- Record: found
- Abstract: found
- Article: not found
Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins.
Author(s):
Alfonso Buil
1 ,
Andrew Anand Brown
2 ,
Tuuli Lappalainen
3 ,
Ana Viñuela
4 ,
Matthew N Davies
4 ,
Hou-Feng Zheng
5 ,
J Brent Richards
6 ,
Daniel Glass
4 ,
Kerrin S Small
4 ,
Richard Durbin
7 ,
Timothy D Spector
4 ,
Emmanouil T Dermitzakis
1
Publication date (Electronic):
Jan 2015
Journal:
Nature genetics
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
Understanding the genetic architecture of gene expression is an intermediate step in understanding the genetic architecture of complex diseases. RNA sequencing technologies have improved the quantification of gene expression and allow measurement of allele-specific expression (ASE). ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed thus far. In this study, we take advantage of a sample of twins to measure the relative contributions of genetic and environmental effects to ASE, and we find substantial effects from gene × gene (G×G) and gene × environment (G×E) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but where the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants.
Related collections
Most cited references10
- Record: found
- Abstract: found
- Article: found
Single-Tissue and Cross-Tissue Heritability of Gene Expression Via Identity-by-Descent in Related or Unrelated Individuals
Alkes L. Price, Agnar Helgason, Gudmar Thorleifsson … (2011)
Introduction The genome contains a complex set of instructions for the assembly and maintenance of an organism. A fundamental goal in biology is to understand the relationship between genotype and phenotype. This goal can be achieved in part by studying the genetic basis of gene expression, as many genotype-phenotype correlations are a consequence of genetically driven variation in gene expression [1]. A number of studies have mapped individual cis and trans regulatory variants in humans, and recent work has suggested that the majority of regulators act in trans [2]-[5]; regulation of gene expression has also been widely studied in animal models [6]-[9]. However, the bulk of variability in gene expression remains unexplained. Heritability analyses can shed light on the genetic basis of gene expression. Several previous studies have demonstrated substantial overall heritability of gene expression in family data sets, and heritability approaches have also been broadly applied to other phenotypes [10]-[14]. In this study, we used gene expression measurements [11] and genome-wide single nucleotide polymorphism (SNP) data [15] from 722 Icelanders from family cohorts to examine the heritability of gene expression in blood and adipose tissue. By studying more than one tissue type, we were able to analyze the regulation of gene expression both within and across tissues. Our goal was to answer three key questions about gene expression heritability. First, can heritability be partitioned into cis and trans components using local and genome-wide IBD between pairs of individuals? Second, to what extent are heritable components of variance shared across tissues? Third, to what extent does heritability extend to distantly related individuals inheriting IBD segments from distant ancestors? We sought to partition the heritability of gene expression into cis versus trans components by comparing the effects of IBD at the genome-wide level (trans) to those of IBD at the local level (cis), defined as the number of chromosomes (0, 1 or 2) shared IBD at the genomic location containing the expressed gene. Our results show a substantially higher proportion of heritability due to cis regulation, 37% in blood and 24% in adipose tissue, than the 12% reported in a previous ancestry-based study of lymphoblastoid cell lines (LCL) in African Americans [16]. One possible explanation for this discrepancy is transgenerational epigenetic inheritance, which is one of the explanations proposed to account for the “missing heritability” in genetic studies of human traits [17]-[23]. Epigenetic inheritance would regulate gene expression at the cis locus, and would be expected to contribute to cis heritability in family-based analyses but not in ancestry-based analyses, given that this mode of inheritance persists over a relatively short time scale. However, by using IBD in distantly related individuals to produce similar estimates of cis heritability, we were able to rule out this hypothesis. Instead, our analyses indicate that the proportion of heritability attributable to cis regulation is tissue-specific, and that similarities in gene expression across tissues are primarily due to heritable cis effects. Thus, the proportion of gene expression heritability attributable to cis regulation is expected to increase as a function of the number of different cell types