The search for the genetic contribution to autoimmune thyroid disease: the never ending story?

Unlocking the genetic contribution to autoimmune thyroid disease (AITD) will hold one of the keys to understanding disease pathogenesis and developing improved treatments. Significant increases in our understanding of the human genome combined with methodological advances in our ability to search for genetic variation have transformed the way in which we screen the genome for susceptibility loci. From early linkage analysis through to candidate gene studies and most recently genome-wide association screening, each methodology has revealed important insights into not just the heritability of AITD but also the best way of identifying disease causing DNA variants. This review will examine each of the different genome screening techniques, highlighting the successes and failures of each methodology and the lessons learnt which have helped inform the next phase of the disease-gene identification process. We will also look to see where we should be focusing our research efforts in the future.     

Association Studies of Sporadic Parkinson’s Disease in the Genomic Era

Parkinson’s disease is a common age-related progressive neurodegenerative disorder. Over the last 10 years, advances have been made in our understanding of the etiology of the disease with the greatest insights perhaps coming from genetic studies, including genome-wide association approaches. These large scale studies allow the identification of genomic regions harboring common variants associated to disease risk. Since the first genome-wide association study on sporadic Parkinson’s disease performed in 2005, improvements in study design, including the advent of meta-analyses, have allowed the identification of ~21 susceptibility loci. The first loci to be nominated were previously associated to familial PD (SNCA, MAPT, LRRK2) and these have been extensively replicated. For other more recently identified loci (SREBF1, SCARB2, RIT2) independent replication is still warranted. Cumulative risk estimates of associated variants suggest that more loci are still to be discovered. Additional association studies combined with deep re-sequencing of known genome-wide association study loci are necessary to identify the functional variants that drive disease risk. As each of these associated genes and variants are identified they will give insight into the biological pathways involved the etiology of Parkinson’s disease. This will ultimately lead to the identification of molecules that can be used as biomarkers for diagnosis and as targets for the development of better, personalized treatment.     

Cardiovascular disease risk prediction using genetic information (gene scores): is it really informative?

PURPOSE OF REVIEW: DNA-based tests for assessment of genetic predisposition to coronary heart disease need to provide information over and above that of conventional risk factors. The efficacy of selected 'candidate' gene loci in risk algorithms, to improve the predictive accuracy for coronary heart disease, remains to be demonstrated. RECENT FINDINGS: Although many candidate genes for coronary heart disease have been tested, the optimal set of risk genotypes has yet to be identified. There is only a relatively modest risk to be expected in association with any single genotype, published estimates are in the range of 1.12-1.73. Thus the risk associated with any one genotype is modest, but, in combination, selected genotypes may be associated with a clinically significant risk. Since the allele frequency for many of these variants is high, many individuals will carry several 'risk alleles'. A small number of selected single nucleotide polymorphisms should complement the conventional risk factors to identify high-risk individuals in whom correction of 'modifiable risk factors' through lifestyle interventions or medication would be most beneficial. SUMMARY: As our understanding of how genetic variation impacts on common diseases advances, the novel loci identified by genome-wide association scans associated with disease risk will rapidly improve these risk algorithms.     

Genetics of preeclampsia: an approach to genetic linkage studies

Due to its high prevalence during pregnancies, preeclampsia is considered an important public health problem. Many investigators agree in that its expression is related to the interaction between genetic and environmental factors. Many studies have searched for genetic factors, attempting to identify chromosomal regions or candidate genes whose variants may be related to high preeclampsia susceptibility. Several studies have associated a number of susceptibility genes to preeclampsia, but the results have not been replicated consistently in all populations. Mapping of genes and chromosomal regions by linkage analysis has located potential markers on chromosomes 2 and 4. Identification of the genes located in these candidate regions will pinpoint the genetic risk factors, will lead to a better understanding of the syndrome, and will provide clues for its prevention and treatment.     

The thyroglobulin gene: the third locus for autoimmune thyroid disease or a false dawn?

Genetic studies have identified the HLA and CTLA4 regions as susceptibility loci for the development of common autoimmune thyroid diseases (AITDs), including Graves' disease and autoimmune hypothyroidism. Despite numerous studies, the identification of a third locus has remained elusive. Genetic-linkage studies have implicated chromosome 8q24 as a susceptibility locus for AITD. The gene encoding thyroglobulin (Tg), which encodes a major thyroid autoantigen, maps to this region, and a recent study has reported the association of several exonic single-nucleotide polymorphisms (SNPs) with disease. Although these preliminary data are potentially exciting, caution needs to be exercised, and replication of the data sought before Tg can be designated as the third locus for AITD.     

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.     

Genetics of alcohol dependence

Alcohol dependence (AD), a genetically influenced phenotype, is extremely costly to individuals and to society in the United States and throughout the world, contributing to morbidity and mortality and a host of economic, interpersonal, and societal problems. Although until recently the only genes established to affect risk for AD were those encoding several alcohol metabolizing enzymes, there are now several other genes that can be regarded as confirmed risk loci, discovered through linkage and candidate gene association studies. While the mechanism of action of the effects of alcohol-metabolizing enzymes on AD risk is thought to be well understood, we are still in the early stages of understanding the physiology of other risk loci. Further, it is clear that only a small number of the many genes that influence risk for AD have been identified. Newer methodologies (e.g., genomewide association, study of copy number variation, and deep sequencing of candidate loci to identify rare risk variants) that have improved our understanding of other complex traits hold the promise of identifying a greater set of AD susceptibility loci.     

2型糖尿病易感基因的连锁和关联研究

2型糖尿病（T2DM）是由于胰岛素抵抗和β细胞分泌缺陷导致高血糖的一种复杂多基因疾病。遗传因素在T2DM的发生发展中起着重要的作用,其遗传率估计为70％～80％。鉴定2型糖尿病基因将有助于阐明其发病机制,发展更好的诊断、预防和治疗策略。2型糖尿病易感基因的鉴定方法主要有候选基因关联研究和全基因组连锁分析。有3种类型的候选基因：功能候选基因、图位候选基因和表达候选基因。虽然许多候选基因与T2DM的关联分析已经进行,但多数都没有得到一致的重复,过氧化物酶体增殖物激活受-γ,体和β-细胞ATP敏感性钾通道基因是目前最好重复的基因。迄今为止,T2DM的全基因组扫描已在20多个不同的群体中进行,包括欧洲人、美国白人、墨西哥裔美国人、美国本地印度人、非洲裔美国人和亚洲人,这些研究鉴定了一些与T2DM相关的QTLs区域。与T2DM显著和证实连锁的区域包括1q25、2q37．3q28、3p24、6q22、8p23、10q26、12q24、18p11、20q13等,与T2DM提示连锁的区域有1q42、2p21、2q24、4q34、5q13、5q31、7q32、9p24、9q21、10p14、11p13、11q13、12q15、14q23、20p12、Xq23等。鉴定这些区域的T2DMQTLs基因及其作用机制是未来的主要挑战。把DNA微阵列和蛋白质组学技术结合起来应用于传统的连锁分析和关联研究,研究基因-基因间、基因-环境间的互作和多个基因对T2DM的加性效应和综合作用,进一步加强国际协作,T2DM的遗传机制可望在不远的将来得到阐明。本文总结了2型糖尿病基因鉴定的现状,重点在一些得到重复的区域和未来的展望。     

The use of mouse models to unravel genetic architecture of physical activity: a review

The discovery of genetic variants that underlie a complex phenotype is challenging. One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human. Here, we show that PA is a complex phenotype with a strong biological basis and complex genetic architecture. Furthermore, we provide considerations for the translatability of this phenotype between species. Finally, we review studies which point to candidate genetic regions for PA in humans (genetic association and linkage studies) or use mouse models of PA (QTL studies) and we identify candidate genetic regions that overlap between species. On the basis of a large variety of studies in mice and human, statistical analysis reveals that the number of overlapping regions is not higher than expected on a chance level. We conclude that the discovery of new candidate genes for complex phenotypes, such as PA levels, is hampered by various factors, including genetic background differences, phenotype definition and a wide variety of methodological differences between studies .     

