Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank.

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.

BACKGROUND: Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice. RESULTS: In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation. CONCLUSION: This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.

Variation in DNA Methylation in Avian Nestlings Is Largely Determined by Genetic Effects.

As environmental fluctuations are becoming more common, organisms need to rapidly adapt to anthropogenic, climatic, and ecological changes. Epigenetic modifications and DNA methylation in particular provide organisms with a mechanism to shape their phenotypic responses during development. Studies suggest that environmentally induced DNA methylation might allow for adaptive phenotypic plasticity that could last throughout an organism's lifetime. Despite a number of studies demonstrating environmentally induced DNA methylation changes, we know relatively little about what proportion of the epigenome is affected by environmental factors, rather than being a consequence of genetic variation. In the current study, we use a partial cross-foster design in a natural great tit (Parus major) population to disentangle the effects of common origin from common rearing environment on DNA methylation. We found that variance in DNA methylation in 8,315 CpG sites was explained by a common origin and only in 101 by a common rearing environment. Subsequently, we mapped quantitative trait loci for the brood of origin CpG sites and detected 754 cis and 4,202 trans methylation quantitative trait loci, involving 24% of the CpG sites. Our results indicate that the scope for environmentally induced methylation marks independent of the genotype is limited and that the majority of variation in DNA methylation early in life is determined by genetic factors instead. These findings suggest that there may be little opportunity for selection to act on variation in DNA methylation. This implies that most DNA methylation variation likely does not evolve independently of genomic changes.

Pervasive cis effects of variation in copy number of large tandem repeats on local DNA methylation and gene expression.

Variable number tandem repeats (VNTRs) are composed of large tandemly repeated motifs, many of which are highly polymorphic in copy number. However, because of their large size and repetitive nature, they remain poorly studied. To investigate the regulatory potential of VNTRs, we used read-depth data from Illumina whole-genome sequencing to perform association analysis between copy number of ∼70,000 VNTRs (motif size ≥ 10 bp) with both gene expression (404 samples in 48 tissues) and DNA methylation (235 samples in peripheral blood), identifying thousands of VNTRs that are associated with local gene expression (eVNTRs) and DNA methylation levels (mVNTRs). Using an independent cohort, we validated 73%-80% of signals observed in the two discovery cohorts, while allelic analysis of VNTR length and CpG methylation in 30 Oxford Nanopore genomes gave additional support for mVNTR loci, thus providing robust evidence to support that these represent genuine associations. Further, conditional analysis indicated that many eVNTRs and mVNTRs act as QTLs independently of other local variation. We also observed strong enrichments of eVNTRs and mVNTRs for regulatory features such as enhancers and promoters. Using the Human Genome Diversity Panel, we define sets of VNTRs that show highly divergent copy numbers among human populations and show that these are enriched for regulatory effects and preferentially associate with genes that have been linked with human phenotypes through GWASs. Our study provides strong evidence supporting functional variation at thousands of VNTRs and defines candidate sets of VNTRs, copy number variation of which potentially plays a role in numerous human phenotypes.

Beyond pathways: Accelerated flavonoids candidate identification and novel exploration of enzymatic properties using combined mapping populations of wheat.

Although forward-genetics-metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co-mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare-activity-exhibiting flavonoid glycosyl- and methyl-transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward-genetics-metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics-assisted wheat crop improvement.

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

KEY MESSAGE: Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56 kb to 6.37 Mb, with a median of 921 kb, of which 27% had a CI lower than 500 kb and 53% had a CI lower than 1 Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF-AD study.

INTRODUCTION: We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration. METHODS: We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF-AD) study using the EPIC array. RESULTS: We identified Bonferroni-significant differential methylation associated with CSF YKL-40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL-40 levels correlating with plasma YKL-40 levels. A co-localization analysis showed shared genetic variants underlying YKL-40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co-methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development. DISCUSSION: We conducted the most comprehensive epigenome-wide association study (EWAS) of AD-relevant CSF biomarkers to date. Future work should explore the relationship between YKL-40 genotype, DNA methylation, and protein levels in the brain. HIGHLIGHTS: Blood DNA methylation was assessed in the EMIF-AD MBD study. Epigenome-wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)-relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni-significant loci were associated with YKL-40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL-40 co-localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single-nucleotide polymorphisms (SNPs) in YKL-40 on CSF protein levels.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat.

BACKGROUND: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. RESULTS: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. CONCLUSION: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.).

BACKGROUND: Kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes. RESULTS: The analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61-0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%-14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites. CONCLUSIONS: Major genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants.

KEY MESSAGE: Comprehensive metabolomic investigations provide a large set of stress-related metabolites and metabolic pathways, advancing crops under heat stress conditions. Metabolomics-assisted breeding, including mQTL and mGWAS boosted our understanding of improving numerous quantitative traits under heat stress. During the past decade, metabolomics has emerged as a fascinating scientific field that includes documentation, evaluation of metabolites, and chemical methods for cell monitoring programs in numerous plant species. A comprehensive metabolome profiling allowed the investigator to handle the comprehensive data groups of metabolites and the equivalent metabolic pathways in an extraordinary manner. Metabolomics, together with transcriptomics, plays an influential role in discovering connections between stress and genes/metabolite, phenotyping, and biomarkers documentation. Further, it helps to decode several metabolic systems connected with heat stress (HS) tolerance in plants. Heat stress is a critical environmental factor that is globally affecting the growth and productivity of plants. Thus, there is an urgent need to exploit modern breeding and biotechnological tools like metabolomics to develop cultivars with improved HS tolerance. Several studies have reported that amino acids, carbohydrates, nitrogen metabolisms, etc. and metabolites involved in the biosynthesis and catalyzing actions play a game-changing role in HS response and help plants to cope with the HS. The use of metabolomics-assisted breeding (MAB) allows a well-organized transmission of higher yield and HS tolerance at the metabolome level with specific properties. Progressive metabolomics systematic techniques have accelerated metabolic profiling. Nonetheless, continuous developments in bioinformatics, statistical tools, and databases are allowing us to produce ever-progressing, comprehensive insights into the biochemical configuration of plants and by what means this is inclined by genetic and environmental cues. Currently, assimilating metabolomics with post-genomic platforms has allowed a significant division of genetic-phenotypic connotation in several plant species. This review highlights the potential of a state-of-the-art plant metabolomics approach for the improvement of crops under HS. The development of plants with specific properties using integrated omics (metabolomics and transcriptomics) and MAB can provide new directions for future research to enhance HS tolerance in plants to achieve a goal of "zero hunger".

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

Recent applications of metabolomics in plant breeding.

Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.

Nonlinear ridge regression improves cell-type-specific differential expression analysis.

BACKGROUND: Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity. RESULTS: First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data. CONCLUSION: Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas.

DNA methylation and breast cancer-associated variants.

BACKGROUND: A breast cancer polygenic risk score (PRS) comprising 313 common variants reliably predicts disease risk. We examined possible relationships between genetic variation, regulation, and expression to clarify the molecular alterations associated with these variants. METHODS: Genome-wide methylomic variation was quantified (MethylationEPIC) in Asian breast cancer patients (1152 buffy coats from peripheral whole blood). DNA methylation (DNAm) quantitative trait loci (mQTL) mapping was performed for 235 of the 313 variants with minor allele frequencies > 5%. Stability of identified mQTLs (p < 5e-8) across lifetime was examined using a public mQTL database. Identified mQTLs were also mapped to expression quantitative trait loci (eQTLs) in the Genotype-Tissue Expression Project and the eQTLGen Consortium. RESULTS: Breast cancer PRS was not associated with DNAm. A higher proportion of significant cis-mQTLs were observed. Of 822 significant cis-mQTLs (179 unique variants) identified in our dataset, 141 (59 unique variants) were significant (p < 5e-8) in a public mQTL database. Eighty-six percent (121/141) of the matched mQTLs were consistent at multiple time points (birth, childhood, adolescence, pregnancy, middle age, post-diagnosis, or treatment). Ninety-three variants associated with DNAm were also cis-eQTLs (35 variants not genome-wide significant). Multiple loci in the breast cancer PRS are associated with DNAm, contributing to the polygenic nature of the disease. These mQTLs are mostly stable over time. CONCLUSIONS: Consistent results from DNAm and expression data may reveal new candidate genes not previously associated with breast cancer.

DNA methylation near the INS gene is associated with INS genetic variation (rs689) and type 1 diabetes in the Diabetes Autoimmunity Study in the Young.

OBJECTIVE: Mechanisms underlying the role of non-human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non-HLA genetic risk. METHODS: We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case-control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency-matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non-HLA genetic markers known be associated with T1D. Genome-wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) -probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta-analysis was used to combine odds ratios from the two platforms. RESULTS: We identified 10 SNP-methylation probe pairs (false discovery rate (FDR) adjusted P < .05 and validation P < .05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta-P = .0495), cg21574853 (OR: 2.5, meta-P = .0232), and cg25336198 (odds ratio: 6.6, meta-P = .0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site. CONCLUSIONS: We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development.

Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci.

RATIONALE: As the third leading cause of death in the United States, the impact of chronic obstructive pulmonary disease (COPD) makes identification of its molecular mechanisms of great importance. Genome-wide association studies (GWASs) have identified multiple genomic regions associated with COPD. However, genetic variation only explains a small fraction of the susceptibility to COPD, and sub-genome-wide significant loci may play a role in pathogenesis. OBJECTIVES: Regulatory annotation with epigenetic evidence may give priority for further investigation, particularly for GWAS associations in noncoding regions. We performed integrative genomics analyses using DNA methylation profiling and genome-wide SNP genotyping from lung tissue samples from 90 subjects with COPD and 36 control subjects. METHODS: We performed methylation quantitative trait loci (mQTL) analyses, testing for SNPs associated with percent DNA methylation and assessed the colocalization of these results with previous COPD GWAS findings using Bayesian methods in the R package coloc to highlight potential regulatory features of the loci. MEASUREMENTS AND MAIN RESULTS: We identified 942,068 unique SNPs and 33,996 unique CpG sites among the significant (5% false discovery rate) cis-mQTL results. The genome-wide significant and subthreshold (P < 10-4) GWAS SNPs were enriched in the significant mQTL SNPs (hypergeometric test P < 0.00001). We observed enrichment for sites located in CpG shores and shelves, but not CpG islands. Using Bayesian colocalization, we identified loci in regions near KCNK3, EEFSEC, PIK3CD, DCDC2C, TCERG1L, FRMD4B, and IL27. CONCLUSIONS: Colocalization of mQTL and GWAS loci provides regulatory characterization of significant and subthreshold GWAS findings, supporting a role for genetic control of methylation in COPD pathogenesis.

Genome-metabolite associations revealed low heritability, high genetic complexity, and causal relations for leaf metabolites in winter wheat (Triticum aestivum).

We investigated associations between the metabolic phenotype, consisting of quantitative data of 76 metabolites from 135 contrasting winter wheat (Triticum aestivum) lines, and 17 372 single nucleotide polymorphism (SNP) markers. Metabolite profiles were generated from flag leaves of plants from three different environments, with average repeatabilities of 0.5-0.6. The average heritability of 0.25 was unaffected by the heading date. Correlations among metabolites reflected their functional grouping, highlighting the strict coordination of various routes of the citric acid cycle. Genome-wide association studies identified significant associations for six metabolic traits, namely oxalic acid, ornithine, L-arginine, pentose alcohol III, L-tyrosine, and a sugar oligomer (oligo II), with between one and 17 associated SNPs. Notable associations with genes regulating transcription or translation explained between 2.8% and 32.5% of the genotypic variance (pG). Further candidate genes comprised metabolite carriers (pG 32.5-38.1%), regulatory proteins (pG 0.3-11.1%), and metabolic enzymes (pG 2.5-32.5%). The combinatorial use of genomic and metabolic data to construct partially directed networks revealed causal inferences in the correlated metabolite traits and associated SNPs. The evaluated causal relationships will provide a basis for predicting the effects of genetic interferences on groups of correlated metabolic traits, and thus on specific metabolic phenotypes.

Metabolite profiling of barley flag leaves under drought and combined heat and drought stress reveals metabolic QTLs for metabolites associated with antioxidant defense.

Barley (Hordeum vulgare L.) is among the most stress-tolerant crops; however, not much is known about the genetic and environmental control of metabolic adaptation of barley to abiotic stresses. We have subjected a genetically diverse set of 81 barley accessions, consisting of Mediterranean landrace genotypes and German elite breeding lines, to drought and combined heat and drought stress at anthesis. Our aim was to (i) investigate potential differences in morphological, physiological, and metabolic adaptation to the two stress scenarios between the Mediterranean and German barley genotypes and (ii) identify metabolic quantitative trait loci (mQTLs). To this end, we have genotyped the investigated barley lines with an Illumina iSelect 9K array and analyzed a set of 57 metabolites from the primary C and N as well as antioxidant metabolism in flag leaves under control and stress conditions. We found that drought-adapted genotypes attenuate leaf carbon metabolism much more strongly than elite lines during drought stress adaptation. Furthermore, we identified mQTLs for flag leaf γ-tocopherol, glutathione, and succinate content by association genetics that co-localize with genes encoding enzymes of the pathways producing these antioxidant metabolites. Our results provide the molecular basis for breeding barley cultivars with improved abiotic stress tolerance.