Global properties and functional complexity of human gene regulatory variation

Connecting intermediate phenotypes to disease using multi-omics in heart failure

Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.

Harnessing natural variation to identify cis regulators of sex-biased gene expression in a multi-strain mouse liver model

Sex differences in gene expression are widespread in the liver, where many autosomal factors act in tandem with growth hormone signaling to regulate individual variability of sex differences in liver metabolism and disease. Here, we compare hepatic transcriptomic and epigenetic profiles of mouse strains C57BL/6J and CAST/EiJ, representing two subspecies separated by 0.5-1 million years of evolution, to elucidate the actions of genetic factors regulating liver sex differences. We identify 144 protein coding genes and 78 lncRNAs showing strain-conserved sex bias; many have gene ontologies relevant to liver function, are more highly liver-specific and show greater sex bias, and are more proximally regulated than genes whose sex bias is strain-dependent. The strain-conserved genes include key growth hormone-dependent transcriptional regulators of liver sex bias; however, three other transcription factors, Trim24, Tox, and Zfp809, lose their sex-biased expression in CAST/EiJ mouse liver. To elucidate the observed strain specificities in expression, we characterized the strain-dependence of sex-biased chromatin opening and enhancer marks at cis regulatory elements (CREs) within expression quantitative trait loci (eQTL) regulating liver sex-biased genes. Strikingly, 208 of 286 eQTLs with strain-specific, sex-differential effects on expression were associated with a complete gain, loss, or reversal of the sex differences in expression between strains. Moreover, 166 of the 286 eQTLs were linked to the strain-dependent gain or loss of localized sex-biased CREs. Remarkably, a subset of these CREs apparently lacked strain-specific genetic variants yet showed coordinated, strain-dependent sex-biased epigenetic regulation. Thus, we directly link hundreds of strain-specific genetic variants to the high variability in CRE activity and expression of sex-biased genes and uncover underlying genetically-determined epigenetic states controlling liver sex bias in genetically diverse mouse populations.

Post-GWAS functional studies reveal an RA-associated CD40-induced NF-kB signal transduction and transcriptional regulation network targeted by class II HDAC inhibitors

Currently, it remains difficult to identify which single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) are functional and how various functional SNPs (fSNPs) interact and contribute to disease susceptibility. GWAS have identified a CD40 locus that is associated with rheumatoid arthritis (RA). We previously used two techniques developed in our laboratory, single nucleotide polymorphism-next-generation sequencing (SNP-seq) and flanking restriction enhanced DNA pulldown-mass spectrometry (FREP-MS), to determine that the RA risk gene RBPJ regulates CD40 expression via a fSNP at the RA-associated CD40 locus. In the present work, by applying the same approach, we report the identification of six proteins that regulate RBPJ expression via binding to two fSNPs on the RA-associated RBPJ locus. Using these findings, together with the published data, we constructed an RA-associated signal transduction and transcriptional regulation network (STTRN) that functionally connects multiple RA-associated risk genes via transcriptional regulation networks (TRNs) linked by CD40-induced nuclear factor kappa B (NF-kB) signaling. Remarkably, this STTRN provides insight into the potential mechanism of action for the histone deacetylase inhibitor givinostat, an approved therapy for systemic juvenile idiopathic arthritis. Thus, the generation of disease-associated STTRNs based on post-GWAS functional studies is demonstrated as a novel and effective approach to apply GWAS for mechanistic studies and target identification.

Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits

Background

Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles.

Results

Whereas a relatively small number of molecular phenotypes were significantly heritable (h² > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles.

Conclusions

We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.

Robust partitioning of microRNA targets from downstream regulatory changes

The biological impact of microRNAs (miRNAs) is determined by their targets, and robustly identifying direct miRNA targets remains challenging. Existing methods suffer from high false-positive rates and are unable to effectively differentiate direct miRNA targets from downstream regulatory changes. Here, we present an experimental and computational framework to deconvolute post-transcriptional and transcriptional changes using a combination of RNA-seq and PRO-seq. This novel approach allows us to systematically profile the regulatory impact of a miRNA. We refer to this approach as CARP: Combined Analysis of RNA-seq and PRO-seq. We apply CARP to multiple miRNAs and show that it robustly distinguishes direct targets from downstream changes, while greatly reducing false positives. We validate our approach using Argonaute eCLIP-seq and ribosome profiling, demonstrating that CARP defines a comprehensive repertoire of targets. Using this approach, we identify miRNA-specific activity of target sites within the open reading frame. Additionally, we show that CARP facilitates the dissection of complex changes in gene regulatory networks triggered by miRNAs and identification of transcription factors that mediate downstream regulatory changes. Given the robustness of the approach, CARP would be particularly suitable for dissecting miRNA regulatory networks in vivo.

