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Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci
research-article
Author(s):
Sjur Reppe
1
,
2
,
3 ,
Yunpeng Wang
4
,
5
,
6
,
7 ,
Wesley K. Thompson
6 ,
Linda K. McEvoy
8
,
9 ,
Andrew J. Schork
8
,
10 ,
Verena Zuber
4
,
5 ,
Marissa LeBlanc
11
,
12 ,
Francesco Bettella
4
,
5 ,
Ian G. Mills
13 ,
Rahul S. Desikan
8
,
9 ,
Srdjan Djurovic
4
,
5 ,
Kaare M. Gautvik
1
,
2
,
3 ,
Anders M. Dale
6
,
7
,
8
,
9 ,
Ole A. Andreassen
4
,
5
,
6
,
* ,
GEFOS Consortium
Publication date (Electronic):
22 December 2015
Journal:
PLoS ONE
Publisher:
Public Library of Science
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Abstract
Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.
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Most cited references38
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Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
- Record: found
- Abstract: found
- Article: found
High Bone Mineral Density and Fracture Risk in Type 2 Diabetes as Skeletal Complications of Inadequate Glucose Control
Ling Oei, M. Carola Zillikens, Abbas Dehghan … (2013)
OBJECTIVE Individuals with type 2 diabetes have increased fracture risk despite higher bone mineral density (BMD). Our aim was to examine the influence of glucose control on skeletal complications. RESEARCH DESIGN AND METHODS Data of 4,135 participants of the Rotterdam Study, a prospective population-based cohort, were available (mean follow-up 12.2 years). At baseline, 420 participants with type 2 diabetes were classified by glucose control (according to HbA1c calculated from fructosamine), resulting in three comparison groups: adequately controlled diabetes (ACD; n = 203; HbA1c <7.5%), inadequately controlled diabetes (ICD; n = 217; HbA1c ≥7.5%), and no diabetes (n = 3,715). Models adjusted for sex, age, height, and weight (and femoral neck BMD) were used to test for differences in bone parameters and fracture risk (hazard ratio [HR] [95% CI]). RESULTS The ICD group had 1.1–5.6% higher BMD, 4.6–5.6% thicker cortices, and −1.2 to −1.8% narrower femoral necks than ACD and ND, respectively. Participants with ICD had 47–62% higher fracture risk than individuals without diabetes (HR 1.47 [1.12–1.92]) and ACD (1.62 [1.09–2.40]), whereas those with ACD had a risk similar to those without diabetes (0.91 [0.67–1.23]). CONCLUSIONS Poor glycemic control in type 2 diabetes is associated with fracture risk, high BMD, and thicker femoral cortices in narrower bones. We postulate that fragility in apparently “strong” bones in ICD can result from microcrack accumulation and/or cortical porosity, reflecting impaired bone repair.
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All SNPs Are Not Created Equal: Genome-Wide Association Studies Reveal a Consistent Pattern of Enrichment among Functionally Annotated SNPs
Andrew J. Schork, Wesley K. Thompson, Phillip Pham … (2013)
Introduction Complex traits are generally influenced by many genes with small individual effects [1]. This ‘polygenic’ architecture has been difficult to characterize. While Genome-wide association studies (GWAS) [2] have successfully identified thousands of trait-associated single nucleotide polymorphisms (SNPs) [3], even when considered in aggregate, these SNPs explain small portions of the trait heritability [4]. Recent results indicate that GWAS have the potential to explain much of the heritability of common complex phenotypes [5], [6], and more SNPs are likely to be identified in larger samples [7]. However, there are few methods available for identifying more of the SNPs likely to be associated with phenotypes without increasing the sample size, as recognized by recent European and US calls for new statistical genetics methods. The crucial issue is that for most complex traits a large number of SNPs have too small an effect to pass standard GWAS significance thresholds given current sample sizes. We present results suggesting new analytical approaches for GWAS will uncover more of the polygenic effects in complex disorders and traits. We hypothesize that all SNPs in a GWAS are not exchangeable, but come from pre-determinable categories with different distributions of effects. This implies that some categories of SNPs are enriched, i.e. are more likely to be associated with a phenotype than others. This information can be used to calculate the category-specific True Discovery Rate (TDR), or the expected proportion of correctly rejected null hypotheses [8]. SNPs from enriched SNP categories will have an increased TDR for a given effect size, or equivalently, for a given nominal p-value. Stratified False Discovery Rate (sFDR) methods [9] provide an established framework for demonstrating the utility of using enriched genic categories to increase power to discover SNPs likely to replicate in independent samples. Previous work has applied sFDR and related methods to GWAS data stratified by candidate regions determined through prior linkage analysis and/or candidate gene studies [10]–[12] and specific biological pathways related to disease etiology [13]. Others have considered stratification by genome annotations in linkage analysis [14] and Bayesian association analyses [15], demonstrating the utility of this approach for improving power and FDR based discovery where reliable, pre-determinable strata exist. It has been suggested that variation in and around genes harbors more polygenic effects [6], [16]. However, the particular gene elements (i.e., intron, exon, UTRs) containing these variants and the distribution of effect sizes in GWAS have been left to extrapolation and speculation. Further, SNPs in and around genes have been shown to explain more variation [6] and replicate at higher rates [16] than intergenic SNPs. These studies, however, did not parse genic regions down to specific genic elements. We here hypothesize that SNPs in regulatory and coding elements of protein coding genes will show an enrichment of polygenic effects relative to intronic and intergenic SNPs which will be reflected in an increased estimated TDR and empirically confirmed through improved replication rate across independent samples. The association signal of a SNP tested in GWAS is a surrogate for, or ‘tags,’ the potential effects of many other variants. Thus, any of a number of ‘tagged’ variants could underlie the observed association signal. Focusing on the tag SNPs only, without systematically capturing the underlying causal variants within a ‘tagged’ linkage block, limits the functional inferences that can be drawn from GWAS. By incorporating the correlation between SNPs (linkage disequilibrium; LD) we expect a stronger and more consistent differentiation of enrichment among genic annotation categories. In the current study, we use an LD-weighted scoring algorithm that allows quantification of the properties of multi-locus LD structure implicitly captured by each tag SNP to our enrichment analysis. These categories can be leveraged to create strata for established sFDR approaches. We employ a model free strategy to identify enriched strata among phenotypes based on GWAS summary statistics. We first calculate the relative enrichment in different genic elements, using the category-specific empirical cumulative distribution function (cdf) of the nominal p-values after controlling for estimated genomic inflation. For each nominal p-value threshold an estimate of the category-specific TDR = 1−FDR is obtained from these empirical cdfs. This analysis is implemented on summary p-values from ten published GWAS meta-analyses studying 