- Record: found
- Abstract: found
- Article: found
Expression analysis of the osteoarthritis genetic susceptibility locus mapping to an intron of the MCF2L gene and marked by the polymorphism rs11842874
research-article
Author(s):
Colin Shepherd
,
Andrew J. Skelton ,
Michael D. Rushton ,
Louise N. Reynard ,
John Loughlin
Publication date (Electronic):
19 November 2015
Journal:
BMC Medical Genetics
Publisher:
BioMed Central
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Background
Osteoarthritis (OA) is a painful, debilitating disease characterised by loss of articular cartilage with concurrent changes in other tissues of the synovial joint. Genetic association studies have shown that a number of common variants increase the risk of developing OA. Investigating their activity can uncover novel causal pathways and potentially highlight new treatment targets. One of the reported OA association signals is marked by the single nucleotide polymorphism (SNP) rs11842874 at chromosome 13q34. rs11842874 is positioned within a small linkage disequilibrium (LD) block within intron 4 of MCF2L, a gene encoding guanine-nucleotide exchange factor DBS. There are no non-synonymous SNPs that correlate with this association signal and we therefore set out to assess whether its effect on OA susceptibility is mediated by alteration of MCF2L expression.
Methods
Nucleic acid was extracted from cartilage, synovial membrane or infrapatellar fat pad tissues from OA patients. Expression of MCF2L was measured by quantitative PCR and RNA-sequencing whilst the presence of DBS was studied using immunohistochemistry. The functional effect of SNPs within the 13q34 locus was assessed using public databases and in vitro using luciferase reporter analysis.
Results
MCF2L gene and protein expression are detectable in joint tissues, with quantitative differences in the expression of the gene and in the transcript isoforms expressed between the tissues tested. There is an expression quantitative trait locus (eQTL) operating within synovial membrane tissue, with possession of the risk-conferring A allele of rs11842874 correlating with increased MCF2L expression. SNPs within the rs11842874 LD block reside within transcriptional regulatory elements and their direct analysis reveals that several show quantitative differences in regulatory activity at the allelic level.
Conclusions
MCF2L is subject to a cis-acting eQTL in synovial membrane that correlates with the OA association signal. This signal contains several functional SNPs that could account for the susceptibility and which therefore merit further investigation. As far as we are aware, this is the first example of an OA susceptibility locus operating as an eQTL in synovial membrane tissue but not in cartilage.
Related collections
Most cited references20
- Record: found
- Abstract: found
- Article: not found
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
Rob Patro, Stephen Mount, Carl Kingsford (2014)
We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
- Record: found
- Abstract: found
- Article: not found
A functional polymorphism in the 5' UTR of GDF5 is associated with susceptibility to osteoarthritis.
Susumu Saito, Masaharu Takigawa, Y Miyamoto … (2007)
Osteoarthritis (MIM 165720), characterized by degeneration of articular cartilage, is the most common form of human arthritis and a major concern for aging societies worldwide. Epidemiological and genetic studies have shown that osteoarthritis is a polygenic disease. Here, we report that the gene encoding growth differentiation factor 5 (GDF5) is associated with osteoarthritis in Asian populations. A SNP in the 5' UTR of GDF5 (+104T/C; rs143383) showed significant association (P = 1.8 x 10(-13)) with hip osteoarthritis in two independent Japanese populations. This association was replicated for knee osteoarthritis in Japanese (P = 0.0021) and Han Chinese (P = 0.00028) populations. This SNP, located in the GDF5 core promoter, exerts allelic differences on transcriptional activity in chondrogenic cells, with the susceptibility allele showing reduced activity. Our findings implicate GDF5 as a susceptibility gene for osteoarthritis and suggest that decreased GDF5 expression is involved in the pathogenesis of osteoarthritis.
- Record: found
- Abstract: found
- Article: found
Unraveling the Regulatory Mechanisms Underlying Tissue-Dependent Genetic Variation of Gene Expression
Jingyuan Fu, Marcel Wolfs, Patrick Deelen … (2012)
Introduction It has become clear that human genetic variants, such as single nucleotide polymorphisms (SNPs), can in cis affect the expression of nearby genes [1], [2]. Many loci exist that contain genetic variants that affect gene expression (expression quantitative loci, eQTL, usually assessed by investigating single nucleotide polymorphisms (SNPs) and expression probes that are within 250 kb up to 1 Mb apart). These cis-eQTL analyses have been performed in many different human tissues and cell types, including lymphoblastoid cell lines (LCL) [3], [4], liver [5]–[7], blood [8], [9], brain [10], [11], adipose tissues [6], [8], skin [12], [13] and primary fibroblasts [12]. However, considerable heterogeneity of cis-eQTL effects is possible between different tissues: A recent study reported that the proportion of heritability due to gene expression attributable to cis-regulation differs between tissues (37% in blood and 24% in adipose tissue) [14]. By comparing the overlap of significant cis-eQTL at a predefined threshold, estimates on the tissue-dependence of cis-eQTL were between 30% (liver, adipose tissues) and 70–80% (LCLs, fibroblasts, T cells) [8], [9], [15], [16]. However, due to statistical power issues, it is likely that the tissue-dependency of cis-eQTL has been overestimated by studies solely assessing the overlap of cis-eQTL between tissues based on a certain threshold. Realizing this problem, Ding et al. used a refined statistical method to estimate the percentage of overlap by adding a power parameter to the model [12]. They reported that only 30% of cis-eQTL in LCLs were not shared with fibroblast cis-eQTL. Similarly, a recent study by Nica et al. [13] examined the tissue-dependence of cis-eQTL in three human tissues (LCL, skin and fat) in a continuous manner by quantifying the proportion of overlap of cis-eQTL from the enrichment of low P-values. They observed that 29% of cis-eQTL appear to be exclusively tissue-dependent, and also observed that the effect sizes of 10–20% of the cis-eQTL present in multiple tissues differ per tissue type. These observations are in line with a large-scale transcriptomic analysis of 46 human tissues, which found that while only 6.0% of genes were ubiquitously expressed across all the assessed tissues, 3.1% genes were only expressed in a single tissue [17]. To gain a better understanding of this subtle regulation of tissue-dependent regulation and to address the question of how genetic variants mediate tissue-dependent expression, we compared cis-regulation between whole peripheral blood from a large cohort of 1,240 individuals and four smaller primary human tissues (liver, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and skeletal muscle) obtained from a set of 85 subjects. We first applied a robust sampling procedure to estimate accurately how often genes showed different cis-eQTL effects between tissues. We then investigated in what way genes are differently associated with SNPs in different tissues. Finally, we assessed various functional properties for the SNPs involved in tissue-dependent cis-regulation and their association with complex traits. Results Cis-eQTL Mapping in Five Primary Tissues For this study, we collected data for four different tissues from a set of 85 unrelated obese Dutch subjects. We successfully collected data on 74 liver samples, 62 muscle samples, 83 subcutaneous adipose tissue (SAT) samples and 77 visceral adipose tissue (VAT) samples (for 48 individuals all four tissues were available). The fifth tissue, blood, was collected from a different group of 1,240 unrelated Dutch individuals (Table S1). The gene expression levels in all five tissues were profiled using the same Illumina HumanHT12 v3 platform (see Materials and Methods). After normalization, we further removed strong expression differences between these tissues by removing the 50 principal components from this dataset and using the residuals for further analysis (described in [18] and Materials and Methods, Figure S1). We first performed cis-eQTL analysis in each of these datasets separately, by testing the correlation between SNPs and probes that were mapping within 1 Mb distance. At a false-discovery rate (FDR) of 0.05 level, we identified a non-overlapping set of 195,078 probe-SNP pairs that were significant in at least one of the tissues under study: 4,700 probe-SNP pairs were significantly associated in liver, 7,161 pairs significantly in SAT, 5,323 pairs significantly in VAT, 1,971 pairs significantly in muscle, and 190,278 pairs significant in blood (Figure S2). Owing to the much larger sample size, 182,569 probe-SNP pairs (93.6%) were solely detected in blood, while only 601 probe-SNP pairs (0.31%) were significant in each of the five different tissues (Figure S3). Although a previous study showed that the heritability of gene expression levels are higher in blood (37%) compared to adipose tissue (24%) [14], we believe that the large difference in the detected probe-SNP pairs between blood and non-blood tissues is due to statistical power issues that result from substantial sample size differences. As we had initially run cis-eQTL analyses in each of the tissues separately, we subsequently conducted a weighted Z-score meta-analysis across the four non-blood tissues and detected 23,878 probe-SNP pairs at FDR of 0.05. Out of these, 23.2% (5,550 out of 23,878 probe-SNP pairs) had not been identified in any of the single-tissue analyses (Figure S4). In total, the single-tissue analyses and meta-analysis yielded a non-overlapping set of 200,629 significant probe-SNP pairs, corresponding to 103,968 unique expression altering SNPs (eSNPs) and 11,618 probes (eProbes) that represent 8,561 unique genes (eGenes) (Figure S2). Cis-eQTL Effects Differ per Tissue Type To assess the tissue-dependency of the cis-eQTL, we compared the Spearman correlation of each probe-SNP pair between tissues. However, due to the small sample sizes of the non-blood datasets we had very limited statistical power to determine whether there were cis-eQTL effect differences between non-blood tissues. We therefore confined ourselves to comparisons between the large blood dataset and each of the smaller non-blood tissues. To correct for sample size differences, we employed a resampling procedure, permitting us to derive an empirical distribution of association Z-scores (calculated based on the Spearman correlation) of each probe-SNP pair in blood of the same sample size as in non-blood tissues (see Materials and Methods; Figure S5). We observed that 18,456 pairs (9.2% of 200,629 probe-SNP pairs) showed a significantly different Z-score between blood and at least one of the non-blood tissues at P 0.39). Thus, in our data, rs12740374 only exerts an effect on SORT1 gene expression in liver, although we did observe that SORT1 was expressed abundantly in all tissues. Alternative regulation Alternative regulation between tissues refers to a gene that is cis-associated with a SNP in a particular tissue and associated with a different, independent SNP in another tissue. Such an alternative cis-regulation is also a common phenomenon, as we found it applied to on average 14.5% of the probes with tissue-dependent regulation (Figure 2). One particular example is the trans-membrane gene TMEM176A, also known as hepatocellular carcinoma-associated antigen 112. The expression of TMEM176A was associated with intronic SNP rs714885 in liver (N = 74, P = 5.7×10−6) but with the 19.5 kb upstream SNP rs6464104 in blood (N = 1,240, P = 5.07×10−132) (Figure 3B). These two SNPs are unlinked variants (r2 = 0.002 and D′ = 0.054 based on the HapMap phase II CEU panel). We observed the same alternative association for different probes of TMEM176A in an independent liver eQTL dataset (profiled using a custom ink-jet microarrays [7] and in the aforementioned independent blood eQTL dataset that was profiled using Illumina HumanRef-8 v2 BeadChips) (Table S4) [18], [19]. This clearly shows that 1) multiple, unrelated variants can sometimes affect exactly the same gene, and 2) these independent variants sometimes only exert an effect on the gene expression in a particular tissue. Different effect size The different effect size refers to a common phenomenon that a gene is associated with the same SNP with alleles that have the same direction of effect but with a different magnitude in different tissues (Figure 2). For eProbe that showed this, we observed a significantly positive correlation between the association profiles of the tissues. We observed it applies to on average 47.9% of the probes that show tissue-dependent regulation (Figure S2), in line with a previous report [13]. One example is the O-6-methylguanin-DNA-methyltransferase (MGMT) gene that plays an important role in DNA repair and which suppresses tumor development [24]. We observed a cis-eQTL for MGMT across each of the five tissues. However, the effect size in blood was substantially smaller than that in SAT tissues (Figure 3C). Opposite allelic direction Surprisingly, we observed that some genes were associated with the same SNPs in different tissues but with alleles having an opposite effect on the gene expression between tissues. For a probe under this regulation, we then also observed a strong negative correlation between its association profiles across different tissues. This “opposite allelic direction” mechanism accounted for on average 4.4% of the probes under tissue-dependent regulation (Figure 2), which is much less common than the three previous mechanisms. However, this is still much more often than would be expected by chance, as determined by a comparison between two blood datasets in which we found the allelic directions were nearly always identical (Figure S7). One striking opposite allelic direction was observed to D-dopachrome tautomerase (DDT), which showed completely opposite effects between blood and liver (Figure 3D). Consistently, we found this opposite effect in the independent liver [7] and blood dataset,(H8v2), even when different probes were assessed. The minor allele rs5751777-C was associated with higher expression in liver (P = 9.95×10−22 in the discovery set and P = 2.86×10−211 in the validation set), but with lower expression in blood (P = 3.98×10−119 in the discovery set and P = 4.37×10−24 in the validation set) (Table S5). Strikingly, this opposite allelic direction was also observed when comparing liver with SAT, VAT and muscle, tissues that were all obtained from exactly the same set of individuals (Figure S11). Another notable gene with an opposite allelic direction is ORMDL3. Although its function remains unclear, genetic variants near ORMDL3 are associated with various immune-related diseases, including asthma, type 1 diabetes, Crohn's diseases, ulcerative colitis and primary biliary cirrhosis [25]–[29]. ORMDL3 had a genome-wide significant cis-eQTL in blood and its association in SAT was showing near-genome-wide significance (Figure S12). All disease-associated SNPs in this locus showed association in cis with the expression level of ORMDL3 (Table S6), including the functional variant rs12936231 that has been implicated to play a causal role in chromatin remodeling [30]. The risk alleles for asthma and preventive alleles for other autoimmune diseases showed consistent up-regulation in blood (and were also reported in LCLs) [25], [30]. However, to our surprise, the effect in SAT was completely reversed, leading to down-regulation. Although we have only provided a few examples here, these observations indicate that conclusions drawn about mechanistic up- or down-regulation from a single tissue cannot necessarily be translated to other tissues, as they may sometimes lead to completely different conclusions depending on the tissues studied. In the supplementary material (Tables S7, S8, S9, S10 and Figures S13, S14, S15, S16), we have summarized the observed tissue-dependent regulation for 156 genes that have been reported to be associated with complex traits at P = 5×10−8 (based on the genes, mentioned in the Catalog of Published Genome-wide Association Studies, as of 16/09/2011). Some of these plots also show that the genetic regulation of gene expression is sometimes even more complicated than what we have described here: some genes can have multiple cis-eQTL that were either shared or specific to the tissues, e.g, the association of MTMR3 gene that was associated with lung cancer [31], Nephrophaty [32], and inflammatory bowel disease [33], [34] (Figure S17). The four categories of tissue-dependent cis-regulation we have observed can be explained by two molecular models: 1) the tissue-dependent use of the same causal variant, i.e., the same eSNPs tag the same causal variant that is activated differentially by tissue-dependent factors; 2) the tissue-dependent causal variants, i.e., the same or different eSNPs tag different causal variants upon the tissues under study. The extent of the linkage disequilibrium (LD) between the causal variants and tag eSNPs, and the direction of effect of the regulatory factors (e.g., stimulating or suppressing the expression) and the size of their effects could lead to the observations of different categories (Figure 4). 10.1371/journal.pgen.1002431.g004 Figure 4 Molecular models of tissue-dependent cis-regulation. The observed tissue-dependent cis-regulations can be explained by two molecular models: (A) the tissue-dependent use of the same causal variants, or (B) the use of tissue-dependent causal variants. The ovals indicate the two regulatory factors (e.g., transcription factors) that play regulatory roles in different tissues (brown in tissue 1 and purple in tissue 2). These factors can recognize the same or different cis-elements (the yellow region). The genetic variants are shown as SNPs with A/G alleles. The SNPs in red are causal variants and the SNPs in blue are tag SNPs. The red line between them indicates the linkage disequilibrium. The arrows indicate the effect of regulatory factors, here the up arrows represent expression stimulators and the down arrows expression suppressors. The size of the arrows indicates the size of the differences between the expression of A and G alleles, i.e., the cis-eQTL effect size. Discussion Gene expression levels are partly determined by genetic variation, and eQTL mapping in different cell types and tissues has identified many cis-eQTL. However, the effect of cis-eQTL is strongly dependent upon the studied tissue. In this study, we compared the genetic architecture of gene expression regulation in blood and four non-blood primary tissues. We detected that the majority (71.3%) of the detected probes under genetic control (eProbes) show a concordant association across tissues. However, the remaining 28.7% of the eProbes show discordant, tissue-dependent regulation. Strikingly, many of those discordantly associated eProbes are affected by multiple, independent eSNPs. We followed up the genes under tissue-dependent regulation and identified four different mechanisms: specific regulation, alternative regulation, different effect size, and opposite allelic direction. We are the first to provide a comprehensive landscape of the different mechanisms of tissue-dependent cis-regulation. Of the four mechanisms identified, the opposite allelic direction mechanism, where alleles can have opposing effects on gene expression between tissues is of particular interest: Although this mechanism is less common than the other three, it has important implications for inferring the transcriptional effects of alleles from other tissue data, especially on the susceptibility risk alleles for complex diseases. The use of different tissues could result in completely the opposite conclusion! This finding highlights the great importance of investigating disease-relevant tissues in order to correctly characterize the functional effects of disease-associated variants. We observed that SNPs at various transcriptional regulatory regions more often than expected exert tissue-dependent regulation, although most of the eSNPs were located at intergenic and gene intronic regions where functions remain undefined. However, we must emphasize that the causal variants remained undefined. Furthermore, because of the LD structure, although the same eSNPs can be associated with the expression of the same gene in different tissues, this does not necessarily mean that the same regulatory variants act in the different tissues. We have proposed two molecular models and suggested that tissue-dependent cis-regulations can be explained by the tissue-dependent use of the same causal variants or by the use of different tissue-dependent causal variants. Further fine-mapping and functional analyses are needed to identify the causal variants and to understand how they are used in different tissues due to the limited resolution of cis-eQTL mapping: It is known that the size of regulatory cis-elements generally is only a few base pairs (i.e., the binding sites of transcription factors or microRNAs), whereas the size of linkage disequilibrium blocks is generally in a range of 10–100 kb [35]. Furthermore, as the molecular models that we have proposed are quite simple, we cannot exclude other molecular mechanisms acting in these processes, e.g., the competition of different regulatory factors and binding sites in different tissues, or the role of tissue-specific methylation [36], [37] and chromatin remodeling [38], etc. It is well known that trait-associated SNPs are more likely to have effects on gene expression but, to our surprise, we found that they are also more likely to exert tissue-dependent effects. This observation adds an extra layer of complexity to complex traits. We acknowledge that our study has some limitations: We compared cis-regulation between peripheral blood and four rather small non-blood tissues. We lacked statistical power to compare the cis-regulations between two non-blood tissues well. Secondly, the identified discordant eQTLs are determined by the limited tissues that we studied Thirdly, although we corrected for substantial expression differences across samples by employing principal component analysis, it is still possible that some of the observed tissue-dependent cis-regulation can be due to the tissue heterogeneity (i.e. different proportions of cell types per tissue). Likewise it is also possible that some of the identified discordant cis-eQTL could be due to differences in the base-line expression between tissues. However, we observed this to be the case for both concordant and discordant cis-eQTL when investigating the original (non-PCA corrected) expression data (see Table S11). Nevertheless our results indicate that natural genetic varation can affect gene expression levels in complex ways. Further analyses using different tissues and specific cell types and using larger sample sizes are required to gain a deeper understanding of the genetic variation of gene expression and to gain better insight into the full complexity of disease. Materials and Methods Genotyping and Expression Profiling on Liver, Muscle, and Adipose Fat Tissues from the Same Population Subjects From April 2006 to January 2009, 85 morbidly obese Dutch subjects (23 male and 62 female subjects) with a body mass index (BMI) between 35 and 70 were included in the study. They all underwent elective bariatric surgery at the Department of General Surgery, Maastricht University Medical Centre. Patients with acute or chronic inflammatory diseases (e.g., autoimmune diseases), degenerative diseases, reported alcohol consumption (>10 g/day), and/or using anti-inflammatory drugs were excluded. The average age of the subjects was 43.9 with a range of 17 and 67 years. This study was approved by the Medical Ethical Board of Maastricht University Medical Centre, in line with the guidelines of the 1975 Declaration of Helsinki. Informed consent in writing was obtained from each subject personally. The subject information was provided in Table S1. Genotyping Venous blood samples were obtained after 8 hours fasting on the morning of surgery. DNA was extracted from this blood using the Chemagic Magnetic Separation Module 1 (Chemagen) integrated with a Multiprobe II Pipeting robot (PerkinElmer). All samples were genotyped using Illumina HumanOmni1-Quad BeadChips that contain 1,140,419 SNPs. Genotyping was performed according to standard protocols from Illumina. RNA profiling in four tissues Wedge biopsies of liver, visceral adipose tissue (VAT, omentum majus), subcutaneous adipose tissue (SAT, abdominal), and muscle (musculus rectus abdominis) were taken during surgery. RNA was isolated using the Qiagen Lipid Tissue Mini Kit (Qiagen, Crawley, West Sussex, UK, 74804). Assessment of RNA quality and concentration was done with an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, USA). Starting with 200 ng of RNA, the Ambion Illumina TotalPrep Amplification Kit was used for anti-sense RNA synthesis, amplification, and purification according to the protocol provided by the manufacturer (Ambion, Austin, USA). 750 ng of complementary RNA was hybridized to Illumina HumanHT12 BeadChips and scanned on the Illumina BeadArray Reader. Raw probe intensity data for these samples was extracted using Illumina's BeadStudio Gene expression module v3.2 (No background correction was applied, nor did we remove probes with low expression). Genotyping and Expression Profiling on Blood Subjects The genetical genomics samples for blood were collected from unrelated Dutch individuals in four studies: 324 healthy individuals were collected in the University Medical Centre Utrecht, 414 amyotrophic lateral sclerosis (ALS) patients were collected in the University Medical Centre Utrecht, 49 ulcerative colitis (UC) patients from a part of the inflammatory bowel disease (IBD) cohort of the University Medical Centre Groningen, and 453 patients with chronic obstructive pulmonary disease (COPD) were collected with the NELSON study. All samples were collected after informed consent and approved by local ethical review boards. Individual sample information is provided in Table S1. Genotyping and imputation DNA from all samples was hybridized to oligonucleotide arrays from Illumina. 324 healthy individuals and 414 ALS patients were genotyped using the Hap370 platform. The 453 COPD patients and 49 UC patients were genotyped on the 610-Quad platform. Because the subjects with liver, muscle, adipose fat tissues were genotyped using more intensive genotyping platform Illumina HumanOmni1-Quad BeadChips, we further used program IMPUTE v2 to impute the genotypes of SNPs that presented in Omni1-Quad chips but not directly genotyped on Hap370 and 610-Quad platform [39]. The reference panel for imputation was the CEU population from HapMap release 22. The directly genotyped SNPs were coded as 0, 1 or 2, while the imputed SNP dosage values were called at a 0.95 confidence level, ranging between 0 and 2. In this way, we obtained the genotype of the same set of 1,140,419 SNPs for all five tissues under study. RNA profiling Anti-sense RNA was synthesized, amplified and purified using the Ambion Illumina TotalPrep Amplification Kit (Ambion, USA) following the manufacturers' protocol. Complementary RNA was hybridized to Illumina HumanHT-12 arrays and scanned on the Illumina BeadArray Reader. Raw probe intensity data for these samples was extracted using Illumina's BeadStudio Gene expression module v3.2 (No background correction was applied, nor did we remove proves with low expression). Genotyping and Expression Profiling in an Independent Blood Dataset of 229 Samples Subjects To ascertain whether our method for identifying tissue-dependent cis-eQTL was robust, we compared the large peripheral blood with an independent blood eQTL dataset that comprised 229 samples. We have described this cohort in previous studies [9], [18]. In brief, this study comprised 111 English celiac disease patients, 59 Dutch amyotrophic lateral sclerosis patients and 59 Dutch health controls. The peripheral blood (2.5 ml) was collected with the PAXgene system (PreAnalytix GmbH, UK). Genotyping and imputation The samples were genotyped using the Illumina (Illumina, San Diega, USA) HumanHap300 platform. We further used IMPUTE v2 to impute the genotypes of all HapMap II SNPs. The reference panel for imputation was the CEU population from HapMap release 22. The directly genotyped SNPs were coded as 0, 1 or 2, while the imputed SNP dosage values were called at a 0.95 confidence level, ranging between 0 and 2. RNA profiling Anti-sense RNA was synthesized amplified and purified using the Ambion Illumina TltalPrep Amplification Kit (Ambion, USA) following the manufacturers' protocol. Complementary RNA was hybridized to Illumina HumanRef-8 v2 arrays (further referred to as H8v2) and scanned on the Illumina BeadArray Reader. Normalization and PCA Correction The raw expression intensities from five tissues were jointly quantile normalized and log2 transformed. We further applied a principal component analysis (PCA) on expression correlation matrix and observed that genes are differentially expressed among different tissue types (Figure S1). We argue that the dominant principal components (PCs) will primarily capture sample differences in expression that reflect physiological or environmental variation (e.g., tissue type and phenotype difference) as well as systematic experimental variation (e.g. batch and technical effect). In order to target the difference in the genetic variation of expression among tissues, we removed the global variation in expression among tissues by using the residual expression for each probe in each tissue after removing 50 PCs (identical to what we have described before [18]). Our previous analysis on the same dataset showed that the number of significantly detected cis-eQTL probes increased two-fold when 50 PCs were removed from the expression data (see Figure S7 in ref [18]). For the independent blood dataset with 229 subjects, we followed the same quantile normalized and PCA correction. Population Stratification and SNP Quality Control We tested population stratification between the two cohorts using the program PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/strat.shtml). This program uses complete linkage agglomerative clustering, based on pair-wise identity-by-state (IBS) distances. The fact that all the individuals from both cohorts were clustered together indicates there was no population stratification. We also checked the allelic frequencies between the two cohorts by treating the 85 individuals with four tissue samples as cases and the 1,240 individuals for blood samples as controls. For the imputed SNPs, we used the genotype with highest probability as the discrete genotype for QC purposes. We removed SNPs that showed significant differences in allele frequency at P 5%, a call rate >95% and an exact Hardy-Weinberg (HWE) P value>0.001. To make certain on the directions of the allelic effect on gene expression (up-regulating or down-regulating), we further removed SNPs with two types of transversion alleles (A/T and G/C) and confined our analysis to SNPs with transition alleles (A/G or C/T) and other types of transversion alleles (A/C or G/T). This quality control resulted in 710,035 SNPs for further analysis. eQTL Discovery In order to detect cis-eQTLs, analysis was confined to those probe-SNP combinations for which the distance from the probe transcript midpoint to SNP genomic location was ≤1 Mb. For each probe-SNP pair, we used Spearman correlation to detect association between SNPs and the variations of the gene expression in liver, SAT, VAT, muscle and blood, respectively. We calculated the Spearman correlation coefficient and corresponding P values and subsequently transformed this into a Z-score. To maximize the power of eQTL discovery in non-blood tissues, we further performed meta-analysis for four non-blood tissues that combines the association signals across the four non-blood tissues under study. An overall, joint P value was calculated using a weighted (square root of the dataset sample number) Z-method. Please see the ref [40] for a comprehensive overview of this method. To correct for multiple testing, we controlled the false-discovery rate (FDR) at 0.05: the distribution of observed p-values was used to calculate the FDR, by comparison with the distribution obtained from permuting expression phenotypes relative to genotypes 100 times. At FDR = 0.05 level, the significance P value threshold was 1.37×10−5 for significantly associated probe-SNP pairs in liver, 2.07×10−5 for significant association in SAT, 1.54×10−5 for significant association in VAT, 5.64×10−6 for significant association in muscle, 4.8×10−4 for significant association in blood and 1.10×10−4 for significant association in the meta-analysis of four non-blood tissues. For these significant probe-SNP pairs, we termed the corresponding SNP, probe and genes as expression SNP (eSNP), regulated probe (eProbe) and regulated genes (eGenes), respectively. Conditional Regression Analysis to Detect Independent eSNPs Due to the linkage disequilibrium among the tested SNPs, we usually found numerous eSNPs for each eProbe. In order to detect independent eSNPs, we performed conditional regression analysis for the eProbes per tissue type. For each eProbe, we first regressed out the main effect of the top eSNP. We then subjected the residuals to eQTL mapping to detect potential secondary, independent eSNPs. We again controlled the false discovery at 0.05 by running 100, as described before in the method section “eQTL discovery”. If secondary eSNPs were present, we repeated the entire procedure to detect tertiary eSNPs by regressing out both the primary and secondary effect (using appropriate multivariate regression analysis). This procedure was repeated until no significant associations were detected any more. Sampling Approach to Identify Tissue-Dependent eQTL