Will family studies return to prominence in human genetics and genomics? Rare variants and linkage analysis of complex traits

A major focus of modern human genetics has been the search for genetic variations that contribute to human disease. These studies originated in families and used linkage methods as a primary analytical tool. With continued technical improvements, these family-based linkage studies have been very powerful in identifying genes contributing to monogenic disorders. When these methods were applied to disorders with complex, non-Mendelian patterns of inheritance they largely failed. The development of effective capabilities for Genome Wide Association Studies (GWAS) relegated family-based studies to a peripheral role in human genetics research. Despite the remarkable record of GWAS discoveries, common variations identified in GWAS account for a limited (frequently less than 10%) proportion of the heritable risk of qualitative traits or variance of quantitative traits. Next generation sequencing is facilitating a re-examination of family-based methods with surprising and intriguing results. We propose that rare variants of large effect underlie many linkage peaks, including complex quantitative phenotypes, and review the issues underlying this proposed basis for complex traits.     

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.     

Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases

Background  

Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research.     

Genome-wide association studies in systemic lupus erythematosus: a perspective

Genome-wide association studies (GWAS) have been shown to be a powerful way of identifying novel susceptibility genes in systemic lupus erythematosus (SLE), as demonstrated by a series of publications in the past year. Lupus has been a late-comer to the GWAS community, being preceded by success stories for the GWAS approach in other autoimmune diseases, including type I diabetes, ankylosing spondylitis, rheumatoid arthritis, Crohn's disease and ulcerative colitis. The paper by Suarez-Gestal and colleagues seeks to exploit the wealth of data available from a total of four GWAS in SLE, three in European-American populations and one in a Swedish population. The authors describe replication of ten lupus susceptibility alleles in a Spanish SLE case-control study.     

Genetics of atherosclerosis

Genome-wide association studies (GWAS) from the past several years have provided the first unbiased evidence of the genes contributing to common cardiovascular disease traits in European and some Asian populations. The results not only confirmed the importance of prior knowledge, such as the central role of lipoproteins, but also revealed that there is still much to learn about the underlying mechanisms of this disease, as most of the associated genes do not appear to be involved in pathways previously connected to atherosclerosis. In this review, I focus on the common forms of the disease and look at both human and animal model studies. I summarize what was known before GWAS, highlight how the field has been changed by GWAS, and discuss future considerations, such as the limitations of GWAS and strategies that may lead to a more complete, mechanistic understanding of atherosclerosis.     

Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions

Genotyping and phenotyping large natural populations provide opportunities for population genomic analysis and genome-wide association studies (GWAS). Several rice populations have been re-sequenced in the past decade; however, many major Chinese rice cultivars were not included in these studies. Here, we report large-scale genomic and phenotypic datasets for a collection mainly comprised of 1,275 rice accessions of widely planted cultivars and parental hybrid rice lines from China. The population was divided into three indica/Xian and three japonica/Geng phylogenetic subgroups that correlate strongly with their geographic or breeding origins. We acquired a total of 146 phenotypic datasets for 29 agronomic traits under multi-environments for different subpopulations. With GWAS, we identified a total of 143 significant association loci, including three newly identified candidate genes or alleles that control heading date or amylose content. Our genotypic analysis of agronomically important genes in the population revealed that many favorable alleles are underused in elite accessions, suggesting they may be used to provide improvements in future breeding efforts. Our study provides useful resources for rice genetics research and breeding.

     

全基因组关联研究在复杂性疾病遗传研究中的应用

在过去的几年中,人们应用全基因组关联研究(genomewide association studies,GWAS)对多种人类复杂性疾病及性状进行研究,如糖尿病、肿瘤、心血管疾病、神经精神系统疾病、自身免疫性疾病等,且已经鉴定出大量与之密切相关的遗传变异,为进一步探索人类复杂性疾病的遗传特征提供重要线索。但是,由于影响复杂性疾病的因素较多,许多已发现遗传变异对疾病贡献较小,作用机制尚不清楚,现全基因组关联研究亦存在许多问题。今本文就GWAS在复杂性疾病中的应用做一综述,并就其前景做一展望。     

全基因组关联分析的进展与反思

全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,随着人类基因组计划和基因组单倍体图谱计划的实施,人们已通过GWAS方法发现并鉴定了大量与人类性状或复杂性疾病关联的遗传变异,为进一步了解控制人类复杂性疾病发生的遗传特征提供了重要的线索。然而,由于造成复杂性疾病/性状的因素较多,而且GWAS研究系统较为复杂,因此目前GWAS本身亦存在诸多的问题。本文将从研究方式、研究对象、遗传标记,以及统计分析等方面,探讨GWAS的研究现状以及存在的潜在问题,并展望GWAS今后的发展方向。     

Race, ethnicity, and health: can genetics explain disparities?

