Genome-wide association study using family-based cohorts identifies the WLS and CCDC170/ESR1 loci as associated with bone mineral density.

BACKGROUND: Osteoporosis is a common and debilitating bone disease that is characterised by a low bone mineral density (BMD), a highly heritable trait. Genome-wide association studies (GWAS) have proven to be very successful in identifying common genetic variants associated with BMD adjusted for age, gender and weight, however a large portion of the genetic variance for this trait remains unexplained. There is evidence to suggest significant genetic correlation between body size traits and BMD. It has also recently been suggested that unintended bias can be introduced as a result of adjusting a phenotype for a correlated trait. We performed a GWAS meta-analysis in two populations (total n = 6,696) using BMD data adjusted for only age and gender, in an attempt to identify genetic variants associated with BMD including those that may have potential pleiotropic effects on BMD and body size traits. RESULTS: We observed a single variant, rs2566752, associated with spine BMD at the genome-wide significance level in the meta-analysis (P = 3.36 × 10(-09)). Logistic regression analysis also revealed an association between rs2566752 and fracture rate in one of our study cohorts (P = 0.017, n = 5,654). This is an intronic variant located in the wntless Wnt ligand secretion mediator (WLS) gene (1p31.3), a known BMD locus which encodes an integral component of the Wnt ligand secretion pathway. Bioinformatics analyses of variants in moderate LD with rs2566752 produced strong evidence for a regulatory role for the variants rs72670452, rs17130567 and rs1430738. Expression quantitative trait locus (eQTL) analysis suggested that the variants rs12568456 and rs17130567 are associated with expression of the WLS gene in whole blood, cerebellum and temporal cortex brain tissue (P = 0.034-1.19 × 10(-23)). Gene-wide association testing using the VErsatile Gene-based Association Study 2 (VEGAS2) software revealed associations between the coiled-coil domain containing 170 (CCDC170) gene, located adjacent to the oestrogen receptor 1 (ESR1) gene, and BMD at the spine, femoral neck and total hip sites (P = 1.0 × 10(-06), 2.0 × 10(-06) and 2.0 × 10(-06) respectively). CONCLUSIONS: Genetic variation at the WLS and CCDC170/ESR1 loci were found to be significantly associated with BMD adjusted for only age and gender at the genome-wide level in this meta-analysis

Epidemiology and Genetic Epidemiology of the Liver Function Test Proteins

The liver function test (LFT) is among the most commonly used clinical investigations to assess hepatic function, severity of liver diseases and the effect of therapies, as well as to detect drug-induced liver injury (DILI)

The Use of Genome-Wide eQTL Associations in Lymphoblastoid Cell Lines to Identify Novel Genetic Pathways Involved in Complex Traits

Peer reviewe

The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits

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

Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights

Oral Abstracts 7: RA ClinicalO37. Long-Term Outcomes of Early RA Patients Initiated with Adalimumab Plus Methotrexate Compared with Methotrexate Alone Following a Targeted Treatment Approach

Background: This analysis assessed, on a group level, whether there is a long-term advantage for early RA patients treated with adalimumab (ADA) + MTX vs those initially treated with placebo (PBO) + MTX who either responded to therapy or added ADA following inadequate response (IR). Methods: OPTIMA was a 78- week, randomized, controlled trial of ADA + MTX vs PBO + MTX in MTX-naïve early (<1 year) RA patients. Therapy was adjusted at week 26: ADA + MTX-responders (R) who achieved DAS28 (CRP) <3.2 at weeks 22 and 26 (Period 1, P1) were re-randomized to withdraw or continue ADA and PBO + MTX-R continued randomized therapy for 52 weeks (P2); IR-patients received open-label (OL) ADA + MTX during P2. This post hoc analysis evaluated the proportion of patients at week 78 with DAS28 (CRP) <3.2, HAQ-DI <0.5, and/or ΔmTSS ≤0.5 by initial treatment. To account for patients who withdrew ADA during P2, an equivalent proportion of R was imputed from ADA + MTX-R patients. Results: At week 26, significantly more patients had low disease activity, normal function, and/or no radiographic progression with ADA + MTX vs PBO + MTX (Table 1). Differences in clinical and functional outcomes disappeared following additional treatment, when PBO + MTX-IR (n = 348/460) switched to OL ADA + MTX. Addition of OL ADA slowed radiographic progression, but more patients who received ADA + MTX from baseline had no radiographic progression at week 78 than patients who received initial PBO + MTX. Conclusions: Early RA patients treated with PBO + MTX achieved comparable long-term clinical and functional outcomes on a group level as those who began ADA + MTX, but only when therapy was optimized by the addition of ADA in PBO + MTX-IR. Still, ADA + MTX therapy conferred a radiographic benefit although the difference did not appear to translate to an additional functional benefit. Disclosures: P.E., AbbVie, Merck, Pfizer, UCB, Roche, BMS—Provided Expert Advice, Undertaken Trials, AbbVie—AbbVie sponsored the study, contributed to its design, and participated in the collection, analysis, and interpretation of the data, and in the writing, reviewing, and approval of the final version. R.F., AbbVie, Pfizer, Merck, Roche, UCB, Celgene, Amgen, AstraZeneca, BMS, Janssen, Lilly, Novartis—Research Grants, Consultation Fees. S.F., AbbVie—Employee, Stocks. A.K., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, Pfizer, Roche, UCB—Research Grants, Consultation Fees. H.K., AbbVie—Employee, Stocks. S.R., AbbVie—Employee, Stocks. J.S., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, GlaxoSmithKline, Lilly, Pfizer (Wyeth), MSD (Schering-Plough), Novo-Nordisk, Roche, Sandoz, UCB—Research Grants, Consultation Fees. R.V., AbbVie, BMS, GlaxoSmithKline, Human Genome Sciences, Merck, Pfizer, Roche, UCB Pharma—Consultation Fees, Research Support. Table 1.Week 78 clinical, functional, and radiographic outcomes in patients who received continued ADA + MTX vs those who continued PBO + MTX or added open-label ADA following an inadequate response ADA + MTX, n/N (%)a PBO + MTX, n/N (%)b Outcome Week 26 Week 52 Week 78 Week 26 Week 52 Week 78 DAS28 (CRP) <3.2 246/466 (53) 304/465 (65) 303/465 (65) 139/460 (30)*** 284/460 (62) 300/460 (65) HAQ-DI <0.5 211/466 (45) 220/466 (47) 224/466 (48) 150/460 (33)*** 203/460 (44) 208/460 (45) ΔmTSS ≤0.5 402/462 (87) 379/445 (86) 382/443 (86) 330/459 (72)*** 318/440 (72)*** 318/440 (72)*** DAS28 (CRP) <3.2 + ΔmTSS ≤0.5 216/462 (47) 260/443 (59) 266/443 (60) 112/459 (24)*** 196/440 (45) 211/440 (48)*** DAS28 (CRP) <3.2 + HAQ-DI <0.5 + ΔmTSS ≤0.5 146/462 (32) 168/443 (38) 174/443 (39) 82/459 (18)*** 120/440 (27)*** 135/440 (31)** aIncludes patients from the ADA Continuation (n = 105) and OL ADA Carry On (n = 259) arms, as well as the proportional equivalent number of responders from the ADA Withdrawal arm (n = 102). bIncludes patients from the MTX Continuation (n = 112) and Rescue ADA (n = 348) arms. Last observation carried forward: DAS28 (CRP) and HAQ-DI; Multiple imputations: ΔmTSS. ***P < 0.001 and **iP < 0.01, respectively, for differences between initial treatments from chi-squar

Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes