Eight common genetic variants associated with serum dheas levels suggest a key role in ageing mechanisms

Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands-yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15×10-36), SULT2A1 (rs2637125; p = 2.61×10-19), ARPC1A (rs740160; p = 1.56×10-16), TRIM4 (rs17277546; p = 4.50×10-11), BMF (rs7181230; p = 5.44×10-11), HHEX (rs2497306; p = 4.64×10-9), BCL2L11 (rs6738028; p = 1.72×10-8), and CYP2C9 (rs2185570; p = 2.29×10-8). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants.

Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targeted arrays or enrichment methodologies preferentially covering CpG-dense regions, to characterize sufficiently large samples. To overcome this limitation, we present here a new customizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), for sequencing functional methylomes, while simultaneously providing genetic variation information. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adipose tissue (AT) samples and public databases to design AT-specific panels. We establish its efficiency for high-density interrogation of methylome variability by systematic comparisons with other approaches and demonstrate its applicability by identifying novel methylation variation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol, including at CD36. Our more comprehensive AT panel assesses tissue methylation and genotypes in parallel at ∼4 and ∼3 M sites, respectively. Our study demonstrates that MCC-Seq provides comparable accuracy to alternative approaches but enables more efficient cataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS.This work was supported by a Canadian Institute of Health Research (CIHR) team grant awarded to E.G., A.T., M.C.V. and M.L. (TEC-128093) and the CIHR funded Epigeneome Mapping Centre at McGill University (EP1-120608) awarded to T.P. and M.L., and the Swedish Research Council, Knut and Alice Wallenberg Foundation and the Torsten Söderberg Foundation awarded to L.R. F.A. holds studentship from The Research Institute of the McGill University Health Center (MUHC). F.G. is a recipient of a research fellowship award from the Heart and Stroke Foundation of Canada. A.T. is the director of a Research Chair in Bariatric and Metabolic Surgery. M.C.V. is the recipient of the Canada Research Chair in Genomics Applied to Nutrition and Health (Tier 1). E.G. and T.P. are recipients of a Canada Research Chair Tier 2 award. The MuTHER Study was funded by a programme grant from the Wellcome Trust (081917/Z/07/Z) and core funding for the Wellcome Trust Centre for Human Genetics (090532). TwinsUK was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. T.D.S. is a holder of an ERC Advanced Principal Investigator award. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR. Finally, we thank the NIH Roadmap Epigenomics Consortium and the Mapping Centers (http://nihroadmap.nih.gov/epigenomics/) for the production of publicly available reference epigenomes. Specifically, we thank the mapping centre at MGH/BROAD for generation of human adipose reference epigenomes used in this study.This is the final version. It was first published by NPG at http://www.nature.com/ncomms/2015/150529/ncomms8211/full/ncomms8211.html#abstrac

GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium

Decline in muscle strength with aging is an important predictor of health trajectory in the elderly. Several factors, including genetics, are proposed contributors to variability in muscle strength. To identify genetic contributors to muscle strength, a meta-analysis of genomewide association studies of handgrip was conducted. Grip strength was measured using a handheld dynamometer in 27 581 individuals of European descent over 65 years of age from 14 cohort studies. Genomewide association analysis was conducted on ~2.7 million imputed and genotyped variants (SNPs). Replication of the most significant findings was conducted using data from 6393 individuals from three cohorts. GWAS of lower body strength was also characterized in a subset of cohorts. Two genomewide significant (P-value< 5 × 10−8) and 39 suggestive (P-value< 5 × 10−5) associations were observed from meta-analysis of the discovery cohorts. After meta-analysis with replication cohorts, genomewide significant association was observed for rs752045 on chromosome 8 (β = 0.47, SE = 0.08, P-value = 5.20 × 10−10). This SNP is mapped to an intergenic region and is located within an accessible chromatin region (DNase hypersensitivity site) in skeletal muscle myotubes differentiated from the human skeletal muscle myoblasts cell line. This locus alters a binding motif of the CCAAT/enhancer-binding protein-β (CEBPB) that is implicated in muscle repair mechanisms. GWAS of lower body strength did not yield significant results. A common genetic variant in a chromosomal region that regulates myotube differentiation and muscle repair may contribute to variability in grip strength in the elderly. Further studies are needed to uncover the mechanisms that link this genetic variant with muscle strength

Investigating the Causal Relationship of C-Reactive Protein with 32 Complex Somatic and Psychiatric Outcomes: A Large-Scale Cross-Consortium Mendelian Randomization Study.

BACKGROUND: C-reactive protein (CRP) is associated with immune, cardiometabolic, and psychiatric traits and diseases. Yet it is inconclusive whether these associations are causal. METHODS AND FINDINGS: We performed Mendelian randomization (MR) analyses using two genetic risk scores (GRSs) as instrumental variables (IVs). The first GRS consisted of four single nucleotide polymorphisms (SNPs) in the CRP gene (GRSCRP), and the second consisted of 18 SNPs that were significantly associated with CRP levels in the largest genome-wide association study (GWAS) to date (GRSGWAS). To optimize power, we used summary statistics from GWAS consortia and tested the association of these two GRSs with 32 complex somatic and psychiatric outcomes, with up to 123,865 participants per outcome from populations of European ancestry. We performed heterogeneity tests to disentangle the pleiotropic effect of IVs. A Bonferroni-corrected significance level of less than 0.0016 was considered statistically significant. An observed p-value equal to or less than 0.05 was considered nominally significant evidence for a potential causal association, yet to be confirmed. The strengths (F-statistics) of the IVs were 31.92-3,761.29 and 82.32-9,403.21 for GRSCRP and GRSGWAS, respectively. CRP GRSGWAS showed a statistically significant protective relationship of a 10% genetically elevated CRP level with the risk of schizophrenia (odds ratio [OR] 0.86 [95% CI 0.79-0.94]; p < 0.001). We validated this finding with individual-level genotype data from the schizophrenia GWAS (OR 0.96 [95% CI 0.94-0.98]; p < 1.72 × 10-6). Further, we found that a standardized CRP polygenic risk score (CRPPRS) at p-value thresholds of 1 × 10-4, 0.001, 0.01, 0.05, and 0.1 using individual-level data also showed a protective effect (OR < 1.00) against schizophrenia; the first CRPPRS (built of SNPs with p < 1 × 10-4) showed a statistically significant (p < 2.45 × 10-4) protective effect with an OR of 0.97 (95% CI 0.95-0.99). The CRP GRSGWAS showed that a 10% increase in genetically determined CRP level was significantly associated with coronary artery disease (OR 0.88 [95% CI 0.84-0.94]; p < 2.4 × 10-5) and was nominally associated with the risk of inflammatory bowel disease (OR 0.85 [95% CI 0.74-0.98]; p < 0.03), Crohn disease (OR 0.81 [95% CI 0.70-0.94]; p < 0.005), psoriatic arthritis (OR 1.36 [95% CI 1.00-1.84]; p < 0.049), knee osteoarthritis (OR 1.17 [95% CI 1.01-1.36]; p < 0.04), and bipolar disorder (OR 1.21 [95% CI 1.05-1.40]; p < 0.007) and with an increase of 0.72 (95% CI 0.11-1.34; p < 0.02) mm Hg in systolic blood pressure, 0.45 (95% CI 0.06-0.84; p < 0.02) mm Hg in diastolic blood pressure, 0.01 ml/min/1.73 m2 (95% CI 0.003-0.02; p < 0.005) in estimated glomerular filtration rate from serum creatinine, 0.01 g/dl (95% CI 0.0004-0.02; p < 0.04) in serum albumin level, and 0.03 g/dl (95% CI 0.008-0.05; p < 0.009) in serum protein level. However, after adjustment for heterogeneity, neither GRS showed a significant effect of CRP level (at p < 0.0016) on any of these outcomes, including coronary artery disease, nor on the other 20 complex outcomes studied. Our study has two potential limitations: the limited variance explained by our genetic instruments modeling CRP levels in blood and the unobserved bias introduced by the use of summary statistics in our MR analyses. CONCLUSIONS: Genetically elevated CRP levels showed a significant potentially protective causal relationship with risk of schizophrenia. We observed nominal evidence at an observed p < 0.05 using either GRSCRP or GRSGWAS-with persistence after correction for heterogeneity-for a causal relationship of elevated CRP levels with psoriatic osteoarthritis, rheumatoid arthritis, knee osteoarthritis, systolic blood pressure, diastolic blood pressure, serum albumin, and bipolar disorder. These associations remain yet to be confirmed. We cannot verify any causal effect of CRP level on any of the other common somatic and neuropsychiatric outcomes investigated in the present study. This implies that interventions that lower CRP level are unlikely to result in decreased risk for the majority of common complex outcomes

Meta-analysis of human genome-microbiome association studies: The MiBioGen consortium initiative

Background: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. Results: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. Conclusion: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits

Funding Information: This research has been conducted using the UK Biobank Resource. This research has been conducted using the Danish National Biobank resource. The authors are grateful to the Raine Study participants and their families, and to the Raine Study research staff for cohort co-ordination and data collection. QIMR is grateful to the twins and their families for their generous participation in these studies. We would like to thank staff at the Queensland Institute of Medical Research: Anjali Henders, Dixie Statham, Lisa Bowdler, Ann Eldridge, and Marlene Grace for sample collection, processing and genotyping, Scott Gordon, Brian McEvoy, Belinda Cornes and Beben Benyamin for data QC and preparation, and David Smyth and Harry Beeby for IT support. HBCS Acknowledgements: We thank all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Juho Vainio Foundation, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation. Finrisk study is grateful for the THL DNA laboratory for its skillful work to produce the DNA samples used in this study and thanks the Sanger Institute and FIMM genotyping facilities for genotyping the samples. We thank the MOLGENIS team and Genomics Coordination Center of the University Medical Center Groningen for software development and data management, in particular Marieke Bijlsma and Edith Adriaanse. This work was supported by the Leenards Foundation (to Z.K.), the Swiss National Science Foundation (31003A_169929 to Z.K., Sinergia grant CRSII33-133044 to AR), Simons Foundation (SFARI274424 to AR) and SystemsX.ch (51RTP0_151019 to Z.K.). A.R.W., H.Y. and T.M.F. are supported by the European Research Council grant: 323195:SZ-245. M.A.T., M.N.W. and An.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). For full funding information of all participating cohorts see Supplementary Note 2. Publisher Copyright: © 2017 The Author(s).There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01-0.2%), with large effects on height (> 2.4 cm), weight ( 5 kg), and body mass index (BMI) (> 3.5 kg/m(2)). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (P = 2.5 x 10(-10), 6.0 x 10(-5), and 2.9 x 10(-3)). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.Peer reviewe

An Integration of Genome-Wide Association Study and Gene Expression Profiling to Prioritize the Discovery of Novel Susceptibility Loci for Osteoporosis-Related Traits

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6×10−8), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6×10−13; SOX6, p = 6.4×10−10) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation

Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation.

Thyroid dysfunction is an important public health problem, which affects 10% of the general population and increases the risk of cardiovascular morbidity and mortality. Many aspects of thyroid hormone regulation have only partly been elucidated, including its transport, metabolism, and genetic determinants. Here we report a large meta-analysis of genome-wide association studies for thyroid function and dysfunction, testing 8 million genetic variants in up to 72,167 individuals. One-hundred-and-nine independent genetic variants are associated with these traits. A genetic risk score, calculated to assess their combined effects on clinical end points, shows significant associations with increased risk of both overt (Graves' disease) and subclinical thyroid disease, as well as clinical complications. By functional follow-up on selected signals, we identify a novel thyroid hormone transporter (SLC17A4) and a metabolizing enzyme (AADAT). Together, these results provide new knowledge about thyroid hormone physiology and disease, opening new possibilities for therapeutic targets