Cryptic causation of human disease: reading between the (germ) lines

Most cases of complex human diseases arise sporadically. However, usually there is a significant level of familial aggregation of risk and genetic mapping has identified the responsible gene in a few mendelian cases. Although a disease can be causally genetic, intensified mapping efforts have so far been unable to identify genes that account for more than a small fraction of the familial risk, perhaps because the responsible variation arises by somatic mutation (SM). SM explains the kind of epidemiological pattern seen in cancer, and might have a comparable role in many other diseases. For example, in epilepsy, which has largely defied mapping analysis, the underlying disease pathology, undamped neuronal signaling, is closely connected to gene function. Better technologies to detect and characterize SM are becoming available. However, until it is studied directly, SM will remain a cryptic etiological force, even for diseases that are essentially "genetic".     

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a flexible and adaptive test for gene sets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn’s disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.     

Genome-wide multilocus analysis for immune-mediated complex diseases

In postgenomic era, searching and identification of disease genes associated with complex diseases are still one of the great challenge for dissecting human complex diseases. To improve the disease gene localization for complex diseases, a group of closely immune-mediated disease loci were overlapped on each chromosome based on previously reported genome-wide scanning data. Interestingly, five overlapping chromosomal regions (1q21, 2q33, 5q31.1-q33.1, 6p21, and 11q13) were identified by co-localizing disease loci for the following diseases: diabetes, asthma, atopic dermatitis, osteoporosis, and inflammatory bowel disease. The development of specific disease was associated with different combinations of disease loci among five overlapped chromosomal regions. Therefore, the analysis of multiple genetic loci should be considered to determine the effects of multiple genes responsible for complex diseases resulting from the influence of multiple genes.     

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.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

New perspectives for the elucidation of genetic disorders

For almost 15 years, genome research has focused on the search for major risk factors in common diseases, with disappointing results. Only recently, whole-genome association studies have begun to deliver because of the introduction of high-density single-nucleotide-polymorphism arrays and massive enlargement of cohort sizes, but most of the risk factors detected account for only a small proportion of the total genetic risk, and their diagnostic value is negligible. There is reason to believe that the complexity of many "multifactorial" disorders is primarily due to genetic heterogeneity, with defects of different genes causing the same disease. Moreover, de novo copy-number variation has been identified as a major cause of mental retardation and other complex disorders, suggesting that new mutations are an important, previously overlooked factor in the etiology of complex diseases. These observations support the notion that research into the previously neglected monogenic disorders should become a priority of genome research. Because of the introduction of novel high-throughput, low-cost sequencing methods, sequencing and genotyping will soon converge, with far-reaching implications for the elucidation of genetic disease and health care.     

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.     

Gene variants for osteoporosis and their pleiotropic effects in aging

The prevalence of osteoporosis is raising worldwide as improving conditions of living and treatment of other common diseases continuously increases life expectancy. Thus, osteoporosis affects most women above 80 years of age and, at the age of 50, the lifetime risk of suffering an osteoporosis-related fracture approaches 50% in women and 20% in men. Numerous genetic, hormonal, nutritional and life-style factors contribute to the acquisition and maintenance of bone mass. Among them, genetic variations explain as much as 70% of the variance for bone mineral density (BMD) in the population. Dozens of quantitative trait loci (QTLs) for BMD have been identified by genome screening and linkage approaches in humans and mice, and more than 100 candidate gene polymorphisms tested for association with BMD and/or fracture. Sequence variants in the vitamin D receptor (VDR), collagen 1 alpha 1 chain (Col1A1), estrogen receptor alpha (ESR1), interleukin-6 (IL-6) and LDL receptor-related protein 5 (LRP5) genes were all found to be significantly associated with differences in BMD and/or fracture risk in multiple replication studies. Moreover, some genes, such as VDR and IL-6, were shown to interact with non-genetic factors, i.e. calcium intake and estrogens, to modulate BMD. Since these gene variants have also been associated with other complex disorders, including cancer and coronary heart disease, they may represent common genetic susceptibility factors exerting pleiotropic effects during the aging process.     

Contribution of rare and common variants determine complex diseases—Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common ‘low penetrant’ variants in combination with rare or private ‘high penetrant’ variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.     

A Multi-Breed Genome-Wide Association Analysis for Canine Hypothyroidism Identifies a Shared Major Risk Locus on CFA12

Hypothyroidism is a complex clinical condition found in both humans and dogs, thought to be caused by a combination of genetic and environmental factors. In this study we present a multi-breed analysis of predisposing genetic risk factors for hypothyroidism in dogs using three high-risk breeds—the Gordon Setter, Hovawart and the Rhodesian Ridgeback. Using a genome-wide association approach and meta-analysis, we identified a major hypothyroidism risk locus shared by these breeds on chromosome 12 (p = 2.1x10^-11). Further characterisation of the candidate region revealed a shared ~167 kb risk haplotype (4,915,018–5,081,823 bp), tagged by two SNPs in almost complete linkage disequilibrium. This breed-shared risk haplotype includes three genes (LHFPL5, SRPK1 and SLC26A8) and does not extend to the dog leukocyte antigen (DLA) class II gene cluster located in the vicinity. These three genes have not been identified as candidate genes for hypothyroid disease previously, but have functions that could potentially contribute to the development of the disease. Our results implicate the potential involvement of novel genes and pathways for the development of canine hypothyroidism, raising new possibilities for screening, breeding programmes and treatments in dogs. This study may also contribute to our understanding of the genetic etiology of human hypothyroid disease, which is one of the most common endocrine disorders in humans.     

Whole-genome association studies of alcoholism with loci linked to schizophrenia susceptibility

Background

Alcoholism is a complex disease. There have been many reports on significant comorbidity between alcoholism and schizophrenia. For the genetic study of complex diseases, association analysis has been recommended because of its higher power than that of the linkage analysis for detecting genes with modest effects on disease.

Results

To identify alcoholism susceptibility loci, we performed genome-wide single-nucleotide polymorphisms (SNP) association tests, which yielded 489 significant SNPs at the 1% significance level. The association tests showed that tsc0593964 (P-value 0.000013) on chromosome 7 was most significantly associated with alcoholism. From 489 SNPs, 74 genes were identified. Among these genes, GABRA1 is a member of the same gene family with GABRA2 that was recently reported as alcoholism susceptibility gene.

Conclusion

By comparing 74 genes to the published results of various linkage studies of schizophrenia, we identified 13 alcoholism associated genes that were located in the regions reported to be linked to schizophrenia. These 13 identified genes can be important candidate genes to study the genetic mechanism of co-occurrence of both diseases.

     

血脂异常遗传性疾病的研究现状

血脂异常(Dyslipidemia)是指血浆中胆固醇和(或)甘油三酯水平升高, 可导致严重的心血管疾病, 常以冠心病和脑中风为首发表现, 该类疾病严重危害着人们的健康。一些血脂异常疾病具有遗传性, 主要包括孟德尔遗传和多基因遗传。传统检测血脂异常相关基因的方法主要有DNA测序和连锁分析, 适合于孟德尔遗传性血脂异常疾病。最近几年兴起的新一代测序技术(Next-generation sequencing)不仅适用于孟德尔遗传性血脂异常疾病的研究, 同样适用于复杂性血脂异常疾病。2006年至今, 运用全基因组关联分析(Genome wide association study, GWAS)筛出许多与血脂异常疾病相关的基因, 这些基因和早期孟德尔遗传家系确定的基因多数相同。GWAS频谱分析发现, 复杂性疾病相关的基因变异频率存在差异, 并且几乎所有筛查出的与血脂异常疾病相关的单核苷酸多态性(Single nucleotide polymorphisms, SNPs)变异均位于非编码区, 使得人们逐渐对非编码区基因变异展开了研究。血脂异常致病基因的发现和基因变异致病机制的阐明, 为血脂异常疾病提供新的治疗靶点, 并为新一代药物筛选提供新思路。文章对血脂异常遗传性疾病的研究现状进行了综述。     

An analytical enrichment-based review of structural genetic studies on keratoconus

Keratoconus is a progressive bilateral corneal protrusion that leads to irregular astigmatism and impairment of vision. Keratoconus is an etiologically heterogeneous corneal dystrophy and both environmental and genetic factors play a role in its etiopathogenesis. In this analytical review, we have studied all the genes that are structurally associated with keratoconus and have tried to explain the function of each gene and its association with other eye disorders in a concise way. In addition, using gene set enrichment analysis, it was attempted to find the most important impaired metabolic pathways in keratoconus. Several genetic studies have been carried out on keratoconus and several genes have been identified as risk factors involved in the etiology of the disease. In the current study, 16 studies, including nine association studies, five genome-wide association studies, one linkage study, and one meta-analysis, were reviewed and based on the 19 genes found, enrichment was performed and the most important metabolic pathways involved in the disease were identified. The enrichment results indicated that the two pathways, interleukin 1 processing and assembly of collagen fibrils, are significantly associated with the disease. Obviously, the results of this study, in addition to providing information about the genes involved in the disease, can provide an integrated insight into the gene-based etiology of keratoconus and therapeutic opportunities thereof.     

A systems biology approach to genetic studies of complex diseases

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.     

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

BackgroundImmune and skeletal systems physiologically and pathologically interact with each other. Immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown.ObjectiveThis study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia, and fracture).MethodsThe canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. The versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA.ResultsAbout 157 (p<8.19E-6), 319 (p<3.90E-6), and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune diseases, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1, and TSBP1-AS1 (p<E-300), were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, the majority (18) of these 19 putative validated pleiotropic genes were associated with RA.ConclusionThe metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.     

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。     

Genetic susceptibility in Parkinson's disease

It is hoped that an understanding of the genetic basis of Parkinson's disease (PD) will lead to an appreciation of the molecular pathogenesis of disease, which in turn will highlight potential points of therapeutic intervention. It is also hoped that such an understanding will allow identification of individuals at risk for disease prior to the onset of motor symptoms. A large amount of work has already been performed in the identification of genetic risk factors for PD and some of this work, particularly those efforts that focus on genes implicated in monogenic forms of PD, have been successful, although hard won. A new era of gene discovery has begun, with the application of genome wide association studies; these promise to facilitate the identification of common genetic risk loci for complex genetic diseases. This is the first of several high throughput technologies that promise to shed light on the (likely) myriad genetic factors involved in this complex, late-onset neurodegenerative disorder.     

Molecular and genetic mechanisms of osteoporosis: implication for treatment

Osteoporosis is a leading public health problem in our rapidly growing, aging population. It is characterized by reduced bone mass and microarchitectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture risk. Osteoporosis is a complex multifactorial disease, determined by genetic and environmental factors as well as their interactions. A large number of molecular, genetic and environmental factors underlying osteoporosis have been identified in past decades. In this article, we review 1) the molecular mechanisms of several principal systemic and local factors regulating bone metabolism; and 2) the current status of genetic studies searching for genes underlying osteoporosis. Further, we attempt to integrate knowledge from those two fields, and their potential implications for osteoporosis treatment.     

冠心病全基因组关联研究进展

近年来全基因组关联研究在世界范围内发展迅猛,研究者应用全基因组关联研究策略发现了一系列疾病的相关基因或变异,将疾病的基因组研究推向一个新的阶段。冠心病是一种由环境因素和遗传因素共同作用导致的复杂疾病,且是世界范围内死亡和致残的首要原因之一,世界各地的研究者应用此策略发现了候选基因关联研究未曾发现的多个冠心病相关易感区域。文章对近年来世界范围内针对冠心病的全基因组关联研究取得的重要进展进行简要总结,然后就现阶段全基因组关联研究所面临的挑战以及对未来研究的发展趋势进行分析阐述,为进一步探究冠心病的遗传机制提供指导。     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.