Analyses of circulating metabolites in large prospective epidemiological studies could lead to improved prediction and better biological understanding of coronary heart disease (CHD). We performed a ...mass spectrometry-based non-targeted metabolomics study for association with incident CHD events in 1,028 individuals (131 events; 10 y. median follow-up) with validation in 1,670 individuals (282 events; 3.9 y. median follow-up). Four metabolites were replicated and independent of main cardiovascular risk factors lysophosphatidylcholine 18∶1 (hazard ratio HR per standard deviation SD increment = 0.77, P-value<0.001), lysophosphatidylcholine 18∶2 (HR = 0.81, P-value<0.001), monoglyceride 18∶2 (MG 18∶2; HR = 1.18, P-value = 0.011) and sphingomyelin 28∶1 (HR = 0.85, P-value = 0.015). Together they contributed to moderate improvements in discrimination and re-classification in addition to traditional risk factors (C-statistic: 0.76 vs. 0.75; NRI: 9.2%). MG 18∶2 was associated with CHD independently of triglycerides. Lysophosphatidylcholines were negatively associated with body mass index, C-reactive protein and with less evidence of subclinical cardiovascular disease in additional 970 participants; a reverse pattern was observed for MG 18∶2. MG 18∶2 showed an enrichment (P-value = 0.002) of significant associations with CHD-associated SNPs (P-value = 1.2×10-7 for association with rs964184 in the ZNF259/APOA5 region) and a weak, but positive causal effect (odds ratio = 1.05 per SD increment in MG 18∶2, P-value = 0.05) on CHD, as suggested by Mendelian randomization analysis. In conclusion, we identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role in CHD development.
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome ...sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
Cervical carcinoma has a heritable genetic component, but the genetic basis of cervical cancer is still not well understood.
We performed a genome-wide association study of 731 422 single nucleotide ...polymorphisms (SNPs) in 1075 cervical cancer case subjects and 4014 control subjects and replicated it in 1140 case subjects and 1058 control subjects. The association between top SNPs and cervical cancer was estimated by odds ratios (ORs) and 95% confidence intervals (CIs) with unconditional logistic regression. All statistical tests were two-sided.
Three independent loci in the major histocompatibility complex (MHC) region at 6p21.3 were associated with cervical cancer: the first is adjacent to the MHC class I polypeptide-related sequence A gene (MICA) (rs2516448; OR = 1.42, 95% CI = 1.31 to 1.54; P = 1.6×10(-18)); the second is between HLA-DRB1 and HLA-DQA1 (rs9272143; OR = 0.67, 95% CI = 0.62 to 0.72; P = 9.3×10(-24)); and the third is at HLA-DPB2 (rs3117027; OR=1.25, 95% CI = 1.15 to 1.35; P = 4.9×10(-8)). We also confirmed previously reported associations of B*0702 and DRB1*1501-DQB1*0602 with susceptibility to and DRB1*1301-DQA1*0103-DQB1*0603 with protection against cervical cancer. The three new loci are statistically independent of these specific human leukocyte antigen alleles/haplotypes. MICA encodes a membrane-bound protein that acts as a ligand for NKG2D to activate antitumor effects. The risk allele of rs2516448 is in perfect linkage disequilibrium with a frameshift mutation (A5.1) of MICA, which results in a truncated protein. Functional analysis shows that women carrying this mutation have lower levels of membrane-bound MICA.
Three novel loci in the MHC may affect susceptibility to cervical cancer in situ, including the MICA-A5.1 allele that may cause impaired immune activation and increased risk of tumor development.
Frailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Middle-aged ...individuals are also understudied for the association between FI and mortality. Furthermore, the population mortality impact of frailty remains understudied.
We estimated the predictive value of FI for all-cause and cause-specific mortality, taking into account familial factors, and tested whether the associations are time-dependent. We also assessed the proportion of all-cause and cause-specific deaths that are attributable to increased levels of frailty. We analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20 years' mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes, and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.
Increased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality, with the corresponding hazard ratios of 1.28 (1.24, 1.32), 1.31 (1.23, 1.40), and 1.23 (1.11, 1.38) associated with a 10% increase in FI in male single responders, and 1.21 (1.18, 1.25), 1.27 (1.15, 1.34), and 1.26 (1.15, 1.39) in female single responders. No significant associations were observed for cancer mortality. No attenuation of the mortality associations in unrelated individuals was observed when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. Before the age of 80, the proportions of deaths attributable to FI levels > 0.21 were 18.4% of all-cause deaths, 25.4% of CVD deaths, and 20.4% of respiratory-related deaths in men and 19.2% of all-cause deaths, 27.8% of CVD deaths, and 28.5% of respiratory-related deaths in women. After the age of 80, the attributable proportions decreased, most notably for all-cause and CVD mortality.
Increased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI presents a relatively greater risk factor at midlife than in old age. Increased FI has a significant population mortality impact that is greatest through midlife until the age of 80.
Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. ...This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.
Despite considerable progress in schizophrenia genetics, most findings have been for large rare structural variants and common variants in well-imputed regions with few genes implicated from exome ...sequencing. Whole genome sequencing (WGS) can potentially provide a more complete enumeration of etiological genetic variation apart from the exome and regions of high linkage disequilibrium. We analyze high-coverage WGS data from 1162 Swedish schizophrenia cases and 936 ancestry-matched population controls. Our main objective is to evaluate the contribution to schizophrenia etiology from a variety of genetic variants accessible to WGS but not by previous technologies. Our results suggest that ultra-rare structural variants that affect the boundaries of topologically associated domains (TADs) increase risk for schizophrenia. Alterations in TAD boundaries may lead to dysregulation of gene expression. Future mechanistic studies will be needed to determine the precise functional effects of these variants on biology.
A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many ...genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn's disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected.
This study examined the risk of antidepressant-induced manic switch in patients with bipolar disorder treated either with antidepressant monotherapy or with an antidepressant in conjunction with a ...mood stabilizer.
Using Swedish national registries, the authors identified 3,240 patients with bipolar disorder who started treatment with an antidepressant and had no antidepressant treatment during the previous year. Patients were categorized into those receiving antidepressant monotherapy and those receiving an antidepressant plus a mood stabilizer. A within-individual design was used to control for confounding by disorder severity, genetic makeup, and early environmental factors. Cox regression analyses conditioned on individual were used to compare the rate of mania 0-3 months and 3-9 months after the start of antidepressant treatment with a preceding non-treatment period.
Nearly 35% of the patients were treated with antidepressant monotherapy. The increased risk of treatment-emergent mania was confined to patients on antidepressant monotherapy (hazard ratio=2.83, 95% CI=1.12, 7.19). Among patients treated with a concurrent mood stabilizer, no acute change in risk of mania was observed during the 3 months after the start of antidepressant treatment (hazard ratio=0.79, 95% CI=0.54, 1.15), and a decreased risk was observed during the period 3-9 months after treatment initiation (hazard ratio=0.63, 95% CI=0.42, 0.93).
In this national registry study, antidepressant monotherapy was associated with an increased risk of mania. However, no risk of mania was seen in patients receiving an antidepressant while treated with a mood stabilizer. The results highlight the importance of avoiding antidepressant monotherapy in the treatment of bipolar disorder.
The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we ...found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h2g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h2g = 10.6%). The genetic correlation (rg) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h2g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.
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