gene and the changing environment that modulates survival at extreme ages.

Age-dependent DNA methylation patterns on the Y chromosome in elderly males.

The Y chromosome, a sex chromosome that only exists in males, has been ignored in traditional epigenetic association studies for multiple reasons. However, sex differences in aging-related phenotypes and mortality could suggest a critical role of the sex chromosomes in the aging process. We obtained blood-based DNA methylation data on the Y chromosome for 624 men from four cohorts and performed a chromosome-wide epigenetic association analysis to detect Y-linked CpGs differentially methylated over age and cross-validated the significant CpGs in the four cohorts. We identified 40-219 significant CpG sites (false discovery rate <0.05) with >82% of them hypermethylated with increasing age, which is in strong contrast to the patterns reported on the autosomal chromosomes. Comparing the rate of change in the Y-linked DNA methylation across cohorts that represent different age intervals revealed a trend of acceleration in DNA methylation with increasing age. The age-dependent DNA methylation patterns on the Y chromosome were further examined for their association with all-cause mortality with results suggesting that the predominant pattern of age-related hypermethylation on the Y chromosome is associated with reduced risk of death.