14 phenotypes. We then use the sub-study GWAS in Crohn's disease to test if the estimated increased TDR translates to improved replication rates, showing that for a given replication rate the nominal p-value threshold is 100 times larger for the most enriched genic category compared to the intergenic category. Finally, using an established sFDR framework we demonstrate the utility of leveraging enriched categories for improving power to detect SNPs likely to replicate, i.e., to reject more null hypotheses for a fixed FDR. Results LD-Based Enrichment of Genic Elements in Height Under multiple testing paradigms such as GWAS, quantitative estimates of likely true associations can be estimated from the distributions of summary statistics [17], [18]. A common method for visualizing the enrichment of statistical association relative to that expected under the global null hypothesis is through Q-Q plots of the nominal p-values resulting from GWAS. Under the global null hypothesis the theoretical distribution is uniform on the interval [0,1]. Thus, the usual Q-Q curve has as the y-coordinate the nominal p-value, denoted by “p”, and the x-coordinate the value of the empirical cdf at p, which we denote by “q”. As is common in GWAS, we instead plot −log10 p against the −log10 q to emphasize tail probabilities of the theoretical and empirical distributions. In such plots, enrichment results in a leftward shift in the Q-Q curve, corresponding to a larger fraction of SNPs with nominal −log10 p-value greater than or equal to a given threshold (see Material and Methods). The stratified Q-Q plot for height (Figure 1) shows a clear variation in enrichment across genic annotation categories. The separation between the curves for different categories is enhanced when using LD-weighted genic annotation categories in comparison to non LD-weighted positional categories (Figure S3). The parallel shape of these curves is likely caused by the significant but imperfect correlation among categories due to the non-exclusive nature of the annotation scoring (Figure S2). 10.1371/journal.pgen.1003449.g001 Figure 1 Stratified Q-Q plot for height shows enrichment by annotation categories using Linkage-Disequilibrium (LD)-weighted scores. Genic annotation categories were: 1) 10,000 to 1,001 base pairs upstream (10 k Up); 2) 1,000 to 1 base pair upstream (1 k Up); 3) 5′ untranslated region (5′UTR); 4) Exon; 5) Intron; 6) 3′ untranslated region (3′UTR); 7) 1 to 1,000 base pairs downstream (1 k Down); 8) 1,001 to 10,000 base pairs downstream (10 k Down). Q-Q plot of height with non-LD weighted category scores are shown in Figure S3. An earlier departure from the null line (leftward shift) suggests a greater proportion of true associations, for a given nominal p-value. The divergence of the curves for different categories implies that the proportion of non-null effects varies considerably among annotation categories of genic elements. For example, the proportion of SNPs in the 5′UTR category reaching a significance level of −log10(p)>10 is roughly 10 times greater than for all SNPs and 50–100 times greater than for intergenic SNPs. Polygenic Enrichment across Diverse Phenotypes Recently Yang et al [19] demonstrated that an abundance of low p-values beyond what is expected under null hypotheses in GWAS, but not necessarily reaching stringent multiple comparison thresholds, often attributed to ‘spurious inflation,’ is also consistent with an enrichment of true ‘polygenic’ effects [19]. The prevalence of enrichment below the established genome-wide significance threshold of p 7.3;) in height (Figure 2A) is consistent with their hypotheses and strongly suggests that current GWAS do not capture all of the additive ‘tagged variance’ in this phenotype. Importantly, this enrichment varies across genic annotation categories. 10.1371/journal.pgen.1003449.g002 Figure 2 Stratified Q-Q plots and true discovery rates show consistency of enrichment. Upper panel: Stratified Q-Q plots illustrating consistent enrichment of genic annotation categories across diverse phenotypes: (A) Height, (B) Schizophrenia (SCZ), and (C) Cigarettes per Day (CPD). All figures are corrected for inflation using intergenic inflation control. Only nominal p-values below the standard genome-wide significance threshold (p .05 within 1,000,000 basepairs, we estimate the ratio of correlated pairs (r2>.05) to total pairs of p-values at 0.000128. Replication Rate For each of eight sub-studies contributing to the final meta-analysis in the CD report we independently adjusted z-scores using intergenic inflation control. For each of 70 (8 choose 4) possible combinations of four-study discovery and four-study replication sets, we calculated the four-study combined discovery z-score and four-study combined replication z-score for each SNP as the average z-score across the four studies, multiplied by two (the square root of the number of studies). For discovery samples the z-scores were converted to two-tailed p-values, while replication samples were converted to one-tailed p-values preserving the direction of effect in the discovery sample. For each of the 70 discovery-replication pairs cumulative rates of replication were calculated over 1000 equally-spaced bins spanning the range of negative log10(p-values) observed in the discovery samples. The cumulative replication rate for any bin was calculated as the proportion of SNPs with a −log10(discovery p-value) greater than the lower bound of the bin with a replication p-value 0.2. For each SNP the sum of LD with each genic annotation category was recorded. SNPs were assigned to categories by thresholding continuous scores with an inclusive lower bound of 1.0. Positional (non LD-weighted) scores were recorded as the annotation for the GWAS tag SNP's location only. (TIF) Click here for additional data file. Figure S2 Correlations among annotation categories and scores. (A) Heat map displaying the Spearman's correlation coefficients among continuous valued LD-weighted annotation scores. (B) Heat map displaying the Spearman's correlation coefficients among thresholded and binarized annotation categories presented in Q-Q plots. Correlations are reported using the annotations for the union of SNPs across all GWAS (2,558,411 SNPs). (TIF) Click here for additional data file. Figure S3 Enrichment in Height without LD weighted annotation. Q-Q plot showing enrichment of genic annotation categories using positional scores (non LD-weighted). Enrichment patterns are present, but less apparent than using LD-weighted annotation scores (Figure 1). No inflation correction was performed by our group. (TIF) Click here for additional data file. Figure S4 Height before and after Intergenic Inflation Control. (A) Q-Q plot of height without correction for genomic inflation. (B) Q-Q plot of height after correction for genomic inflation using the ‘intergenic inflation control’. Note the overcorrection (grey line below null-hypothesis line, marked by red arrows) in the un-corrected Q-Q plot in Panel A is resolved in Panel B. Although slight, because of the log scaling of these plots, this slight deflation (left of 1.5 on the x-axis of Panel A) occurs over a much greater proportion of the distribution and thus has a stronger effect on the mean and median of the distribution than the more visually apparent inflation in the extreme tails (right of 2 on the x-axis of Panel A). For lambda values, see Table S4. Only nominal p-values below the standard genome-wide significance threshold (p 0.05 and within 2 megabases (original scoring: r2>0.2 and within 1 megabase). BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (TIF) Click here for additional data file. Figure S15 Replication rate among categories with alternate scoring parameters. A regenerated cumulative replication plot (Figure 4B) showing the average rate of replication (p 0.05 and all SNPs within 2 megabases) results in a similar pattern of increased replication as with the original parameters (including r2>0.2 and all SNPs within 1 megabases), with the exception of the intergenic category, which shows a noticeable decrease in the replication rate. (TIF) Click here for additional data file. Figure S16 Relationship between total categorical total LD and z-score2. The mean (z2) of each category, using the height GWAS, as we change the threshold for inclusion for both the original (A; including r2>0.2 and within 1 megabases), and alternate (B; r2>0.05 and within 2 megabases) parameters for LD weighted scoring. The mean(z2) increases approximately monotonically each category, but with noticeably different slopes. The 5′UTR category in figure A becomes unstable at high thresholds because there are very few SNPs remaining. Changing to a more inclusive LD weighted scoring increases the number of SNPs with high scores and improves the relationship. This suggests that even greater enrichment could be achieved by tuning the categorical inclusion threshold upwards. (TIF) Click here for additional data file. Figure S17 Parametric mixture model fits to Q-Q plots. Q-Q Plot for Height (A) and Crohn's Disease (B). Solid black lines are actual data. Dotted black lines are Q-Q curves under the global null hypothesis. Solid red lines are fitted Q-Q curves from Weibull mixture model for transformed p-values. Note, upper limit in Q-Q plot y-axes is 7.3, corresponding to GWAS-significance threshold of p = 5×10−8. (TIF) Click here for additional data file. Figure S18 Effect of non-null proportion on Q-Q plots. Predicted Q-Q Plot for Crohn's Disease (CD; solid black line) from parametric Weibull mixture model fit (model given by Equation [S9]). The blue line is the predicted Q-Q curve of the CD data if the non-null proportion π1 were 0.001 instead of the value 0.026 estimated from the CD data. The red line is the predicted Q-Q curve if the non-null proportion π1 were 0.10. (TIF) Click here for additional data file. Figure S19 Effect of sample size on Q-Q plots. Predicted Q-Q Plot for Crohn's Disease (CD; solid black line) from parametric Weibull mixture model fit (model given by Equation [S9]). The blue line is the predicted Q-Q curve of the CD data if the sample size were half as large as the true sample size (n = 51,109). The red line is the predicted Q-Q curve of the CD data if the sample size were five times as large as the true sample size. (TIF) Click here for additional data file. Table S1 Descriptive statistics for each GWAS study. All traits are highly heritable and summary statistics are from well-powered studies. All Studies were imputed with using the HapMap phase II as a reference, with the exception of CD, UC and SCZ that used HapMap phase III as a reference. These statistics describe the results of the study in the form they were obtained by our group. (XLSX) Click here for additional data file. Table S2 LD-weighted score distribution for the union of SNPs across all studies. The average score for different categories varies widely and reflects the relative abundance of the different elements within the genome. *Note intergenic scores are binary, with a score of 1 denoting an intergenic SNP. (XLSX) Click here for additional data file. Table S3 The number of SNPs per annotation category. The table shows the number of tag SNPs in each annotation category from each GWAS without LD based annotation (using only positional information (No LD) and after LD based annotation (LD). Note the increased number of SNPs in all annotation categories, especially in annotation categories such as 3′UTR and 5′UTR when using LD-weighted categories. BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (XLSX) Click here for additional data file. Table S4 Estimated genomic inflation factors before and after intergenic inflation control (IIC). We present the estimates from either all SNPs or intergenic SNPs. The λGC values calculated before IIC were calculated from the summary statistics as they were made available to us, either by collaborators or public data repositories. Many of these studies already had performed a standard genomic control procedure, adjusting the test statistics down, to correct for inflation. For these studies our procedure may correct statistics upwards, increasing the computed λGC values. We leveraged the intergenic SNPs to estimate inflation because their relative depletion of associations suggests they provide a robust estimate of true null SNPs that is less contaminated by polygenic effects. Using annotation categories in this fashion is important given concerns posed by recent GWAS [8] about the over-correction of test statistics using standard genomic control [15]. Values greater than 1 indicate inflation and values less than 1 indicate an over correction, relative to the theoretical empirical null distribution. λGC was calculated as the ratio of the median z-score2 to the expected median of a Chi-square distribution with 1 degree of freedom, for all SNPs and intergenic SNPs independently. IIC, Intergenic Inflation Control; BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (XLSX) Click here for additional data file. Table S5 Significance of QQ-plot enrichment. The p-values of the enrichment of the Q-Q plots for all phenotypes compare intergenic annotation category with each other annotation category. Each p-value corresponds to the median Kolmogorov-Smirnov (KS) statistic from 10 iterations of each comparison for 10 different random prunings of SNPs to approximate independence (r2 .2) relating each GWAS tag SNP to all 1KGP SNPs within 1,000,000 base pairs. Note the consistent pattern across phenotypes, with large variation between annotaion categories, with highest LD score in 5′UTR. BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (XLSX) Click here for additional data file. Table S8 Per category average per SNP number of tagged SNPs. The average total number of SNP tagged (r2>0.2) by a tag SNP per genic annotation category for each phenotype is shown. Note the consistent pattern across phenotypes, with variation between categories, and highest number in 5′UTR. The distribution of block sizes does match the ordering of enrichment by category. BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (XLSX) Click here for additional data file. Table S9 Per category average MAF. The average minor allele frequency of GWAS tag SNPs in each genic annotation category for every phenotype is not consistent with this effect driving our enrichment patterns. Note the similarities across phenotypes and annotation categories. BD, Bipolar Disorder; BMI, Body Mass Index; CD, Crohn's disease; CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL, High density lipoprotein; LDL, Low density lipoprotein; SBP, systolic blood pressure; SCZ, Schizophrenia; TC, total