present in the tissue being assayed, consistent with results obtained from blood, adipose tissue and LCL. Methods Ethics statement This research was approved by the Data Protection Commission of Iceland and the National Bioethics Committee of Iceland. The appropriate informed consent was obtained for all sample donors. Icelandic Family Blood cohort Relative abundances of 23,720 transcripts were obtained for blood samples from each of 1,001 individuals from the IFB cohort, as described previously [11] (see Web Resources). Values were adjusted for sex and age. We removed 4,985 transcripts that either had >5% missing data, did not map to an autosomal chromosome, or mapped to more than one genomic location. We removed 16 individuals with >5% missing data and 269 individuals for which long-range phased SNP data were not available. This left 18,735 transcripts and 716 individuals. Most of out analyses focused on 2,233 related pairs (individuals from the same family pedigree with genome-wide IBD >0.05) spanning a subset of 687 individuals. Icelandic Family Adipose cohort Relative abundances of 23,720 transcripts were obtained for adipose tissue samples from each of 673 individuals from the IFA cohort, as described previously [11] (see Web Resources). Values were adjusted for sex, age and body mass index (BMI), restricting to 638 individuals with BMI data. We removed 4,621 transcripts that either had >5% missing data, did not map to an autosomal chromosome, or mapped to more than one genomic location. We removed 2 individuals with >5% missing data and 67 individuals for which long-range phased SNP data were not available. This left 19,099 transcripts and 569 individuals. Most of our analyses focused on 1,700 related pairs (individuals from the same family pedigree with genome-wide IBD >0.05) spanning a subset of 531 individuals. Local IBD estimates Individuals were genotyped using the Illumina 300K chip. Owing to the sensitive nature of genotype data, access to these data can only be granted at the headquarters of deCODE Genetics in Iceland. Given long-range phased Illumina 300K data [15] for a pair of individuals, we partitioned the genome into 2cM blocks and for each block performed 2×2 = 4 comparisons between haplotypes from the two individuals. We declared two haplotypes to be IBD if they matched at >95% of alleles in the block, non-IBD if they matched at 5% of SNPs excluded. We defined local IBD as the total number of comparisons producing a match. We verified that this approach infers 0∶1∶2 copies IBD between parent-child pairs with probabilities 0.2%∶99.3%∶0.4% and 0∶1∶2: copies IBD between sibling pairs with probabilities 24.9%∶50.1%∶24.9%, excluding from this computation the 7% of pairs and blocks for which inferred IBD was unknown. These numbers are a function of the thresholds we used to define IBD and non-IBD; the thresholds were largely chosen for specificity rather than sensitivity since for our application it does not matter that inferred IBD is sometimes unknown. The numbers are very close to the expected theoretical probabilities (for parent-child pairs, 2 copies IBD is expected to occasionally occur due to IBD in “unrelated” parents). This validates our use of long-range phased SNP genotypes to compute local IBD estimates. We computed genome-wide IBD estimates as the average of local IBD estimates across all 2cM blocks. Heritability estimates using genome-wide IBD only We applied variance-components methods to estimate narrow-sense heritability [14], [24]. The source code used in our heritability analyses is available for download (see Web Resources). Let egs denote the gene expression for gene g and individual s, normalized to have mean 0 and variance 1 across individuals. Let θst denote the genome-wide IBD between individuals s and t (0≤θst ≤1) and be the N×N matrix of genome-wide IBD, where N is the number of individuals. Let Vg denote the covariance matrix of normalized gene expression for gene g. We consider the model and fit hg 2, the heritability of gene g, to the observed normalized gene expression values egs by maximizing the likelihood , where . Values of egs , hg and Vg vary with tissue type, but we view tissue type as an implicit index rather than an explicit index for simplicity of notation. For both blood and adipose tissue, the estimated values of hg 2 were ∼80% correlated to values that were computed previously using similar methods [11], despite the fact that the current analysis was restricted to a subset of individuals for which long-range phased SNP data was available for local IBD inference. We declare hg 2>0 to be nominally significant (P 0.05. For the analysis of gene expression in adipose tissue, we similarly analyzed 19,099 mRNA transcripts of 531 individuals from the IFA cohort, focusing on 1,700 related pairs with genome-wide IBD >0.05 (see Methods). The IFA cohort largely overlaps the IFB cohort, with 496 of the 722 individuals analyzed appearing in both cohorts. 