Human genetics: the hidden text of genome-wide associations

Genome-wide association studies are finally leading geneticists straight to the genetic susceptibility factors for complex diseases. Several challenges lie ahead, including translation of the findings into practical public health outcomes, and integrating genetic analysis with broader biological understanding.     

A comparative study of discriminating human heart failure etiology using gene expression profiles

Background  

Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clinically with many similar decreases in ventricular function, emerging work suggests that they are distinct diseases with different responses to therapy. The ability to distinguish between ischemic and non-ischemic heart failure may be essential to guide appropriate therapy and determine prognosis for successful treatment. In this paper we consider discriminating the etiologies of heart failure using gene expression libraries from two separate institutions.     

Multifactorial diseases: a nightmare for the geneticist

Common diseases are often familial, but they do not show in most families, a simple pattern of inheritance. In a few families these diseases may be caused by a mutation in a single gene. In most families these diseases are multifactorial, they result from a complex interaction between a genetic component which is often polygenic and many environmental factors. Two major, model free, methods are used to locate and identify susceptibility genes that predispose to multifactorial diseases. The first is a non parametric linkage analysis that relies on affected sib pairs, or an affected pedigree member, the second method is association studies which looks for increase frequency of particular alleles or genotypes in affected compared with unaffected individuals in the population. Most of the results have not been replicated, identifying susceptibility genes is proving much more difficult than most geneticists imagined 20 years ago. The main reason for this irreproducibility is genetic heterogeneity.     

The genetics of cardiac birth defects

Congenital heart disease likely results from a complex mixture of environmental and genetic factors. Recent work has elucidated rare single gene mutations that cause a variety of cardiac defects, but the etiologies of more common disease remains unknown. Here, we review the known genetic causes of cardiac malformations and discuss future approaches for addressing sporadic congenital heart disease as a complex trait.     

Microarray,SAGE and their applications to cardiovascular diseases

The wealth of DNA data generated by the human genome project coupling with recently invented high-throughput gene expression profiling techniques has dramatically sped up the process for biomedical researchers on elucidating the role of genes in human diseases. One powerful method to reveal insight into gene functions is the systematic analysis of gene expression. Two popular high-throughput gene expression technologies, microarray and Serial Analysis of Gene Expression (SAGE) are capable of producing large amounts of gene expression data with the potential of providing novel insights into fundamental disease processes, especially complex syndromes such as cardiovascular disease, whose etiologies are due to multiple genetic factors and their interplay with the environment. Microarray and SAGE have already been used to examine gene expression patterns of cell-culture, animal and human tissues models of cardiovascular diseases. In this review, we will first give a brief introduction of microarray and SAGE     

Genome-wide association studies: a new window into immune-mediated diseases

Given the recent explosion of genetic discoveries, 2007 is becoming known to human geneticists as the year of genome-wide association studies. In fact, more genetic risk factors for common diseases were identified in this one year than had been collectively reported before 2007. In particular, 2007 witnessed the discovery of many genes that influence susceptibility to individual immune-mediated diseases, as well as other genes that are associated with susceptibility to more than one disease. Although much work remains to be done, in this Review we discuss what effect these studies are having on our understanding of disease pathogenesis and their potential impact on future immunology studies.     

Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits.

Recent advances in molecular biology have provided geneticists with ever-increasing numbers of highly polymorphic genetic markers that have made possible linkage mapping of loci responsible for many human diseases. However, nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, we explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. We compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. We also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, we also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that we consider, we find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, we also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. We also discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models.     

The Collaborative Cross, a groundbreaking tool to tackle complex traits

Complex traits, like the susceptibility to common diseases, are controlled by numerous genomic regions which individual effect is generally weak. These observations led geneticists to develop an experimental system to dissect the genetic of complex traits in the mouse. The Collaborative Cross (CC) is a genetic reference population of over 300 inbred lines derived from eight inbred strains of three Mus musculus sub-species that captures 90% of the genetic variation known in the mouse genome. We present here the generation and the characteristics of the CC and we report the results of the first experiments with partially inbred CC lines.     

Selection strategies for linkage studies using twins.

Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.     

Human diseases: clues to cracking the connexin code?

The vertebrate gap junctions formed by the connexin family of transmembrane proteins came to the attention of geneticists in 1993 with the identification of mutations linked to a form of demyelinating neuropathy. Since then, several other genetic disorders have been linked to mutations in specific connexin genes. Also, different diseases can result from different mutations in the same connexin gene. In addition, specific connexin knockout mice have surprising phenotypes. This is leading cell biologists to look afresh at connexins and their involvement in intercellular communication through gap junctions, a process that seems central to coordinating cell function within tissues. Here, we comment on how genetic studies are giving a new impetus to the cell biology of gap junctions.     

Quantifying and modeling birth order effects in autism

Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.     

