Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (～1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.     

Testing gene set enrichment for subset of genes: Sub-GSE

Background  

Many methods have been developed to test the enrichment of genes related to certain phenotypes or cell states in gene sets. These approaches usually combine gene expression data with functionally related gene sets as defined in databases such as GeneOntology (GO), KEGG, or BioCarta. The results based on gene set analysis are generally more biologically interpretable, accurate and robust than the results based on individual gene analysis. However, while most available methods for gene set enrichment analysis test the enrichment of the entire gene set, it is more likely that only a subset of the genes in the gene set may be related to the phenotypes of interest.     

遗传风险评分在复杂疾病遗传学研究中的应用

心血管疾病、2型糖尿病、原发性高血压、哮喘、肥胖、肿瘤等复杂疾病在全球范围内流行,并成为人类死亡的主要原因。越来越多的人开始关注遗传易感性在复杂疾病发病机制中的作用。至今,与复杂疾病相关的易感基因和基因序列变异仍未完全清楚。人们希望通过遗传关联研究来阐明复杂疾病的遗传基础。近年来,全基因组关联研究和候选基因研究发现了大量与复杂疾病有关的基因序列变异。这些与复杂疾病有因果和(或)关联关系的基因序列变异的发现促进了复杂疾病预测和防治方法的产生和发展。遗传风险评分(Genetic risk score,GRS)作为探索单核苷酸多态(Single nucleotide polymorphisms,SNPs)与复杂疾病临床表型之间关系的新兴方法,综合了若干SNPs的微弱效应,使基因多态对疾病的预测性大幅度提升。该方法在许多复杂疾病遗传学研究中得到成功应用。本文重点介绍了GRS的计算方法和评价标准,简要列举了运用GRS取得的系列成果,并对运用过程中所存在的局限性进行了探讨,最后对遗传风险评分的未来发展方向进行了展望。     

Haploinsufficiency predictions without study bias

Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants.     

Whole-exome sequencing reveals insights into genetic susceptibility to Congenital Zika Syndrome

Congenital Zika Syndrome (CZS) is a critical illness with a wide range of severity caused by Zika virus (ZIKV) infection during pregnancy. Life-threatening neurodevelopmental dysfunctions are among the most common phenotypes observed in affected newborns. Risk factors that contribute to susceptibility and response to ZIKV infection may be related to the virus itself, the environment, and maternal genetic background. Nevertheless, the newborn’s genetic contribution to the critical illness is still not elucidated. Here, we aimed to identify possible genetic variants as well as relevant biological pathways that might be associated with CZS phenotypes. For this purpose, we performed a whole-exome sequencing in 40 children born to women with confirmed exposure to ZIKV during pregnancy. We investigated the occurrence of rare harmful single-nucleotide variants (SNVs) possibly associated with inborn errors in genes ontologically related to CZS phenotypes. Moreover, an exome-wide association analysis was also performed using a case-control design (29 CZS cases and 11 controls), for both common and rare variants. Five out of the 29 CZS patients harbored known pathogenic variants likely to contribute to mild to severe manifestations observed. Approximately, 30% of affected individuals carried at least one pathogenic or likely pathogenic SNV in genes candidates to play a role in CZS. Our common variant association analysis detected a suggestive protective effect of the rs2076469 in DISP3 gene (p-value: 1.39 x 10⁻⁵). The IL12RB2 gene (p-value: 2.18x10^-11) also showed an unusual distribution of nonsynonymous rare SNVs in control samples. Finally, genes harboring harmful variants are involved in processes related to CZS phenotypes such as neurological development and immunity. Therefore, both rare and common variations may be likely to contribute as the underlying genetic cause of CZS susceptibility. The variations and pathways identified in this study may also have implications for the development of therapeutic strategies in the future.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Bibliometric Survey of the Citation Impacts of GWAS and Candidate Gene Studies Published during the Same Period and in the Same Journals

In genetic epidemiology, genome-wide association studies (GWAS) are used to rapidly scan a large set of genetic variants and thus to identify associations with a particular trait or disease. The GWAS philosophy is different to that of conventional candidate-gene-based approaches, which directly test the effects of genetic variants of potentially contributory genes in an association study. One controversial question is whether GWAS provide relevant scientific outcomes by comparison with candidate-gene studies. We thus performed a bibliometric study using two citation metrics to assess whether the GWAS have contributed a capital gain in knowledge discovery by comparison with candidate-gene approaches. We selected GWAS published between 2005 and 2009 and matched them with candidate-gene studies on the same topic and published in the same period of time. We observed that the GWAS papers have received, on average, 30±55 citations more than the candidate gene papers, 1 year after their publication date, and 39±58 citations more 2 years after their publication date. The GWAS papers were, on average, 2.8±2.4 and 2.9±2.4 times more cited than expected, 1 and 2 years after their publication date; whereas the candidate gene papers were 1.5±1.2 and 1.5±1.4 times more cited than expected. While the evaluation of the contribution to scientific research through citation metrics may be challenged, it cannot be denied that GWAS are great hypothesis generators, and are a powerful complement to candidate gene studies.     

G-protein-coupled receptors and asthma endophenotypes: the cysteinyl leukotriene system in perspective

Genetic variation in specific G-protein coupled receptors (GPCRs) is associated with a spectrum of respiratory disease predispositions and drug response phenotypes. Although certain GPCR gene variants can be disease-causing through the expression of inactive, overactive, or constitutively active receptor proteins, many more GPCR gene variants confer risk for potentially deleterious endophenotypes. Endophenotypes are traits, such as bronchiole hyperactivity, atopy, and aspirin intolerant asthma, which have a strong genetic component and are risk factors for a variety of more complex outcomes that may include disease states. GPCR genes implicated in asthma endophenotypes include variants of the cysteinyl leukotriene receptors (CYSLTR1 and CYSLTR2), and prostaglandin D2 receptors (PTGDR and CRTH2), thromboxane A2 receptor (TBXA2R), beta2-adrenergic receptor (ADRB2), chemokine receptor 5 (CCR5), and the G protein-coupled receptor associated with asthma (GPRA). This review of the contribution of variability in these genes places the contribution of the cysteinyl leukotriene system to respiratory endophenotypes in perspective. The genetic variant(s) of receptors that are associated with endophenotypes are discussed in the context of the extent to which they contribute to a disease phenotype or altered drug efficacy.     

Recent advances in asthma genetics

There are over 100 genes that have been reported to be associated with asthma or related phenotypes. In 2006–2007 alone there were 53 novel candidate gene associations reported in the literature. Replication of genetic associations and demonstration of a functional mechanism for the associated variants are needed to confirm an asthma susceptibility gene. For most of the candidate genes there is little functional information. In a previous review by Hoffjan et al. published in 2003, functional information was reported for 40 polymorphisms and here we list another 22 genes which have such data. Some important genes such as filaggrin, interleukin-13, interleukin-17 and the cysteinyl leukotriene receptor-1 which not only were replicated by independent association studies but also have functional data are reviewed in this article.     

Revealing the Genetic Basis of Natural Bacterial Phenotypic Divergence

Divergent phenotypes for distantly related strains of bacteria, such as differing antibiotic resistances or organic solvent tolerances, are of keen interest both from an evolutionary perspective and for the engineering of novel microbial organisms and consortia in synthetic biology applications. A prerequisite for any practical application of this phenotypic diversity is knowledge of the genetic determinants for each trait of interest. Sequence divergence between strains is often so extensive as to make brute-force approaches to identifying the loci contributing to a given trait impractical. Here we describe a global linkage analysis approach, GLINT, for rapid discovery of the causal genetic variants underlying phenotypic divergence between distantly related strains of Escherichia coli. This general strategy will also be usable, with minor modifications, for revealing genotype-phenotype associations between naturally occurring strains of other bacterial species.     

Interpretation of phenotype in genetically engineered mice.

BACKGROUND AND PURPOSE: In mice, genetic engineering involves two general approaches-addition of an exogenous gene, resulting in transgenic mice, and use of knockout mice, which have a targeted mutation of an endogenous gene. The advantages of these approaches is that questions can be asked about the function of a particular gene in a living mammalian organism, taking into account interactions among cells, tissues, and organs under normal, disease, injury, and stress situations. METHODS: Review of the literature concentrating principally on knockout mice and questions of unexpected phenotypes, lack of phenotype, redundancy, and effect of genetic background on phenotype will be discussed. CONCLUSION: There is little gene redundancy in mammals; knockout phenotypes exist even if none are immediately apparent; and investigating phenotypes in colonies of mixed genetic background may reveal not only more phenotypes, but also may lead to better understanding of the molecular or cellular mechanism underlying the phenotype and to discovery of modifier gene(s).     

Recent advances in the genetics of osteoporosis

It has been known for over 20 years that osteoporosis is highly influenced by genetic factors. Bone mineral density (BMD) has also been shown to be highly heritable. Other known risk factors for osteoporotic fractures such as reduced bone quality, femoral neck geometry and bone turnover are now also known to be heritable. Susceptibility to osteoporosis is mediated, in all likelihood, by multiple genes each having small effect. Different approaches are being used currently to identify the many genes responsible. These include linkage studies in man and experimental animals as well as candidate gene studies and alterations in gene expression. Linkage studies have identified multiple quantitative trait loci (QTL) for regulation of BMD and, with twin studies, have indicated that the effects of these loci are partly site-dependent and sex-specific. On the whole, the genes responsible for BMD regulation at these QTL have not yet been isolated. Most studies have used the candidate gene approach. The vitamin D receptor gene (VDR), the collagen type I alpha 1 gene (COLIA1) and estrogen receptor gene (ER) alpha have been most widely investigated and found to play a role in regulating BMD, but the effects are modest and together probably account for less than 5% of the heritable contribution to BMD. Genes may vary in their influence of particular intermediate phenotypes, and we now know that not all genes influencing BMD will be important in fracture. In addition, the study of other diseases such as osteoarthritis and metabolic bone syndromes may prove fruitful in highlighting genes which overlap to osteoporosis as well. As large scale genetic testing becomes more cost-effective, recent findings have illustrated the potential of novel approaches. These include combining large multi-national populations for candidate gene analysis, meta-analyses, DNA pooling studies and gene expression studies.     

Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women''s Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.     

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.     

An Exponential Combination Procedure for Set-Based Association Tests in Sequencing Studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives—that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk—a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.     

Multiple regression methods show great potential for rare variant association tests

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.     

A polygenic heterogeneity model for common epilepsies with complex genetics

Approximately 40% of epilepsy has a complex genetic basis with an unknown number of susceptibility genes. The effect of each susceptibility gene acting alone is insufficient to account for seizure phenotypes, but certain numbers or combinations of variations in susceptibility genes are predicted to raise the level of neuronal hyperexcitability above a seizure threshold for a given individual in a given environment. Identities of susceptibility genes are beginning to be determined, initially by translation of knowledge gained from gene discovery in the monogenic epilepsies. This entrée into idiopathic epilepsies with complex genetics has led to the experimental validation of susceptibility variants in the first few susceptibility genes. The genetic architecture so far emerging from these results is consistent with what we have designated as a polygenic heterogeneity model for the epilepsies with complex genetics.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.     

Introduction to Deep Sequencing and Its Application to Drug Addiction Research with a Focus on Rare Variants

Through linkage analysis, candidate gene approach, and genome-wide association studies (GWAS), many genetic susceptibility factors for substance dependence have been discovered such as the alcohol dehydrogenase gene (ALDH2) for alcohol dependence (AD) and nicotinic acetylcholine receptor (nAChR) subunit variants on chromosomes 8 and 15 for nicotine dependence (ND). However, these confirmed genetic factors contribute only a small portion of the heritability responsible for each addiction. Among many potential factors, rare variants in those identified and unidentified susceptibility genes are supposed to contribute greatly to the missing heritability. Several studies focusing on rare variants have been conducted by taking advantage of next-generation sequencing technologies, which revealed that some rare variants of nAChR subunits are associated with ND in both genetic and functional studies. However, these studies investigated variants for only a small number of genes and need to be expanded to broad regions/genes in a larger population. This review presents an update on recently developed methods for rare-variant identification and association analysis and on studies focused on rare-variant discovery and function related to addictions.     

Genotype-based databases for variants causing rare diseases

Inherited diseases are the result of DNA sequence changes. In recessive diseases, the clinical phenotype results from the combined functional effects of variants in both copies of the gene. In some diseases there is often considerable variability of clinical presentation or disease severity, which may be predicted by the genotype. Additional effects may be triggered by environmental factors, as well as genetic modifiers which could be nucleotide polymorphisms in related genes, e.g. maternal ApoE or ABCA1 genotypes which may have an influence on the phenotype of SLOS individuals. Here we report the establishment of genotype variation databases for various rare diseases which provide individual clinical phenotypes associated with genotypes and include data about possible genetic modifiers. These databases aim to be an easy public access to information on rare and private variants with clinical data, which will facilitate the interpretation of genetic variants.