Over the past decade, numerous studies have documented profound racial and ethnic disparities in disease in the United States. This essay examines how popular and scientific concepts of race and ethnicity converge with dominant understandings of genetics to inform the design and interpretation of research, public health policy, and medical practice. Although there is some acknowledgment in the biomedical community that racial and ethnic categories are social and not genetic, ideas about race and ethnicity that circulate in biomedicine are contradictory. Thus, in practice genetic explanations for observed differences are common both in the scientific literature and in popular media accounts of biomedical research. Such explanations naturalize racial and ethnic difference and create a conceptual barrier to developing a research program that explores the complex ways in which social inequality and experiences of racial discrimination interact with human biology to influence patterns of disease. Importantly, genetically based ideas lead to disease prevention policies that are bound to be ineffective.     

A new methodology to associate SNPs with human diseases according to their pathway related context

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.     

Gene-diet interactions in childhood obesity

Childhood overweight and obesity have reached epidemic proportions worldwide, and the increase in weight-associated co-morbidities including premature type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease will soon become major healthcare and economic problems. A number of studies now indicate that the childhood obesity epidemic which has emerged during the past 30 years is a complex multi-factorial disease resulting from interaction of susceptibility genes with an obesogenic environment. This review will focus on gene-diet interactions suspected of having a prominent role in promoting childhood obesity. In particular, the specific genes that will be presented (FTO, MC4R, and NPC1) have recently been associated with childhood obesity through a genome-wide association study (GWAS) and were shown to interact with nutritional components to increase weight gain. Although a fourth gene (APOA2) has not yet been associated with childhood obesity, this review will also present information on what now represents the best characterized gene-diet interaction in promoting weight gain.     

Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.     

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。     

Deep resequencing identifies candidate functional genes in leprosy GWAS loci

Leprosy is the second most prevalent mycobacterial disease globally. Despite the existence of an effective therapy, leprosy incidence has consistently remained above 200,000 cases per year since 2010. Numerous host genetic factors have been identified for leprosy that contribute to the persistently high case numbers. In the past decade, genetic epidemiology approaches, including genome-wide association studies (GWAS), identified more than 30 loci contributing to leprosy susceptibility. However, GWAS loci commonly encompass multiple genes, which poses a challenge to define causal candidates for each locus. To address this problem, we hypothesized that genes contributing to leprosy susceptibility differ in their frequencies of rare protein-altering variants between cases and controls. Using deep resequencing we assessed protein-coding variants for 34 genes located in GWAS or linkage loci in 555 Vietnamese leprosy cases and 500 healthy controls. We observed 234 nonsynonymous mutations in the targeted genes. A significant depletion of protein-altering variants was detected for the IL18R1 and BCL10 genes in leprosy cases. The IL18R1 gene is clustered with IL18RAP and IL1RL1 in the leprosy GWAS locus on chromosome 2q12.1. Moreover, in a recent GWAS we identified an HLA-independent signal of association with leprosy on chromosome 6p21. Here, we report amino acid changes in the CDSN and PSORS1C2 genes depleted in leprosy cases, indicating them as candidate genes in the chromosome 6p21 locus. Our results show that deep resequencing can identify leprosy candidate susceptibility genes that had been missed by classic linkage and association approaches.     

GWAS在2型糖尿病相关基因研究中的应用

2型糖尿病(type 2 diabetes,T2D)是一种常见的复杂疾病,其发病受到遗传和环境因素的共同作用.全基因组关联研究(genome-wide association study,GWAS)是一种可在全基因组范围筛查疾病相关的序列变异的新型群体关联研究方法.近年来,采用GWAS以及在此基础上展开的meta分析,已分别在TCF7L2、HHEX-IDE、SLC30A8、CDKAL1、CDKN2A-CDKN2B、IGF2BP2、NOTCH2、CDC123-CAMK1D、ADAMTS9、THADA、TSPAN8-LGR5、JAZF1等12个基因区域鉴定出多个T2D相关的多态位点.已有的研究提示,上述多个基因可能在胰岛β细胞发育和功能维持方面扮演着重要角色.本文集中介绍了GWAS的原理及其在T2D研究中的优势;回顾了GWAS在T2D研究中的主要发现;并对运用GWAS在T2D研究中尚需解决的问题进行了总结和展望.     

Genetic Insights into Sporadic Parkinson's Disease Pathogenesis

Intensive research over the last 15 years has led to the identification of several autosomal recessive and dominant genes that cause familial Parkinson’s disease (PD). Importantly, the functional characterization of these genes has shed considerable insights into the molecular mechanisms underlying the etiology and pathogenesis of PD. Collectively; these studies implicate aberrant protein and mitochondrial homeostasis as key contributors to the development of PD, with oxidative stress likely acting as an important nexus between the two pathogenic events. Interestingly, recent genome-wide association studies (GWAS) have revealed variations in at least two of the identified familial PD genes (i.e. α-synuclein and LRRK2) as significant risk factors for the development of sporadic PD. At the same time, the studies also uncovered variability in novel alleles that is associated with increased risk for the disease. Additionally, in-silico meta-analyses of GWAS data have allowed major steps into the investigation of the roles of gene-gene and gene-environment interactions in sporadic PD. The emergent picture from the progress made thus far is that the etiology of sporadic PD is multi-factorial and presumably involves a complex interplay between a multitude of gene networks and the environment. Nonetheless, the biochemical pathways underlying familial and sporadic forms of PD are likely to be shared.     

A better prognosis for genetic association studies in mice

Although inbred mouse strains have been the premier model organism used in biomedical research, multiple studies and analyses have indicated that genome-wide association studies (GWAS) cannot be productively performed using inbred mouse strains. However, there is one type of GWAS in mice that has successfully identified the genetic basis for many biomedical traits of interest: haplotype-based computational genetic mapping (HBCGM). Here, we describe how the methodological basis for a HBCGM study significantly differs from that of a conventional murine GWAS, and how an integrative analysis of its output within the context of other 'omic' information can enable genetic discovery. Consideration of these factors will substantially improve the prognosis for the utility of murine genetic association studies for biomedical discovery.     

From a sow's ear to a silk purse: real progress in porcine genomics

An incredible amount of progress has occurred in the past decade since the pig genome map began to develop. The porcine genetic linkage map now has nearly 5,000 loci including several hundred genes, microsatellites and amplified fragment length polymorphism (AFLP) markers being added to the map. Thanks to somatic cell hybrid panels and then radiation hybrid panels the physical genetic map is also growing rapidly and now has over 4,000 genes and markers. Many quantitative trait loci (QTL) scans have been completed and together with candidate gene analyses have identified important chromosomal regions and individual genes associated with traits of economic interests. Using marker assisted selection (MAS) the commercial pig industry is actively using this information and traditional performance information to improve pig production. Large scale pig arrays are just now beginning to be used and co-expression of thousands of genes is now advancing our understanding of gene function. The pig's role in xenotransplantation and biomedical research makes the study of its genome important for the study of human disease. Sequencing of the pig genome appears on the near horizon. This commentary will discuss recent advances in pig genomics, directions for future research and the implications to both the pig industry and human health.     

Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life

Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum’s one gene—one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems’ responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.     

Gene map of the extended human MHC

The major histocompatibility complex (MHC) is the most important region in the vertebrate genome with respect to infection and autoimmunity, and is crucial in adaptive and innate immunity. Decades of biomedical research have revealed many MHC genes that are duplicated, polymorphic and associated with more diseases than any other region of the human genome. The recent completion of several large-scale studies offers the opportunity to assimilate the latest data into an integrated gene map of the extended human MHC. Here, we present this map and review its content in relation to paralogy, polymorphism, immune function and disease.