The Roles of FMRP-Regulated Genes in Autism Spectrum Disorder: Single- and Multiple-Hit Genetic Etiologies

Autism spectrum disorder (ASD) is a highly heritable complex neurodevelopmental condition characterized by impairments in social interaction and communication and restricted and repetitive behaviors. Although roles for both de novo and familial genetic variation have been documented, the underlying disease mechanisms remain poorly elucidated. In this study, we defined and explored distinct etiologies of genetic variants that affect genes regulated by Fragile-X mental retardation protein (FMRP), thought to play a key role in neuroplasticity and neuronal translation, in ASD-affected individuals. In particular, we developed the Trend test, a pathway-association test that is able to robustly detect multiple-hit etiologies and is more powerful than existing approaches. Exploiting detailed spatiotemporal maps of gene expression within the human brain, we identified four discrete FMRP-target subpopulations that exhibit distinct functional biases and contribute to ASD via different types of genetic variation. We also demonstrated that FMRP target genes are more likely than other genes with similar expression patterns to contribute to disease. We developed the hypothesis that FMRP targets contribute to ASD via two distinct etiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets (“single-hit etiology”) or (2) the combination of multiple less penetrant disruptions of nonembryonic, synaptic FMRP targets (“multiple-hit etiology”). The Trend test provides rigorous support for a multiple-hit genetic etiology in a subset of autism cases and is easily extendible to combining information from multiple types of genetic variation (i.e., copy-number and exome variants), increasing its value to next-generation sequencing approaches.     

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.     

Gene mapping in the 20th and 21st centuries: statistical methods, data analysis, and experimental design

In the 20th century geneticists began to unravel some of the simpler aspects of the etiology of inherited diseases in humans. The theory of linkage analysis was developed and applied long before the advent of molecular biology, but only the technological advances of the second half of the 20th century made large-scale gene mapping with a dense genome-spanning set of markers a reality. More recently, the primary topic of interest has shifted from simple Mendelian diseases, for which genotypes of some gene are the cause of disease, to more complex diseases, for which genotypes of some set of genes together with environmental factors merely alter the probability that an individual gets the disease, although individual factors are typically insufficient to cause the disease outright. To this end, a great deal of dogma has evolved about the best way to skin this cat, although to date success has been minimal with any approach. We postulate that the main reason for this is a lack of attention to experimental design. Once the data have been ascertained, the most powerful statistical methods will not be able to salvage an inappropriately designed study (Andersen 1990). Each phenotype and/or population mandates its own individually tailored study design to maximize the chances of successful gene mapping. We suggest that careful consideration of the available data from real genotype-phenotype correlation studies (as opposed to oversimplified theoretically tractable models), and the practical feasibility of different ascertainment schemes dictate how one should proceed. In this review we review the theory and practice of gene mapping at the close of the 20th century, showing that most methods of linkage and linkage disequilibrium analysis are similar in a fundamental sense, with the differences being related more to study design and ascertainment than to technical details of the underlying statistical analysis. To this end, we propose a new focus in the field of statistical genetics that more explicitly highlights the primacy of study design as the means to increase power for gene mapping.     

Metagenomics and personalized medicine

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn's disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes.     

Wild mice: an ever-increasing contribution to a popular mammalian model

Classical laboratory inbred strains of mice have been extremely helpful for research in immunology and oncology, and more generally, for the analysis of complex traits. Unfortunately, because they all derive from a relatively small pool of ancestors, their genetic polymorphism is rather limited. However, recently strains belonging to different species of Mus have been established from wild progenitors. These are an interesting addition to the arsenal of mouse geneticists, because they can be crossed with classical laboratory strains to produce viable and fertile offspring with a large number of polymorphisms of natural origin. These strains are helpful for making genome annotations because they permit highly refined genotype-phenotype correlations. They also allow the interpretation of molecular variation within a clear evolutionary framework. In this article, we provide examples with the aim of promoting the use of these new strains.     

Admixture mapping: from paradigms of race and ethnicity to population history

Admixture mapping is a whole genome association strategy that takes advantage of population history-or genetic ancestry-to map genes for complex diseases. However, because it uses racial/ethnic groupings to examine differential disease risk, admixture mapping raises ethical and social concerns. While there has been much theoretical commentary regarding the ethical and social implications of population-based genetic research, empirical data from stakeholders most closely involved with these studies is limited. One of the first admixture mapping studies carried out was a scan for Multiple Sclerosis (MS) risk factors in an African-American population. Applying qualitative research methods, we used this example to explore developing views, experiences and perceptions of the ethical and social implications of admixture mapping and other population-based research-their value, risks and benefits, and the future prospects of the field. Additionally, we sought to understand how social and ethical risks might be mitigated, and the benefits of this research optimized. We draw on in-depth, one-on-one interviews with leading population geneticists, genome scientists, bioethicists, and African-Americans with MS. Here we present our findings from this unique group of key informants and stakeholders.     

Genome-virome interactions: examining the role of common viral infections in complex disease

New technologies have widened our view of 'complex diseases': those with both genetic and environmental risk factors. In this Review, we explore recent genetic and virological evidence implicating host-virus interactions in three diseases: type 1 diabetes, inflammatory bowel disease and asthma. The viruses implicated in these diseases cause mucosal infections that affect most of the population but are asymptomatic or mild in many hosts. These findings place a new emphasis on common viral infections as important environmental factors in the pathogenesis of complex diseases, and they compel the field to pursue a better understanding of host interactions with the human virome.     

复杂疾病全基因组关联研究进展—— 研究设计和遗传标记

实现全基因组关联研究(Genome-wide association study, GWA)在数年前还是遗传学家们的梦想, 如今它已经变成了现实。自2005年Science杂志报道了第一项有关年龄相关性(视网膜)黄斑变性全基因组关联研究研究以来, 有关与复杂疾病的全基因组关联研究如雨后春笋般层出不穷。文中介绍了近两年来全基因组关联研究在复杂疾病研究领域内的主要发现、全基因组关联研究设计原理、遗传标记的选择、比较及相关商品信息。最后介绍了人类基因组拷贝数变异的研究进展, 总结了人类全基因组关联研究所取得成就和存在的问题, 并对全基因组关联研究未来的研究重点和要解决的问题进行了展望。