10.1371/journal.pgen.1001317.g001 Figure 1 Local and genome-wide IBD. We plot the local relatedness (0, 1 or 2 copies IBD) between two siblings from the IFB cohort at each 2cM block on chromosome 1. The dotted line represents their genome-wide relatedness of 0.568, which is within the expected range for siblings [46]. We estimated the overall heritability hg 2 for each gene g using variance-component methods [14] (see Methods). Although estimates for each gene g are statistically noisy at these sample sizes, histograms show a clear positive bias for both IFB and IFA cohorts (Figure S1 and Table S1), and hg 2>0 was nominally significant (P = 0.05; see Methods) for an excess of genes: 42% for IFB and 63% for IFA. We computed the average h 2 as the average of hg 2 across genes g. A relevant question is whether or not to allow negative values of hg 2 when computing this average [26]. Such values have no biological interpretation (except in the case of negative correlation among siblings in traits that depend on birth order). However, because values close to zero may be either increased or decreased by statistical noise—leading to negative estimates of hg 2 for 3,031 of 18,735 genes for IFB and 1,038 of 19,099 genes for IFA—we elected to allow negative values in our main computations so as to produce an unbiased estimate of average h 2. We obtained estimates of h 2 = 0.150 for blood and h 2 = 0.234 for adipose tissue. We obtained similar results when using a regression-based approach to estimate average h 2 (Text S1), which more readily lends itself to visualization (Figure 2A and 2B). (When clipping negative hg 2 values to zero, we obtained h 2 = 0.159 for blood and h 2 = 0.237 for adipose tissue.) Our results are consistent with previous analyses reporting that expression levels of a substantial fraction of genes are significantly heritable at the level of h 2 = 0.3 or higher [10]-[13], [26]. 10.1371/journal.pgen.1001317.g002 Figure 2 Family heritability in the IFB and IFA cohorts. (A) Gene expression covariance (average value of product of normalized gene expression measurements) between related individuals in the IFB cohort varies with genome-wide IBD. Each point represents one pair of related individuals. The slope of this plot corresponds to the regression-based estimate of h 2. (B) Same as (A), for IFA cohort. (C) Gene expression covariance between siblings for genes with 0, 1 or 2 copies IBD at the cis locus, minus total covariance as displayed above. The slope of this plot corresponds to the regression-based estimate of hcis 2. The signal to noise ratio is higher in this plot due to reduced effects of systematic noise covariance. (D) Same as (C), for IFA cohort. Cis versus trans heritability of gene expression While estimates of overall heritability are based on genome-wide IBD, it is possible to estimate cis versus trans heritability by extending variance components to consider both local (cis) IBD at the genomic location close to the expressed gene, and genome-wide (trans) IBD (see Methods). As before, analyses were restricted to 2,233 and 1,700 related pairs from the IFB and IFA cohorts, respectively. Histograms of hg,cis 2 and hg,trans 2 estimates for each gene g show a clear positive bias for both IFB and IFA cohorts (Figure S2 and Table S1), with an excess of nominally significant (P 0: 16%; hg,trans 2>0: 19%) and IFA (hg,cis 2>0: 16%; hg,trans 2>0: 30%). For IFB, we obtained average cis and trans heritability estimates of hcis 2 = 0.055 and htrans 2 = 0.095, respectively, which sum to h 2 = 0.150. This leads to the conclusion that the proportion of heritability of expression due to cis variants in blood is πcis = 37%. For IFA, we obtained estimates of hcis 2 = 0.057 and htrans 2 = 0.177, which sum to h 2 = 0.234. This yields an estimate of πcis = 24% in adipose tissue. The values of h 2 and htrans 2 in adipose tissue are significantly higher than for blood, but hcis 2 is similar, leading to a lower value of πcis . We obtained similar results when using a regression-based approach to estimate average hcis 2 and htrans 2 (Text S1; Figure 2C and 2D). We note that there is considerably less statistical uncertainty in estimates of hcis 2 (Figure 2C and 2D) than in estimates of h 2 (Figure 2A and 2B). Indeed, we obtained standard errors of h 2 = 0.150±0.011, hcis 2 = 0.055±0.001 and htrans 2 = 0.095±0.010 for blood and h 2 = 0.234±0.011, hcis 2 = 0.057±0.002 and htrans 2 = 0.177±0.010 for adipose tissue (see Methods). These standard errors are 7-100 times lower than standard errors for single-gene heritability estimates, which are inadequate for estimating πcis (see Text S1). The much lower standard errors for hcis 2 are a consequence of variation in cis IBD across the genome that decouples the estimation of this parameter from the systematic noise covariance structure across all pairs of individuals (see Text S1). Based on these standard errors for hcis 2 and htrans 2, πcis has little statistical uncertainty, although results may be affected by modeling uncertainty. Our heritability model does not account for the possibility of phenotypic similarity in related individuals due to shared environment, which can confound estimates of heritability [14]. We note that such effects would inflate estimates of h 2 and htrans 2, but have a negligible impact on hcis 2, since the extent of shared environment would be related to genome-wide (trans) rather than local (cis) IBD. To investigate the possibility of confounding due to shared environment, we computed the average correlation in gene expression between spouses, who are genetically unrelated but have a shared environment. We observed average correlations of 0.074±0.042 in 33 IFB spouse pairs and 0.076±0.035 in 28 IFA spouse pairs, which are similar in magnitude to correlations between sib-sib or parent-child pairs that correspond to the average heritabilities reported above (see Text S1 and Table S2). Thus, there is strong evidence that shared environment can lead to similarity in gene expression phenotypes. We further investigated whether the gene by gene signature of correlations in spouse pairs matches the signature of correlations in sib-sib or parent-child pairs or estimates of hg 2, but found that it does not (see Text S1 and Table S3). Thus, we hypothesize that the correlations in spouse pairs are due to very recent shared environment (e.g. diet) arising from sharing the same household, whereas the correlations in sib-sib and parent-child pairs in this study (who are unlikely to share the same household, since only adult individuals were sampled) are due to genetic heritability. However, we cannot rule out a small amount of inflation in h 2 and htrans 2 estimates due to shared environment in related individuals. Assessing the impact of epigenetic inheritance on cis heritability Our family-based estimates of πcis in blood and adipose tissue are considerably greater than a previous estimate of 12±3% obtained using lymphoblastoid cell lines (LCL) from African-Americans, in which local versus genome-wide European ancestry was used to infer the relative contribution of cis versus trans heritability [16]. An analogous ancestry-based analysis of LCL gene expression data [27] from admixed HapMap 3 Mexican-Americans [28] has produced a similarly low value of πcis = 13±9%. One possible explanation for the lower values as compared to family-based estimates could be the epigenetic inheritance of cis-acting factors other than DNA sequence that are transmitted from parent to offspring. Given the relatively short time scale of epigenetic inheritance, this would be expected to have a much greater impact on family-based estimates of πcis than those based on ancestry [22]-[23]. To further explore the epigenetic hypothesis, we repeated the cis versus trans analysis using subsets of unrelated or distantly related individuals (genome-wide IBD 0 was nominally significant (P = 0.05) for 20% of genes, a significant excess. We next investigated the relationship between an individual’s blood expression and a related individual’s adipose expression, using variance-components methods (see Methods). This revealed that cross-tissue similarity varies with the level of family relatedness, with an average cross-tissue heritability estimate of ξ 2 = 0.030±0.006. Analogous to the analyses for single tissues, we partitioned the cross-tissue heritability into cis and trans components, yielding values of ξcis 2 = 0.031±0.001 and ξtrans 2 = -0.001±0.006. We obtained similar results using regression-based approaches (Text S1; Figure 3A and 3B). Histograms of cross-heritability estimates for each gene g show a positive bias for ξg 2 and ξg,cis 2, but not ξg,trans 2, for which the histogram is symmetric about zero (Figure S4). While our estimate of ξtrans 2 is not significantly different from zero, ξcis 2 is highly significant and explains the bulk of our estimate of ρ. This implies that the extent to which gene expression in blood and adipose tissue is similar across genes and individuals is dominated by heritable effects at the cis locus. 