Association of LINC00673 rs11655237 polymorphism with cancer susceptibility: A meta-analysis based on 23,478 subjects

BACKGROUND

Some studies on the relationship between LINC00673 polymorphism and cancer susceptibility have been inconsistent. To perform a more comprehensively quantitative assessment of LINC00673 rs11655237 and risk of overall cancer, we operated this meta-analysis for the first time.

METHODS

A comprehensive search was conducted to obtain relevant literature up to November 20, 2019. Pooled odds ratios and 95% confidence intervals were utilized to assess rs11655237 and cancer susceptibility under five different genetic models.

RESULTS

Eventually, 11 case-control studies from 9 articles were included. We found that LINC00673 rs11655237 polymorphism increased the susceptibility to overall cancer under all genetic models in the overall population. By dividing ethnicity and cancer type into subgroups, we also obtained similar positive results in subgroups of Chinese population, pancreatic cancer, cervical cancer, neuroblastoma, hepatoblastoma and gastric cancer.

CONCLUSION

Overall, this meta-analysis has demonstrated for the first time that LINC00673 rs11655237 could increase susceptibility to cancer.

Bayesian multivariate reanalysis of large genetic studies identifies many new associations

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.

Genome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

Variants in oxidative stress-related genes affect the chemosensitivity through Nrf2-mediated signaling pathway in biliary tract cancer

Background

Oxidative stress and their effectors play critical roles in carcinogenesis and chemoresistance. However, the role of oxidative stress-related genes variants in biliary tract cancer (BTC) chemoresistance remains unknown. In this work, we aim to investigate oxidative stress-dependent molecular mechanisms underlying chemoresistance, and find potential biomarkers to predict chemotherapy response for BTC.

Methods

Sixty-six SNPs in 21 oxidative stress-related genes were genotyped and analyzed in 367 BTC patients. Immunoblot, immunohistochemical, immunofluorescent, quantitative PCR, chromatin immunoprecipitation analysis and study of animal xenograft models were performed to discover oxidative stress-related susceptibility genes underlying chemoresistance mechanism of BTC.

Findings

We found that 3 functional polymorphisms (CAT_rs769217, GPX4_rs4807542, and GSR_rs3779647), which were shown to affect their respective gene expression levels, modified the effect of chemotherapy on overall survival (OS). We then demonstrated that knockdown of GPX4, CAT, or GSR induced chemoresistance through elevation of ROS level and activation of Nrf2-ABCG2 pathway in BTC cell lines. Moreover, the association between Nrf2 expression and BTC prognosis is only found in patients who received chemotherapy. Knockdown of Nrf2 enhanced chemosensitivity or even eliminated postoperative recurrence in BTC xenograft mouse models. Importantly, upon chemotherapy treatment patients harboring high oxidative stress-related score received higher survival benefit from adjuvant chemotherapy compared with patients with low oxidative stress-related score.

Interpretation

The result of our study suggests, for the first time, that the oxidative stress-related score calculated by combining variations in CAT, GPX4, and GSR or Nrf2 expression could be used for predicting the chemosensitivity of BTC patients.

Fund

This work was supported by the National Science Foundation of China, Foundation of Shanghai Shen Kang Hospital Development Center, and Shanghai Outstanding Academic Leaders Plan.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

Evaluation of the Relationship Between Common Variants in the TLR-9 Gene and Hip Osteoarthritis Susceptibility

Objective: Hip osteoarthritis (HOA) is one of the most common types of osteoarthritis and affects nearly 10% of men and 18% of women who are >60 years of age worldwide. It has been demonstrated to be a genetic disease with a 50% heritability risk. Recently, the TLR-9 gene has been associated with knee OA in both Turkish and Chinese populations, but the relationship between the TLR-9 gene and HOA has not been evaluated. In this study, we aimed to evaluate the relationship between the common genetic variants in the TLR-9 gene and the predisposition of Han Chinese individuals to HOA. Methods: A total of 730 HOA patients and 1220 healthy controls were recruited in a hospital-based case-control study. Six common single nucleotide polymorphisms (SNPs) of the TLR-9 gene were selected for genotyping, and genetic association analyses were performed using both single-marker and haplotype-based methods. Results: The SNP rs187084 was found to be significantly associated with the risk of HOA after a Bonferroni correction (adjusted allelic p-values with age, gender, and body mass index [BMI] = 0.0008). The results indicated that the A allele of rs187084 is a risk allele for HOA and is likely to be a predisposing factor leading to an increased risk of HOA (adjusted odds ratio with age, gender, and BMI = 1.26, 95% confidence interval = 1.10-1.43). The results of the haplotype analyses confirmed a similar pattern to the SNP analyses. Conclusions: Our study provides strong evidence that variations in the TLR-9 gene are closely linked with genetic susceptibility to HOA in the Han Chinese population. This finding furthers the role of TLR-9 in the development and occurrence of OA in general.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

The HOTAIR, PRNCR1 and POLR2E polymorphisms are associated with cancer risk: a meta-analysis

Long non-coding RNAs (LncRNAs) have been widely studied and aberrant expression of lncRNAs are involved in diverse cancers. Genetic variation in lncRNAs can influence the lncRNAs expression and function. At present, there are many studies to investigate the association between lncRNAs polymorphisms and cancer susceptibility. However, it has no systematic study to evaluate the association. We performed a meta-analysis to summarize the results of common lncRNAs (HOTAIR, PRNCR1, POLR2E and H19) polymorphisms on cancer risk, by using the random-effect model to obtain the odds ratio (ORs) and 95% confidence interval (95%CI). We also applied the meta-regression and publication bias analysis to seek the source of heterogeneity and evaluate the stability of results, respectively. The summary results indicated that HOTAIR rs920778 increased the cancer risk in recessive model (OR = 1.61, 95% CI = 1.08-2.41, P_{heterogeneity}<0.001). For PRNCR1 (rs1016343, rs16901946) and POLR2E (rs3787016), we also found the significant association with incresed risk of cancer (all P<0.05). However, we did not observe any significant association between H19 rs2107425 and cancer risk. Our meta-analysis results revealed that these four lncRNAs polymorphisms (HOTAIR rs920778, PRNCR1 rs1016343 and rs16901946, POLR2E rs3787016) can contribute to cancer risk. Further studies should confirm these findings.

Interplay of cis and trans mechanisms driving transcription factor binding and gene expression evolution

Noncoding regulatory variants play a central role in the genetics of human diseases and in evolution. Here we measure allele-specific transcription factor binding occupancy of three liver-specific transcription factors between crosses of two inbred mouse strains to elucidate the regulatory mechanisms underlying transcription factor binding variations in mammals. Our results highlight the pre-eminence of cis-acting variants on transcription factor occupancy divergence. Transcription factor binding differences linked to cis-acting variants generally exhibit additive inheritance, while those linked to trans-acting variants are most often dominantly inherited. Cis-acting variants lead to local coordination of transcription factor occupancies that decay with distance; distal coordination is also observed and may be modulated by long-range chromatin contacts. Our results reveal the regulatory mechanisms that interplay to drive transcription factor occupancy, chromatin state, and gene expression in complex mammalian cell states.

Association between TLR-9 gene rs187084 polymorphism and knee osteoarthritis in a Chinese population

Osteoarthritis (OA) is a complex disease that is induced by many genetic risk variants and other factors. To examine the role of toll-like receptor 9 (TLR-9) in OA patients, we conducted a case–control study involving 215 knee OA (KOA) patients and 215 controls in a Chinese population. Genotyping with a custom-by-design 48-Plex single nucleotide polymorphism Scan™ Kit showed the TLR-9 gene rs187084 polymorphism was associated with an increased risk of KOA. Stratification analyses further validated this finding among old people (age ≥ 55 years). In conclusion, TLR-9 gene rs187084 polymorphism is positively correlated with susceptibility to KOA, especially among old people. Nevertheless, this finding should be confirmed by larger size studies with more ethnic populations.

The Association Between XPG Gene Polymorphism and Gastric Cancer Risk

PURPOSE

Studies exploring the association between the Xeroderma pigmentosum group G (XPG) gene polymorphisms and gastric cancer (GC) risk provide conflicting findings. Thus, this meta-analysis was performed.

MATERIALS AND METHODS

The PubMed and EMBASE databases were comprehensively searched to identify studies for the inclusion in the meta-analysis. The strength of the association was evaluated by calculating pooled odds ratios and 95% confidence intervals.

RESULTS

Nine case-control studies involving 3540 cases and 3953 controls were included in the meta-analysis, which revealed that the XPG rs751402 polymorphism is positively associated with GC risk and could be viewed as a risk factor of GC in three genetic models.

CONCLUSION

The XPG gene rs751402 polymorphism is associated with an increased risk of GC in Chinese Han populations. This finding should be verified by larger studies that include additional ethnic groups.

Association of insulin-like growth factor I gene polymorphisms with the risk of osteoporosis in a Chinese population

Osteoporosis is a systemic metabolic and serious skeletal disease commonly observed among the elderly. Insulin-like growth factors (IGFs) are critical regulators for bone cell function. We estimated the role of IGF-I rs35767, rs2288377 and rs5742612 polymorphisms in the susceptibility to osteoporosis in a population of China, and assessed gene-environment interactions. A total of 346 patients with osteoporosis and 346 controls were enrolled. Genotyping of IGF-I rs35767, rs2288377 and rs5742612 was amplified and performed with the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. The TA and AA genotypes displayed elevated risk of developing osteoporosis (TA vs TT: OR=1.54, 95% CI=1.11-2.15; AA vs TT: OR=3.65, 95% CI=2.09-6.37). Compared with TT individuals, individuals with the TA+AA genotype had a substantial increased susceptibility to osteoporosis (OR=1.80, 95% CI=1.31-2.46). In recessive model, the AA genotype of rs2288377 displayed 2.89 folds risk of osteoporosis (adjusted OR=2.89, 95% CI=1.70-4.89). A significant negative interaction was found between IGF-I rs2288377 and BMD levels for femoral neck (r=-0.14, P<0.001), total hip (r=-0.09, P<0.001) and trochanter (r=-0.13, P<0.001). In conclusion, we suggest that IGF-I rs2288377 polymorphism had a strong influence on osteoporosis susceptibility in this Chinese population.

Time-dependent genetic effects on gene expression implicate aging processes

Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature.

The Genetic Architecture of Gene Expression in Peripheral Blood

We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h²) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (h_COJO²) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (h_g²) accounted for on average 48% (0.093/0.192) of h². Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.

Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease

Primary sclerosing cholangitis (PSC) is a rare progressive disorder leading to bile duct destruction; ∼75% of patients have comorbid inflammatory bowel disease (IBD). We undertook the largest genome-wide association study of PSC (4,796 cases and 19,955 population controls) and identified four new genome-wide significant loci. The most associated SNP at one locus affects splicing and expression of UBASH3A, with the protective allele (C) predicted to cause nonstop-mediated mRNA decay and lower expression of UBASH3A. Further analyses based on common variants suggested that the genome-wide genetic correlation (r_G) between PSC and ulcerative colitis (UC) (r_G = 0.29) was significantly greater than that between PSC and Crohn's disease (CD) (r_G = 0.04) (P = 2.55 × 10^-15). UC and CD were genetically more similar to each other (r_G = 0.56) than either was to PSC (P < 1.0 × 10^-15). Our study represents a substantial advance in understanding of the genetics of PSC.

Genetic variations and miRNA-target interactions contribute to natural phenotypic variations in Populus

Variation in regulatory factors, including microRNAs (miRNAs), contributes to variation in quantitative and complex traits. However, in plants, variants in miRNAs and their target genes that contribute to natural phenotypic variation, and the underlying regulatory networks, remain poorly characterized. We investigated the associations and interactions of single-nucleotide polymorphisms (SNPs) in miRNAs and their target genes with phenotypes in 435 individuals from a natural population of Populus. We used RNA-seq to identify 217 miRNAs differentially expressed in a tension wood system, and identified 1196 candidate target genes; degradome sequencing confirmed 60 of the target sites. In addition, 72 miRNA-target pairs showed significant co-expression. Gene ontology (GO) term analysis showed that most of the genes in the co-regulated pairs participate in biological regulation. Genome resequencing found 5383 common SNPs (frequency ≥ 0.05) in 139 miRNAs and 31 037 SNPs in 819 target genes. Single-SNP association analyses identified 232 significant associations between wood traits (P ≤ 0.05) and SNPs in 102 miRNAs and 1387 associations with 478 target genes. Among these, 102 miRNA-target pairs associated with the same traits. Multi-SNP associations found 102 epistatic pairs associated with traits. Furthermore, a reconstructed regulatory network contained 12 significantly co-expressed pairs, including eight miRNAs and nine targets associated with traits. Lastly, both expression and genetic association showed that miR156i, miR156j, miR396a and miR6445b were involved in the formation of tension wood. This study shows that variants in miRNAs and target genes contribute to natural phenotypic variation and annotated roles and interactions of miRNAs and their target genes by genetic association analysis.

A potential role for genome structure in the translation of mechanical force during immune cell development

Immune cells react to a wide range of environments, both chemical and physical. While the former has been extensively studied, there is growing evidence that physical and in particular mechanical forces also affect immune cell behavior and development. In order to elicit a response that affects immune cell behavior or development, environmental signals must often reach the nucleus. Chemical and mechanical signals can initiate signal transduction pathways, but mechanical forces may also have a more direct route to the nucleus, altering nuclear shape via mechanotransduction. The three-dimensional organization of DNA allows for the possibility that altering nuclear shape directly remodels chromatin, redistributing critical regulatory elements and proteins, and resulting in wide-scale gene expression changes. As such, integrating mechanotransduction and genome architecture into the immunology toolkit will improve our understanding of immune development and disease.

Transethnic Genetic-Correlation Estimates from Summary Statistics

The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.

Natural Genetic Variation Differentially Affects the Proteome and Transcriptome in Caenorhabditis elegans

Natural genetic variation is the raw material of evolution and influences disease development and progression. An important question is how this genetic variation translates into variation in protein abundance. To analyze the effects of the genetic background on gene and protein expression in the nematode Caenorhabditis elegans, we quantitatively compared the two genetically highly divergent wild-type strains N2 and CB4856. Gene expression was analyzed by microarray assays, and proteins were quantified using stable isotope labeling by amino acids in cell culture. Among all transcribed genes, we found 1,532 genes to be differentially transcribed between the two wild types. Of the total 3,238 quantified proteins, 129 proteins were significantly differentially expressed between N2 and CB4856. The differentially expressed proteins were enriched for genes that function in insulin-signaling and stress-response pathways, underlining strong divergence of these pathways in nematodes. The protein abundance of the two wild-type strains correlates more strongly than protein abundance versus transcript abundance within each wild type. Our findings indicate that in C. elegans only a fraction of the changes in protein abundance can be explained by the changes in mRNA abundance. These findings corroborate with the observations made across species.

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes

Alternative splicing is a widespread process contributing to structural transcript variation and proteome diversity. In cancer, the splicing process is commonly disrupted, resulting in both functional and non-functional end-products. Cancer-specific splicing events are known to contribute to disease progression; however, the dysregulated splicing patterns found on a genome-wide scale have until recently been less well-studied. In this review, we provide an overview of aberrant RNA splicing and its regulation in cancer. We then focus on the executors of the splicing process. Based on a comprehensive catalog of splicing factor encoding genes and analyses of available gene expression and somatic mutation data, we identify cancer-associated patterns of dysregulation. Splicing factor genes are shown to be significantly differentially expressed between cancer and corresponding normal samples, and to have reduced inter-individual expression variation in cancer. Furthermore, we identify enrichment of predicted cancer-critical genes among the splicing factors. In addition to previously described oncogenic splicing factor genes, we propose 24 novel cancer-critical splicing factors predicted from somatic mutations.

Why the activity of a gene depends on its neighbors

Sixty years ago, the position of a gene on a chromosome was seen to be a major determinant of gene activity; however, position effects are rarely central to current discussions of gene expression. We describe a comprehensive and simplifying view of how position in 1D sequence and 3D nuclear space underlies expression. We suggest that apparently-different regulatory motifs including enhancers, silencers, insulators, barriers, and boundaries act similarly - they are active promoters that tether target genes close to, or distant from, appropriate transcription sites or 'factories'. We also suggest that any active transcription unit regulates the firing of its neighbors - and thus can be categorized as one or other type of motif; this is consistent with expression quantitative trait loci (eQTLs) being widely dispersed.

Evolutionary genetic bases of longevity and senescence

Senescence, as a time-dependent developmental process, affects all organisms at every stage in their development and growth. During this process, genetic, epigenetic and environmental factors are known to introduce a wide range of variation for longevity among individuals. As an important life-history trait, longevity shows ontogenetic relationships with other complex traits, and hence may be viewed as a composite trait. Factors that influence the origin and maintenance of diversity of life are ultimately governed by Darwinian processes. Here we review evolutionary genetic mechanisms underlying longevity and senescence in humans from a life-history and genotype-epigenetic-phenotype (G-E-P) map prospective. We suggest that synergistic and cascading effects of cis-ruptive mechanisms in the genome, and epigenetic disruptive processes in relation to environmental factors may lead to sequential slippage in the G-E-P space. These mechanisms accompany age, stage and individual specific senescent processes, influenced by positive pleiotropy of certain genes, superior genome integrity, negative-frequency dependent selection and other factors that universally regulate rarity in nature. Finally we interpret life span as an inherent property of self-organizing systems that, accordingly, maintain species-specific limits for the entire complex of fitness traits. We conclude that Darwinian approaches provide unique opportunities to discover the biological bases of longevity as well as devise individual specific medical or other interventions toward improving health span.

Genetic variants in lncRNA HOTAIR are associated with risk of colorectal cancer

Long non-coding RNA HOX transcript antisenseRNA (HOTAIR) has been widely identified to participate in tumour pathogenesis, acting as a promoter in colorectal cancer carcinogenesis. However, the association between genetic variants in HOTAIR and cancer risk has not yet been reported. In the present study, we performed a two-stage case-control study to investigate the association between HOTAIR tagSNPs and the risk of colorectal cancer. We found that individuals with rs7958904 CC genotype had a significantly decreased risk of colorectal cancer in both Stage 1 and 2, compared with those carrying GG genotype [odds ratio (OR) = 0.70, 95% confidence interval (CI) = 0.51-0.97 in Stage 1; OR = 0.58, 95% CI = 0.37-0.91 in Stage 2; OR = 0.67, 95% CI = 0.51-0.87 in combined stage]. The subsequently stratified analyses showed that the protective effect of rs7958904 was more pronounced in several subgroups. In summary, our study showed that genetic variants in HOTAIR were associated with risk of colorectal cancer and rs7958904 may act as a potential biomarker for predicting the risk of colorectal cancer.

Dissecting the causal genetic mechanisms of coronary heart disease

Large-scale genome-wide association studies (GWAS) have identified 46 loci that are associated with coronary heart disease (CHD). Additionally, 104 independent candidate variants (false discovery rate of 5 %) have been identified (Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H et al. Nat Genet 43:333-8, 2011; Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR et al. Nat Genet 45:25-33, 2012; C4D Genetics Consortium. Nat Genet 43:339-44, 2011). The majority of the causal genes in these loci function independently of conventional risk factors. It is postulated that a number of the CHD-associated genes regulate basic processes in the vascular cells involved in atherosclerosis, and that study of the signaling pathways that are modulated in this cell type by causal regulatory variation will provide critical new insights for targeting the initiation and progression of disease. In this review, we will discuss the types of experimental approaches and data that are critical to understanding the molecular processes that underlie the disease risk at 9p21.3, TCF21, SORT1, and other CHD-associated loci.

Hybrid mice reveal parent-of-origin and Cis- and trans-regulatory effects in the retina

A fundamental challenge in genomics is to map DNA sequence variants onto changes in gene expression. Gene expression is regulated by cis-regulatory elements (CREs, i.e., enhancers, promoters, and silencers) and the trans factors (e.g., transcription factors) that act upon them. A powerful approach to dissecting cis and trans effects is to compare F1 hybrids with F0 homozygotes. Using this approach and taking advantage of the high frequency of polymorphisms in wild-derived inbred Cast/EiJ mice relative to the reference strain C57BL/6J, we conducted allele-specific mRNA-seq analysis in the adult mouse retina, a disease-relevant neural tissue. We found that cis effects account for the bulk of gene regulatory divergence in the retina. Many CREs contained functional (i.e., activating or silencing) cis-regulatory variants mapping onto altered expression of genes, including genes associated with retinal disease. By comparing our retinal data with previously published liver data, we found that most of the cis effects identified were tissue-specific. Lastly, by comparing reciprocal F1 hybrids, we identified evidence of imprinting in the retina for the first time. Our study provides a framework and resource for mapping cis-regulatory variants onto changes in gene expression, and underscores the importance of studying cis-regulatory variants in the context of retinal disease.

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels.

Individuals in a species differ from each other in many ways. For many traits, a fraction of this variation is genetic—it is caused by DNA sequence variants in the genome of each individual. Some of these variants influence traits by altering how much certain genes are expressed, i.e. how many mRNA and protein molecules are made in different individuals. Surprisingly, earlier work has found that the effects of genetic variants on mRNA and protein levels for the same genes appear to be very different. Many variants appeared to influence only mRNA (but not protein) levels, and vice versa. In this paper, we studied this question by using a technique called “ribosome profiling” to measure translation (the cellular process of reading mRNA molecules and synthesizing protein molecules) in two yeast strains. We found that the genetic differences between these two strains influence translation for hundreds of genes. Because most of these effects were small in magnitude, they explain at most a small fraction of the discrepancies between the effects of genetic variants on mRNA and protein levels.

Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.

Cis and trans effects of human genomic variants on gene expression

Gene expression is a heritable cellular phenotype that defines the function of a cell and can lead to diseases in case of misregulation. In order to detect genetic variations affecting gene expression, we performed association analysis of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) with gene expression measured in 869 lymphoblastoid cell lines of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort in cis and in trans. We discovered that 3,534 genes (false discovery rate (FDR) = 5%) are affected by an expression quantitative trait locus (eQTL) in cis and 48 genes are affected in trans. We observed that CNVs are more likely to be eQTLs than SNPs. In addition, we found that variants associated to complex traits and diseases are enriched for trans-eQTLs and that trans-eQTLs are enriched for cis-eQTLs. As a variant affecting both a gene in cis and in trans suggests that the cis gene is functionally linked to the trans gene expression, we looked specifically for trans effects of cis-eQTLs. We discovered that 26 cis-eQTLs are associated to 92 genes in trans with the cis-eQTLs of the transcriptions factors BATF3 and HMX2 affecting the most genes. We then explored if the variation of the level of expression of the cis genes were causally affecting the level of expression of the trans genes and discovered several causal relationships between variation in the level of expression of the cis gene and variation of the level of expression of the trans gene. This analysis shows that a large sample size allows the discovery of secondary effects of human variations on gene expression that can be used to construct short directed gene regulatory networks.

Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs

BACKGROUND

Gene expression genetic studies in human tissues and cells identify cis- and trans-acting expression quantitative trait loci (eQTLs). These eQTLs provide insights into regulatory mechanisms underlying disease risk. However, few studies systematically characterized eQTL results across cell and tissues types. We synthesized eQTL results from >50 datasets, including new primary data from human brain, peripheral plaque and kidney samples, in order to discover features of human eQTLs.

RESULTS

We find a substantial number of robust cis-eQTLs and far fewer trans-eQTLs consistent across tissues. Analysis of 45 full human GWAS scans indicates eQTLs are enriched overall, and above nSNPs, among positive statistical signals in genetic mapping studies, and account for a significant fraction of the strongest human trait effects. Expression QTLs are enriched for gene centricity, higher population allele frequencies, in housekeeping genes, and for coincidence with regulatory features, though there is little evidence of 5' or 3' positional bias. Several regulatory categories are not enriched including microRNAs and their predicted binding sites and long, intergenic non-coding RNAs. Among the most tissue-ubiquitous cis-eQTLs, there is enrichment for genes involved in xenobiotic metabolism and mitochondrial function, suggesting these eQTLs may have adaptive origins. Several strong eQTLs (CDK5RAP2, NBPFs) coincide with regions of reported human lineage selection. The intersection of new kidney and plaque eQTLs with related GWAS suggest possible gene prioritization. For example, butyrophilins are now linked to arterial pathogenesis via multiple genetic and expression studies. Expression QTL and GWAS results are made available as a community resource through the NHLBI GRASP database [http://apps.nhlbi.nih.gov/grasp/].

CONCLUSIONS

Expression QTLs inform the interpretation of human trait variability, and may account for a greater fraction of phenotypic variability than protein-coding variants. The synthesis of available tissue eQTL data highlights many strong cis-eQTLs that may have important biologic roles and could serve as positive controls in future studies. Our results indicate some strong tissue-ubiquitous eQTLs may have adaptive origins in humans. Efforts to expand the genetic, splicing and tissue coverage of known eQTLs will provide further insights into human gene regulation.

Genetic background drives transcriptional variation in human induced pluripotent stem cells

Human iPS cells have been generated using a diverse range of tissues from a variety of donors using different reprogramming vectors. However, these cell lines are heterogeneous, which presents a limitation for their use in disease modeling and personalized medicine. To explore the basis of this heterogeneity we generated 25 iPS cell lines under normalised conditions from the same set of somatic tissues across a number of donors. RNA-seq data sets from each cell line were compared to identify the majority contributors to transcriptional heterogeneity. We found that genetic differences between individual donors were the major cause of transcriptional variation between lines. In contrast, residual signatures from the somatic cell of origin, so called epigenetic memory, contributed relatively little to transcriptional variation. Thus, underlying genetic background variation is responsible for most heterogeneity between human iPS cell lines. We conclude that epigenetic effects in hIPSCs are minimal, and that hIPSCs are a stable, robust and powerful platform for large-scale studies of the function of genetic differences between individuals. Our data also suggest that future studies using hIPSCs as a model system should focus most effort on collection of large numbers of donors, rather than generating large numbers of lines from the same donor.

Human induced pluripotent stem (hiPS) cells are a potentially powerful model system for studying human disease and development, and a resource for personalized medicine. However, it has been reported that hiPS cells exhibit substantial heterogeneity which could limit their use as model systems. Clearly, knowledge of the source of heterogeneity is key for deeper understanding of the use of human iPS cells for basic and therapeutic applications. One source of this heterogeneity has been presumed to be “memory” of the adult somatic cell from which the hIPS cells were derived, but the evidence to support this view is scant. We have generated a set of human iPS cells from a set of somatic cell types from different donors. Our study shows that cell lines from different somatic sources but from the same donor (i.e. with the same genome) are more similar than cell lines isolated from the same tissue type but from different donors. Once genetic changes are accounted for, all aspects of gene expression, including mRNA levels, splicing and imprinting are highly similar between iPS cells derived from different human tissues. Thus, most of the previously described transcriptional variation between cell lines is likely to be genetic in origin.

Spurious transcription factor binding: non-functional or genetically redundant?

Transcription factor binding sites (TFBSs) on the DNA are generally accepted as the key nodes of gene control. However, the multitudes of TFBSs identified in genome-wide studies, some of them seemingly unconstrained in evolution, have prompted the view that in many cases TF binding may serve no biological function. Yet, insights from transcriptional biochemistry, population genetics and functional genomics suggest that rather than segregating into 'functional' or 'non-functional', TFBS inputs to their target genes may be generally cumulative, with varying degrees of potency and redundancy. As TFBS redundancy can be diminished by mutations and environmental stress, some of the apparently 'spurious' sites may turn out to be important for maintaining adequate transcriptional regulation under these conditions. This has significant implications for interpreting the phenotypic effects of TFBS mutations, particularly in the context of genome-wide association studies for complex traits.

Heritability and genetic basis of protein level variation in an outbred population

The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population’s average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.

Modeling genomic regulatory networks with big data

High-throughput sequencing, large-scale data generation projects, and web-based cloud computing are changing how computational biology is performed, who performs it, and what biological insights it can deliver. I review here the latest developments in available data, methods, and software, focusing on the modeling and analysis of the gene regulatory interactions in cells. Three key findings are: (i) although sophisticated computational resources are increasingly available to bench biologists, tailored ongoing education is necessary to avoid the erroneous use of these resources. (ii) Current models of the regulation of gene expression are far too simplistic and need updating. (iii) Integrative computational analysis of large-scale datasets is becoming a fundamental component of molecular biology. I discuss current and near-term opportunities and challenges related to these three points.

Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression

To systematically investigate the impact of immune stimulation upon regulatory variant activity, we exposed primary monocytes from 432 healthy Europeans to interferon-γ (IFN-γ) or differing durations of lipopolysaccharide and mapped expression quantitative trait loci (eQTLs). More than half of cis-eQTLs identified, involving hundreds of genes and associated pathways, are detected specifically in stimulated monocytes. Induced innate immune activity reveals multiple master regulatory trans-eQTLs including the major histocompatibility complex (MHC), coding variants altering enzyme and receptor function, an IFN-β cytokine network showing temporal specificity, and an interferon regulatory factor 2 (IRF2) transcription factor-modulated network. Induced eQTL are significantly enriched for genome-wide association study loci, identifying context-specific associations to putative causal genes including CARD9, ATM, and IRF8. Thus, applying pathophysiologically relevant immune stimuli assists resolution of functional genetic variants.