A HapMap harvest of insights into the genetics of common disease

HTRA1 synergizes with oxidized phospholipids in promoting inflammation and macrophage infiltration essential for ocular VEGF expression

Understanding oxidative stress and HTRA1 locus in abnormal angiogenesis resulting in wet AMD pathology is an important step in developing a novel therapeutic approach. Using subretinal injection of oxLDL into C57BL/6 mice, we observed a lesion resembling the features of choroidal neovascularization (CNV), including macrophage infiltration, increased VEGF expression, and neovascularization. However, incubating ARPE-19 cells with oxLDL–a carrier of oxidized phospholipids–resulted in increased expression of inflammatory cytokines and chemoattractant proteins that recruited monocytes, but no substantial increase in expression of VEGF. Furthermore, incubation of ARPE-19 with oxLDL induced higher expression of HTRA1, which we showed to synergize with oxLDL in elevating the expression of inflammatory cytokines and chemoattractant factors. To investigate the role of macrophage infiltration on these expression changes, we treated cultured J774 macrophages with oxLDL and applied the conditioned medium onto ARPE-19 cells. This treatment was found to greatly enhance the expression of VEGF in ARPE-19, indicating the necessity of macrophage secretory products to induce increased expression of VEGF in retinal pigment epithelium. Gene expression analysis revealed that oxLDL induced the expression of Wnt3A in macrophages, a key activator of canonical Wnt signaling pathways. In addition, western blot analysis showed that the macrophage conditioned media further enhanced the reduction of phosphorylated β-catenin induced by oxLDL. Lastly, we investigated HTRA1 as a potential target for AMD therapeutics. We demonstrated the ability of anti-HTRA1 antibody in vitro to neutralize the protease activity of HTRA1 and reduce the inflammatory and angiogenic response to oxidative stress. Finally, we validated the neutralizing effect of anti-HTRA1 antibody in vivo by evaluating lesion size and protein expression in a laser-photocoagulation murine model of CNV. We found that the combination of oxLDL and HTRA1 enhanced CNV size, which was reversed by the addition of anti-HTRA1 antibody. This study not only provides preliminary evidence that HTRA1 may be a viable target for AMD therapeutics but also elucidates the biochemical mechanisms by which this therapeutic effect may be mediated.

PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies

To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.e. the LD structure of the reference panel can differ substantially from the true LD structure in the study population. In simulation studies and in applications to population-based and family-based studies, we illustrate the features of the proposed GEE framework. Our results suggest that our approach can be up to 100% more efficient than existing methodology.

Variation in complement factor H affects complement activation in immunoglobulin A vasculitis with nephritis

BACKGROUND

Immunoglobulin A (IgA) vasculitis with nephritis (IgAVN) and IgA nephropathy (IgAN) are widely considered as related diseases. Considerable evidences support the notion of involvement of complement activation in both IgAVN and IgAN. Our previous studies identified a genetic variant in complement factor H (CFH), rs6677604, as an IgAN-susceptible variant by genome-wide association study, and further confirmed its linkage to CFHR3-1Δ and proved its influence on complement activation and thereby on IgAN susceptibility.

AIM

To explore the role of rs6677604 in complement activation of IgAVN.

METHODS

In this study, we enrolled 632 patients with IgAVN, 1178 patients with IgAN and 902 healthy controls. The genotype of rs6677604 was measured by TaqMan allele discrimination assays or was extracted from genome-wide association study data.

RESULTS

The frequency of the rs6677604-A allele was significantly higher in IgAVN than in IgAN. However, no significant differences were observed between IgAVN and the controls. Higher complement factor H (FH) levels were observed in IgAVN than IgAN, and positive correlation between circulating FH and C3 levels was present in IgAVN. In both IgAVN and IgAN, rs6677604-A was associated with less intensity of glomerular C3 deposits. In agreement with the higher frequency of rs6677604-A in IgAVN, the glomerular C3 deposits of patients with IgAVN were less intense than those in IgAN.

CONCLUSION

Our findings suggest that genetic variation in CFH (rs6677604) is involved in the phenotype of complement activation in both IgAVN and IgAN. Moreover, rs6677604 might contribute to the difference of complement activation intensity between IgAVN and IgAN.

Entering the post-epigenomic age: back to epigenetics

It is undeniably one of the greatest findings in biology that (with some very minor exceptions) every cell in the body possesses the whole genetic information needed to generate a complete individual. Today, this concept has been so thoroughly assimilated that we struggle to still see how surprising this finding actually was: all cellular phenotypes naturally occurring in one person are generated from genetic uniformity, and thus are per definition epigenetic. Transcriptional mechanisms are clearly critical for developing and protecting cell identities, because a mis-expression of few or even single genes can efficiently induce inappropriate cellular programmes. However, how transcriptional activities are molecularly controlled and which of the many known epigenomic features have causal roles remains unclear. Today, clarification of this issue is more pressing than ever because profiling efforts and epigenome-wide association studies (EWAS) continuously provide comprehensive datasets depicting epigenomic differences between tissues and disease states. In this commentary, we propagate the idea of a widespread follow-up use of epigenome editing technology in EWAS studies. This would enable them to address the questions of which features, where in the genome, and which circumstances are essential to shape development and trigger disease states.

RNA Biology Provides New Therapeutic Targets for Human Disease

RNA is the messenger molecule that conveys information from the genome and allows the production of biomolecules required for life in a responsive and regulated way. Most genes are able to produce multiple mRNA products in response to different internal or external environmental signals, in different tissues and organs, and at specific times in development or later life. This fine tuning of gene expression is dependent on the coordinated effects of a large and intricate set of regulatory machinery, which together orchestrate the genomic output at each locus and ensure that each gene is expressed at the right amount, at the right time and in the correct location. This complexity of control, and the requirement for both sequence elements and the entities that bind them, results in multiple points at which errors may occur. Errors of RNA biology are common and found in association with both rare, single gene disorders, but also more common, chronic diseases. Fortunately, complexity also brings opportunity. The existence of many regulatory steps also offers multiple levels of potential therapeutic intervention which can be exploited. In this review, I will outline the specific points at which coding RNAs may be regulated, indicate potential means of intervention at each stage, and outline with examples some of the progress that has been made in this area. Finally, I will outline some of the remaining challenges with the delivery of RNA-based therapeutics but indicate why there are reasons for optimism.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Linkage Analysis of the Chromosome 5q31-33 Region Identifies JAKMIP2 as a Risk Factor for Graves' Disease in the Chinese Han Population

Background

This study aimed to investigate susceptibility to Graves’s disease and the association with the 5q32–33.1 region on chromosome 5 in a Chinese Han population.

Material/Methods

Eighty Chinese Han multiplex families included first-degree and second-degree relatives with Graves’ disease. Eight microsatellite markers on chromosome 5 at the 5q32–33.1 region underwent linkage analysis and the association between the regions D5S1480–D5S2014 were studied.

Results

The maximal heterogeneity logarithm of the odds (HLOD) score of D5S2090 was 4.29 (α=0.42) and of D5S2014 was 4.01 (α=0.34). A nonparametric linkage (NPL) score of 3.14 (P<0.001) was found for D5S2014. The D5S1480–D5S2014 region on chromosome 5 was associated with Graves’ disease, with eight haplotype domains. There were significant differences in the sixth and eighth haplotype domains between patients with Graves’ disease compared with normal individuals. Tagging single nucleotide polymorphisms (SNPs) of the sixth and eighth haplotype domains showed that individuals with SNP62 (rs12653715 G/C) who were GG homozygous had a significantly increased risk of Graves’ disease compared GC heterozygous or CC homozygous individuals. The transmission disequilibrium test (TDT) indicated that SNP62 (rs12653715) and SNP63 (rs12653081) loci in the Janus kinase and microtubule interacting protein 2 (JAKMIP2) gene showed dominant transmission from heterozygous parents to the affected offspring, and SNPs in the secretoglobin family 3A member 2 (SCGB3A2) gene showed no transmission disequilibrium. The haplotype JAKMIP2-1 was identified as being particularly significant.

Conclusions

JAKMIP2 gene polymorphism require further study as potential risk factors for Graves’ disease in the Chinese Han population.

Shared additive genetic variation for alcohol dependence among subjects of African and European ancestry

Alcohol dependence (AD) affects individuals from all racial/ethnic groups, and previous research suggests that there is considerable variation in AD risk between and among various ancestrally defined groups in the United States. Although the reasons for these differences are likely due in part to contributions of complex sociocultural factors, limited research has attempted to examine whether similar genetic variation plays a role across ancestral groups. Using a pooled sample of individuals of African and European ancestry (AA/EA) obtained through data shared within the Database for Genotypes and Phenotypes, we estimated the extent to which additive genetic similarity for AD between AA and EAs using common single nucleotide polymorphisms overlapped across the two populations. AD was represented as a factor score by using Diagnostic and Statistical Manual dependence criteria, and genetic data were imputed by using the 1000 Genomes Reference Panel. Analyses revealed a significant single nucleotide polymorphism-based heritability of 17 percent (SE = 5) in EAs and 24 percent (SE = 15) in AAs. Further, a significant genetic correlation of 0.77 (SE = 0.46) suggests that the allelic architecture influencing the AD factor for EAs and AAs is largely similar across the two populations. Analyses indicated that investigating the genetic underpinnings of alcohol dependence in different ethnic groups may serve to highlight core etiological factors common to both groups and unique etiological factors specific to each ethnic group.

Recent Advances in the Genetics of Fractures in Osteoporosis

Genetic susceptibility, together with old age, female sex, and low bone mineral density (BMD) are amongst the strongest determinants of fracture risk. Tmost recent large-scale genome-wide association study (GWAS) meta-analysis has yielded fifteen loci. This review focuses on the advances in the research of genetic determinants of fracture risk. We first discuss the genetic architecture of fracture risk, touching upon different methods and overall findings. We then discuss in a second paragraph the most recent advances in the field and focus on the genetics of fracture risk and also of other endophenotypes closely related to fracture risk such as bone mineral density (BMD). Application of state-of-the-art methodology such as Mendelian randzation in fracture GWAS are reviewed. The final part of this review touches upon potential future directions in genetic research of osteoporotic fractures.

Enabling precision medicine via standard communication of HTS provenance, analysis, and results

A personalized approach based on a patient's or pathogen’s unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the “Open-Stand.org principles for collaborative open standards development.” With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

This Community Page article presents a communication standard for the provenance of high-throughput sequencing data; a BioCompute Object (BCO) can serve as a history of what was computed, be used as part of a validation process, or provide clarity and transparency of an experimental process to collaborators.

Interleukin-6 gene variants are associated with reduced risk of chronicity in hepatitis B virus infection in a Malaysian population

Interleukin-6 (IL-6) is a cytokine with a critical role in regulating the immune response to infectious disease. Studies have indicated that polymorphisms in the IL-6 gene may be linked to hepatitis B virus (HBV) infection. The purpose of the present study was to examine the association among IL-6 SNPs and haplotypes with HBV infection risk in a Malaysian population. A total of 1,246 Malaysian subjects with and without chronic hepatitis B were recruited for this study. Three IL-6 polymorphisms (rs2069837, rs1800796 and rs2066992) were genotyped using a Sequenom MassARRAY^® platform. The results suggested that GC and CC genotypes of rs1800796 as well as GT and TT genotypes of rs2066992 were associated with protection against HBV infection (P<0.001). Furthermore, haplotypes GG and CT exhibited a significant association with protection against HBV (P=0.003 and =0.005, respectively); and haplotypes GG and CT exhibited a significant association with clearance of HBV infection (P=0.035 and =0.037, respectively). The present study indicates that two IL-6 SNPs (rs1800796 and rs2066992) are associated with clearance of chronic HBV or protection against HBV infection at allelic, genotypic and haplotypic levels.

Analysis of two susceptibility SNPs in HLA region and evidence of interaction between rs6457617 in HLA-DQB1 and HLA-DRB1*04 locus on Tunisian rheumatoid arthritis

Previous genomewide association studies (GWAS) and meta-analyses have enumerated several genes/loci in major histocompatibility complex region, which are consistently associated with rheumatoid arthritis (RA) in different ethnic populations. Given the genetic heterogeneity of the disease, it is necessary to replicate these susceptibility loci in other populations. In this case, we investigate the analysis of two SNPs, rs13192471 and rs6457617, from the human leukocyte antigen (HLA) region with the risk of RA in Tunisian population. These SNPs were previously identified to have a strong RA association signal in several GWAS studies. A case-control sample composed of 142 RA patients and 123 healthy controls was analysed. Genotyping of rs13192471 and rs6457617 was carried out using real-time PCR methods by TaqMan allelic discrimination assay. A trend of significant association was found in rs6457617 TT genotype with susceptibility to RA (P = 0.04, pc = 0.08, OR = 1.73). Moreover, using multivariable analysis, the combination of rs6457617*TT-HLA-DRB1*04+ increased risk of RA (OR = 2.38), which suggest a gene-gene interaction event between rs6457617 located within the HLA-DQB1 and HLA-DRB1. Additionally, haplotypic analysis highlighted a significant association of rs6457617*T-HLA-DRB1*04+ haplotype with susceptibility to RA (P = 0.018, p_c = 0.036, OR = 1.72). An evidence of association was shown subsequently in antiCCP+ subgroup with rs6457617 both in T allele and TT genotype (P = 0.01, p_c = 0.03, OR = 1.66 and P = 0.008, p_c = 0.024, OR = 1.28, respectively). However, no association was shown for rs13192471 polymorphism with susceptibility and severity to RA. This study suggests the involvement of rs6457617 locus as risk variant for susceptibility/severity to RA in Tunisian population. Secondly, it highlights the gene-gene interaction between HLA-DQB1 and HLA-DRB1.

A single nucleotide polymorphism of the interferon-γ gene and susceptibility to hepatitis B virus-related cirrhosis: a randomized controlled trial

OBJECTIVE

This study aimed to explore the correlation between a single nucleotide polymorphism (SNP) of the interferon-γ (IFN-γ) gene and susceptibility to hepatitis B virus (HBV) -related cirrhosis in the Chinese population.

METHOD

PCR-LDR was employed for the genotyping of two SNPs, rs1861494 and rs2069718, of the IFN-γ gene in 230 patients with HBV-related cirrhosis and 320 inactive HBsAg carriers without cirrhosis. It was then determined whether the Hardy-Weinberg (H-W) equilibrium was satisfied. The odds ratio (OR) and 95% confidence interval (CI) were analyzed using the chi-square test and univariate non-conditional logistic regression. Haplotypes were established using SHEsis and SNPStats online software and their interaction with non-genetic factors was analyzed.

RESULTS

Compared with the AA genotype of rs1861494 SNP, AG+GG genotypes increased the risk of HBV-related cirrhosis. There was a significant difference in the distribution of A and G alleles between the case group and the control group (P<0.05). There was also a significant difference in the distribution of AA, AG, GG, AA, and AG+GG genotypes and A/G alleles between the case group and the control group (P<0.05). This indicated that the G allele may be a risk factor for HBV-related cirrhosis. By analyzing the different distribution of haplotypes in the case group and the control group, we observed significant differences in the distribution of AA, AG and GA haplotypes between the case and control groups (P<0.05). Haplotypes of the IFN-γ gene did not interact with other relevant factors.

CONCLUSION

The G allele of rs1861494 SNP as well as AG and GG genotypes and G allele of rs2069718 SNP may be risk factors of HBV-related cirrhosis. The AA haplotype may be a protective factor for HBV-related cirrhosis, while the AG haplotype is a risk factor for HBV-related cirrhosis.

Navigating the nuances of clinical sequence variant interpretation in Mendelian disease

Understanding clinical genetic test results in the era of next-generation sequencing has become increasingly complex, necessitating clear and thorough guidelines for sequence variant interpretation. To meet this need the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for a systematic approach for sequence variant interpretation in 2015. This framework is intended to be adaptable to any Mendelian condition, promoting transparency and consistency in variant interpretation, yet its comprehensive nature yields important challenges and caveats that end users must understand. In this review, we address some of these nuances and discuss the evolving efforts to refine and adapt this framework. We also consider the added complexity of distinguishing between variant-level interpretations and case-level conclusions, particularly in the context of the large gene panel approach to clinical diagnostics.

Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning

Motivation

One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data.

Results

The purpose of this review is to provide a structured overview of practical solutions for the interpretation of ROIs with the help of epigenomic data. Starting with epigenomic enrichment analysis, we discuss leading tools and machine learning methods utilizing epigenomic and 3D genome structure data. The hierarchy of tools and methods reviewed here presents a practical guide for the interpretation of genome-wide ROIs within an epigenomic context.

Contact

mikhail.dozmorov@vcuhealth.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA

Summary

Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype-phenotype relationship to guide the estimation of genetic correlation and the association mapping. The application of graph-GPA to GWAS datasets for 12 complex diseases with a prior disease graph obtained from a text mining of biomedical literature illustrates its power to improve the identification of risk genetic variants and to facilitate understanding of genetic relationship among complex diseases.

Availability and implementation

graph-GPA is implemented as an R package 'GGPA', which is publicly available at http://dongjunchung.github.io/GGPA/. DDNet, a web interface to query diseases of interest and download a prior disease graph obtained from a text mining of biomedical literature, is publicly available at http://www.chunglab.io/ddnet/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Novel Methods for Family-Based Genetic Studies

The recent development of microarray and sequencing technology allows identification of disease susceptibility genes. Although the genome-wide association studies (GWAS) have successfully identified many genetic markers related to human diseases, the traditional statistical methods are not powerful to detect rare genetic markers. The rare genetic markers are usually grouped together and tested at the set level. One of such methods is the sequence kernel association test (SKAT), which has been commonly used in the rare genetic marker analysis. In recent publications, SKAT has been extended to be applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.

Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations

Unbiased genetic association studies, including genome-wide association and whole-genome sequencing studies, have uncovered many novel disease-associated variants. Relatively few of these associated regions, however, have led to insights that are biologically mechanistic or clinically actionable due in part to the difficulty in designing appropriate functional validation studies to understand how variants contribute to disease. Asthma is a complex inflammatory lung disease for which many genetic associations have been identified. Using asthma as a disease model, we designed Reducing Associations by Linking Genes And transcriptomic Results (REALGAR), an app that facilitates the design of functional validation studies by integrating cell- and tissue-specific results of diseaserelevant gene expression and other omics studies. Via specific examples, we demonstrate how integrated gene- centric and disease-specific information leads to asthma insights, and more broadly, can help understand complex diseases.

Drug development in the era of precision medicine

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.

Nutritional Genomics and Direct-to-Consumer Genetic Testing: An Overview

The increasing prevalence in polygenic diseases, such as obesity, cardiovascular disease, and type 2 diabetes, observed over the past few decades is more likely linked to a rapid transition in lifestyle rather than to changes in the sequence of the nuclear genome. In the new era of precision medicine, nutritional genomics holds the promise to be translated into tailored nutritional strategies to prevent and manage polygenic diseases more effectively. Nutritional genomics aims to prevent, treat, and manage polygenic diseases through targeted therapies formulated from individuals' genetic makeup and dietary intake. Direct-to-consumer genetic testing (DTC-GT) has become commercially available to equip individuals with information on their genetic vulnerability to different diseases. This information may potentially prompt behavioral changes against adverse factors. However, scientific evidence behind the clinical recommendations is a matter of continuous debate, and behavioral modifications after disclosing genetic information remain inconclusive. In this review, we provide an overview of nutritional genomics and related nutritional DTC-GT services and discuss whether available data are sufficient to be translated into clinical recommendations and public health initiatives. Overall, the scientific evidence supporting the dissemination of genomic information for nutrigenomic purposes remains sparse. Therefore, additional knowledge needs to be generated, particularly for polygenic traits.

ShinyGPA: An interactive visualization toolkit for investigating pleiotropic architecture using GWAS datasets

In spite of accumulating evidence suggesting that different complex traits share a common risk basis, namely pleiotropy, effective investigation of pleiotropic architecture still remains challenging. In order to address this challenge, we developed ShinyGPA, an interactive and dynamic visualization toolkit to investigate pleiotropic structure. ShinyGPA requires only the summary statistics from genome-wide association studies (GWAS), which reduces the burden on researchers using this tool. ShinyGPA allows users to effectively investigate genetic relationships among phenotypes using a flexible low-dimensional visualization and an intuitive user interface. In addition, ShinyGPA provides joint association mapping functionality that can facilitate biological understanding of the pleiotropic architecture. We analyzed GWAS summary statistics for 12 phenotypes using ShinyGPA and obtained visualization results and joint association mapping results that are well supported by the literature. The visualization produced by ShinyGPA can also be used as a hypothesis generating tool for relationships between phenotypes, which might also be used to improve the design of future genetic studies. ShinyGPA is currently available at https://dongjunchung.github.io/GPA/.

The genetics of obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a common chronic disease and is associated with high social and economic costs. OSA is heritable, and there is evidence of both direct genetic contributions to OSA susceptibility and indirect contributions via 'intermediate' phenotypes such as obesity, craniofacial structure, neurological control of upper airway muscles and of sleep and circadian rhythm. Investigation of the genetics of OSA is an important research area and may lead to improved understanding of disease aetiology, pathogenesis, adverse health consequences and new preventive strategies and treatments. Genetic studies of OSA have lagged behind other chronic diseases; however recent gene discovery efforts have been successful in finding genetic loci contributing to OSA-associated intermediate phenotypes. Nevertheless, many of the seminal questions relating to the genetic epidemiology of OSA and associated factors remain unanswered. This paper reviews the current state of knowledge of the genetics of OSA, with a focus on genomic approaches to understanding sleep apnoea.

Rethinking ovarian cancer genomics: where genome-wide association studies stand?

Genome-wide association studies (GWAS) allow the finding of genetic variants associated with several traits. Regarding ovarian cancer (OC), 15 GWAS have been conducted since 2009, with the discovery of 49 SNPs associated with disease susceptibility and 46 with impact in the clinical outcome of patients (p < 5.00 × 10-2). Among them, 14 variants reached the genome-wide significance (p < 5.00 × 10−8). Despite the results obtained, they should be validated in independent sets. So far, five validation studies have been conducted which could confirm the association of 12 OC susceptibility SNPs. Consequently, post-GWAS studies are crucial unravel the biological plausibility of GWAS’ findings and the allelic spectrum of OC.

Genetic predisposition to lung adenocarcinoma among never-smoking Chinese with different epidermal growth factor receptor mutation status

OBJECTIVES

The inconsistent findings from genetic association studies may be related to the heterogeneity in different molecular subtypes of lung cancer. This study evaluated the predisposing single-nucleotide polymorphisms (SNPs) in epidermal growth factor receptor (EGFR) mutant and EGFR wild-type lung adenocarcinoma separately among never-smokers.

MATERIALS AND METHODS

This was a two-stage case-control study. Never-smokers with pathologically confirmed lung adenocarcinoma and healthy controls were recruited in Hong Kong and Macau. Genomic DNA was extracted and genotyped by MassARRAY. In the discovery stage, 51 SNPs were investigated at the SNP, gene and pathway level among 103 EGFR mutant and 78 EGFR wild-type lung adenocarcinoma cases compared with matched controls. In the validation stage, SNPs that were identified with significant lung cancer risk were replicated in a separate cohort of 84 lung adenocarcinoma cases and compared with 103 Chinese Han, Beijing and 105 Chinese Han, Southern public controls from the 1000 genome database.

RESULTS AND CONCLUSION

The genetic association of IL-6 rs2069840 with EGFR mutant lung adenocarcinoma was ascertained. In the discovery stage, haplotype GGG in three SNPs (rs2069840, rs2069852, rs2066992) of IL-6, synergetic effects of IL-6 rs2069840 and environmental tobacco smoke in the workplace were found to be related to EGFR mutant lung adenocarcinoma. ERCC2 rs238406 showed a marginally significant association with EGFR mutant lung adenocarcinoma in the validation stage (P=0.096). ERCC2 rs50871 and ATM rs611646 showed significant association with EGFR wild-type lung adenocarcinoma in the discovery stage. In conclusion, IL-6 rs2069840 conferred susceptibility to EGFR mutant lung adenocarcinoma in a Hong Kong and Macau never-smoking Chinese population.

The human microbiome

Until recently, human microbiology was based on the identification of single microbes, such as bacteria, fungi and viruses, frequently isolated from patients with acute or chronic infections. Novel culture-independent molecular biochemical analyses (genomics, transcriptomics, proteomics, metabolomics) allow today to detect and classify the diverse microorganisms in a given ecosystem (microbiota), such as the gastrointestinal tract, the skin, the airway system, the urogenital tract and others, and to assess all genomes in these ecosystems (microbiome) as well as their gene products. These analyses revealed that each individual has its own microbiota that plays a role in health and disease. In addition, they greatly contributed to the recent advances in the understanding of the pathogenesis of a wide range of human diseases. It is to be expected that these new insights will translate into diagnostic, therapeutic and preventive measures in the context of personalized/precision medicine.

New Rodent Population Models May Inform Human Health Risk Assessment and Identification of Genetic Susceptibility to Environmental Exposures

BACKGROUND

This paper provides an introduction for environmental health scientists to emerging population-based rodent resources. Mouse reference populations provide an opportunity to model environmental exposures and gene-environment interactions in human disease and to inform human health risk assessment.

OBJECTIVES

This review will describe several mouse populations for toxicity assessment, including older models such as the Mouse Diversity Panel (MDP), and newer models that include the Collaborative Cross (CC) and Diversity Outbred (DO) models.

METHODS

This review will outline the features of the MDP, CC, and DO mouse models and will discuss published case studies investigating the use of these mouse population resources in each step of the risk assessment paradigm.

DISCUSSION

These unique resources have the potential to be powerful tools for generating hypotheses related to gene-environment interplay in human disease, performing controlled exposure studies to understand the differential responses in humans for susceptibility or resistance to environmental exposures, and identifying gene variants that influence sensitivity to toxicity and disease states.

CONCLUSIONS

These new resources offer substantial advances to classical toxicity testing paradigms by including genetically sensitive individuals that may inform toxicity risks for sensitive subpopulations. Both in vivo and complementary in vitro resources provide platforms with which to reduce uncertainty by providing population-level data around biological variability. https://doi.org/10.1289/EHP1274.

Genome-Wide Association Mapping of Loci Associated with Plant Growth and Forage Production under Salt Stress in Alfalfa (Medicago sativa L.)

Salinity tolerance is highly desirable to sustain alfalfa production in marginal lands that have been rendered saline. In this study, we used a diverse panel of 198 alfalfa accessions for mapping loci associated with plant growth and forage production under salt stress using genome-wide association studies (GWAS). The plants were genotyped using genotyping-by-sequencing (GBS). A greenhouse procedure was used for phenotyping four agronomic and physiological traits affected by salt stress, including dry weight (DW), plant height (PH), leaf chlorophyll content (LCC), and stomatal conductance (SC). For each trait, a stress susceptibility index (SSI) was used to evaluate plant performance under stressed and non-stressed conditions. Marker-trait association identified a total of 42 markers significantly associated with salt tolerance. They were located on all chromosomes except chromosome 2 based on the alignment of their flanking sequences to the reference genome (Medicago truncatula). Of those identified, 13 were associated with multiple traits. Several loci identified in the present study were also identified in previous reports. BLAST search revealed that 19 putative candidate genes linked to 24 significant markers. Among them, B3 DNA-binding protein, Thiaminepyrophosphokinase and IQ calmodulin-binding motif protein were identified among multiple traits in the present and previous studies. With further investigation, these markers and candidates would be useful for developing markers for marker-assisted selection in breeding programs to improve alfalfa cultivars with enhanced tolerance to salt stress.

SNP in human ARHGEF3 promoter is associated with DNase hypersensitivity, transcript level and platelet function, and Arhgef3 KO mice have increased mean platelet volume

Genome-wide association studies have identified a genetic variant at 3p14.3 (SNP rs1354034) that strongly associates with platelet number and mean platelet volume in humans. While originally proposed to be intronic, analysis of mRNA expression in primary human hematopoietic subpopulations reveals that this SNP is located directly upstream of the predominantly expressed ARHGEF3 isoform in megakaryocytes (MK). We found that ARHGEF3, which encodes a Rho guanine exchange factor, is dramatically upregulated during both human and murine MK maturation. We show that the SNP (rs1354034) is located in a DNase I hypersensitive region in human MKs and is an expression quantitative locus (eQTL) associated with ARHGEF3 expression level in human platelets, suggesting that it may be the causal SNP that accounts for the variations observed in human platelet traits and ARHGEF3 expression. In vitro human platelet activation assays revealed that rs1354034 is highly correlated with human platelet activation by ADP. In order to test whether ARHGEF3 plays a role in MK development and/or platelet function, we developed an Arhgef3 KO/LacZ reporter mouse model. Reflecting changes in gene expression, LacZ expression increases during MK maturation in these mice. Although Arhgef3 KO mice have significantly larger platelets, loss of Arhgef3 does not affect baseline MK or platelets nor does it affect platelet function or platelet recovery in response to antibody-mediated platelet depletion compared to littermate controls. In summary, our data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human MKs and human platelet function—a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.

New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis

This article summarizes key advances in hypnosis research during the past two decades, including (i) clinical research supporting the efficacy of hypnosis for managing a number of clinical symptoms and conditions, (ii) research supporting the role of various divisions in the anterior cingulate and prefrontal cortices in hypnotic responding, and (iii) an emerging finding that high hypnotic suggestibility is associated with atypical brain connectivity profiles. Key recommendations for a research agenda for the next decade include the recommendations that (i) laboratory hypnosis researchers should strongly consider how they assess hypnotic suggestibility in their studies, (ii) inclusion of study participants who score in the middle range of hypnotic suggestibility, and (iii) use of expanding research designs that more clearly delineate the roles of inductions and specific suggestions. Finally, we make two specific suggestions for helping to move the field forward including (i) the use of data sharing and (ii) redirecting resources away from contrasting state and nonstate positions toward studying (a) the efficacy of hypnotic treatments for clinical conditions influenced by central nervous system processes and (b) the neurophysiological underpinnings of hypnotic phenomena. As we learn more about the neurophysiological mechanisms underlying hypnosis and suggestion, we will strengthen our knowledge of both basic brain functions and a host of different psychological functions.

Systems Genetic Analysis in GeneNetwork.org

Genome-wide association studies (GWAS) have emerged as a powerful tool to identify alleles and molecular pathways that influence susceptibility to psychiatric disorders and other diseases. Forward genetics using mouse mapping populations allows for a complementary approach that provides rigorous genetic and environmental control. In this unit, we describe techniques and tools that reduce the technical burden traditionally associated with genetic mapping in mice and enhance their translational utility to human psychiatric disorders. We provide guidance on choosing the appropriate mapping population, discuss the importance of phenotype, and offer detailed instructions on using the Web-based resource GeneNetwork to aid neuroscientists in better understanding the mechanisms through which genes influence behavior. We believe that the continued development of mouse mapping populations, genetic tools, bioinformatics resources, and statistical methodologies should remain a parallel strategy by which to investigate the genetic and environmental underpinnings of psychiatric disorders and other diseases in humans. © 2017 by John Wiley & Sons, Inc.

Genome-wide analysis identifies 12 loci influencing human reproductive behavior

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

Sphingomyelin Phosphodiesterase 3 Enhances Cytodifferentiation of Periodontal Ligament Cells

Sphingomyelin phosphodiesterase 3 ( Smpd3), which encodes neutral sphingomyelinase 2 (nSMase2), is a key molecule for skeletal development as well as for the cytodifferentiation of odontoblasts and alveolar bone. However, the effects of nSMase2 on the cytodifferentiation of periodontal ligament (PDL) cells are still unclear. In this study, the authors analyzed the effects of Smpd3 on the cytodifferentiation of human PDL (HPDL) cells. The authors found that Smpd3 increases the mRNA expression of calcification-related genes, such as alkaline phosphatase (ALPase), type I collagen, osteopontin, Osterix (Osx), and runt-related transcription factor (Runx)-2 in HPDL cells. In contrast, GW4869, an inhibitor of nSMase2, clearly decreased the mRNA expression of ALPase, type I collagen, and osteocalcin in HPDL cells, suggesting that Smpd3 enhances HPDL cytodifferentiation. Next, the authors used exome sequencing to evaluate the genetic variants of Smpd3 in a Japanese population with aggressive periodontitis (AgP). Among 44 unrelated subjects, the authors identified a single nucleotide polymorphism (SNP), rs145616324, in Smpd3 as a putative genetic variant for AgP among Japanese people. Moreover, Smpd3 harboring this SNP did not increase the sphingomyelinase activity or mRNA expression of ALPase, type I collagen, osteopontin, Osx, or Runx2, suggesting that this SNP inhibits Smpd3 such that it has no effect on the cytodifferentiation of HPDL cells. These data suggest that Smpd3 plays a crucial role in maintaining the homeostasis of PDL tissue.

Copy number variation analysis reveals additional variants contributing to endometriosis development

PURPOSE

Endometriosis is a gynecological disease influenced by multiple genetic and environmental factors. The aim of the current study was to use SNP-array technology to identify genomic aberrations that may possibly contribute to the development of endometriosis.

METHODS

We performed an SNP-array genotyping of pooled DNA samples from both patients (n = 100) and controls (n = 50). Copy number variation (CNV) calling and association analyses were performed using PennCNV software. MLPA and TaqMan Copy-Number assays were used for validation of CNVs discovered.

RESULTS

We detected 49 CNV loci that were present in patients with endometriosis and absent in the control group. After validation procedures, we confirmed six CNV loci in the subtelomeric regions, including 1p36.33, 16p13.3, 19p13.3, and 20p13, representing gains, while 17q25.3 and 20q13.33 showed losses. Among the intrachromosomal regions, our results revealed duplication at 19q13.1 within the FCGBP gene (p = 0.007).

CONCLUSIONS

We identified CNVs previously associated with endometriosis, together with six suggestive novel loci possibly involved in this disease. The intergenic locus on chromosome 19q13.1 shows strong association with endometriosis and is under further functional investigation.

A Putative Association of a Single Nucleotide Polymorphism in GPR126 with Aggressive Periodontitis in a Japanese Population

Periodontitis is an inflammatory disease causing loss of tooth-supporting periodontal tissue. Disease susceptibility to the rapidly progressive form of periodontitis, aggressive periodontitis (AgP), appears to be influenced by genetic risk factors. To identify these in a Japanese population, we performed whole exome sequencing of 41 unrelated generalized or localized AgP patients. We found that AgP is putatively associated with single nucleotide polymorphism (SNP) rs536714306 in the G-protein coupled receptor 126 gene, GPR126 [c.3086 G>A (p.Arg1029Gln)]. Since GPR126 activates the cAMP/PKA signaling pathway, we performed cAMP ELISA analysis of cAMP concentrations, and found that rs536714306 impaired the signal transactivation of GPR126. Moreover, transfection of human periodontal ligament (HPDL) cells with wild-type or mutant GPR126 containing rs536714306 showed that wild-type GPR126 significantly increased the mRNA expression of bone sialoprotein, osteopontin, and Runx2 genes, while mutant GPR126 had no effect on the expression of these calcification-related genes. The increase in expression of these genes was through the GPR126-induced increase of bone morphogenic protein-2, inhibitor of DNA binding (ID) 2, and ID4 expression. These data indicate that GPR126 might be important in maintaining the homeostasis of periodontal ligament tissues through regulating the cytodifferentiation of HPDL cells. The GPR126 SNP rs536714306 negatively influences this homeostasis, leading to the development of AgP, suggesting that it is a candidate genetic risk factor for AgP in the Japanese population.

Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice

Although mice are the most widely used mammalian model organism, genetic studies have suffered from limited mapping resolution due to extensive linkage disequilibrium (LD) that is characteristic of crosses among inbred strains. Carworth Farms White (CFW) mice are a commercially available outbred mouse population that exhibit rapid LD decay in comparison to other available mouse populations. We performed a genome-wide association study (GWAS) of behavioral, physiological and gene expression phenotypes using 1,200 male CFW mice. We used genotyping by sequencing (GBS) to obtain genotypes at 92,734 SNPs. We also measured gene expression using RNA sequencing in three brain regions. Our study identified numerous behavioral, physiological and expression quantitative trait loci (QTLs). We integrated the behavioral QTL and eQTL results to implicate specific genes, including Azi2 in sensitivity to methamphetamine and Zmynd11 in anxiety-like behavior. The combination of CFW mice, GBS and RNA sequencing constitutes a powerful approach to GWAS in mice.

Functional characterisation of ADIPOQ variants using individuals recruited by genotype

Four non-coding GWAS variants in or near the ADIPOQ gene (rs17300539, rs17366653, rs3821799 and rs56354395) together explain 4% of the variation in circulating adiponectin. The functional basis for this is unknown. We tested the effect of these variants on ADIPOQ transcription, splicing and stability respectively in adipose tissue samples from participants recruited by rs17366653 genotype. Transcripts carrying rs17300539 demonstrated a 17% increase in expression (p = 0.001). Variant rs17366653 was associated with disruption of ADIPOQ splicing leading to a 7 fold increase in levels of a non-functional transcript (p = 0.002). Transcripts carrying rs56354395 demonstrated a 59% decrease in expression (p = <0.0001). No effects of rs3821799 genotype on expression was observed. Association between variation in the ADIPOQ gene and serum adiponectin may arise from effects on mRNA transcription, splicing or stability. These studies illustrate the utility of recruit-by-genotype studies in relevant human tissues in functional interpretation of GWAS signals.

A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies

An epigenome-wide association study (EWAS) is a large-scale study of human disease-associated epigenetic variation, specifically variation in DNA methylation. High throughput technologies enable simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. The clustering of correlated DNA methylation at CpGs is reportedly similar to that of linkage-disequilibrium (LD) correlation in genetic single nucleotide polymorphisms (SNP) variation. However, current analysis methods, such as the t-test and rank-sum test, may be underpowered to detect differentially methylated markers. We propose to test the association between the outcome (e.g case or control) and a set of CpG sites jointly. Here, we compared the performance of five CpG set analysis approaches: principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), sequence kernel association test (SKAT), and sliced inverse regression (SIR) with Hotelling's T2 test and t-test using Bonferroni correction. The simulation results revealed that the first six methods can control the type I error at the significance level, while the t-test is conservative. SPCA and SKAT performed better than other approaches when the correlation among CpG sites was strong. For illustration, these methods were also applied to a real methylation dataset.

Fast and Accurate Construction of Confidence Intervals for Heritability

Estimation of heritability is fundamental in genetic studies. Recently, heritability estimation using linear mixed models (LMMs) has gained popularity because these estimates can be obtained from unrelated individuals collected in genome-wide association studies. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. Existing methods for the construction of confidence intervals and estimators of SEs for REML rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals. Here, we show that the estimation of confidence intervals by state-of-the-art methods is inaccurate, especially when the true heritability is relatively low or relatively high. We further show that these inaccuracies occur in datasets including thousands of individuals. Such biases are present, for example, in estimates of heritability of gene expression in the Genotype-Tissue Expression project and of lipid profiles in the Ludwigshafen Risk and Cardiovascular Health study. We also show that often the probability that the genetic component is estimated as 0 is high even when the true heritability is bounded away from 0, emphasizing the need for accurate confidence intervals. We propose a computationally efficient method, ALBI (accurate LMM-based heritability bootstrap confidence intervals), for estimating the distribution of the heritability estimator and for constructing accurate confidence intervals. Our method can be used as an add-on to existing methods for estimating heritability and variance components, such as GCTA, FaST-LMM, GEMMA, or EMMAX.

Genetic Factors That Affect Risk of Alcoholic and Nonalcoholic Fatty Liver Disease

Genome-wide association studies and candidate gene studies have informed our understanding of factors contributing to the well-recognized interindividual variation in the progression and outcomes of alcoholic liver disease and nonalcoholic fatty liver disease. We discuss the mounting evidence for shared modifiers and common pathophysiological processes that contribute to development of both diseases. We discuss the functions of proteins encoded by risk variants of genes including patatin-like phospholipase domain-containing 3 and transmembrane 6 superfamily member 2, as well as epigenetic factors that contribute to the pathogenesis of alcoholic liver disease and nonalcoholic fatty liver disease. We also discuss important areas of future genetic research and their potential to affect clinical management of patients.

Building towards precision medicine: empowering medical professionals for the next revolution

A new paradigm in disease classification, diagnosis and treatment is rapidly approaching. Known as precision medicine, this new healthcare model incorporates and integrates genetic information, microbiome data, and information on patients’ environment and lifestyle to better identify and classify disease processes, and to provide custom-tailored therapeutic solutions. In spite of its promises, precision medicine faces several challenges that need to be overcome to successfully implement this new healthcare model. In this paper we identify four main areas that require attention: data, tools and systems, regulations, and people. While there are important ongoing efforts for addressing the first three areas, we argue that the human factor needs to be taken into consideration as well. In particular, we discuss several studies that show how primary care physicians and clinicians in general feel underequipped to interpret genetic tests and direct-to-consumer genomic tests. Considering the importance of genetic information for precision medicine applications, this is a pressing issue that needs to be addressed. To increase the number of professionals with the necessary expertise to correctly interpret the genomics profiles of their patients, we propose several strategies that involve medical curriculum reforms, specialist training, and ongoing physician training.

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples

OBJECTIVE

The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.

METHODS

This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM).

RESULTS

In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18).

CONCLUSION

We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

DNA context represents transcription regulation of the gene in mouse embryonic stem cells

Understanding gene regulatory information in DNA remains a significant challenge in biomedical research. This study presents a computational approach to infer gene regulatory programs from primary DNA sequences. Using DNA around transcription start sites as attributes, our model predicts gene regulation in the gene. We find that H3K27ac around TSS is an informative descriptor of the transcription program in mouse embryonic stem cells. We build a computational model inferring the cell-type-specific H3K27ac signatures in the DNA around TSS. A comparison of embryonic stem cell and liver cell-specific H3K27ac signatures in DNA shows that the H3K27ac signatures in DNA around TSS efficiently distinguish the cell-type specific H3K27ac peaks and the gene regulation. The arrangement of the H3K27ac signatures inferred from the DNA represents the transcription regulation of the gene in mESC. We show that the DNA around transcription start sites is associated with the gene regulatory program by specific interaction with H3K27ac.

Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin

Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer-promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer-promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.

Glutamatergic medication in the treatment of obsessive compulsive disorder (OCD) and autism spectrum disorder (ASD) - study protocol for a randomised controlled trial

BACKGROUND

Compulsivity is a cross-disorder trait underlying phenotypically distinct psychiatric disorders that emerge in childhood or adolescence. Despite the effectiveness of serotonergic compounds in the treatment of obsessive-compulsive disorder, treatment-resistant symptoms remaining in 40 to 60 % of patients present a pressing clinical problem. There are currently no medications that effectively treat the core impairments of autism spectrum disorder. There is an urgent need for the development of conceptually novel pharmacological strategies. Agents targeting glutamate neurotransmission, such as memantine, represent promising candidates. This proof-of-concept clinical study will allow pilot-testing of memantine for both clinical effectiveness and tolerability/safety. Memantine is an N-methyl-D-aspartate receptor antagonist, approved for the treatment of Alzheimer's dementia in a number of countries.

METHODS/DESIGN

This 12-week study has an add-on, randomised, double-blind, placebo-controlled design of treatment with memantine, including an up-titration phase (forced flexible dose design, 5-15 mg/day), in patients aged 6-17 years and 9 months with obsessive-compulsive disorder or autism spectrum disorder. It is planned to include patients with obsessive-compulsive disorder (N = 50) or autism spectrum disorder (N = 50) across four centres in three European countries. Patients will be randomly assigned to memantine or placebo in a 1:1 ratio. Primary objectives are the investigation of the effectiveness of memantine in paediatric patients for improving symptoms of compulsivity (primary outcome measure: total score on the Children's Yale-Brown Obsessive-Compulsive Scale) and to explore its tolerability and safety. Secondary objectives are to explore the effects of memantine at the level of structure, function and biochemistry of the fronto-striatal circuits, and to collect blood for genetic analyses and biomarkers. Tertiary objectives are to explore the role of new candidate genes and pathways for compulsivity by linking genes to clinical phenotypes, response to treatment, neurocognitive test performance, and key structural and functional neuroimaging measures of the fronto-striatal circuits and to explore biomarkers/proteomics for compulsivity traits.

DISCUSSION

This study is part of the large, translational project TACTICS ( http://www.tactics-project.eu/ ) that is funded by the European Union and investigates the neural, genetic and molecular factors involved in the pathogenesis of compulsivity. Its results will provide clinically relevant solid information on potential new mechanisms and medication treatment in obsessive-compulsive and autism spectrum disorders.

TRIAL REGISTRATION

EudraCT Number: 2014-003080-38 , date of registration: 14 July 2014.