A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.

A report on the 12th International Congress of Human Genetics, joint with the 61st annual American Society of Human Genetics conference, Montreal, Quebec, 11-15 October 2011.

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation [1] and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome–wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript.

Phenotypic variation results from variation in gene expression, which is modulated by genetic and/or epigenetic factors. To understand the molecular basis of human disease, interaction between genetic and epigenetic factors needs to be taken into account. The asthma-associated region 17q12-q21 harbors three genes, the zona pellucida binding protein 2 (ZPBP2), gasdermin B (GSDMB) and ORM1-like 3 (ORMDL3), that show allele-specific differences in expression levels in lymphoblastoid cell lines (LCLs) and CD4+ T cells. Here, we report a molecular dissection of allele-specific transcriptional regulation of the genes within the chromosomal region 17q12-q21 combining in vitro transfection, formaldehyde-assisted isolation of regulatory elements, chromatin immunoprecipitation and DNA methylation assays in LCLs. We found that a single nucleotide polymorphism rs4795397 influences the activity of ZPBP2 promoter in vitro in an allele-dependent fashion, and also leads to nucleosome repositioning on the asthma-associated allele. However, variable methylation of exon 1 of ZPBP2 masks the strong genetic effect on ZPBP2 promoter activity in LCLs. In contrast, the ORMDL3 promoter is fully unmethylated, which allows detection of genetic effects on its transcription. We conclude that the cis-regulatory effects on 17q12-q21 gene expression result from interaction between several regulatory polymorphisms and epigenetic factors within the cis-regulatory haplotype region.

Electronic supplementary material

The online version of this article (doi:10.1007/s00439-012-1142-x) contains supplementary material, which is available to authorized users.

Genome-wide association studies of human gene expression promise to identify functional regulatory genetic variation that contributes to phenotypic diversity. However, it is unclear how useful this approach will be for the identification of disease-susceptibility variants. We generated gene expression profiles for 22 184 mRNA transcripts using RNA derived from peripheral blood CD4+ lymphocytes, and genome-wide genotype data for 516 512 autosomal markers in 200 subjects. We screened for cis-acting variants by testing variants mapping within 50 kb of expressed transcripts for association with transcript abundance using generalized linear models. Significant associations were identified for 1585 genes at a false discovery rate of 0.05 (corresponding to P-values ranging from 1 × 10−91 to 7 × 10−4). Importantly, we identified evidence of regulatory variation for 119 previously mapped disease genes, including 24 examples where the variant with the strongest evidence of disease-association demonstrates strong association with specific transcript abundance. The prevalence of cis-acting variants among disease-associated genes was 63% higher than the genome-wide rate in our data set (P = 6.41 × 10−6), and although many of the implicated loci were associated with immune-related diseases (including asthma, connective tissue disorders and inflammatory bowel disease), associations with genes implicated in non-immune-related diseases including lipid profiles, anthropomorphic measurements, cancer and neurologic disease were also observed. Genetic variants that confer inter-individual differences in gene expression represent an important subset of variants that contribute to disease susceptibility. Population-based integrative genetic approaches can help identify such variation and enhance our understanding of the genetic basis of complex traits.

The commercialization of academic research has been promoted by North American policy makers for over 30 years as a means of increasing university financing and to ensure that promising research would eventually find its way to the marketplace. The following issues paper constitutes a reflection on the impact of the Canadian commercialization framework on academic research in the field of genomics. It was written following two workshops and two independent studies organized by academic groups in Quebec (Centre of Genomics and Policy) and Alberta (Health Law Institute). The full sets of recommendations are available upon request to the authors.

Short-acting b2-adrenergic receptor agonists are commonly used bronchodilators for symptom relief in asthmatics. The aim of this study was to test whether genetic variants in PDE4D gene, a key regulator of b2-adrenoceptor-induced cAMP turnover in airway smooth muscle cells, affect the response to short-acting b2-agonists. Bronchodilator responsiveness was assessed in 133 asthmatic children by % change in baseline forced expiratory volume in one second (FEV1) after administration of albuterol. The analyses were performed in patients with airway obstruction (FEV1/FVC ratio below 90%, n = 93). FEV1  % change adjusted for baseline FEV1 values was significantly different between genotypes of rs1544791 G/A polymorphism (P = 0.006) and −1345 C/T (rs1504982) promoter variation (P = 0.03). The association remained significant with inclusion of age, sex, atopy, and controller medication into multivariate model (P = 0.004 and P = 0.02, resp.). Our work identifies new genetic variants implicated in modulation of asthma treatment, one of them (rs1544791) previously associated with asthma phenotype.

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Background

Parent-of-origin-dependent expression of alleles, imprinting, has been suggested to impact a substantial proportion of mammalian genes. Its discovery requires allele-specific detection of expressed transcripts, but in some cases detected allelic expression bias has been interpreted as imprinting without demonstrating compatible transmission patterns and excluding heritable variation. Therefore, we utilized a genome-wide tool exploiting high density genotyping arrays in parallel measurements of genotypes in RNA and DNA to determine allelic expression across the transcriptome in lymphoblastoid cell lines (LCLs) and skin fibroblasts derived from families.

Results

We were able to validate 43% of imprinted genes with previous demonstration of compatible transmission patterns in LCLs and fibroblasts. In contrast, we only validated 8% of genes suggested to be imprinted in the literature, but without clear evidence of parent-of-origin-determined expression. We also detected five novel imprinted genes and delineated regions of imprinted expression surrounding annotated imprinted genes. More subtle parent-of-origin-dependent expression, or partial imprinting, could be verified in four genes. Despite higher prevalence of monoallelic expression, immortalized LCLs showed consistent imprinting in fewer loci than primary cells. Random monoallelic expression has previously been observed in LCLs and we show that random monoallelic expression in LCLs can be partly explained by aberrant methylation in the genome.

Conclusions

Our results indicate that widespread parent-of-origin-dependent expression observed recently in rodents is unlikely to be captured by assessment of human cells derived from adult tissues where genome-wide assessment of both primary and immortalized cells yields few new imprinted loci.

Genetic variants altering cis-regulation of normal gene expression (cis-eQTLs) have been extensively mapped in human cells and tissues, but the extent by which controlled, environmental perturbation influences cis-eQTLs is unclear. We carried out large-scale induction experiments using primary human bone cells derived from unrelated donors of Swedish origin treated with 18 different stimuli (7 treatments and 2 controls, each assessed at 2 time points). The treatments with the largest impact on the transcriptome, verified on two independent expression arrays, included BMP-2 (t = 2h), dexamethasone (DEX) (t = 24h), and PGE2 (t = 24h). Using these treatments and control, we performed expression profiling for 18,144 RefSeq transcripts on biological replicates of the complete study cohort of 113 individuals (ntotal = 782) and combined it with genome-wide SNP-genotyping data in order to map treatment-specific cis-eQTLs (defined as SNPs located within the gene ±250 kb). We found that 93% of cis-eQTLs at 1% FDR were observed in at least one additional treatment, and in fact, on average, only 1.4% of the cis-eQTLs were considered as treatment-specific at high confidence. The relative invariability of cis-regulation following perturbation was reiterated independently by genome-wide allelic expression tests where only a small proportion of variance could be attributed to treatment. Treatment-specific cis-regulatory effects were, however, 2- to 6-fold more abundant among differently expressed genes upon treatment. We further followed-up and validated the DEX–specific cis-regulation of the MYO6 and TNC loci and found top cis-regulatory variants located 180 kb and 250 kb upstream of the transcription start sites, respectively. Our results suggest that, as opposed to tissue-specificity of cis-eQTLs, the interactions between cellular environment and cis-variants are relatively rare (∼1.5%), but that detection of such specific interactions can be achieved by a combination of functional genomic approaches as described here.

Author Summary

Population variation in normal gene expression has been convincingly shown to be under strong genetic control where the main genetic variants are located within close proximity to the gene itself (so called cis-acting). However, the extent to which controlled, environmental stimuli influences cis-regulation of gene expression is unclear. Here, we combine different functional genomic approaches and examine the role of common genetic variants on induced gene expression in a population panel of primary human cells derived from ∼100 unrelated donors treated under multiple conditions. Using these approaches, we find that the interaction between cellular environment and cis-variants are relatively rare, with only a small proportion of the identified genetic variants being specific to treatment. However, although treatment-specific genetic regulation of gene expression seems to be infrequent, we prove its existence by thorough validation of treatment-specific effects of the glucocorticoid-specific regulation of TNC expression. Taken together, these findings indicate that the regulatory landscape within a cell is very stable but, by combining functional genomic tools gene-environmental interactions of clinical importance, can be detected and possibly used as biomarkers in future pharmacogenomic studies.

Allelic imbalance (AI) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions. Recently, Bing et al. have described the use of genotyping arrays to assay AI at a high resolution (∼750,000 SNPs across the autosomes). In this paper, we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner. We propose two families of approaches: (i) a statistical approach based on z-score computations, and (ii) a family of machine learning approaches based on Hidden Markov Models. Each method is evaluated using previously published experimental data sets as well as with permutation testing. When applied to whole genome data from 53 HapMap samples, our approaches reveal that allelic imbalance is widespread (most expressed genes show evidence of AI in at least one of our 53 samples) and that most AI regions in a given individual are also found in at least a few other individuals. While many AI regions identified in the genome correspond to known protein-coding transcripts, others overlap with recently discovered long non-coding RNAs. We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels, but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end. The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases.

Author Summary

Measures of gene expression, and the search for regulatory regions in the genome responsible for differences in levels of gene expression, is one of the key paths of research used to identify disease causing genes, as well as explain differences between healthy individuals. Typically, experiments have measured and compared gene expression in multiple individuals, and used this information to attempt to map regulatory regions responsible. Differences in environment between individuals can, however, cause differences in gene expression unrelated to the underlying regulatory sequence. New genotyping technologies enable the measurement of expression of both copies of a particular gene, at loci that are heterozygous within a particular individual. This will therefore act as an internal control, as environmental factors will continue to affect the expression of both copies of a gene at presumably equal levels, and differences in expression are more likely to be explicable by differences in regulatory regions specific to the two copies of the gene itself. Differences between regulatory regions are expected to lead to differences in expression of the two copies (or the two alleles) of a particular gene, also known as allelic imbalance. We describe a set of signal processing methods for the reliable detection of allelic expression within the genome.

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

Author Summary

BMD and hip geometry are two major predictors of osteoporotic fractures, the most severe consequence of osteoporosis in elderly persons. We performed sex-specific genome-wide association studies (GWAS) for BMD at the lumbar spine and femor neck skeletal sites as well as hip geometric indices (NSA, NL, and NW) in the Framingham Osteoporosis Study and then replicated the top findings in two independent studies. Three novel loci were significant: in women, including chromosome 1p13.2 (RAP1A) for NW; in men, 2q11.2 (TBC1D8) for NSA and 18q11.2 (OSBPL1A) for NW. We confirmed a previously reported region on 8q24.12 (TNFRSF11B/OPG) for lumbar spine BMD in women. In addition, we integrated GWAS signals with eQTL in several tissues and publicly available expression signature profiling in cellular and whole-animal models, and prioritized 16 candidate genes/loci based on their potential involvement in skeletal metabolism. Among three prioritized loci (GPR177, SOX6, and CASR genes) associated with BMD in women, GPR177 and SOX6 have been successfully replicated later in a large-scale meta-analysis, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of using expression profiling to support the candidacy of suggestive GWAS signals that may contain important genes of interest.

Bone mineral density (BMD) is a heritable complex trait used in the clinical diagnosis of osteoporosis and the assessment of fracture risk. We performed meta-analysis of five genome-wide association studies of femoral neck and lumbar spine BMD in 19,195 subjects of Northern European descent. We identified 20 loci reaching genome-wide significance (GWS; P<5×10−8), of which 13 map to new regions including 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4; ARHGAP1; F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1). The metaanalysis also confirmed at GWS level, seven known BMD loci on 1p36 (ZBTB40), 6q25 (ESR1), 8q24 (TNFRSF11B), 11q13.4 (LRP5), 12q13 (SP7), 13q14 (TNFSF11), and 18q21 (TNFRSF11A). The numerous SNPs associated with BMD map to genes in signaling pathways with relevance to bone metabolism, and highlight the complex genetic architecture underlying osteoporosis and BMD variation.

Current genome-wide association studies (GWAS) are moving towards the use of large cohorts of primary cell lines to study a disease of interest and to assign biological relevance to the genetic signals identified. Here, we use a panel of human osteoblasts (HObs) to carry out a transcriptomic survey, similar to recent studies in lymphoblastoid cell lines (LCLs). The distinct nature of HObs and LCLs is reflected by the preferential grouping of cell type–specific genes within biologically and functionally relevant pathways unique to each tissue type. We performed cis-association analysis with SNP genotypes to identify genetic variations of transcript isoforms, and our analysis indicates that differential expression of transcript isoforms in HObs is also partly controlled by cis-regulatory genetic variants. These isoforms are regulated by genetic variants in both a tissue-specific and tissue-independent fashion, and these associations have been confirmed by RT–PCR validation. Our study suggests that multiple transcript isoforms are often present in both tissues and that genetic control may affect the relative expression of one isoform to another, rather than having an all-or-none effect. Examination of the top SNPs from a GWAS of bone mineral density show overlap with probeset associations observed in this study. The top hit corresponding to the FAM118A gene was tested for association studies in two additional clinical studies, revealing a novel transcript isoform variant. Our approach to examining transcriptome variation in multiple tissue types is useful for detecting the proportion of genetic variation common to different cell types and for the identification of cell-specific isoform variants that may be functionally relevant, an important follow-up step for GWAS.

Author Summary

The transcriptome of any given cell type is a complex program of controlled gene expression underlying its biological function. An additional layer of molecular complexity involving individual genetic variation can modulate the transcriptome within the same tissue type, conferring potential phenotypic differences between individuals at the cellular level. This study highlights common and unique aspects of the transcriptome between the well-characterized lymphoblastoid cell lines from the International HapMap Project and those of a cultured primary cell type, human osteoblasts. We observe that inter-individual genetic variation can regulate transcript isoform expression in tissue-specific and tissue-independent manners, indicating that genetic differences among individuals can alter the transcriptome in one or more tissues, ultimately leading to altered biological functions within the lymphoblasts and/or osteoblasts. Pursuant to this, genome wide association studies on bone mineral density (BMD) have identified a number of significant loci and polymorphisms highly linked to the BMD quantitative phenotype. A small proportion of these polymorphisms overlap with our highly significant SNPs regulating the osteoblast transcriptome, revealing a potential molecular basis for this phenotype at the transcriptional level. This study highlights the importance of examining the differing transcriptomes and cis-regulatory mechanisms governing the biological and functional roles of varied tissue types.

It is likely that evolutionary differences among species are driven by sequence changes in regulatory regions. Likewise, polymorphisms in the promoter regions may be responsible for interindividual differences at the level of populations. We present an unbiased survey of genetic variation in 2-kb segments upstream of the transcription start sites of 28 protein-coding genes, characterized in five population groups of different geographic origin. On average, we found 9.1 polymorphisms and 8.8 haplotypes per segment with corresponding nucleotide and haplotype diversities of 0.082% and 58%, respectively. We characterized these segments through different summary statistics, Hardy-Weinberg equilibria fixation index (Fst) estimates, and neutrality tests, as well as by analyzing the distributions of haplotype allelic classes, introduced here to assess the departure from neutrality and examined by coalescent simulations under a simple population model, assuming recombinations or different demography. Our results suggest that genetic diversity in some of these regions could have been shaped by purifying selection and driven by adaptive changes in the other, thus explaining the relatively large variance in the corresponding genetic diversity indices loci. However, some of these effects could be also due to linkage with surrounding sequences, and the neutralists' explanations cannot be ruled out given uncertainty in the underlying demographic histories and the possibility of random effects due to the small size of the studied segments. Hum Mutat 28(5), 441–450, 2007. © 2007 Wiley-Liss, Inc.

Systemic lupus erythematosus (SLE) is the prototype autoimmune disease where genes regulated by type I interferon (IFN) are over-expressed and contribute to the disease pathogenesis. Because signal transducer and activator of transcription 4 (STAT4) plays a key role in the type I IFN receptor signaling, we performed a candidate gene study of a comprehensive set of single nucleotide polymorphism (SNPs) in STAT4 in Swedish patients with SLE. We found that 10 out of 53 analyzed SNPs in STAT4 were associated with SLE, with the strongest signal of association (P = 7.1 × 10−8) for two perfectly linked SNPs rs10181656 and rs7582694. The risk alleles of these 10 SNPs form a common risk haplotype for SLE (P = 1.7 × 10−5). According to conditional logistic regression analysis the SNP rs10181656 or rs7582694 accounts for all of the observed association signal. By quantitative analysis of the allelic expression of STAT4 we found that the risk allele of STAT4 was over-expressed in primary human cells of mesenchymal origin, but not in B-cells, and that the risk allele of STAT4 was over-expressed (P = 8.4 × 10−5) in cells carrying the risk haplotype for SLE compared with cells with a non-risk haplotype. The risk allele of the SNP rs7582694 in STAT4 correlated to production of anti-dsDNA (double-stranded DNA) antibodies and displayed a multiplicatively increased, 1.82-fold risk of SLE with two independent risk alleles of the IRF5 (interferon regulatory factor 5) gene.

An efficient, cost-effective and open-source approach is described for high-throughput genotyping of large fixed panels of diploid individuals.

Association mapping aimed at identifying DNA polymorphisms that contribute to variation in complex traits entails genotyping a large number of single-nucleotide polymorphisms (SNPs) in a very large panel of individuals. Few technologies, however, provide inexpensive high-throughput genotyping. Here, we present an efficient approach developed specifically for genotyping large fixed panels of diploid individuals. The cost-effective, open-source nature of our methodology may make it particularly attractive to those working in nonmodel systems.