Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS

The PsychENCODE project

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions

In this study, ant colony optimisation (ACO) algorithm is used to derive near-optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome-wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.

Systematic Integration of Brain eQTL and GWAS Identifies ZNF323 as a Novel Schizophrenia Risk Gene and Suggests Recent Positive Selection Based on Compensatory Advantage on Pulmonary Function

Genome-wide association studies have identified multiple risk variants and loci that show robust association with schizophrenia. Nevertheless, it remains unclear how these variants confer risk to schizophrenia. In addition, the driving force that maintains the schizophrenia risk variants in human gene pool is poorly understood. To investigate whether expression-associated genetic variants contribute to schizophrenia susceptibility, we systematically integrated brain expression quantitative trait loci and genome-wide association data of schizophrenia using Sherlock, a Bayesian statistical framework. Our analyses identified ZNF323 as a schizophrenia risk gene (P = 2.22×10(-6)). Subsequent analyses confirmed the association of the ZNF323 and its expression-associated single nucleotide polymorphism rs1150711 in independent samples (gene-expression: P = 1.40×10(-6); single-marker meta-analysis in the combined discovery and replication sample comprising 44123 individuals: P = 6.85×10(-10)). We found that the ZNF323 was significantly downregulated in hippocampus and frontal cortex of schizophrenia patients (P = .0038 and P = .0233, respectively). Evidence for pleiotropic effects was detected (association of rs1150711 with lung function and gene expression of ZNF323 in lung: P = 6.62×10(-5) and P = 9.00×10(-5), respectively) with the risk allele (T allele) for schizophrenia acting as protective allele for lung function. Subsequent population genetics analyses suggest that the risk allele (T) of rs1150711 might have undergone recent positive selection in human population. Our findings suggest that the ZNF323 is a schizophrenia susceptibility gene whose expression may influence schizophrenia risk. Our study also illustrates a possible mechanism for maintaining schizophrenia risk variants in the human gene pool.

Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

Molecular mechanisms underlying variations in lung function: a systems genetics analysis

BACKGROUND

Lung function measures reflect the physiological state of the lung, and are essential to the diagnosis of chronic obstructive pulmonary disease (COPD). The SpiroMeta-CHARGE consortium undertook the largest genome-wide association study (GWAS) so far (n=48,201) for forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in the general population. The lung expression quantitative trait loci (eQTLs) study mapped the genetic architecture of gene expression in lung tissue from 1111 individuals. We used a systems genetics approach to identify single nucleotide polymorphisms (SNPs) associated with lung function that act as eQTLs and change the level of expression of their target genes in lung tissue; termed eSNPs.

METHODS

The SpiroMeta-CHARGE GWAS results were integrated with lung eQTLs to map eSNPs and the genes and pathways underlying the associations in lung tissue. For comparison, a similar analysis was done in peripheral blood. The lung mRNA expression levels of the eSNP-regulated genes were tested for associations with lung function measures in 727 individuals. Additional analyses identified the pleiotropic effects of eSNPs from the published GWAS catalogue, and mapped enrichment in regulatory regions from the ENCODE project. Finally, the Connectivity Map database was used to identify potential therapeutics in silico that could reverse the COPD lung tissue gene signature.

FINDINGS

SNPs associated with lung function measures were more likely to be eQTLs and vice versa. The integration mapped the specific genes underlying the GWAS signals in lung tissue. The eSNP-regulated genes were enriched for developmental and inflammatory pathways; by comparison, SNPs associated with lung function that were eQTLs in blood, but not in lung, were only involved in inflammatory pathways. Lung function eSNPs were enriched for regulatory elements and were over-represented among genes showing differential expression during fetal lung development. An mRNA gene expression signature for COPD was identified in lung tissue and compared with the Connectivity Map. This in-silico drug repurposing approach suggested several compounds that reverse the COPD gene expression signature, including a nicotine receptor antagonist. These findings represent novel therapeutic pathways for COPD.

INTERPRETATION

The system genetics approach identified lung tissue genes driving the variation in lung function and susceptibility to COPD. The identification of these genes and the pathways in which they are enriched is essential to understand the pathophysiology of airway obstruction and to identify novel therapeutic targets and biomarkers for COPD, including drugs that reverse the COPD gene signature in silico.

FUNDING

The research reported in this article was not specifically funded by any agency. See Acknowledgments for a full list of funders of the lung eQTL study and the Spiro-Meta CHARGE GWAS.

Genetic basis of interindividual susceptibility to cancer cachexia: selection of potential candidate gene polymorphisms for association studies

Cancer cachexia is a complex and multifactorial disease. Evolving definitions highlight the fact that a diverse range of biological processes contribute to cancer cachexia. Part of the variation in who will and who will not develop cancer cachexia may be genetically determined. As new definitions, classifications and biological targets continue to evolve, there is a need for reappraisal of the literature for future candidate association studies. This review summarizes genes identified or implicated as well as putative candidate genes contributing to cachexia, identified through diverse technology platforms and model systems to further guide association studies. A systematic search covering 1986-2012 was performed for potential candidate genes / genetic polymorphisms relating to cancer cachexia. All candidate genes were reviewed for functional polymorphisms or clinically significant polymorphisms associated with cachexia using the OMIM and GeneRIF databases. Pathway analysis software was used to reveal possible network associations between genes. Functionality of SNPs/genes was explored based on published literature, algorithms for detecting putative deleterious SNPs and interrogating the database for expression of quantitative trait loci (eQTLs). A total of 154 genes associated with cancer cachexia were identified and explored for functional polymorphisms. Of these 154 genes, 119 had a combined total of 281 polymorphisms with functional and/or clinical significance in terms of cachexia associated with them. Of these, 80 polymorphisms (in 51 genes) were replicated in more than one study with 24 polymorphisms found to influence two or more hallmarks of cachexia (i.e., inflammation, loss of fat mass and/or lean mass and reduced survival). Selection of candidate genes and polymorphisms is a key element of multigene study design. The present study provides a contemporary basis to select genes and/or polymorphisms for further association studies in cancer cachexia, and to develop their potential as susceptibility biomarkers of cachexia.

Improved integrative framework combining association data with gene expression features to prioritize Crohn's disease genes

Genome-wide association studies in Crohn's disease (CD) have identified 140 genome-wide significant loci. However, identification of genes driving association signals remains challenging. Furthermore, genome-wide significant thresholds limit false positives at the expense of decreased sensitivity. In this study, we explored gene features contributing to CD pathogenicity, including gene-based association data from CD and autoimmune (AI) diseases, as well as gene expression features (eQTLs, epigenetic markers of expression and intestinal gene expression data). We developed an integrative model based on a CD reference gene set. This integrative approach outperformed gene-based association signals alone in identifying CD-related genes based on statistical validation, gene ontology enrichment, differential expression between M1 and M2 macrophages and a validation using genes causing monogenic forms of inflammatory bowel disease as a reference. Besides gene-level CD association P-values, association with AI diseases was the strongest predictor, highlighting generalized mechanisms of inflammation, and the interferon-γ pathway particularly. Within the 140 high-confidence CD regions, 598 of 1328 genes had low prioritization scores, highlighting genes unlikely to contribute to CD pathogenesis. For select regions, comparably high integrative model scores were observed for multiple genes. This is particularly evident for regions having extensive linkage disequilibrium such as the IBD5 locus. Our analyses provide a standardized reference for prioritizing potential CD-related genes, in regions with both highly significant and nominally significant gene-level association P-values. Our integrative model may be particularly valuable in prioritizing rare, potentially private, missense variants for which genome-wide evidence for association may be unattainable.

Embryonal Fyn-associated substrate (EFS) and CASS4: The lesser-known CAS protein family members

The CAS (Crk-associated substrate) adaptor protein family consists of four members: CASS1/BCAR1/p130Cas, CASS2/NEDD9/HEF1/Cas-L, CASS3/EFS/Sin and CASS4/HEPL. While CAS proteins lack enzymatic activity, they contain specific recognition and binding sites for assembly of larger signaling complexes that are essential for cell proliferation, survival, migration, and other processes. All family members are intermediates in integrin-dependent signaling pathways mediated at focal adhesions, and associate with FAK and SRC family kinases to activate downstream effectors regulating the actin cytoskeleton. Most studies of CAS proteins to date have been focused on the first two members, BCAR1 and NEDD9, with altered expression of these proteins now appreciated as influencing disease development and prognosis for cancer and other serious pathological conditions. For these family members, additional mechanisms of action have been defined in receptor tyrosine kinase (RTK) signaling, estrogen receptor signaling or cell cycle progression, involving discrete partner proteins such as SHC, NSP proteins, or AURKA. By contrast, EFS and CASS4 have been less studied, although structure-function analyses indicate they conserve many elements with the better-known family members. Intriguingly, a number of recent studies have implicated these proteins in immune system function, and the pathogenesis of developmental disorders, autoimmune disorders including Crohn's disease, Alzheimer's disease, cancer and other diseases. In this review, we summarize the current understanding of EFS and CASS4 protein function in the context of the larger CAS family group.

Pharmacoethnicity in Paclitaxel-Induced Sensory Peripheral Neuropathy

PURPOSE

Paclitaxel is used worldwide in the treatment of breast, lung, ovarian, and other cancers. Sensory peripheral neuropathy is an associated adverse effect that cannot be predicted, prevented, or mitigated. To better understand the contribution of germline genetic variation to paclitaxel-induced peripheral neuropathy, we undertook an integrative approach that combines genome-wide association study (GWAS) data generated from HapMap lymphoblastoid cell lines (LCL) and Asian patients.

METHODS

GWAS was performed with paclitaxel-induced cytotoxicity generated in 363 LCLs and with paclitaxel-induced neuropathy from 145 Asian patients. A gene-based approach was used to identify overlapping genes and compare with a European clinical cohort of paclitaxel-induced neuropathy. Neurons derived from human-induced pluripotent stem cells were used for functional validation of candidate genes.

RESULTS

SNPs near AIPL1 were significantly associated with paclitaxel-induced cytotoxicity in Asian LCLs (P < 10(-6)). Decreased expression of AIPL1 resulted in decreased sensitivity of neurons to paclitaxel by inducing neurite morphologic changes as measured by increased relative total outgrowth, number of processes and mean process length. Using a gene-based analysis, there were 32 genes that overlapped between Asian LCL cytotoxicity and Asian patient neuropathy (P < 0.05), including BCR. Upon BCR knockdown, there was an increase in neuronal sensitivity to paclitaxel as measured by neurite morphologic characteristics.

CONCLUSIONS

We identified genetic variants associated with Asian paclitaxel-induced cytotoxicity and functionally validated the AIPL1 and BCR in a neuronal cell model. Furthermore, the integrative pharmacogenomics approach of LCL/patient GWAS may help prioritize target genes associated with chemotherapeutic-induced peripheral neuropathy.

A meta-analysis strategy for gene prioritization using gene expression, SNP genotype, and eQTL data

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources.

Path from schizophrenia genomics to biology: gene regulation and perturbation in neurons derived from induced pluripotent stem cells and genome editing

Schizophrenia (SZ) is a devastating mental disorder afflicting 1% of the population. Recent genome-wide association studies (GWASs) of SZ have identified >100 risk loci. However, the causal variants/genes and the causal mechanisms remain largely unknown, which hinders the translation of GWAS findings into disease biology and drug targets. Most risk variants are noncoding, thus likely regulate gene expression. A major mechanism of transcriptional regulation is chromatin remodeling, and open chromatin is a versatile predictor of regulatory sequences. MicroRNA-mediated post-transcriptional regulation plays an important role in SZ pathogenesis. Neurons differentiated from patient-specific induced pluripotent stem cells (iPSCs) provide an experimental model to characterize the genetic perturbation of regulatory variants that are often specific to cell type and/or developmental stage. The emerging genome-editing technology enables the creation of isogenic iPSCs and neurons to efficiently characterize the effects of SZ-associated regulatory variants on SZ-relevant molecular and cellular phenotypes involving dopaminergic, glutamatergic, and GABAergic neurotransmissions. SZ GWAS findings equipped with the emerging functional genomics approaches provide an unprecedented opportunity for understanding new disease biology and identifying novel drug targets.

Generalized functional linear models for gene-based case-control association studies

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.

Genetic variation in HTR4 and lung function: GWAS follow-up in mouse

Human genome-wide association studies (GWASs) have identified numerous associations between single nucleotide polymorphisms (SNPs) and pulmonary function. Proving that there is a causal relationship between GWAS SNPs, many of which are noncoding and without known functional impact, and these traits has been elusive. Furthermore, noncoding GWAS-identified SNPs may exert trans-regulatory effects rather than impact the proximal gene. Noncoding variants in 5-hydroxytryptamine (serotonin) receptor 4 (HTR4) are associated with pulmonary function in human GWASs. To gain insight into whether this association is causal, we tested whether Htr4-null mice have altered pulmonary function. We found that HTR4-deficient mice have 12% higher baseline lung resistance and also increased methacholine-induced airway hyperresponsiveness (AHR) as measured by lung resistance (27%), tissue resistance (48%), and tissue elastance (30%). Furthermore, Htr4-null mice were more sensitive to serotonin-induced AHR. In models of exposure to bacterial lipopolysaccharide, bleomycin, and allergic airway inflammation induced by house dust mites, pulmonary function and cytokine profiles in Htr4-null mice differed little from their wild-type controls. The findings of altered baseline lung function and increased AHR in Htr4-null mice support a causal relationship between genetic variation in HTR4 and pulmonary function identified in human GWAS.

A meta-analysis of gene expression quantitative trait loci in brain

Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10(-9)), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.

Five regulatory genes detected by matching signatures of eQTL and GWAS in psoriasis

BACKGROUND

Psoriasis is a common immune-mediated inflammatory skin disease with strong genetic dispositions. Although more than 40 susceptibility loci have been revealed mostly through psoriasis genome wide association studies, genetic variants with small effect remain to be identified.

OBJECTIVE

In order to explore the susceptibility genes with potential regulatory function, we queried jointly two psoriasis genome wide association cohorts and an expression dataset.

METHODS

We integrated conventional genome-wide association evidences in 2326 Han Chinese and 2719 Caucasian populations, and the signature of expression quantitative trait loci (eQTL) in lymphoblastoid B cells, with application of Bayesian algorithm.

RESULTS

Five genes with implied regulatory effect were revealed to be associated significantly with the risk of psoriasis, with one novel signal in FAM20B gene which is significantly expressed (P=3.24×10(-5)). Besides, seven single nucleotide polymorphisms were identified to be involved in the mechanism of psoriasis through eQTL effect.

CONCLUSIONS

We identified FAM20B as a risk regulatory gene in the etiology of psoriasis at first time. This study shed a spotlight on the immune regulatory mechanism in psoriasis.

More powerful genetic association testing via a new statistical framework for integrative genomics

Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.

Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression

Understanding the genetic basis of human traits/diseases and the underlying mechanisms of how these traits/diseases are affected by genetic variations is critical for public health. Current genome-wide functional genomics data uncovered a large number of functional elements in the noncoding regions of human genome, providing new opportunities to study regulatory variants (RVs). RVs play important roles in transcription factor bindings, chromatin states and epigenetic modifications. Here, we systematically review an array of methods currently used to map RVs as well as the computational approaches in annotating and interpreting their regulatory effects, with emphasis on regulatory single-nucleotide polymorphism. We also briefly introduce experimental methods to validate these functional RVs.

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

A symbiotic liaison between the genetic and epigenetic code

With rapid advances in sequencing technologies, we are undergoing a paradigm shift from hypothesis- to data-driven research. Genome-wide profiling efforts have given informative insights into biological processes; however, considering the wealth of variation, the major challenge still remains in their meaningful interpretation. In particular sequence variation in non-coding contexts is often challenging to interpret. Here, data integration approaches for the identification of functional genetic variability represent a possible solution. Exemplary, functional linkage analysis integrating genotype and expression data determined regulatory quantitative trait loci and proposed causal relationships. In addition to gene expression, epigenetic regulation and specifically DNA methylation was established as highly valuable surrogate mark for functional variance of the genetic code. Epigenetic modification has served as powerful mediator trait to elucidate mechanisms forming phenotypes in health and disease. Particularly, integrative studies of genetic and DNA methylation data have been able to guide interpretation strategies of risk genotypes, but also proved their value for physiological traits, such as natural human variation and aging. This Review seeks to illustrate the power of data integration in the genomic era exemplified by DNA methylation quantitative trait loci. However, the model is further extendable to virtually all traceable molecular traits.

Genome-wide expression quantitative trait loci analysis in asthma

PURPOSE OF REVIEW

Expression quantitative trait loci (eQTL) mapping studies are the next most important step in genomics to identify susceptibility genes and molecular pathways involved in human diseases following the completion of genome-wide association studies (GWAS). This article reviews the emerging concepts in genetics of gene expression and the empirical value of eQTL mapping to refine GWAS asthma susceptibility loci.

RECENT FINDINGS

eQTL mapping studies were paramount to reveal the cis and trans control of gene expression, the cell type and tissue specificity of eQTLs, and the pleiotropic nature of eQTL single nucleotide polymorphisms. A small number of eQTL studies were recently performed in tissues and cell types that are relevant for asthma and are used to interpret the biology underpinning GWAS loci including the most robust asthma susceptibility locus on 17q21.

SUMMARY

The full potential of eQTL mapping studies is just starting to be revealed. Imminent progress is expected owing to the accelerating advances in sequencing technologies to map genetic variants and transcriptomes as well as the development of bioinformatics and computational algorithms to exploit integrative genomic approaches. A short-term challenge in the field of asthma is the creation of well powered eQTL datasets testing gene expression and other molecular phenotypes in disease-relevant tissues.

Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility

Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript abundance or expression quantitative trait (eQTL) analysis unravels the genomic architecture of regulatory variants. Availability of eQTL information from tissues relevant for glucose homeostasis in humans opens a new avenue to prioritize GWAS-implicated variants that may be involved in triggering a causal chain of events leading to T2D. In this article, we review the progress made in the field of eQTL research and knowledge gained from those studies in understanding transcription regulatory mechanisms in human subjects. We highlight several novel approaches that can integrate eQTL analysis with multiple layers of biological information to identify ethnic-specific causal variants and gene-environment interactions relevant to T2D pathogenesis. Finally, we discuss how the eQTL analysis mediated search for “missing heritability” may lead us to novel biological and molecular mechanisms involved in susceptibility to T2D.

Synergistic effect of interleukin-10-receptor variants in a case of early-onset ulcerative colitis

AIM: To investigated the molecular cause of very early-onset ulcerative colitis (UC) in an 18-mo-old affected child.

METHODS: We analysed the interleukin-10 (IL10) receptor genes at the DNA and RNA level in the proband and his relatives. Beta catenin and tumor necrosis factor-α (TNFα) receptors were analysed in the proteins extracted from peripheral blood cells of the proband, his relatives and familial adenomatous polyposis (FAP) and PTEN hamartoma tumor syndrome (PHTS) patients. Samples were also collected from the proband’s inflamed colorectal mucosa and compared to healthy and tumour mucosa collected from a FAP patient and patients affected by sporadic colorectal cancer (CRC). Finally, we examined mesalazine and azathioprine effects on primary fibroblasts stabilised from UC and FAP patients.

RESULTS: Our patient was a compound heterozygote for the IL10RB E47K polymorphism, inherited from his father, and for a novel point mutation within the IL10RA promoter (the -413G->T), inherited from his mother. Beta catenin and tumour necrosis factor α receptors-I (TNFRI) protein were both over-expressed in peripheral blood cells of the proband’s relatives more than the proband. However, TNFRII was over-expressed only in the proband. Finally, both TNFα-receptors were shown to be under-expressed in the inflamed colon mucosa and colorectal cancer tissue compared to healthy colon mucosa. Consistent with this observation, mesalazine and azathioprine induced, in primary fibroblasts, IL10RB and TNFRII over-expression and TNFRI and TNFα under-expression. We suggest that β-catenin and TNFRI protein expression in peripheral blood cells could represent molecular markers of sub-clinical disease in apparently healthy relatives of patients with early-onset UC.

CONCLUSION: A synergistic effect of several variant alleles of the IL10 receptor genes, inherited in a Mendelian manner, is involved in UC onset in this young child.

Proteomic studies related to genetic determinants of variability in protein concentrations

Genetic variation has multiple effects on the proteome. It may influence the expression level of proteins, modify their sequences through single nucleotide polymorphisms, the occurrence of allelic variants, or alternative splicing (ASP) events. This perspective paper summarizes the major effects of genetic variability on protein expression and isoforms and provides an overview of proteomics techniques and methods that allow studying the effects of genetic variability at different levels of the proteome. The paper provides an overview of recent quantitative trait loci studies performed to explore the effect of genetic variation on protein expression (pQTL). Finally it gives a perspective view on advances in proteomics technology and the role of the Chromosome-Centric Human Proteome Project (C-HPP) by creating large-scale resources that may facilitate performing more comprehensive pQTL experiments in the future.

Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing

Rapid advances in high-throughput sequencing facilitate variant discovery and genotyping, but linking variants into a single haplotype remains challenging. Here we demonstrate HaploSeq, an approach for assembling chromosome-scale haplotypes by exploiting the existence of 'chromosome territories'. We use proximity ligation and sequencing to show that alleles on homologous chromosomes occupy distinct territories, and therefore this experimental protocol preferentially recovers physically linked DNA variants on a homolog. Computational analysis of such data sets allows for accurate (∼99.5%) reconstruction of chromosome-spanning haplotypes for ∼95% of alleles in hybrid mouse cells with 30× sequencing coverage. To resolve haplotypes for a human genome, which has a low density of variants, we coupled HaploSeq with local conditional phasing to obtain haplotypes for ∼81% of alleles with ∼98% accuracy from just 17× sequencing. Whereas methods based on proximity ligation were originally designed to investigate spatial organization of genomes, our results lend support for their use as a general tool for haplotyping.

Systems biology approaches to epidemiological studies of complex diseases

Systems biology approaches to epidemiological studies of complex diseases include collection of genetic, genomic, epigenomic, and metagenomic data in large-scale epidemiological studies of complex phenotypes. Designs and analyses of such studies raise many statistical challenges. This article reviews some issues related to integrative analysis of such high dimensional and inter-related datasets and outline some possible solutions. I focus my review on integrative approaches for genome-wide genetic variants and gene expression data, methods for joint analysis of genetic and epigenetic variants, and methods for analysis of microbiome data. Statistical methods such as mediation analysis, high-dimensional instrumental variable regression, sparse signal recovery, and compositional data regression provide potential frameworks for integrative analysis of these high-dimensional genomic data.