Recent advances in the genetic investigation of osteoarthritis

Osteoarthritis (OA) demonstrates considerable clinical heterogeneity, generating heated debate over whether OA is a single disease or a complex mix of disparate diseases and concerning which tissues are principally involved in disease initiation and progression. Epidemiological studies have demonstrated a major genetic component to OA risk. However, these studies have also revealed differences in risk between males and females and for disease at different skeletal sites. This observation has resulted in the concept of genes for specific sites rather than a generalised OA phenotype. Recent breakthroughs have shed considerable light on the nature of OA genetic susceptibility. Many candidate genes have been confirmed, such as the interleukin-1 gene cluster and the oestrogen alpha-receptor gene ESR1. Genome-wide linkage scans have revealed several regions harbouring novel loci, some of which are beginning to yield their genes.     

Molecular genetics of myocardial infarction

Myocardial infarction (MI) is an important clinical problem because of its large contribution to mortality. The main causal and treatable risk factors for MI include hypertension, hypercholesterolemia or dyslipidemia, diabetes mellitus, and smoking. In addition to these risk factors, recent studies have shown the importance of genetic factors and interactions between multiple genes and environmental factors. Disease prevention is an important strategy for reducing the overall burden of MI, with the identification of markers for disease risk being key both for risk prediction and for potential intervention to lower the chance of future events. Although genetic linkage analyses of families and sib-pairs as well as candidate gene and genome-wide association studies have implicated several loci and candidate genes in predisposition to coronary heart disease (CHD) or MI, the genes that contribute to genetic susceptibility to these conditions remain to be identified definitively. In this review, we summarize both candidate loci for CHD or MI identified by linkage analyses and candidate genes examined by association studies. We also review in more detail studies that have revealed the association with MI or CHD of polymorphisms in MTHFR, LPL, and APOE by the candidate gene approach and those in LTA and at chromosomal region 9p21.3 by genome-wide scans. Such studies may provide insight into the function of implicated genes as well as into the role of genetic factors in the development of CHD and MI.     

Molecular genetic approaches for studying the etiology of diabetic nephropathy

A critical challenge faced by clinical nephrologists today is the escalating number of patients developing end stage renal disease, a major proportion of which is attributed to diabetic nephropathy (DN). The need for new measures to prevent and treat this disease cannot be overemphasized. To this end, modern genetic approaches provide powerful tools to investigate the etiology of DN. Human studies have already established the importance of genetic susceptibility for DN. Several major susceptibility loci have been identified using linkage studies. In addition, linkage studies in rodents have pinpointed promising chromosomal segments that influence renal traits. Besides augmenting our understanding of disease pathogenesis, these animal studies may facilitate the cloning of disease susceptibility genes in man through the identification of homologous regions that contribute to renal disease. In human diabetes, various genes have been evaluated for their risk contribution to DN. This widespread strategy has been propelled by our knowledge of the glucose-activated pathways underlying DN. Evidence has emerged that a true association does indeed exist for some candidate genes. Furthermore, the in vivo manipulation of gene expression has shown that these genes can modify features of DN in transgenic and knockout rodent models, thus corroborating the findings from human association studies. Still, the exact molecular mechanisms involving these genes remain to be fully elucidated. This formidable task may be accomplished by continuing to harness the synergy between human and experimental genetic approaches. In this respect, our review provides a first synthesis of the current literature to facilitate this challenging effort.     

The search for human osteoporosis genes

Osteoporosis is the most prevalent metabolic bone disease and a major clinical and public health problem. Heredity plays an important and well-established role in determining the lifetime risk of this disease. Major efforts are currently underway to identify the specific genes and their allelic variations that contribute to the heritable component to osteoporosis. A number of laboratories are using quantitative trait locus (QTL) methods of genome scanning in families and animal models to identify candidate genomic regions and, ultimately, the genes and genetic variations that lead to osteoporosis. Several chromosomal regions of the human genome have now been linked to osteoporosis-related phenotypes. Although the specific genes contributing to the majority of these linkage signals have not been identified, two positional candidate genes have now been identified: low density lipoprotein receptor-related protein 5 (LRP5) and bone morphogenetic protein 2 (BMP2). A number of QTL has also been identified by cross-breeding strains of mice with variable bone density and several of these QTL have been fine mapped, providing a rich new base for understanding osteoporosis. Genetic association analyses have also provided evidence for a modest relationship between allelic variants in several biological candidate genes and bone mass and the risk of fracture. These ongoing animal and human studies will provide a continuing source of new insight into the genetic regulation of bone and mineral metabolism and the molecular etiology of osteoporosis. The new insight that will emerge from this ongoing research should lead to new ways of diagnosing, preventing and treating the growing clinical and public health problem of osteoporosis.     

Association study designs for complex diseases

Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.     

基于功能一致性和网络拓扑属性预测冠心病致病基因

基于功能一致性利用蛋白质互作网络挖掘潜在的疾病致病基因,对于了解疾病致病机理和改进临床治疗至关重要．基于基因功能一致性和其在蛋白质互作网络中的拓扑属性将基因与疾病之间建立关联,对疾病风险位点内的基因进行了致病风险预测,并通过GO及KEGG功能富集分析方法进一步筛选,预测出新的致病基因．预测出了51个新的冠心病致病基因,分析发现大部分基因参与了冠心病的致病过程．为疾病基因的挖掘提出一个新的思路,从而有助于复杂疾病致病机理的研究．     

The emerging genetic architecture of type 2 diabetes

Type 2 diabetes is a genetically heterogeneous disease, with several relatively rare monogenic forms and a number of more common forms resulting from a complex interaction of genetic and environmental factors. Previous studies using a candidate gene approach, family linkage studies, and gene expression profiling uncovered a number of type 2 genes, but the genetic basis of common type 2 diabetes remained unknown. Recently, a new window has opened on defining potential type 2 diabetes genes through genome-wide SNP association studies of very large populations of individuals with diabetes. This review explores the pathway leading to discovery of these genetic effects, the impact of these genetic loci on diabetes risk, the potential mechanisms of action of the genes to alter glucose homeostasis, and the limitations of these studies in defining the role of genetics in this important disease.     

Integration of expression profiles and genetic mapping data to identify candidate genes in intracranial aneurysm.

Intracranial aneurysm (IA) is a complex genetic disease for which, to date, 10 loci have been identified by linkage. Identification of the risk-conferring genes in the loci has proven difficult, since the regions often contain several hundreds of genes. An approach to prioritize positional candidate genes for further studies is to use gene expression data from diseased and nondiseased tissue. Genes that are not expressed, either in diseased or nondiseased tissue, are ranked as unlikely to contribute to the disease. We demonstrate an approach for integrating expression and genetic mapping data to identify likely pathways involved in the pathogenesis of a disease. We used expression profiles for IAs and nonaneurysmal intracranial arteries (IVs) together with the 10 reported linkage intervals for IA. Expressed genes were analyzed for membership in Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways. The 10 IA loci harbor 1,858 candidate genes, of which 1,561 (84%) were represented on the microarrays. We identified 810 positional candidate genes for IA that were expressed in IVs or IAs. Pathway information was available for 294 of these genes and involved 32 KEGG biological function pathways represented on at least 2 loci. A likelihood-based score was calculated to rank pathways for involvement in the pathogenesis of IA. Adherens junction, MAPK, and Notch signaling pathways ranked high. Integration of gene expression profiles with genetic mapping data for IA provides an approach to identify candidate genes that are more likely to function in the pathology of IA.     

The thyroglobulin gene as the first thyroid-specific susceptibility gene for autoimmune thyroid disease

Recent linkage and association studies provide evidence for thyroglobulin (Tg) being an autoimmune thyroid disease (AITD) susceptibility gene. The Tg locus has been reported to be linked with AITD in two independent studies, and further analysis demonstrated that markers within the Tg gene were associated with AITD. Furthermore, missense single-nucleotide polymorphisms (SNPs) in the Tg gene were shown to be associated with autoimmune thyroiditis in both mice and humans. If Tg is confirmed as a susceptibility gene for AITD, it could provide a novel therapeutic target.     

Genome-wide association studies and type 2 diabetes

In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ～10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.