Cholesterol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-ratio. (XLSX) Click here for additional data file. Table S10 Multiple regression analysis predicting log(Z2) in height. A multiple regression analysis reveals a minimal, but significant, effect of total LD on the log z2 for height. This represents a minimal, but significant, effect of overall LD block size on enrichment. Categorical effects remain independently strong in this analysis with an effect size order that mirrors enrichment. (DOCX) Click here for additional data file. Table S11 Null GWAS Simulations. We present simulations of categorical enrichment based on multiple independent null GWAS simulations using subjects with European ancestry from the 1000 Genomes Project. Random phenotypes were generated unrelated to genotypes for each subject, association z-scores were computed for each tag SNP, and mean(z2) was computed for each annotation category, using the same procedure as applied to the actual GWAS data. The means and standard deviations were computed from 20 independent simulation runs. The results demonstrate that the observed differential enrichment of annotation categories cannot be explained by category-specific spurious sources of genomic inflation due to differential LD or MAF. (DOCX) Click here for additional data file. Table S12 FDR versus sFDR Discovery. Leveraging the enriched genic annotation categories to create strata among the SNPs we show that the stratified false discovery rate (sFDR) method improves the discovery of SNPs for a given FDR threshold, across all phenotypes. The numbers reported are after pruning SNPs for LD at a threshold of r2≤0.2. (XLSX) Click here for additional data file. Text S1 Supplementary text extending Materials and Methods and presenting supporting analyses. More details are provided with respect to the acquisition, processing and annotating of the GWAS data used for the main results. The relationship between QQ-plots and False Discovery Rate is extended and related to our measures of enrichment. Also, the main results are described within the context of a mixture-modeling framework. Finally, a series of control experiments are described and supplementary references are enumerated. (DOCX) Click here for additional data file.
Author and article information
Contributors
Graham R. Wallace: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Electronic):
1932-6203
Publication date
(Electronic):
22
December
2015
Publication date Collection: 2015
Volume: 10
Issue: 12
Electronic Location Identifier: e0144531
Affiliations
[4
]NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine,
University of Oslo, Oslo, Norway
[6
]Department of Psychiatry, University of California San Diego, La Jolla, California,
United States of America
[7
]Department of Neurosciences, University of California San Diego, La Jolla, California,
United States of America
[8
]Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California,
United States of America
[9
]Department of Radiology, University of California San Diego, La Jolla, California,
United States of America
[10
]Cognitive Sciences Graduate Program, University of California San Diego, La Jolla,
California, United States of America
[11
]Department of Clinical Molecular Biology, Institute of Clinical Medicine, University
of Oslo, Oslo, Norway
[12
]Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University
of Oslo, Oslo, Norway
Author notes
Conceived and designed the experiments: AMD OAA. Performed the experiments: YW ML VZ SR AMD. Analyzed the data: IGM YW FB AJS RSD WKT VZ AMD KMG LKM SD. Contributed reagents/materials/analysis tools: SR KMG SD. Wrote the paper: OAA SR YW.
* E-mail:
o.a.andreassen@
123456medisin.uio.no
Article
Publisher ID: PONE-D-15-21924DOI: 10.1371/journal.pone.0144531
PMC ID: 4687843
PubMed ID: 26695485
SO-VID: 2f821dad-943d-4b47-9236-194e61f762e4
Copyright © © 2015 Reppe et al
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
History
Date
received
: 21
May
2015
Date
accepted
: 19
November
2015
Page count
Figures: 3,
Tables: 2,
Pages: 20
Funding
This work was supported by the Research Council of Norway [grant number 183782/V50
to OAA]; the South East Norway Health Authority [grant number 2010-074 to OAA and
52009/8029 to KMG]; the National Institutes of Health [grant number T32 EB005970 to
RSD, RC2DA029475 and R01HD061414 to AJS]; the Robert J. Glushko and Pamela Samuelson
Graduate Fellowship to AJS; the 6th EU framework program [grant number LSHM-CT-2003-502941
to KMG and SR]; and Oslo University Hospital, Ullevaal [grant number 52009/8029 to
KMG]. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
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Data Availability The primary microarray expression data have been submitted to the European Bioinformatics
Institute (EMBL-EBI; ID: E-MEXP-1618).
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Data availability:
ScienceOpen disciplines: Uncategorized