Comparing blood and non-blood tissues For each of the 200,629 probe-SNP pairs that was significantly associated at FDR 0.05 level, we further assessed whether the detected Z-scores differed per tissue. We used the Z-scores in blood as a reference because the blood samples were independent from other tissue samples and the sample size was much larger. To correct for the sample size difference, we, out of the 1,240 blood samples, randomly selected without replacement the same number of samples for the comparison with liver (N = 74), SAT (N = 83), VAT (N = 77) and muscle (N = 62). For a certain probe-SNP pair, we re-calculated the association Z-score in blood for the selected sample size. The sampling procedure was repeated 100 times. We subsequently fitted a generalized extreme value distribution (GEVD) for the Z-scores of 100× sampling procedures in blood. GEVD is a flexible model with three parameters: location (γ), scale (β) and shape (α). GEVD can resemble different distributions with different settings of parameters. For example, when α = 0, it resembles the Gumbel types of distributions (Type I); when α>0, it resembles the Frechet types of distributions (Type II); when α 6.23×10−8 were called “concordant associations”. The expression profiling in all five tissues used the same platform. Therefore, the discordant association cannot be explained by the hybridization efficiency. Because all of the tested SNPs were directly genotyped in non-blood tissues but most of them were imputed in blood, we further checked whether the discordance was caused by the imputation. We did not observe that imputation accuracy might confound our results: 69.3% of the discordant eSNPs were imputed in blood whereas 68.0% of the concordant eSNPs were imputed in blood (Fisher's exact test P value = 0.60). We also assessed whether there was heterogeneity in effect present when comparing the different subgroups of phenotypes. We did not find evidence this to be the case (see Table S6 in ref [18]). Comparing two independent blood datasets To further validate the tissue-dependent effect we had detected, we compared the cis-eQTL effects between the blood dataset HT12 and H8v2, using the same sampling procedure as described above. Because of the difference of expression platform, we could only make comparisons for those probes that were present in both datasets. We only investigated SNPs that showed similar allele frequencies between the two blood datasets (SNPs with allele frequency P<0.01 were excluded from analysis and as the H8v2 dataset contained 111 celiac disease patients that were nearly all HLA-DQ2.2 or HLA-DQ2.5 positive we also excluded the HLA from this analysis). After filtering we could compare 93,656 probe-SNP pairs. Enrichment for SNP Properties The minor allele frequency (MAF) and function properties of eSNPs were annotated by the web-based tool SNP Annotation and Proxy Search (SNAP) (www.broadinstitute.rog/mpg/snap) [41], using the CEU population panel from HapMap release 22. We performed Fisher's exact test to compare the enrichment between eSNPs with a tissue-dependent effect on expression across tissues and eSNPs with a static effect. Cis-eQTL Analysis of Trait-Associated SNPs To directly assess the effect of trait-associated SNPs on gene expression, we confined our cis-eQTL analysis to 1,954 SNPs (with alleles A/G) that were associated with complex traits at P<5.0×10−8 in the ‘Catalog of Published Genome-wide Associated Studies’ (per 16 September 2011) [20] and assessed the tissue-dependency of eQTL effect across the tissues, following the same analysis and permutation procedures. The cis-eQTL significance threshold P values were set at P = 4.6×10−3 in blood, 2.6×10−4 in liver, 2.5×10−4 in muscle, 1.8×10−4 in VAT and 3.2×10−5 in SAT, and 1.1×10−3 for the meta-analysis of four non-blood tissue. At these levels, a total of 2,990 probe-SNP pairs were significant in at least one eQTL analysis. Characterizing the Tissue-Dependent Mechanisms of Cis-Regulation To characterize the tissue-dependent mechanisms of cis-regulation, we reasoned that comparing the association at a single probe-SNP level cannot provide a complete picture of the tissue-dependent genetic determinants of gene expression. To gain further insight into the tissue-dependent cis-regulation, we extended analysis for the eProbes with discordant cis-eQTL that were determined by single probe-SNP comparison and compared their whole association profiles across tissues. The association profile refers to the set of the absolute Z-scores of all N number of the tested SNPs within 1 Mb distance from the middle point of probe under study: i.e., {|Z1 |, |Z2|, |Z3 |, … |Zn |}. Such a profile can represent the combined association signals of the multiple independent eSNPs and their linkage disequilibrium. Most of the eProbes only showed significant association in blood and were not significantly associated in the smaller non-blood tissues. For those eProbes, we had limited statistical power to determine whether the association in non-blood tissues is truly absent or is not detected due to power issues. Therefore, we confined our comparison of association profiles to the eProbes that were significantly associated in non-blood tissues and compared them to those in blood. To assess the similarity of association profiles across tissues, we computed Pearson correlations coefficient ( ) of the association profiles between two tissues. Because the SNPs were likely in strong linkage equilibrium, there is strong dependency among the Z-scores within the association profile. To determine the empirical threshold for the significance of the correlation between the association profiles and considering the dependency of the SNPs, we performed permutation analysis by randomly assigning genomes to the individuals per tissue type. We thus obtained the association profiles per probe per tissue for the permuted genotypes. These permuted association profiles retained the same correlation structure among SNPs and the Pearson correlation coefficient between the permuted association profiles (r0 ) would mainly explain the correlation among SNPs. We repeated this permutation 100 times and determined the empirical threshold rthres = 0.21 at FDR 0.05 level using the model (FDR = n0 {r0 ≥rthres }/n1 {r≥rthres ), where r and r0 refer to the Pearson correlation coefficient of real data and permuted data, respectively; n refers to the number of probes where r≥rthres and n0 refers to the average number of probes where r0≥rthres from 100× permutations. Based on the correlation of association profiles between tissues, we identified four different categories of tissue-dependent genetic regulation of gene expression. If the association profiles for one single probe did not correlate at all between two tissues (r<0.21), we further checked whether the eProbe was significant in both tissues: if the probe had a significant association in one tissue but not in the other, we deemed this “specific cis-regulation”; if instead the eProbe was significant in both tissues, but was associated to different (unlinked) eSNPs in the different tissues, we deemed it “alternative cis-regulation”. For those association profiles where two tissues showed a correlation (r≥0.21), we checked the direction and the effect size of allelic effect on gene expression: if the allelic direction was the same and the effect size was different, we concluded the eProbe belonged to the category “different effect size”; if the allelic direction was instead opposite, the probes had tissue-dependent regulation with an “opposite allelic direction”. Differential Expression For the probes with tissue-dependent cis-regulation, we assessed whether they were also differential expressed between the tissues where they showed different cis-regulation. To do so, we relied upon the quantile-normalized expression intensity before any removal of the first 50 principal components. For each discordant eProbe, we used a Wilcoxon Mann-Whitney U test to assess the differential expression between the tissues. We performed the same analysis for a random set of concordant eProbes, equal in size to the set of discordant eProbes. The significance of differential expression was controlled at a Bonferroni-corrected P value 0.05 level. Accession Numbers Expression data for both blood tissue and four non-blood dataset have been deposited in GEO with accession numbers GSE20142 (1,240 peripheral blood samples, hybridized to HT12 arrays) and GSE22070 (subcutaneous adipose, visceral adipose, muscle and liver samples). The expression data of the validation blood eQTL dataset (229 samples) has been deposited in GEO with accession number GSE203332. Supporting Information Figure S1 The effect of removing principal components from expression data. (PDF) Click here for additional data file. Figure S2 Flowchart for the analysis of the tissue-dependent cis-eQTL across the five human tissues. (PDF) Click here for additional data file. Figure S3 Overlap of the associated probe-SNP pairs across the tissues. (PDF) Click here for additional data file. Figure S4 Overlap of the associated probe-SNP pairs across the single-tissue analysis and meta-analysis. (PDF) Click here for additional data file. Figure S5 Sampling procedure. We assessed the difference of association strength between blood and four other tissues (liver, SAT, VAT and muscle). As an example, for liver, we randomly sampled 74 subjects out of the 1,240 blood subjects (making the same sample size as for the liver tissue dataset) and re-measured the association strength for each significantly associated probe-SNP pair, in terms of Z-scores. This sampling procedure was repeated 100 times. The histogram showed the Z-scores distribution of a certain cis-eQTL in 74 blood subjects. We then assessed the deviation of the Z-scores detected in liver (the red arrow) from the distribution of Z-scoress in blood, by fitting the extreme value distribution (EVD) (the red line). The same analysis was performed for comparing blood with SAT, VAT and muscle, by randomly sampling N number of blood subjects (N = 83 for the SAT sample size; 77 for the VAT sample size, and 62 for the muscle sample size, respectively). (PDF) Click here for additional data file. Figure S6 The overlap of discordantly associated probe-SNP pairs. (PDF) Click here for additional data file. Figure S7 The comparison of Z-scores between two independent blood datasets. The comparison of cis-eQTL effect was confined to the set of 93,656 probe-SNP pairs that have been tested in two independent blood datasets, e.g., a discovery set of 1,240 subjects profiled on the Illumina HT12 expression platform (HT12) and a validation set of 229 subjects profiled on the Illumina H8v2 expression platform (H8v2). The Z-scores of cis-eQTL in the discovery set were the mean of Z-scores from 100× taking a sample of 229 out of the 1,240 blood subjects. The gray dots indicate the concordantly associated probe-SNP pairs between the two blood samples. The red dots indicate the discordantly associated probe-SNP pairs (the false-positive tissue-dependent association). The black line is the diagonal line. (PDF) Click here for additional data file. Figure S8 The probes-SNP distance for associated probe-SNP pairs. The distance was calculated by the base pair position (bp) of SNPs minus the bp position of the middle point of the probes. (PNG) Click here for additional data file. Figure S9 Probe-SNP distance for 2,794 eProbes in blood with multiple independent eSNPs. (PDF) Click here for additional data file. Figure S10 The discordant probe-SNP pairs vs. the probe-SNP distance. The histogram shows the number the probe-SNP pairs with different distance. The numbers on each bar show the total number of probe-SNP pairs and the percentage of pairs with discordant association. The 2×2 table for Fisher's exact test is shown. (PDF) Click here for additional data file. Figure S11 The direction of allelic effect of rs5751777 on DDT expression. The correlation between the genotype of rs5751777 and the expression intensity of DDT gene (residual variance after 50 PCs removed) in five tissues. Each dot represents one subject, red for females and blue for males. The X-axis represents the genotypes and the Y-axis represents the expression rank of the probes. (PDF) Click here for additional data file. Figure S12 The opposite association of ORMDL3 gene between blood and SAT. The x-axis is the genome position based on genome build 36.3 (in Mb). The y-axis at the left is the association profiles in terms of Z-scores. The Z-scores in blood, represented as the red dots, has been weighted by the square root of the sample size, corresponding to the compared tissue. The blue dots represent the Z-scores in SAT. The dashed green line indicates the significance level at FDR 0.05. For a better illustration of allelic direction, we assigned the association Z-scores in blood a positive value. If the allelic direction in SAT is the same as that in blood, the Z-scores in SAT are positive too; otherwise, the Z-scores in SAT are negative. (PDF) Click here for additional data file. Figure S13 The association profiles of the selected trait-associated genes that show discordant association between blood and liver. The x-axis is the genome position based on genome build 36.3. The y-axis at the left is the association profiles in terms of the Z-score. The Z-score in blood, represented as the red dots or orange dots. The red dots refer to the Z-scores that have been weighted by the square root of the sample sizes, corresponding to the compared tissue. For the clarity of subtle effect in blood, the weak association in blood was shown as orange dots if the Z-scores have not been weighted by the sample size, i.e., the Z-scores reported in 1,240 subjects. The blue dots represent the Z-scores in liver. The dashed green line indicates the Z-score 3.49, representing the significance level in blood at FDR 0.05. The right panel shows the correlation of the absolute association Z-scores between two tissues. The rho-value indicates the correlation coefficient of the Pearson correlation. (PDF) Click here for additional data file. Figure S14 The association profiles of the selected trait-associated genes that show discordant association between blood and SAT. The x-axis is the genome position based on genome build 36.3. The y-axis at the left is the association profiles in terms of the Z-score. The Z-score in blood, represented as the red dots or orange dots. The red dots refer to the Z-scores that have been weighted by the square root of the sample sizes, corresponding to the compared tissue. For the clarity of subtle effect in blood, the weak association in blood was shown as orange dots if the Z-scores have not been weighted by the sample size, i.e., the Z-scores reported in 1,240 subjects. The blue dots represent the Z-scores in SAT. The dashed green line indicates the Z-score 3.49, representing the significance level in blood at FDR 0.05. The right panel shows the correlation of the absolute association Z-scores between two tissues. The rho-value indicates the correlation coefficient of the Pearson correlation. (PDF) Click here for additional data file. Figure S15 The association profiles of the selected trait-associated genes that show discordant association between blood and VAT. The x-axis is the genome position based on genome build 36.3. The y-axis at the left is the association profiles in terms of the Z-score. The Z-score in blood, represented as the red dots or orange dots. The red dots refer to the Z-scores that have been weighted by the square root of the sample sizes, corresponding to the compared tissue. For the clarity of subtle effect in blood, the weak association in blood was shown as orange dots if the Z-scores have not been weighted by the sample size, i.e., the Z-scores reported in 1,240 subjects. The blue dots represent the Z-scores in VAT. The dashed green line indicates the Z-score 3.49, representing the significance level in blood at FDR 0.05. The right panel shows the correlation of the absolute association Z-scores between two tissues. The rho-value indicates the correlation coefficient of the Pearson correlation. (PDF) Click here for additional data file. Figure S16 The association profiles of the selected trait-associated genes that show discordant association between blood and muscle. The x-axis is the genome position based on genome build 36.3. The y-axis at the left is the association profiles in terms of the Z-score. The Z-score in blood, represented as the red dots or orange dots. The red dots refer to the Z-scores that have been weighted by the square root of the sample sizes, corresponding to the compared tissue. For the clarity of subtle effect in blood, the weak association in blood was shown as orange dots if the Z-scores have not been weighted by the sample size, i.e., the Z-scores reported in 1,240 subjects. The blue dots represent the Z-scores in muscle. The dashed green line indicates the Z-score 3.49, representing the significance level in blood at FDR 0.05. The right panel shows the correlation of the absolute association Z-scores between two tissues. The rho-value indicates the correlation coefficient of the Pearson correlation. (PDF) Click here for additional data file. Figure S17 Association profiles of MTMR3 in blood and liver. The x-axis is the genome position based on genome build 36.3 (in Mb). The y-axis at the left indicates the association Z-score. The Z-scores in blood, represented as the red dots, have been weighted by the square root of the sample size, corresponding to the compared tissue. The blue dots represent the Z-scores in SAT. The dashed green line indicates the Z-scores 3.49, representing the significance level in blood at FDR 0.05. The right panel shows the correlation of the absolute association Z-scores between two tissues. The r-value indicates the correlation coefficient of the Pearson correlation. (PDF) Click here for additional data file. Table S1 Characteristics of Samples. (XLS) Click here for additional data file. Table S2 The number of discordant cis-eQTL between blood and non-blood tissues. (DOC) Click here for additional data file. Table S3 The Number of independent eSNPs per probe. (DOC) Click here for additional data file. Table S4 Replication of tissue-alternative cis-eQTL of TMEM176A. (DOC) Click here for additional data file. Table S5 Replication of cis-eQTL of DDT in blood and liver that show opposite allelic direction. (DOC) Click here for additional data file. Table S6 Allelic effect of disease-associated SNPs on the expression of ORMLD3. (DOC) Click here for additional data file. Table S7 The tissue-dependent regulation of 45 trait-associated genes that show discordant association between blood and liver. (XLS) Click here for additional data file. Table S8 The tissue-dependent regulation of 50 trait-associated genes that show discordant association between blood and SAT. (XLS) Click here for additional data file. Table S9 The tissue-dependent regulation of 46 trait-associated genes that show discordant association between blood and VAT. (XLS) Click here for additional data file. Table S10 The tissue-dependent regulation of 19 trait-associated genes that show discordant association between blood and Muscle. (XLS) Click here for additional data file. Table S11 The number of the differentially expressed eProbes. (DOC) Click here for additional data file.
Author and article information
Contributors
Colin Shepherd:
ORCID: http://orcid.org/0000-0003-3334-3379
colin.shepherd@newcastle.ac.uk
Andrew J. Skelton: andrew.skelton@newcastle.ac.uk
Michael D. Rushton: michael.rushton@newcastle.ac.uk
Louise N. Reynard: louise.reynard@newcastle.ac.uk
John Loughlin: john.loughlin@newcastle.ac.uk
Journal
Journal ID (nlm-ta): BMC Med Genet
Journal ID (iso-abbrev): BMC Med. Genet
Title:
BMC Medical Genetics
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1471-2350
Publication date
(Electronic):
19
November
2015
Publication date PMC-release: 19
November
2015
Publication date Collection: 2015
Volume: 16
Electronic Location Identifier: 108
Affiliations
Author information
Colin Shepherd
http://orcid.org/0000-0003-3334-3379
Article
Publisher ID: 254DOI: 10.1186/s12881-015-0254-2
PMC ID: 4653905
SO-VID: 17ef354b-7564-4fb5-b5c4-a1dd7428239e
Copyright © © Shepherd et al. 2015
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 27
August
2015
Date
accepted
: 11
November
2015
Categories
Subject:
Research Article
Custom metadata
issue-copyright-statement © The Author(s) 2015
ScienceOpen disciplines: Genetics
Data availability:
ScienceOpen disciplines: Genetics