10.1371/journal.pgen.1001317.g003 Figure 3 Cross-tissue heritability in the IFB and IFA cohorts. Plots are analogous to those in Figure 2, except that we analyze the covariance between related individuals in different tissues, instead of between related individuals in the same tissue. (A) Cross-tissue covariance between related individuals in the intersection of IFB and IFA varies with genome-wide IBD. The slope of this plot corresponds to the regression-based estimate of ξ 2. (B) Cross-tissue covariance between siblings in the intersection of IFB and IFA with 0, 1 or 2 copies IBD at the cis locus, minus total covariance as displayed in (A). The slope of this plot corresponds to ξcis 2. Averaging across cell types with shared cis effects increases the value of πcis Our finding that cross-tissue similarities are dominated by heritable cis effects leads to the mathematical result that πcis is expected to increase with tissue heterogeneity: as the number of cell types represented in a tissue increases, the strongly correlated cis effects will add linearly but the uncorrelated trans effects will be diluted. In detail, let x and y denote cells types and suppose that Cov(exgs ,exgt ) = Cov(eygs ,eygt ) = hcis 2 γgst +htrans 2 θst for all genes g and individuals s≠t, and that all cis effects (but no trans or non-genetic effects) are shared across cell types. Thus, Cov(exgs ,eygt ) = hcis 2 γgst . Now consider a tissue z containing cell types x and y. Up to a normalization constant, Cov(ezgs ,ezgt ) = Cov(0.5(exgs +eygs ),0.5(exgt +eygt )) = hcis 2 γgst +0.5htrans 2 θst , so that πcis,z = hcis 2 /(hcis 2+0.5htrans 2) is larger than πcis,x = πcis,y = hcis 2 /(hcis 2+htrans 2). We verified this theoretical result empirically by defining ezgs = ebgs + eags as the average of normalized gene expression in blood and adipose tissue, normalized to mean 0 and variance 1. For synthetic tissue z, we obtained the value πcis = 0.41, which is larger than the value of πcis for either blood or adipose tissue, and similar to the predicted value of 0.055/(0.055+0.25(0.095+0.177)) = 0.45 based on hcis 2 and htrans 2 (πcis < 0.45 is actually expected since not all cis effects are shared). Thus, the variability in πcis across tissue types (0.12 for LCL, 0.24 for adipose, 0.37 for blood) is consistent with the fact that LCL represent a single cell type, whereas adipose tissue and blood contain many cell types: adipose tissue contains smooth muscle cells, fibroblasts, adipocytes, mast-cells and endothelial cells, while blood contains erythrocytes, thrombocytes, neutrophils, lymphocytes, monocytes, eosinophils and basophils in proportions that vary across individuals [32]-[34]. This also explains why studies of individual cell types have been more successful in identifying trans eQTLs than studies of whole tissues, and why most replications across tissue types occur at cis eQTLs [11], [34]-[37]. Discussion In this study, we observed a greater contribution of cis regulation in blood and adipose tissue than in a previous ancestry-based analysis of LCL in African-Americans [16]. This result is not sensitive to sample size, because although estimates for individual genes are statistically noisy, we considered averages across genes. We also observed that cross-tissue similarity between blood and adipose expression is genetically heritable and dominated by cis effects. These two results are highly concordant. Due to the dilution of trans effects that are not shared across cell types, cis regulation is expected to explain a greater proportion of heritability in tissue types that are heterogeneous in their cell composition, such as blood and adipose tissue—particularly blood, in which cell type proportions may vary among individuals. This highlights the importance of considering different tissue types [16]. However, other explanations for the higher contribution of cis regulation in this study than in the ancestry-based analysis are also possible. For example, epistasis between two neighboring cis variants would be included in cis heritabilities estimated via IBD, but not in the ancestry-based analysis in which ancestry is a partial proxy for SNP genotype but a very poor proxy for both genotypes of two interacting SNPs. In addition, epistatic interactions involving multiple loci may potentially be important, and may confound estimates of narrow-sense heritability, but are outside the scope of this study. A further possibility is that trans effects in LCL could be overstated due to genetically heritable variation of in vitro factors such as the response to EBV virus, which would mimic trans regulation in heritability analyses but does not reflect true biological trans regulation [38]. Distinguishing between these possibilities is an important direction of future work. Efforts to understand cis regulation are likely to benefit from combining information from many cell or tissue types, since underlying mechanisms can be either shared or cell-type specific. Indeed, our finding that on average roughly half single-tissue cis heritability (hcis 2) is shared across tissues (ξcis 2) is consistent with a recent study focusing on cis eQTLs, which reported that 54%, 50% and 54% of cis eQTLs in fibroblasts, LCLs and T cells, respectively, are cell-type specific [36]. Those percentages would be expected to be higher when considering only two cell types, but lower at larger sample sizes. On the other hand, studies of trans regulation should focus on a single cell type to avoid diluting trans effects that are not shared across cell types. New technologies to assay cell type-specific gene expression in complex tissues may also prove valuable [39]. Future experiments will shed light on whether similarity between tissues other than blood and adipose is also predominantly explained by heritable cis effects. Results may vary by organism as well as tissue type. Recent studies of fat, kidney, adrenal and heart tissues in rat recombinant inbred strains also observed reduced trans effects in more heterogeneous tissues, but reported some evidence of cross-tissue regulation in trans as well as in cis [8]-[9]. The similarity of cis heritability results using IBD in closely related versus distantly related individuals has significant implications. It has been suggested that epigenetic inheritance, defined as the transmission across generations of epigenetic changes not due to variation in DNA sequence, is a potential source of the “missing heritability” in genetic association studies [17]-[21]. Epigenetic inheritance would be expected to influence expression at the cis locus, and would be expected to contribute to cis heritability between closely related individuals but not between distantly related individuals, given that this mode of inheritance persists over a relatively short time scale [22]-[23]. Our failure to observe any such discordance suggests that transgenerational epigenetic inheritance is unlikely to play a major role in the missing heritability of gene expression and other traits, although it does not rule out a very small aggregate effect across all genes or large effects at certain metastable epialleles [40]-[41], nor does it shed light on the importance of mitotically conserved epigenetic effects that are not transmitted from parent to offspring. Our results highlight the utility of using IBD in distantly related individuals to make inferences about heritability. This approach will be particularly valuable as sample sizes increase, since the number of pairs of individuals increases quadratically with sample size. Indeed, IBD in distantly related individuals has already proven useful for mapping specific loci [42], and heritability-related analyses using identity-by-state (IBS) instead of IBD have also yielded important insights [43]-[45]. By using IBD segments shorter than those analyzed here to consider IBD sharing at different distances from genes, it may even be possible draw conclusions about the distribution of genomic distances at which cis regulation contributes to heritability. Supporting Information Figure S1 Histograms of heritability estimates for each gene. We plot histograms of (a) hg 2 estimates for IFB and (b) hg 2 estimates for IFA, across genes g. (0.23 MB TIF) Click here for additional data file. Figure S2 Histograms of cis and trans heritability estimates for each gene. We plot histograms of (a) hg,cis 2 estimates for IFB, (b) hg,trans 2 estimates for IFB, (c) hg,cis 2 estimates for IFA and (d) hg,trans 2 estimates for IFA, across genes g. (0.19 MB TIF) Click here for additional data file. Figure S3 Histograms of cross-tissue correlations for each gene. We plot a histogram of observed gene-specific cross-tissue correlations ρg . (0.14 MB TIF) Click here for additional data file. Figure S4 Histograms of cross-tissue heritability estimates for each gene. We plot histograms of (a) ξg 2 estimates, (b) ξg,cis 2 estimates and (c) ξg,trans 2 estimates, across genes. (0.20 MB TIF) Click here for additional data file. Table S1 Heritability results for each gene. (1.82 MB TXT) Click here for additional data file. Table S2 Average correlations between spouse-spouse, sib-sib, and parent-child pairs. We list the average correlation for each pair type and cohort, averaging across correlations for each gene g. We also list standard errors, computed via jackknife. (0.03 MB DOC) Click here for additional data file. Table S3 Concordance of gene-by-gene signatures of correlations in each pair type. We list values of Rsib-sib,parent-child , Rspouse,sib-sib and Rspouse,parent-child for each cohort (see text), along with the number of pairs of each type used to compute those values. For comparison purposes, we also list (in italics) values of Rsib-sib,parent-child computed using smaller subsets of pairs to match the number of pairs used to compute Rspouse,sib-sib or Rspouse,parent-child , as a smaller number of pairs leads to lower values of R. (0.03 MB DOC) Click here for additional data file. Text S1 Supplementary Note. (0.04 MB DOC) Click here for additional data file.
