Population genomics of human gene expression

Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants

Background

Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype.

Methods and findings

We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates.

Conclusions

Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.

Multi-omics integrative analysis identified SNP-methylation-mRNA: Interaction in peripheral blood mononuclear cells

Genetic variants have potential influence on DNA methylation and thereby regulate mRNA expression. This study aimed to comprehensively reveal the relationships among SNP, methylation and mRNA, and identify methylation-mediated regulation patterns in human peripheral blood mononuclear cells (PBMCs). Based on in-house multi-omics datasets from 43 Chinese Han female subjects, genome-wide association trios were constructed by simultaneously testing the following three association pairs: SNP-methylation, methylation-mRNA and SNP-mRNA. Causal inference test (CIT) was used to identify methylation-mediated genetic effects on mRNA. A total of 64,184 significant cis-methylation quantitative trait loci (meQTLs) were identified (FDR < 0.05). Among the 745 constructed trios, 464 trios formed SNP-methylation-mRNA regulation chains (CIT). Network analysis (Cytoscape 3.3.0) constructed multiple complex regulation networks among SNP, methylation and mRNA (eg a total of 43 SNPs simultaneously connected to cg22517527 and further to PRMT2, DIP2A and YBEY). The regulation chains were supported by the evidence from 4DGenome database, relevant to immune or inflammatory related diseases/traits, and overlapped with previous eQTLs from dbGaP and GTEx. The results provide new insights into the regulation patterns among SNP, DNA methylation and mRNA expression, especially for the methylation-mediated effects, and also increase our understanding of functional mechanisms underlying the established associations.

Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations

In recent years, large scale meta-analysis of genome-wide association studies (GWAS) have reliably identified genetic polymorphisms associated with neuropsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD). However, the majority of disease-associated single nucleotide polymorphisms (SNPs) appear within functionally ambiguous non-coding genomic regions. Recently, increased emphasis has been placed on identifying the functional relevance of disease-associated variants via correlating risk polymorphisms with gene expression levels in etiologically relevant tissues. For neuropsychiatric disorders, the etiologically relevant tissue is brain, which requires robust postmortem sample sizes from varying genetic backgrounds. While small sample sizes are of decreasing concern, postmortem brain databases are composed almost exclusively of Caucasian samples, which significantly limits study design and result interpretation. In this review, we highlight the importance of gene expression and expression quantitative loci (eQTL) studies in clinically relevant postmortem tissue while addressing the current limitations of existing postmortem brain databases. Finally, we introduce future collaborations to develop postmortem brain databases for neuropsychiatric disorders from Chinese and Asian subpopulations.

Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.

Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci

BACKGROUND

Linking genetic risk loci identified by genome-wide association studies (GWAS) to their causal genes remains a major challenge. Disease-associated genetic variants are concentrated in regions containing regulatory DNA elements, such as promoters and enhancers. Although researchers have previously published DNA maps of these regulatory regions for kidney tubule cells and glomerular endothelial cells, maps for podocytes and mesangial cells have not been available.

METHODS

We generated regulatory DNA maps (DNase-seq) and paired gene expression profiles (RNA-seq) from primary outgrowth cultures of human glomeruli that were composed mainly of podocytes and mesangial cells. We generated similar datasets from renal cortex cultures, to compare with those of the glomerular cultures. Because regulatory DNA elements can act on target genes across large genomic distances, we also generated a chromatin conformation map from freshly isolated human glomeruli.

RESULTS

We identified thousands of unique regulatory DNA elements, many located close to transcription factor genes, which the glomerular and cortex samples expressed at different levels. We found that genetic variants associated with kidney diseases (GWAS) and kidney expression quantitative trait loci were enriched in regulatory DNA regions. By combining GWAS, epigenomic, and chromatin conformation data, we functionally annotated 46 kidney disease genes.

CONCLUSIONS

We demonstrate a powerful approach to functionally connect kidney disease-/trait-associated loci to their target genes by leveraging unique regulatory DNA maps and integrated epigenomic and genetic analysis. This process can be applied to other kidney cell types and will enhance our understanding of genome regulation and its effects on gene expression in kidney disease.

Immune system and new avenues in Parkinson’s disease research and treatment

Parkinson’s disease (PD) is a progressive neurological disorder characterized by degeneration of dopaminergic neurons in the substantia nigra. However, although 200 years have now passed since the primary clinical description of PD by James Parkinson, the etiology and mechanisms of neuronal loss in this disease are still not fully understood. In addition to genetic and environmental factors, activation of immunologic responses seems to have a crucial role in PD pathology. Intraneuronal accumulation of α-synuclein (α-Syn), as the main pathological hallmark of PD, potentially mediates initiation of the autoimmune and inflammatory events through, possibly, auto-reactive T cells. While current therapeutic regimens are mainly used to symptomatically suppress PD signs, application of the disease-modifying therapies including immunomodulatory strategies may slow down the progressive neurodegeneration process of PD. The aim of this review is to summarize knowledge regarding previous studies on the relationships between autoimmune reactions and PD pathology as well as to discuss current opportunities for immunomodulatory therapy.

eQTL analysis from co-localization of 2739 GWAS loci detects associated genes across 14 human cancers

Genetic variants can predict other "linked" diseases because alterations in one or more genes in vivo may affect relevant phenotype properties. Our study systematically explored the pan-cancer common gene and cancer type-specific genes based on GWAS loci and TCGA data of multiple cancers. It was found that there were 17 SNPs were significantly associated with the expression of 18 genes. Associations between the 18 cis-regulatory genes and the pathologic stage of each cancer showed that MYL2 and PTGFR in HNSC, 4 genes (F8, SATB2, G6PD and UGT1A6) in KIRP, 3 genes (CHMP4C, MAP3K1 and MECP2) in LUAD were all strongly associated with cancer stage levels. Additionally, the survival association analysis showed that SATB2 was correlated with HNSC survival, and MPP1 was strongly associated with the survival of SARC. This study will shed light on the biological pathways involved in cancer-genetic associations, and has the potential to be applied to the predictions of the risk of cancers developing in healthy individuals.

Novel hypotheses emerging from GWAS in migraine?

Recent technical advances in genetics made large-scale genome-wide association studies (GWAS) in migraine feasible and have identified over 40 common DNA sequence variants that affect risk for migraine types. Most of the variants, which are all single nucleotide polymorphisms (SNPs), show robust association with migraine as evidenced by the fact that the vast majority replicate in subsequent independent studies. However, despite thorough bioinformatic efforts aimed at linking the migraine risk SNPs with genes and their molecular pathways, there remains quite some discussion as to how successful this endeavour has been, and their current practical use for the diagnosis and treatment of migraine patients. Although existing genetic information seems to favour involvement of vascular mechanisms, but also neuronal and other mechanisms such as metal ion homeostasis and neuronal migration, the complexity of the underlying genetic pathophysiology presents challenges to advancing genetic knowledge to clinical use. A major issue is to what extent one can rely on bioinformatics to pinpoint the actual disease genes, and from this the linked pathways. In this Commentary, we will provide an overview of findings from GWAS in migraine, current hypotheses of the disease pathways that emerged from these findings, and some of the major drawbacks of the approaches used to identify the genes and pathways. We argue that more functional research is urgently needed to turn the hypotheses that emerge from GWAS in migraine to clinically useful information.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the 'Prioritization of candidate causal Genes at Molecular QTLs' (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of 'true positive' causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Expression QTL (eQTLs) Analyses Reveal Candidate Genes Associated With Fruit Flesh Softening Rate in Peach [Prunus persica (L.) Batsch]

Significant differences in softening rate have been reported between melting flesh in peach and nectarine varieties. This trait seems to be controlled by several genes. We aimed to identify candidate genes involved in fruit softening rate by integrating quantitative trait loci (QTL) and expression QTL (eQTL) analyses, comparing siblings with contrasting softening rates. We used a segregating population derived from nectarine cv. 'Venus' selfing, which was phenotyped for softening rate during three seasons. Six siblings with high (HSR) and six with low softening rate (LSR) were sequenced using RNA-Seq. A group of 5,041 differentially expressed genes was identified. Also, we found a QTL with a LOD (logarithm of odds) score of 9.7 on LG4 in all analyzed seasons. Furthermore, we detected 1,062 eQTLs, of which 133 were found co-localizing with the identified QTL. Gene Ontology (GO) analysis showed 'Response to auxin' as one the main over-represented categories. Our findings suggest over-expression of auxin biosynthetic related genes in the HSR group, which implies a higher expression and/or accumulation of auxin, thereby triggering fast softening. Conversely, the LSR phenotype might be explained by an altered auxin-homeostasis associated with low auxin levels. This work will contribute to unraveling the genetic mechanisms responsible for the softening rate in peaches and nectarines and lead to the development of molecular markers.

Evidence for Weak Selective Constraint on Human Gene Expression

Variation in human complex traits is connected to variation in gene expression, and selection on complex traits can be reflected in selection on gene expression. Here, Glassberg and Gao et al. analyze polymorphic....

Gene expression variation is a major contributor to phenotypic variation in human complex traits. Selection on complex traits may therefore be reflected in constraint on gene expression. Here, we explore the effects of stabilizing selection on cis-regulatory genetic variation in humans. We analyze patterns of expression variation at copy number variants and find evidence for selection against large increases in gene expression. Using allele-specific expression (ASE) data, we further show evidence of selection against smaller-effect variants. We estimate that, across all genes, singletons in a sample of 122 individuals have ∼2.2× greater effects on expression variation than the average variant across allele frequencies. Despite their increased effect size relative to common variants, we estimate that singletons in the sample studied explain, on average, only 5% of the heritability of gene expression from cis-regulatory variants. Finally, we show that genes depleted for loss-of-function variants are also depleted for cis-eQTLs and have low levels of allelic imbalance, confirming tighter constraint on the expression levels of these genes. We conclude that constraint on gene expression is present, but has relatively weak effects on most cis-regulatory variants, thus permitting high levels of gene-regulatory genetic variation.

Genome-Wide Maps of Transcription Regulatory Elements and Transcription Enhancers in Development and Disease

Gene expression is regulated by numerous elements including enhancers, insulators, transcription factors, and architectural proteins. Regions of DNA distal to the transcriptional start site, called enhancers, play a central role in the temporal and tissue-specific regulation of gene expression through RNA polymerase II. The identification of enhancers and other cis regulatory elements has largely been possible due to advances in next generation sequencing technologies. Enhancers regulate gene expression through chromatin loops mediated by architectural proteins such as YY1, CTCF, the cohesin complex, and LDB1. Additionally, enhancers can be transcribed to produce noncoding RNAs termed enhancer RNAs that likely participate in transcriptional regulation. The central role of enhancers in regulating gene expression implicates them in both normal physiology but also many disease states. The importance of enhancers is evident by the suggested role of SNPs, duplications, and other alterations of enhancer function in many diseases, ranging from cancer to atherosclerosis to chronic kidney disease. Although much progress has been made in recent years, the field of enhancer biology and our knowledge of the cis regulome remains a work in progress. This review will highlight recent seminal studies which demonstrate the role of enhancers in normal physiology and disease pathogenesis. © 2019 American Physiological Society. Compr Physiol 9:439-455, 2019.

BRCA1 and BRCA2 5' noncoding region variants identified in breast cancer patients alter promoter activity and protein binding

The widespread use of next generation sequencing for clinical testing is detecting an escalating number of variants in noncoding regions of the genome. The clinical significance of the majority of these variants is currently unknown, which presents a significant clinical challenge. We have screened over 6,000 early-onset and/or familial breast cancer (BC) cases collected by the ENIGMA consortium for sequence variants in the 5' noncoding regions of BC susceptibility genes BRCA1 and BRCA2, and identified 141 rare variants with global minor allele frequency < 0.01, 76 of which have not been reported previously. Bioinformatic analysis identified a set of 21 variants most likely to impact transcriptional regulation, and luciferase reporter assays detected altered promoter activity for four of these variants. Electrophoretic mobility shift assays demonstrated that three of these altered the binding of proteins to the respective BRCA1 or BRCA2 promoter regions, including NFYA binding to BRCA1:c.-287C>T and PAX5 binding to BRCA2:c.-296C>T. Clinical classification of variants affecting promoter activity, using existing prediction models, found no evidence to suggest that these variants confer a high risk of disease. Further studies are required to determine if such variation may be associated with a moderate or low risk of BC.

Cell-type-specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes

Most expression quantitative trait locus (eQTL) studies to date have been performed in heterogeneous tissues as opposed to specific cell types. To better understand the cell-type-specific regulatory landscape of human melanocytes, which give rise to melanoma but account for <5% of typical human skin biopsies, we performed an eQTL analysis in primary melanocyte cultures from 106 newborn males. We identified 597,335 cis-eQTL SNPs prior to linkage disequilibrium (LD) pruning and 4997 eGenes (FDR < 0.05). Melanocyte eQTLs differed considerably from those identified in the 44 GTEx tissue types, including skin. Over a third of melanocyte eGenes, including key genes in melanin synthesis pathways, were unique to melanocytes compared to those of GTEx skin tissues or TCGA melanomas. The melanocyte data set also identified trans-eQTLs, including those connecting a pigmentation-associated functional SNP with four genes, likely through cis-regulation of IRF4 Melanocyte eQTLs are enriched in cis-regulatory signatures found in melanocytes as well as in melanoma-associated variants identified through genome-wide association studies. Melanocyte eQTLs also colocalized with melanoma GWAS variants in five known loci. Finally, a transcriptome-wide association study using melanocyte eQTLs uncovered four novel susceptibility loci, where imputed expression levels of five genes (ZFP90, HEBP1, MSC, CBWD1, and RP11-383H13.1) were associated with melanoma at genome-wide significant P-values. Our data highlight the utility of lineage-specific eQTL resources for annotating GWAS findings, and present a robust database for genomic research of melanoma risk and melanocyte biology.

Colon-specific eQTL analysis to inform on functional SNPs

Background

Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs).

Methods

We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data.

Results

The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported.

Conclusions

This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci.

High-throughput characterization of genetic effects on DNA-protein binding and gene transcription

Many variants associated with complex traits are in noncoding regions and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, Biallelic Targeted STARR-seq (BiT-STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2720 SNPs with significant ASE (FDR < 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high-throughput allele-specific binding assay for NFKB1. We identified 2684 SNPs with allele-specific binding (ASB) (FDR < 10%); 256 of these SNPs also had ASE (OR = 1.97, P-value = 0.0006). Of variants associated with complex traits, 1531 resulted in ASE, and 1662 showed ASB. For example, we characterized that the Crohn's disease risk variant for rs3810936 increases NFKB1 binding and results in altered gene expression.

Architecture of polymorphisms in the human genome reveals functionally important and positively selected variants in immune response and drug transporter genes

Background

Genetic polymorphisms can contribute to phenotypic differences amongst individuals, including disease risk and drug response. Characterization of genetic polymorphisms that modulate gene expression and/or protein function may facilitate the identification of the causal variants. Here, we present the architecture of genetic polymorphisms in the human genome focusing on those predicted to be potentially functional/under natural selection and the pathways that they reside.

Results

In the human genome, polymorphisms that directly affect protein sequences and potentially affect function are the most constrained variants with the lowest single-nucleotide variant (SNV) density, least population differentiation and most significant enrichment of rare alleles. SNVs which potentially alter various regulatory sites, e.g. splicing regulatory elements, are also generally under negative selection.

Interestingly, genes that regulate the expression of transcription/splicing factors and histones are conserved as a higher proportion of these genes is non-polymorphic, contain ultra-conserved elements (UCEs) and/or has no non-synonymous SNVs (nsSNVs)/coding INDELs. On the other hand, major histocompatibility complex (MHC) genes are the most polymorphic with SNVs potentially affecting the binding of transcription/splicing factors and microRNAs (miRNA) exhibiting recent positive selection (RPS). The drug transporter genes carry the most number of potentially deleterious nsSNVs and exhibit signatures of RPS and/or population differentiation. These observations suggest that genes that interact with the environment are highly polymorphic and targeted by RPS.

Conclusions

In conclusion, selective constraints are observed in coding regions, master regulator genes, and potentially functional SNVs. In contrast, genes that modulate response to the environment are highly polymorphic and under positive selection.

Electronic supplementary material

The online version of this article (10.1186/s40246-018-0175-1) contains supplementary material, which is available to authorized users.

Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue

Schizophrenia and the affective disorders, here comprising bipolar disorder and major depressive disorder, are psychiatric illnesses that lead to significant morbidity and mortality worldwide. Whilst understanding of their pathobiology remains limited, large case-control studies have recently identified single nucleotide polymorphisms (SNPs) associated with these disorders. However, discerning the functional effects of these SNPs has been difficult as the associated causal genes are unknown. Here we evaluated whether schizophrenia and affective disorder associated-SNPs are correlated with gene expression within human brain tissue. Specifically, to identify expression quantitative trait loci (eQTLs), we leveraged disorder-associated SNPs identified from 11 genome-wide association studies with gene expression levels in post-mortem, neurologically-normal tissue from two independent human brain tissue expression datasets (UK Brain Expression Consortium (UKBEC) and Genotype-Tissue Expression (GTEx)). Utilizing stringent multi-region meta-analyses, we identified 2,224 cis-eQTLs associated with expression of 40 genes, including 11 non-coding RNAs. One cis-eQTL, rs16969968, results in a functionally disruptive missense mutation in CHRNA5, a schizophrenia-implicated gene. Importantly, comparing across tissues, we find that blood eQTLs capture < 10% of brain cis-eQTLs. Contrastingly, > 30% of brain-associated eQTLs are significant in tibial nerve. This study identifies putatively causal genes whose expression in region-specific tissue may contribute to the risk of schizophrenia and affective disorders.

An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease

Pathogens have been a strong driving force for natural selection. Therefore, understanding how human genetic differences impact infection-related cellular traits can mechanistically link genetic variation to disease susceptibility. Here we report the Hi-HOST Phenome Project (H2P2): a catalog of cellular genome-wide association studies (GWAS) comprising 79 infection-related phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance for infection-associated phenotypes ranging from pathogen replication to cytokine production. We combined H2P2 with clinical association data from patients to identify a SNP near CXCL10 as a risk factor for inflammatory bowel disease. A SNP in the transcriptional repressor ZBTB20 demonstrated pleiotropy, likely through suppression of multiple target genes, and was associated with viral hepatitis. These data are available on a web portal to facilitate interpreting human genome variation through the lens of cell biology and should serve as a rich resource for the research community.

The Associations between Toll-Like Receptor 9 Gene Polymorphisms and Cervical Cancer Susceptibility

This meta-analysis systematically reviews the association between Toll-like receptor 9 polymorphisms and the risk of cervical cancer. Case-control studies focused on the association were collected from the PubMed, Web of Science, Cochrane Library, Embase, MEDLINE, CNKI, VIP, and Wanfang databases from inception to July 2017. We screened the studies and assessed the methodological quality of the included studies and extracted data. A meta-analysis was performed using RevMan 5.3 and Stata 12.0 software. Pooled odds ratios and 95% confidence intervals were employed to evaluate the strength of the associations between Toll-like receptor 9 polymorphisms and cervical cancer risk. A total of 9 studies comprising 3331 cervical cancer patients and 4109 healthy controls met the inclusion criteria. Of these, 8 studies contained information about G2848A (rs352140) and 4 studies contained information about −1486T/C (rs187084). Our results revealed that the associations between rs187084 and cervical cancer risk in the dominant model (p = 0.002) and heterozygous model (p = 0.002) were significant, with 1.30- and 1.32-fold increases in susceptibility, respectively, compared to that in the wild-type model. However, rs352140 was not related to cervical cancer regardless of whether the subgroup analysis was conducted (p > 0.05). In conclusion, there is a significant correlation between rs187084 and cervical cancer risk with the minor C allele increasing the risk of occurrence of cervical cancer. However, rs352140 is not associated with the occurrence of cervical cancer.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Genetic determinants of co-accessible chromatin regions in activated T cells across humans

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4⁺ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.

Polymorphism in lncRNA AC016683.6 and its interaction with smoking exposure on the susceptibility of lung cancer

Background

Long non-coding RNAs play pivotal roles in the carcinogenesis of multiple types of cancers. This study is firstly to evaluate influence of rs4848320 and rs1110839 polymorphisms in long non-coding RNA AC016683.6 on the susceptibility of lung cancer.

Methods

The present study was a hospital-based case–control study with 434 lung cancer patients and 593 cancer-free controls. Genotyping of the two SNPs detected by Taqman^® allelic discrimination method.

Results

There were no statistically significant associations between rs4848320 and rs1110839 polymorphisms in AC016683.6 and risk of lung cancer in overall population. However, in the smoking population, rs4848320 and rs1110839 polymorphisms significantly increased the risk of lung cancer in dominant and homozygous models (Rs4848320: P = 0.029; Rs1110839: P = 0.034), respectively. In male population, rs1110839 genetic variant was related to the risk of lung cancer in all genetic models (GG vs. TT: P = 0.008; Dominant model: P = 0.029; Recessive model: P = 0.027) rather than heterozygous model. The crossover analyses provided rs4848320 and rs1110839 risk genotypes carriers combined with smoking exposure 2.218-fold, 1.755-fold increased risk of lung cancer (Rs4848320: P = 0.005; Rs1110839: P = 0.017). Additionally, there were significantly positive multiplicative interaction of rs4848320 polymorphism with smoking status, with adjusted OR of 2.244 (1.162–4.334), but rs1110839 polymorphism did not exist.

Conclusions

Rs4848320 and rs1110839 polymorphisms may be associated with lung cancer susceptibility. Interaction of rs4848320 risk genotypes with smoking exposure may strengthen the risk effect on lung cancer.

Estimation of cis-eQTL effect sizes using a log of linear model

The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Unfortunately, these approaches do not correspond to the simplest model of eQTL action, and thus yield estimates of eQTL association that can be uninterpretable and inaccurate. In this article, we propose a new, log-of-linear model for eQTL action, termed ACME, that captures allelic contributions to cis-acting eQTLs in an additive fashion, yielding effect size estimates that correspond to a biologically coherent model of cis-eQTLs. We describe a non-linear least-squares algorithm to fit the model by maximum likelihood, and obtain corresponding p-values. We perform careful investigation of the model using a combination of simulated data and data from the Genotype Tissue Expression (GTEx) project. Our results reveal little evidence for dominance effects, a parsimonious result that accords with a simple biological model for allele-specific expression and supports use of the ACME model. We show that Type-I error is well-controlled under our approach in a realistic setting, so that rank-based normalizations are unnecessary. Furthermore, we show that such normalizations can be detrimental to power and estimation accuracy under the proposed model. We then show, through effect size analyses of whole-genome cis-eQTLs in the GTEx data, that using standard normalizations instead of ACME noticeably affects the ranking and sign of estimates.

Polymorphism studies on microRNA targetome of thalassemia

Thalassemia is one of the most prevalent hemoglobin disorders. It is caused by the decreased or absent synthesis of one globin chain that leads to moderate to severe hemolytic anemia in clinical complications. Some genetic factors cause these phenotypic variations by the alteration of gene expression. MicroRNAs (miRNAs) are post-transcriptional regulators in gene expression. Therefore, variations in 3'-untranslated region (3'-UTR) of target genes may affect gene expression. It is of interest to evaluate the impact of noncoding SNPs in thalassemia related genes on miRNA: mRNA interactions in the severity of thalassemia. Polymorphisms that alter miRNA: mRNA interactions were predicted using PolymiRTS and Mirsnpscore tools. Then, the effect of predicted target SNPs on thermodynamic stability, local RNA structure and regulatory elements was investigated using RNAhybrid, RNAsnp and RegulomeDB, respectively. The molecular functions and the Biological process of candidate genes were extracted and interaction network was created. Forty-six SNPs were predicted to affect 188 miRNA interactions. These results suggest that 3'-UTR SNP may affect gene expression and cause phenotypic variation in thalassemia patients.

A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles

Recent large-scale genome-wide association studies have identified tens of genetic loci robustly associated with Body Mass Index (BMI). Gene expression profiles were also found to be associated with BMI. However, accurate prediction of obesity risk utilizing genetic data remains challenging. In a cohort of 75 individuals, we integrated 27 BMI-associated SNPs and obesity-associated gene expression profiles. Genetic risk score was computed by adding BMI-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. An adjusted R2 of 0.556 and accuracy of 76% was achieved for the linear regression and support vector machine models, respectively. In this paper, we report a new mathematical method to predict obesity genetic risk. We constructed obesity prediction models based on genetic information for a small cohort. Our computational framework serves as an example for using genetic information to predict obesity risk for specific cohorts.

ARE DISCOVERIES SPURIOUS? DISTRIBUTIONS OF MAXIMUM SPURIOUS CORRELATIONS AND THEIR APPLICATIONS

Over the last two decades, many exciting variable selection methods have been developed for finding a small group of covariates that are associated with the response from a large pool. Can the discoveries by such data mining approaches be spurious due to high dimensionality and limited sample size? Can our fundamental assumptions on exogeneity of covariates needed for such variable selection be validated with the data? To answer these questions, we need to derive the distributions of the maximum spurious correlations given certain number of predictors, namely, the distribution of the correlation of a response variable Y with the best s linear combinations of p covariates X, even when X and Y are independent. When the covariance matrix of X possesses the restricted eigenvalue property, we derive such distributions for both finite s and diverging s, using Gaussian approximation and empirical process techniques. However, such a distribution depends on the unknown covariance matrix of X. Hence, we use the multiplier bootstrap procedure to approximate the unknown distributions and establish the consistency of such a simple bootstrap approach. The results are further extended to the situation where residuals are from regularized fits. Our approach is then applied to construct the upper confidence limit for the maximum spurious correlation and testing exogeneity of covariates. The former provides a baseline for guarding against false discoveries due to data mining and the latter tests whether our fundamental assumptions for high-dimensional model selection are statistically valid. Our techniques and results are illustrated by both numerical examples and real data analysis.

Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity

Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. We show that transcription factors (TFs) occupy multiple loci of individual complex genetic disorders using novel computational methods. Application to 213 phenotypes and 1,544 TF binding datasets identifies 2,264 relationships between hundreds of TFs and 94 phenotypes, including AR in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of the systemic lupus erythematosus risk loci are occupied by the Epstein-Barr virus EBNA2 protein and many co-clustering human TFs, revealing gene-environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis, and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent upon EBNA2. Our results nominate mechanisms that operate across risk loci within disease phenotypes, suggesting new paradigms for disease origins.

A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver

Genome-wide association studies (GWAS) have identified numerous genetic variants in the human genome associated with diseases and traits. Nevertheless, for most loci the causative variant is still unknown. Expression quantitative trait loci (eQTL) in disease relevant tissues is an excellent approach to correlate genetic association with gene expression. While liver is the primary site of gene transcription for two pathways relevant to age-related macular degeneration (AMD), namely the complement system and cholesterol metabolism, we explored the contribution of AMD associated variants to modulate liver gene expression. We extracted publicly available data and computed the largest eQTL data set for liver tissue to date. Genotypes and expression data from all studies underwent rigorous quality control. Subsequently, Matrix eQTL was used to identify significant local eQTL. In total, liver samples from 588 individuals revealed 202,489 significant eQTL variants affecting 1,959 genes (Q-Value < 0.001). In addition, a further 101 independent eQTL signals were identified in 93 of the 1,959 eQTL genes. Importantly, our results independently reinforce the notion that high density lipoprotein metabolism plays a role in AMD pathogenesis. Taken together, our study generated a first comprehensive map reflecting the genetic regulatory landscape of gene expression in liver.

Enhanced methods to detect haplotypic effects on gene expression

Motivation

Expression quantitative trait loci (eQTLs), genetic variants associated with gene expression levels, are identified in eQTL mapping studies. Such studies typically test for an association between single nucleotide polymorphisms (SNPs) and expression under an additive model, which ignores interaction and haplotypic effects. Mismatches between the model tested and the underlying genetic architecture can lead to a loss of association power. Here we introduce a new haplotype-based test for eQTL studies that looks for haplotypic effects on expression levels. Our test is motivated by compound heterozygous architectures, a common disease model for recessive monogenic disorders, where two different alleles can have the same effect on a gene's function.

Results

When the underlying true causal architecture for a simulated gene is a compound heterozygote, our method is better able to capture the signal than the marginal SNP method. When the underlying model is a single SNP, there is no difference in the power of our method relative to the marginal SNP method. We apply our method to empirical gene expression data measured in 373 European individuals from the GEUVADIS study and find 29 more eGenes (genes with at least one association) than the standard marginal SNP method. Furthermore, in 974 of the 3529 total eGenes, our haplotype-based method results in a stronger association signal than the standard marginal SNP method. This demonstrates our method both increases power over the standard method and provides evidence of haplotypic architectures regulating gene expression.

Availability and Implementation

http://bogdan.bioinformatics.ucla.edu/software/.

Contact

rob.brown@ucla.edu or pasaniuc@ucla.edu.

QuASAR-MPRA: accurate allele-specific analysis for massively parallel reporter assays

Motivation

The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets.

Results

We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays.

Availability and implementation

http://github.com/piquelab/QuASAR/tree/master/mpra.

Contact

fluca@wayne.edu or rpique@wayne.edu.

Supplementary information

Supplementary data are available online at Bioinformatics.

Applications of the 1000 Genomes Project resources

The 1000 Genomes Project created a valuable, worldwide reference for human genetic variation. Common uses of the 1000 Genomes dataset include genotype imputation supporting Genome-wide Association Studies, mapping expression Quantitative Trait Loci, filtering non-pathogenic variants from exome, whole genome and cancer genome sequencing projects, and genetic analysis of population structure and molecular evolution. In this article, we will highlight some of the multiple ways that the 1000 Genomes data can be and has been utilized for genetic studies.

ABCG2 regulatory single-nucleotide polymorphisms alter in vivo enhancer activity and expression

OBJECTIVES

The expression and activity of the breast cancer resistance protein (ABCG2) contributes toward the pharmacokinetics of endogenous and xenobiotic substrates. The effect of genetic variation on the activity of cis-regulatory elements and nuclear response elements in the ABCG2 locus and their contribution toward ABCG2 expression have not been investigated systematically. In this study, the effect of genetic variation on the in vitro and in vivo enhancer activity of six previously identified liver enhancers in the ABCG2 locus was examined.

METHODS

Reference and variant liver enhancers were tested for their ability to alter luciferase activity in vitro in HepG2 and HEK293T cell lines and in vivo using a hydrodynamic tail vein assay. Positive in vivo single-nucleotide polymorphisms (SNPs) were tested for association with gene expression and for altered protein binding in electrophoretic mobility shift assays.

RESULTS

Multiple SNPs were found to alter enhancer activity in vitro. Four of these variants (rs9999111, rs12508471, ABCG2RE1*2, and rs149713212) decreased and one (rs2725263) increased enhancer activity in vivo. In addition, rs9999111 and rs12508471 were associated with ABCG2 expression in lymphoblastoid cell lines, lymphocytes, and T cells, and showed increased HepG2 nuclear protein binding.

CONCLUSION

This study identifies SNPs within regulatory regions of the ABCG2 locus that alter enhancer activity in vitro and in vivo. Several of these SNPs correlate with tissue-specific ABCG2 expression and alter DNA/protein binding. These SNPs could contribute toward reported tissue-specific variability in ABCG2 expression and may influence the correlation between ABCG2 expression and disease risk or the pharmacokinetics and pharmacodynamics of breast cancer resistance protein substrates.

Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks (“modules”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.

Single nucleotide polymorphisms in ZNRD1-AS1 increase cancer risk in an Asian population

Single nucleotide polymorphisms (SNPs) in human zinc ribbon domain containing 1 antisense RNA 1 (ZNRD1-AS1) have been associated with cancer development. In this meta-analysis, we more precisely estimated the associations between three expression quantitative trait loci SNPs in ZNRD1-AS1 (rs3757328, rs6940552, and rs9261204) and cancer susceptibility. The data for three SNPs were extracted from eligible studies, which included 5,293 patients and 5,440 controls. Overall, no significant associations between SNPs in ZNRD1-AS1 (rs3757328, rs6940552, and rs9261204) and cancer risk were observed. However, in further subgroup analyses based on cancer type, we found that the A allele of rs3757328 increased the risk of some cancer in both allele contrast (OR = 1.15, 95% CI = 1.05 – 1.25) and recessive models (OR = 1.79; 95% CI = 1.33 – 2.41). The A allele of rs6940552 and the G allele of rs9261204 also increased the risk of some cancer in an Asian population in allele contrast (OR = 1.17, 95% CI = 1.08 – 1.26, and OR = 1.25, 95% CI = 1.16 – 1.34, respectively) and recessive models (OR = 1.44, 95% CI = 1.18 – 1.77, and OR = 1.49; 95% CI = 1.23 – 1.80, respectively). Thus, rs3757328, rs6940552, and rs9261204 in ZNRD1-AS1 are all associated with increased some cancer risk in an Asian population.

Cis-acting single nucleotide polymorphisms alter MicroRNA-mediated regulation of human brain-expressed transcripts

Substantial variability exists in the presentation of complex neurological disorders, and the study of single nucleotide polymorphisms (SNPs) has shed light on disease mechanisms and pathophysiological variability in some cases. However, the vast majority of disease-linked SNPs have unidentified pathophysiological relevance. Here, we tested the hypothesis that SNPs within the miRNA recognition element (MRE; the region of the target transcript to which the miRNA binds) can impart changes in the expression of those genes, either by enhancing or reducing transcript and protein levels. To test this, we cross-referenced 7,153 miRNA-MRE brain interactions with the SNP database (dbSNP) to identify candidates, and functionally assessed 24 SNPs located in the 3’UTR or the coding sequence (CDS) of targets. For over half of the candidates tested, SNPs either enhanced (4 genes) or disrupted (10 genes) miRNA binding and target regulation. Additionally, SNPs causing a shift from a common to rare codon within the CDS facilitated miRNA binding downstream of the SNP, dramatically repressing target gene expression. The biological activity of the SNPs on miRNA regulation was also confirmed in induced pluripotent stem cell (iPSC) lines. These studies strongly support the notion that SNPs in the 3’UTR or the coding sequence of disease-relevant genes may be important in disease pathogenesis and should be reconsidered as candidate modifiers.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

BACKGROUND

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets.

RESULTS

Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.

CONCLUSIONS

The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.

A null variant in the apolipoprotein L3 gene is associated with non-diabetic nephropathy

Background

Inheritance of apolipoprotein L1 gene (APOL1) renal-risk variants in a recessive pattern strongly associates with non-diabetic end-stage kidney disease (ESKD). Further evidence supports risk modifiers in APOL1-associated nephropathy; some studies demonstrate that heterozygotes possess excess risk for ESKD or show earlier age at ESKD, relative to those with zero risk alleles. Nearby loci are also associated with ESKD in non-African Americans.

Methods

We assessed the role of the APOL3 null allele rs11089781 on risk of non-diabetic ESKD. Four cohorts containing 2781 ESKD cases and 2474 controls were analyzed.

Results

Stratifying by APOL1 risk genotype (recessive) and adjusting for African ancestry identified a significant additive association between rs11089781 and ESKD in each stratum and in a meta-analysis [meta-analysis P  =  0.0070; odds ratio (OR) = 1.29]; ORs were consistent across APOL1 risk strata. The biological significance of this association is supported by the finding that the APOL3 gene is co-regulated with APOL1, and that APOL3 protein was able to bind to APOL1 protein.

Conclusions

Taken together, the genetic and biological data support the concept that other APOL proteins besides APOL1 may also influence the risk of non-diabetic ESKD.

Association studies of WD repeat domain 3 and chitobiosyldiphosphodolichol beta-mannosyltransferase genes with schizophrenia in a Japanese population

Schizophrenia and schizophrenia-like symptoms induced by the dopamine agonists and N-methyl-D aspartate type glutamate receptor antagonists occur only after the adolescent period. Similarly, animal models of schizophrenia by these drugs are also induced after the critical period around postnatal week three. Based upon the development-dependent onsets of these psychotomimetic effects, by using a DNA microarray technique, we identified the WD repeat domain 3 (WDR3) and chitobiosyldiphosphodolichol beta-mannosyltransferase (ALG1) genes as novel candidates for schizophrenia-related molecules, whose mRNAs were up-regulated in the adult (postnatal week seven), but not in the infant (postnatal week one) rats by an indirect dopamine agonist, and phencyclidine, an antagonist of the NMDA receptor. WDR3 and other related proteins are the nuclear proteins presumably involved in various cellular activities, such as cell cycle progression, signal transduction, apoptosis, and gene regulation. ALG1 is presumed to be involved in the regulation of the protein N-glycosylation. To further elucidate the molecular pathophysiology of schizophrenia, we have evaluated the genetic association of WDR3 and ALG1 in schizophrenia. We examined 21 single nucleotide polymorphisms [SNPs; W1 (rs1812607)-W16 (rs6656360), A1 (rs8053916)-A10 (rs9673733)] from these genes using the Japanese case-control sample (1,808 schizophrenics and 2,170 matched controls). No significant genetic associations of these SNPs were identified. However, we detected a significant association of W4 (rs319471) in the female schizophrenics (allelic P = 0.003, genotypic P = 0.008). Based on a haplotype analysis, the observed haplotypes consisting of W4 (rs319471)–W5 (rs379058) also displayed a significant association in the female schizophrenics (P = 0.016). Even after correction for multiple testing, these associations remained significant. Our findings suggest that the WDR3 gene may likely be a sensitive factor in female patients with schizophrenia, and that modification of the WDR3 signaling pathway warrants further investigation as to the pathophysiology of schizophrenia.

Hepatitis B virus genotypes, expression quantitative trait loci for ZNRD1-AS1 and their interactions in hepatocellular carcinoma

Genetic variants in zinc ribbon domain-containing 1 antisense RNA 1 (ZNRD1-AS1) have been reported to be associated with development of hepatocellular carcinoma (HCC). We sought to determine the influences of ZNRD1-AS1 polymorphisms and their interactions with Hepatitis B virus (HBV) genotypes on the risk of HCC. In this study, we conducted a large population case-control study with 1,507 HBV-related HCC cases and 1,560 HBV persistent carriers. Three single-nucleotide polymorphisms (SNPs) in ZNRD1-AS1 (rs3757328, rs6940552 and rs9261204) were genotyped using a TaqMan allelic discrimination assay, and the HBV genotypes were identified by multiplex PCR. We found consistently significant associations between the ZNRD1-AS1 rs6940552 and rs9261204 SNPs with an increased risk of HCC (additive genetic model: adjusted OR = 1.16, 95% CI = 1.03-1.32 for rs6940552; adjusted OR =1.20, 95% CI = 1.06-1.35 for rs9261204) and found a borderline association between rs3757328 and HCC risk. Besides, we observed a dose-dependent relationship between increasing numbers of variant alleles of the SNPs and HCC risk (P for trend <0.001). Moreover, we observed a stronger combined effect of the three SNPs on HCC risk among the subjects infected with non-B genotype HBV (adjusted OR = 1.26, 95% CI = 1.05-1.50) compared with HBV B-related genotypes (adjusted OR = 0.89, 95% CI = 0.69-1.15; P= 0.029 for heterogeneity test). We also found that a multiplicative interaction between the variant alleles and the HBV genotype significantly affected HCC susceptibility (P = 0.030). Together, these results indicate that ZNRD1-AS1 may influence HCC risk accompanied by HBV genotypes.

Quantification of classical HLA class I mRNA by allele-specific, real-time polymerase chain reaction for most Han individuals

Recent studies have shown that expression levels of different alleles at the same HLA class I locus can vary dramatically, which might have a broad influence on human disease. However, precise quantification of the relative expression level of each HLA allele is challenging, because distinguishing different alleles on the same locus is difficult. Here, we developed a series of allele-specific, real-time polymerase chain reaction assays for quantifying HLA class I allele mRNA in most Han individuals. The alleles of almost all heterozygous genotypes with a frequency higher than 0.5% in our population (78 alleles on HLA-A locus, 124 alleles on HLA-B locus, and 74 alleles on HLA-C locus) were specifically amplified. The specificity of the amplification was strictly validated by setting the corresponding negative control for each allele of each genotype. The amplification efficiency of each reaction was determined, and the slopes of the reactions were compared. This study provides a tool for detecting the comprehensive expression profile of HLA class I alleles and will be useful not only for the investigation of the molecular mechanism underlying HLA allele expression regulation but also for exploration of immunological mechanisms involving HLA expression in the fields of tumour immune evasion, viral infection, auto-immune disorders, and graft vs host disease after haematopoietic stem cell transplantation.

Long non-coding RNA PAX8-AS1 polymorphisms increase the risk of childhood acute lymphoblastic leukemia

The present case-control study was conducted on 110 children with acute lymphoblastic leukemia (ALL) and 120 healthy children to determine the impact of polymorphisms in paired-box gene 8 (PAX8) antisense RNA 1 (PAX8-AS1), namely rs4848320 C>T, rs6726151 T>G and rs1110839 G>T, on ALL risk. Genotyping was performed through the polymerase chain reaction-restriction fragment length polymorphism method. The findings indicated that the rs4848320 variant increased the risk of ALL in codominant [CT vs. CC: odds ratio (OR)=2.13, 95% confidence interval (CI)=1.16-3.90, P=0.014; and TT vs. CC: OR=2.21, 95% CI=1.03-4.74, P=0.041], dominant (CT+TT vs. CC: OR=2.15, 95% CI=1.22-3.81, P=0.009,) and allele (T vs. C: OR=1.55, 95% CI=1.07-2.25, P=0.024) inheritance models. The rs6726151 variant significantly increased the risk of ALL in codominant (GT vs. GG: OR=1.88, 95% CI=1.08-3.27, P=0.036) and overdominant (GT vs. GG+TT: OR=2.08, 95% CI=1.23-3.53, P=0.008) inheritance models. No significant relationship was identified between the rs1110839 G>T variant and disease risk/protection in childhood ALL. In conclusion, the findings of the present study indicated that rs4848320 and rs6726151 polymorphisms of PAX8-AS1 may be a risk factor for the development of childhood ALL. Further studies with larger sample sizes and different ethnicities are now required to confirm these findings.

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele-or both-in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10-6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10-10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10-11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10-6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.

cis-Acting Complex-Trait-Associated lincRNA Expression Correlates with Modulation of Chromosomal Architecture

Intergenic long noncoding RNAs (lincRNAs) are the largest class of transcripts in the human genome. Although many have recently been linked to complex human traits, the underlying mechanisms for most of these transcripts remain undetermined. We investigated the regulatory roles of a high-confidence and reproducible set of 69 trait-relevant lincRNAs (TR-lincRNAs) in human lymphoblastoid cells whose biological relevance is supported by their evolutionary conservation during recent human history and genetic interactions with other trait-associated loci. Their enrichment in enhancer-like chromatin signatures, interactions with nearby trait-relevant protein-coding loci, and preferential location at topologically associated domain (TAD) boundaries provide evidence that TR-lincRNAs likely regulate proximal trait-relevant gene expression in cis by modulating local chromosomal architecture. This is consistent with the positive and significant correlation found between TR-lincRNA abundance and intra-TAD DNA-DNA contacts. Our results provide insights into the molecular mode of action by which TR-lincRNAs contribute to complex human traits.

Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models

Understanding the functional mechanism of SNPs identified in GWAS on complex diseases is currently a challenging task. The studies of expression quantitative trait loci (eQTL) have shown that regulatory variants play a crucial role in the function of associated SNPs. Detecting significant genes (called eGenes) in eQTL studies and analyzing the effect sizes of cis-SNPs can offer important implications on the genetic architecture of associated SNPs and interpretations of the molecular basis of diseases. We applied linear mixed models (LMM) to the gene expression level and constructed likelihood ratio tests (LRT) to test for eGene in the Geuvadis data. We identified about 11% genes as eGenes in the Geuvadis data and found some eGenes were enriched in approximately independent linkage disequilibrium (LD) blocks (e.g. MHC). We further performed PrediXcan analysis for seven diseases in the WTCCC data with weights estimated using LMM and identified 64, 5, 21 and 1 significant genes (p < 0.05 after Bonferroni correction) associated with T1D, CD, RA and T2D. We found most of the significant genes of T1D and RA were also located within the MHC region. Our results provide strong evidence that gene expression plays an intermediate role for the associated variants in GWAS.

The effect of genetic variation on promoter usage and enhancer activity

The identification of genetic variants affecting gene expression, namely expression quantitative trait loci (eQTLs), has contributed to the understanding of mechanisms underlying human traits and diseases. The majority of these variants map in non-coding regulatory regions of the genome and their identification remains challenging. Here, we use natural genetic variation and CAGE transcriptomes from 154 EBV-transformed lymphoblastoid cell lines, derived from unrelated individuals, to map 5376 and 110 regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs), respectively. We characterize five categories of genes associated with puQTLs, distinguishing single from multi-promoter genes. Among multi-promoter genes, we find puQTL effects either specific to a single promoter or to multiple promoters with variable effect orientations. Regulatory variants associated with opposite effects on different mRNA isoforms suggest compensatory mechanisms occurring between alternative promoters. Our analyses identify differential promoter usage and modulation of enhancer activity as molecular mechanisms underlying eQTLs related to regulatory elements.

Expression quantitative trait loci (eQTL) are widely studied, yet the mechanisms by which they exert their effects are largely unknown. Here, performing CAGE-seq on 154 lymphoblastoid cell lines, the authors map regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs).