Methylation Quantitative Trait Loci Research Articles

Trajectories of body mass index and adolescent hair cortisol: importance of persistence, variability, and developmental timing

Longevity gene klotho (KL) is associated with age-related phenotypes including lifespan, cardiometabolic disorders, cognition, and brain morphology, in part, by conferring protection against inflammation. We hypothesized that the KL/inflammation association might be altered in the presence of psychiatric stress and operate via epigenetic pathways. We examined KL polymorphisms, and their interaction with posttraumatic stress disorder (PTSD) symptoms, in association with KL DNA methylation in blood. We further examined KL DNA methylation as a predictor of longitudinal changes in a peripheral biomarker of inflammation (C-reactive protein; CRP).The sample comprised 309 white non-Hispanic military veterans (93.5 % male; mean age: 32 years, range: 19–65; 30 % PTSD per structured diagnostic interview); 111 were reassessed approximately two years later.Analyses revealed a methylation quantitative trait locus at rs9527025 (C370S, previously implicated in numerous studies of aging) in association with a Cytosine-phosphate-Guanine site (cg00129557; B = −.65, p = 1.29 X 10−20), located within a DNase hypersensitivity site in the body of KL. There was also a rs9527025 x PTSD severity interaction (B = .004, p = .035) on methylation at this locus such that the minor allele was associated with reduced cg00129557 methylation in individuals with few or no PTSD symptoms while this effect was attenuated in those with elevated levels of PTSD. Path models revealed that methylation at cg00129557 was inversely associated with CRP over time (B = −.14, p = .005), controlling for baseline CRP. There was also an indirect effect of rs9527025 X PTSD on subsequent CRP via cg00129557 methylation (indirect B = −.002, p = .033).Results contribute to our understanding of the epigenetic correlates of inflammation in PTSD and suggest that KL methylation may be a mechanism by which KL genotype confers risk vs. resilience to accelerated aging in those experiencing traumatic stress.

Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease.

Alzheimer’s disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

Obsessive-compulsive disorder and attention-deficit/hyperactivity disorder: distinct associations with DNA methylation and genetic variation

BackgroundA growing body of research has demonstrated associations between specific neurodevelopmental disorders and variation in DNA methylation (DNAm), implicating this molecular mark as a possible contributor to the molecular etiology of these disorders and/or as a novel disease biomarker. Furthermore, genetic risk variants of neurodevelopmental disorders have been found to be enriched at loci associated with DNAm patterns, referred to as methylation quantitative trait loci (mQTLs).MethodsWe conducted two epigenome-wide association studies in individuals with attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD) (aged 4–18 years) using DNA extracted from saliva. DNAm data generated on the Illumina Human Methylation 450 K array were used to examine the interaction between genetic variation and DNAm patterns associated with these disorders.ResultsUsing linear regression followed by principal component analysis, individuals with the most endorsed symptoms of ADHD or OCD were found to have significantly more distinct DNAm patterns from controls, as compared to all cases. This suggested that the phenotypic heterogeneity of these disorders is reflected in altered DNAm at specific sites. Further investigations of the DNAm sites associated with each disorder revealed that despite little overlap of these DNAm sites across the two disorders, both disorders were significantly enriched for mQTLs within our sample.ConclusionsOur DNAm data provide insights into the regulatory changes associated with genetic variation, highlighting their potential utility both in directing GWAS and in elucidating the pathophysiology of neurodevelopmental disorders.

Identification of Transcription Factor/Gene Axis in Colon Cancer Using a Methylome Approach.

Colon cancer is one of the most commonly diagnosed cancers worldwide. Both environmental and molecular characters can influence its development. DNA methylation has been heralded as a promising marker for use in cancer prevention, diagnosis, and treatment. It has been shown to facilitate cancer progression through multiple mechanisms. Changes in DNA methylation can inhibit or promote the binding of transcription factors (TFs) and further disturb gene regulation. Detection of DNA methylation-mediated regulatory events in colon cancer are critical for mining novel biomarkers. Here, we explore the influence of CpG sites located at promoter regions of differentially expressed genes and identify methylation–gene relationships using expression–methylation quantitative trait loci. We find that promoter methylation sites mainly negatively regulate the corresponding genes. We also identify candidate TFs that can bind to these sites in a sequence-dependent manner. By integrating transcriptome and methylome profiles, we construct a TF–CpG–gene regulatory network for colon cancer, which is used to determine the roles of TFs and methylation in the transcription process. Finally, based on TF–CpG–gene relationships, we design a framework to evaluate patient prognosis, which shows that one TF–CpG–gene triplet is significantly associated with patient survival rate and represents a potential novel biomarker for use in colon cancer prognosis and treatment.

Systemic Investigation of Promoter-wide Methylome and Genome Variations in Gout.

Current knowledge of gout centers on hyperuricemia. Relatively little is known regarding the pathogenesis of gouty inflammation. To investigate the epigenetic background of gouty inflammation independent of hyperuricemia and its relationship to genetics, 69 gout patients and 1455 non-gout controls were included. Promoter-wide methylation was profiled with EPIC array. Whole-genome sequencing data were included for genetic and methylation quantitative trait loci (meQTL) analyses and causal inference tests. Identified loci were subjected to co-methylation analysis and functional localization with DNase hypersensitivity and histone marks analysis. An expression database was queried to clarify biologic functions of identified loci. A transcription factor dataset was integrated to identify transcription factors coordinating respective expression. In total, seven CpG loci involved in interleukin-1β production survived genetic/meQTL analyses, or causal inference tests. None had a significant relationship with various metabolic traits. Additional analysis suggested gouty inflammation, instead of hyperuricemia, provides the link between these CpG sites and gout. Six (PGGT1B, INSIG1, ANGPTL2, JNK1, UBAP1, and RAPTOR) were novel genes in the field of gout. One (CNTN5) was previously associated with gouty inflammation. Transcription factor mapping identified several potential transcription factors implicated in the link between differential methylation, interleukin-1β production, and gouty inflammation. In conclusion, this study revealed several novel genes specific to gouty inflammation and provided enhanced insight into the biological basis of gouty inflammation.

Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis.

Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of ‘druggable’ genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein–protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.

Integrative analysis of genome-wide association study and common meQTLs for exploring the effects of DNA methylation on the development of neuroticism

BackgroundNeuroticism is one of the important traits of personality, which has strong genetic components. However, the underlying genetic mechanism is still unclear. MethodsTo better understand the genetic basis of neuroticism, we conducted an integrative analysis of genome-wide association studies (GWAS) and life course consistent methylation quantitative trait loci (meQTLs) data. The GWAS data of neuroticism was derived from a published study of neuroticism (including 170,906 subjects). Life course consistent meQTLs were obtained from a large scale longitudinal meQTLs analysis (including 1,018 mother–child pairs).Gene prioritization, pathway and tissue/cell type enrichment analyses were implemented by DEPICT. ResultsWe identified multiple genes, pathways and tissues associated with neuroticism, such as NEIL2 (P value = 1.31 × 10−2), ARHGAP27 (P value = 1.40 × 10−2), REACTOME_CLATHRIN_DERIVED_VESICLE_BUDDING(P value =4.92 × 10−6) ,REACTOME_TRANS:GOLGI_NETWORK_VESICLE_BUDDING (P value =4.92 × 10−6), frontal lobe(P value =3.83 × 10−3) and visual cortex (P value =8.46 × 10−3). ConclusionsOur results provide novel insights for understanding the genetic mechanism of neuroticism.

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

BackgroundRecent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels. MethodsWe applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels. ResultsWe identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before. ConclusionsOur findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

Summary-Based Methylome-Wide Association Analyses Suggest Potential Genetically Driven Epigenetic Heterogeneity of Alzheimer's Disease.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with no curative treatment available. Exploring the genetic and non-genetic contributors to AD pathogenesis is essential to better understand its underlying biological mechanisms, and to develop novel preventive and therapeutic strategies. We investigated potential genetically driven epigenetic heterogeneity of AD through summary data-based Mendelian randomization (SMR), which combined results from our previous genome-wide association analyses with those from two publicly available methylation quantitative trait loci studies of blood and brain tissue samples. We found that 152 probes corresponding to 113 genes were epigenetically associated with AD at a Bonferroni-adjusted significance level of 5.49E-07. Of these, 10 genes had significant probes in both brain-specific and blood-based analyses. Comparing males vs. females and hypertensive vs. non-hypertensive subjects, we found that 22 and 79 probes had group-specific associations with AD, respectively, suggesting a potential role for such epigenetic modifications in the heterogeneous nature of AD. Our analyses provided stronger evidence for possible roles of four genes (i.e., AIM2, C16orf80, DGUOK, and ST14) in AD pathogenesis as they were also transcriptionally associated with AD. The identified associations suggest a list of prioritized genes for follow-up functional studies and advance our understanding of AD pathogenesis.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (n = 1980), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

PTSD and the klotho longevity gene: Evaluation of longitudinal effects on inflammation via DNA methylation

BackgroundLongevity gene klotho (KL) is associated with age-related phenotypes including lifespan, cardiometabolic disorders, cognition, and brain morphology, in part, by conferring protection against inflammation. We hypothesized that the KL/inflammation association might be altered in the presence of psychiatric stress and operate via epigenetic pathways. We examined KL polymorphisms, and their interaction with posttraumatic stress disorder (PTSD) symptoms, in association with KL DNA methylation in blood. We further examined KL DNA methylation as a predictor of longitudinal changes in a peripheral biomarker of inflammation (C-reactive protein; CRP). MethodsThe sample comprised 309 white non-Hispanic military veterans (93.5 % male; mean age: 32 years, range: 19–65; 30 % PTSD per structured diagnostic interview); 111 were reassessed approximately two years later. ResultsAnalyses revealed a methylation quantitative trait locus at rs9527025 (C370S, previously implicated in numerous studies of aging) in association with a Cytosine-phosphate-Guanine site (cg00129557; B = −.65, p = 1.29 X 10−20), located within a DNase hypersensitivity site in the body of KL. There was also a rs9527025 x PTSD severity interaction (B = .004, p = .035) on methylation at this locus such that the minor allele was associated with reduced cg00129557 methylation in individuals with few or no PTSD symptoms while this effect was attenuated in those with elevated levels of PTSD. Path models revealed that methylation at cg00129557 was inversely associated with CRP over time (B = −.14, p = .005), controlling for baseline CRP. There was also an indirect effect of rs9527025 X PTSD on subsequent CRP via cg00129557 methylation (indirect B = −.002, p = .033). ConclusionsResults contribute to our understanding of the epigenetic correlates of inflammation in PTSD and suggest that KL methylation may be a mechanism by which KL genotype confers risk vs. resilience to accelerated aging in those experiencing traumatic stress.

Molecular genetic and epigenetic analysis of the osteoarthritis risk residing downstream of the gene TGFB1

Purpose: Over 90 genome-wide significant association signals have been reported for osteoarthritis (OA). Risk-conferring variants (typically single nucleotide polymorphisms [SNPs]) are often intergenic and thought to operate by mediating differential expression of their target genes. SNP genotype can also correlate with differential DNA methylation (DNAm) at CG dinucleotides (CpGs), known as methylation quantitative trait loci (mQTLs). DNAm has been shown to act as a conduit through which genotype can exert a functional effect upon gene expression.

O11 Lymphocyte DNA methylation mediates genetic risk at RA risk loci that are shared with other immune mediated diseases

Abstract Background The aetiology of rheumatoid arthritis (RA) is complex. In particular, the vast majority of disease-associated variants implicated by genome-wide association studies are non-coding, leaving genetic mechanisms of adaptive immune dysregulation unresolved. The contribution to this process of epigenetic factors, including the addition of methyl groups to DNA, also remains uncertain. To address these issues and prioritise causal genes for downstream study, genome-wide data incorporating DNA methylation and gene expression measurements from lymphocyte subsets in an early arthritis inception cohort, were available. Methods Whole genome methylation and transcription data from isolated CD4+ T cells and B cells of > 100 well-characterised inflammatory arthritis patients, all of whom were naïve to immunomodulatory treatments and of Northern European ancestry, were obtained (Illumina HumanHT-12 v4 Expression BeadChip and Infinium MethylationEPIC BeadChip arrays, respectively). Genotyping was undertaken using the Illumina Human CoreExome-24 version 1-0 array. After independent pre-processing, normalisation and quality control of paired CD4+ and B lymphocyte data, methylation quantitative trait loci (meQTLs) were first modelled using the MatrixEQTL package in each cell type. Next, at RA risk-associated cis-CpGs, correlations between site-specific methylation and the expression of genes within ±500Kb identified quantitative trait methylations (eQTMs). To infer directionality of SNP-CpG-transcript associations a causal inference test (CIT) was applied. Multiple testing was accounted for, and in vitro assays were used to validate meQTLs at loci of interest and confirm regulatory mechanisms. Further analysis integrated GWAS data from other immune mediated diseases (IMDs) and additional publically available resources. Results We found strong evidence that disease-associated DNA variants regulate cis-CpG methylation of DNA in CD4+ T and/or B cells at 37% RA loci. In general we observed these variants to preferentially modify methylation at sites mapping to lymphocyte enhancers and regions flanking transcription start sites, and at positions bound by the NFκB transcription factor. Using paired, cell-specific transcriptomic data and a statistical approach to infer causality, we then identified examples where site-specific DNA methylation in turn mediates gene expression, including ORMDL3/GSDMB, IL6ST/ANKRD55, FCRL3 and JAZF1 in CD4+ lymphocytes. Leveraging GWAS data we noted that a number of genes regulated in this way highlight mechanisms common to RA, multiple sclerosis and asthma, distinguishing these IMDs from osteoarthritis which is considered a primarily degenerative disease. To validate our findings, cis-meQTL effects at sentinel loci were replicated by pyrosequencing in an independent cohort of genotyped early arthritis patients, and methylation-mediated regulation of FCRL3 expression downstream of the regulatory SNP was confirmed experimentally using a luciferase reporter assay in Jurkat T-cells. Conclusion Our observations highlight important mechanisms of genetic risk in RA and the wider context of autoimmunity. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritisation and, potentially, therapeutic targeting in complex IMD. Disclosures A.D. Clark None. N. Nair None. A.E. Anderson None. N. Thalayasingam None. N. Naamane None. A.J. Skelton None. J. Diboll None. A. Barton None. S. Eyre None. J.D. Isaacs None. L.N. Reynard None. A.G. Pratt Grants/research support; I am a recipient of an unrestricted, investigator initiated research grant from Pfizer, paid to Newcastle University.

The molecular genetics and epigenetics of COLGALT2, a risk locus for osteoarthritis

Purpose: Genome wide association studies (GWAS) have identified over 90 association signals for osteoarthritis (OA). The majority of these mediate OA susceptibility by affecting the expression of their target genes, and are known as expression quantitative trait loci (eQTLs). Epigenetic modifications, such as DNA methylation (DNAm), can also influence gene expression and act as mediators between genotype at a single nucleotide polymorphism (SNP) and gene expression. Correlation between SNP genotype and DNAm at a CG dinucleotide (CpG) is known as a methylation quantitative trait locus (mQTL).

Osteoarthritis-associated epigenetic effects operate in cartilage during human fetal development

Purpose: Almost 100 genome-wide significant association signals have been reported for osteoarthritis (OA). Risk-conferring variants typically operate by altering expression of a target gene. Epigenetic mechanisms within chondrocytes can mediate this regulation. DNA methylation (DNAm) at a CpG dinucleotide (CpG) can correlate with genotype at single nucleotide polymorphisms (SNPs), creating methylation quantitative trait loci (mQTLs). Previously, we have reported 18 mQTLs correlating with OA risk alleles in cartilage from patients who have undergone arthroplasty.

Metabolomics analysis and metabolite-agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines.

SummaryPlants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high‐density genetic map, we conducted a comprehensive metabolome study via widely targeted LC‐MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty‐four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite‐agronomic traits with the co‐localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co‐localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.

DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs.

Genome-wide association studies have identified more than 100 SNPs that increase the risk of prostate cancer (PrCa). We identify and compare expression quantitative trait loci (eQTLs) and CpG methylation quantitative trait loci (meQTLs) among 147 established PrCa risk SNPs in primary prostate tumors (n = 355 from a Seattle-based study and n = 495 from The Cancer Genome Atlas, TCGA) and tumor-adjacent, histologically benign samples (n = 471 from a Mayo Clinic study). The role of DNA methylation in eQTL regulation of gene expression was investigated by data triangulation using several causal inference approaches, including a proposed adaptation of the Causal Inference Test (CIT) for causal direction. Comparing eQTLs between tumors and benign samples, we show that 98 of the 147 risk SNPs were identified as eQTLs in the tumor-adjacent benign samples, and almost all 34 eQTL identified in tumor sets were also eQTLs in the benign samples. Three lines of results support the causal role of DNA methylation. First, nearly 100 of the 147 risk SNPs were identified as meQTLs in one tumor set, and almost all eQTLs in tumors were meQTLs. Second, the loss of eQTLs in tumors relative to benign samples was associated with altered DNA methylation. Third, among risk SNPs identified as both eQTLs and meQTLs, mediation analyses suggest that over two-thirds have evidence of a causal role for DNA methylation, mostly mediating genetic influence on gene expression. In summary, we provide a comprehensive catalog of eQTLs, meQTLs and putative cancer genes for known PrCa risk SNPs. We observe that a substantial portion of germline eQTL regulatory mechanisms are maintained in the tumor development, despite somatic alterations in tumor genome. Finally, our mediation analyses illuminate the likely intermediary role of CpG methylation in eQTL regulation of gene expression.

Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

DNA methylation QTL analysis identifies new regulators of human longevity.

Human longevity is a complex trait influenced by both genetic and environmental factors, whose interaction is mediated by epigenetic mechanisms like DNA methylation. Here, we generated genome-wide whole-blood methylome data from 267 individuals, of which 71 were long-lived (90–104 years), by applying reduced representation bisulfite sequencing. We followed a stringent two-stage analysis procedure using discovery and replication samples to detect differentially methylated sites (DMSs) between young and long-lived study participants. Additionally, we performed a DNA methylation quantitative trait loci analysis to identify DMSs that underlie the longevity phenotype. We combined the DMSs results with gene expression data as an indicator of functional relevance. This approach yielded 21 new candidate genes, the majority of which are involved in neurophysiological processes or cancer. Notably, two candidates (PVRL2, ERCC1) are located on chromosome 19q, in close proximity to the well-known longevity- and Alzheimer’s disease-associated loci APOE and TOMM40. We propose this region as a longevity hub, operating on both a genetic (APOE, TOMM40) and an epigenetic (PVRL2, ERCC1) level. We hypothesize that the heritable methylation and associated gene expression changes reported here are overall advantageous for the LLI and may prevent/postpone age-related diseases and facilitate survival into very old age.