- Record: found
- Abstract: found
- Article: not found
Detection and replication of epistasis influencing transcription in humans
Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits 1 and contributes to their variation 2,3 is a fundamental question in evolution and human genetics. Though often demonstrated in artificial gene manipulation studies in model organisms 4,5 , and some examples have been reported in other species 6 , few examples exist for epistasis amongst natural polymorphisms in human traits 7,8 . Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits 2,3 , but an alternative view is that it has previously been too technically challenging to detect due to statistical and computational issues 9 . Here we show that, using advanced computation 10 and a gene expression study design, many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (p < 2.91 × 10−16). Replication of these interactions in two independent data sets 11,12 showed both concordance of direction of epistatic effects (p = 5.56 ×10−31) and enrichment of interaction p-values, with 30 being significant at a conservative threshold of p < 0.05/501. Forty-four of the genetic interactions are located within 2Mb of regions of known physical chromosome interactions 13 (p = 1.8 × 10−10). Epistatic networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis-acting SNP is modulated by several trans-acting SNPs. For example MBNL1 is influenced by an additive effect at rs13069559 which itself is masked by trans-SNPs on 14 different chromosomes, with nearly identical genotype-phenotype (GP) maps for each cis-trans interaction. This study presents the first evidence for multiple instances of segregating common polymorphisms interacting to influence human traits.
- Record: found
- Abstract: found
- Article: not found
Quantifying RNA allelic ratios by microfluidics-based multiplex PCR and deep sequencing
Rui Zhang, Xin Li, Gokul Ramaswami … (2013)
We developed a targeted RNA sequencing method that couples microfluidics-based multiplex PCR and deep sequencing (mmPCR-seq) to uniformly and simultaneously amplify up to 960 loci in 48 samples independently of their gene expression levels, and accurately and cost-effectively measure allelic ratios even for low-quantity or low-quality RNA samples. We applied mmPCR-seq to RNA editing and allele-specific expression studies. mmPCR-seq complements RNA-seq and provides a highly desirable solution for future applications.
Author and article information
Journal
Journal ID (iso-abbrev): Nat. Genet.
Title:
Nature genetics
ISSN
(Electronic):
1546-1718
ISSN
(Print):
1061-4036
Publication date
(Electronic):
Jan 2015
Volume: 47
Issue: 1
Affiliations
[1
]
1] Department of Genetic Medicine and Development, University of Geneva Medical School,
Geneva, Switzerland. [2] Institute of Genetics and Genomics in Geneva, University
of Geneva, Geneva, Switzerland. [3] Swiss Institute of Bioinformatics, Geneva, Switzerland.
[2
]
1] Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK. [2] NORMENT, KG Jebsen
Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University
Hospital, Oslo, Norway.
[3
]
1] Department of Genetic Medicine and Development, University of Geneva Medical School,
Geneva, Switzerland. [2] Institute of Genetics and Genomics in Geneva, University
of Geneva, Geneva, Switzerland. [3] Swiss Institute of Bioinformatics, Geneva, Switzerland.
[4] Department of Genetics, Stanford University, Stanford, California, USA.
[5
]
1] Department of Medicine, McGill University, Montreal, Quebec, Canada. [2] Department
of Human Genetics, McGill University, Montreal, Quebec, Canada. [3] Department of
Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
[6
]
1] Department of Twin Research, King's College London, London, UK. [2] Department
of Medicine, McGill University, Montreal, Quebec, Canada. [3] Department of Human
Genetics, McGill University, Montreal, Quebec, Canada. [4] Department of Epidemiology
and Biostatistics, McGill University, Montreal, Quebec, Canada.
Article
Publisher Item ID: ng.3162 Mid ID: EMS65857DOI: 10.1038/ng.3162
PubMed ID: 25436857
SO-VID: fca74319-1353-47bb-ab22-1a3934e496bd
History
Data availability: