Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

The HUNT study: A population-based cohort for genetic research.

The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

Epigenome-wide association in adipose tissue from the METSIM cohort.

Most epigenome-wide association studies to date have been conducted in blood. However, metabolic syndrome is mediated by a dysregulation of adiposity and therefore it is critical to study adipose tissue in order to understand the effects of this syndrome on epigenomes. To determine if natural variation in DNA methylation was associated with metabolic syndrome traits, we profiled global methylation levels in subcutaneous abdominal adipose tissue. We measured association between 32 clinical traits related to diabetes and obesity in 201 people from the Metabolic Syndrome in Men cohort. We performed epigenome-wide association studies between DNA methylation levels and traits, and identified associations for 13 clinical traits in 21 loci. We prioritized candidate genes in these loci using expression quantitative trait loci, and identified 18 high confidence candidate genes, including known and novel genes associated with diabetes and obesity traits. Using methylation deconvolution, we examined which cell types may be mediating the associations, and concluded that most of the loci we identified were specific to adipocytes. We determined whether the abundance of cell types varies with metabolic traits, and found that macrophages increased in abundance with the severity of metabolic syndrome traits. Finally, we developed a DNA methylation-based biomarker to assess type 2 diabetes risk in adipose tissue. In conclusion, our results demonstrate that profiling DNA methylation in adipose tissue is a powerful tool for understanding the molecular effects of metabolic syndrome on adipose tissue, and can be used in conjunction with traditional genetic analyses to further characterize this disorder.

A Partial Loss-of-Function Variant in AKT2 Is Associated With Reduced Insulin-Mediated Glucose Uptake in Multiple Insulin-Sensitive Tissues: A Genotype-Based Callback Positron Emission Tomography Study.

Rare fully penetrant mutations in AKT2 are an established cause of monogenic disorders of glucose metabolism. Recently, a novel partial loss-of-function AKT2 coding variant (p.Pro50Thr) was identified that is nearly specific to Finns (frequency 1.1%), with the low-frequency allele associated with an increase in fasting plasma insulin level and risk of type 2 diabetes. The effects of the p.Pro50Thr AKT2 variant (p.P50T/AKT2) on insulin-stimulated glucose uptake (GU) in the whole body and in different tissues have not previously been investigated. We identified carriers (N = 20) and matched noncarriers (N = 25) for this allele in the population-based Metabolic Syndrome in Men (METSIM)study and invited these individuals back for positron emission tomography study with [18F]-fluorodeoxyglucose during euglycemic hyperinsulinemia. When we compared p.P50T/AKT2 carriers to noncarriers, we found a 39.4% reduction in whole-body GU (P = 0.006) and a 55.6% increase in the rate of endogenous glucose production (P = 0.038). We found significant reductions in GU in multiple tissues-skeletal muscle (36.4%), liver (16.1%), brown adipose (29.7%), and bone marrow (32.9%)-and increases of 16.8-19.1% in seven tested brain regions. These data demonstrate that the p.P50T substitution of AKT2 influences insulin-mediated GU in multiple insulin-sensitive tissues and may explain, at least in part, the increased risk of type 2 diabetes in p.P50T/AKT2 carriers.

Trans-ancestry Fine Mapping and Molecular Assays Identify Regulatory Variants at the ANGPTL8 HDL-C GWAS Locus.

Recent genome-wide association studies (GWAS) have identified variants associated with high-density lipoprotein cholesterol (HDL-C) located in or near the ANGPTL8 gene. Given the extensive sharing of GWAS loci across populations, we hypothesized that at least one shared variant at this locus affects HDL-C. The HDL-C-associated variants are coincident with expression quantitative trait loci for ANGPTL8 and DOCK6 in subcutaneous adipose tissue; however, only ANGPTL8 expression levels are associated with HDL-C levels. We identified a 400-bp promoter region of ANGPTL8 and enhancer regions within 5 kb that contribute to regulating expression in liver and adipose. To identify variants functionally responsible for the HDL-C association, we performed fine-mapping analyses and selected 13 candidate variants that overlap putative regulatory regions to test for allelic differences in regulatory function. Of these variants, rs12463177-G increased transcriptional activity (1.5-fold, P = 0.004) and showed differential protein binding. Six additional variants (rs17699089, rs200788077, rs56322906, rs3760782, rs737337, and rs3745683) showed evidence of allelic differences in transcriptional activity and/or protein binding. Taken together, these data suggest a regulatory mechanism at the ANGPTL8 HDL-C GWAS locus involving tissue-selective expression and at least one functional variant.

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases.

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.

A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease.

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.

A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans.

BACKGROUND: Experiments in mice initially suggested a role for the protein angiopoietin-like 8 (ANGPTL8) in glucose homeostasis. However, subsequent experiments in model systems have challenged this proposed role. We sought to better understand the importance of ANGPTL8 in human glucose homeostasis by examining the association of a null mutation in ANGPTL8 with fasting glucose levels and risk for type 2 diabetes. METHODS: A naturally-occurring null mutation in human ANGPTL8 (rs145464906; c.361C > T; p.Q121X) is carried by ~1 in 1000 individuals of European ancestry and is associated with higher levels of plasma high-density lipoprotein cholesterol, suggesting that this mutation has functional significance. We examined the association of p.Q121X with fasting glucose levels and risk for type 2 diabetes in up to 95,558 individuals (14,824 type 2 diabetics and 80,734 controls). RESULTS: We found no significant association of p.Q121X with either fasting glucose or type 2 diabetes (p-value = 0.90 and 0.65, respectively). Given our sample sizes, we had >98 % power to detect at least a 0.23 mmol/L effect on plasma glucose and >95 % power to detect a 70 % increase in risk for type 2 diabetes. CONCLUSION: Disruption of ANGPTL8 function in humans does not seem to have a large effect on measures of glucose tolerance.

Multiple Hepatic Regulatory Variants at the GALNT2 GWAS Locus Associated with High-Density Lipoprotein Cholesterol.

Genome-wide association studies (GWASs) have identified more than 150 loci associated with blood lipid and cholesterol levels; however, the functional and molecular mechanisms for many associations are unknown. We examined the functional regulatory effects of candidate variants at the GALNT2 locus associated with high-density lipoprotein cholesterol (HDL-C). Fine-mapping and conditional analyses in the METSIM study identified a single locus harboring 25 noncoding variants (r(2) > 0.7 with the lead GWAS variants) strongly associated with total cholesterol in medium-sized HDL (e.g., rs17315646, p = 3.5 × 10(-12)). We used luciferase reporter assays in HepG2 cells to test all 25 variants for allelic differences in regulatory enhancer activity. rs2281721 showed allelic differences in transcriptional activity (75-fold [T] versus 27-fold [C] more than the empty-vector control), as did a separate 780-bp segment containing rs4846913, rs2144300, and rs6143660 (49-fold [AT(-) haplotype] versus 16-fold [CC(+) haplotype] more). Using electrophoretic mobility shift assays, we observed differential CEBPB binding to rs4846913, and we confirmed this binding in a native chromatin context by performing chromatin-immunoprecipitation (ChIP) assays in HepG2 and Huh-7 cell lines of differing genotypes. Additionally, sequence reads in HepG2 DNase-I-hypersensitivity and CEBPB ChIP-seq signals spanning rs4846913 showed significant allelic imbalance. Allelic-expression-imbalance assays performed with RNA from primary human hepatocyte samples and expression-quantitative-trait-locus (eQTL) data in human subcutaneous adipose tissue samples confirmed that alleles associated with increased HDL-C are associated with a modest increase in GALNT2 expression. Together, these data suggest that at least rs4846913 and rs2281721 play key roles in influencing GALNT2 expression at this HDL-C locus.

Congenital cataracts: de novo gene conversion event in CRYBB2.

PURPOSE: To identify the cause of congenital cataracts in a consanguineous family of Ashkenazi Jewish ancestry. METHODS: We performed genome-wide linkage analysis and whole-exome sequencing for the initial discovery of variants, and we confirmed the variants using gene-specific primers and Sanger sequencing. RESULTS: We found significant evidence of linkage to chromosome 22, under an autosomal dominant inheritance model, with a maximum logarithm of the odds (LOD) score of 3.91 (16.918 to 25.641 Mb). Exome sequencing identified three nonsynonymous changes in the CRYBB2 exon 5 coding sequence that are consistent with the sequence of the corresponding region of the pseudogene CRYBB2P1. The identification of these changes was complicated by possible mismapping of some mutated CRYBB2 sequences to CRYBB2P1. Sequencing with gene-specific primers confirmed that the changes--rs2330991, c.433 C>T (p.R145W); rs2330992, c.440A>G (p.Q147R); and rs4049504, c.449C>T (p.T150M)--present in all ten affected family members are located in CRYBB2 and are not artifacts of cross-reaction with CRYBB2P1. We did not find these changes in six unaffected family members, including the unaffected grandfather who contributed the affected haplotype, nor did we find them in the 100 Ashkenazi Jewish controls. CONCLUSIONS: Our data are consistent with a de novo gene conversion event, transferring 270 base pairs at most from CRYBB2P1 to exon 5 of CRYBB2. This study highlights how linkage mapping can be complicated by de novo mutation events, as well as how sequence-analysis pipeline mapping of short reads from next-generation sequencing can be complicated by the existence of pseudogenes or other highly homologous sequences.

Age-related clonal hematopoiesis associated with adverse outcomes.

BACKGROUND: The incidence of hematologic cancers increases with age. These cancers are associated with recurrent somatic mutations in specific genes. We hypothesized that such mutations would be detectable in the blood of some persons who are not known to have hematologic disorders. METHODS: We analyzed whole-exome sequencing data from DNA in the peripheral-blood cells of 17,182 persons who were unselected for hematologic phenotypes. We looked for somatic mutations by identifying previously characterized single-nucleotide variants and small insertions or deletions in 160 genes that are recurrently mutated in hematologic cancers. The presence of mutations was analyzed for an association with hematologic phenotypes, survival, and cardiovascular events. RESULTS: Detectable somatic mutations were rare in persons younger than 40 years of age but rose appreciably in frequency with age. Among persons 70 to 79 years of age, 80 to 89 years of age, and 90 to 108 years of age, these clonal mutations were observed in 9.5% (219 of 2300 persons), 11.7% (37 of 317), and 18.4% (19 of 103), respectively. The majority of the variants occurred in three genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with an increase in the risk of hematologic cancer (hazard ratio, 11.1; 95% confidence interval [CI], 3.9 to 32.6), an increase in all-cause mortality (hazard ratio, 1.4; 95% CI, 1.1 to 1.8), and increases in the risks of incident coronary heart disease (hazard ratio, 2.0; 95% CI, 1.2 to 3.4) and ischemic stroke (hazard ratio, 2.6; 95% CI, 1.4 to 4.8). CONCLUSIONS: Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease. (Funded by the National Institutes of Health and others.).

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.