Leprosy as a genetic model for susceptibility to common infectious diseases

Leprosy (Hansen’s disease) is a human infectious disease that can be effectively treated with long-term administration of multi-drug therapy. In 2006, over 250,000 new cases were reported to the World Health Organization. In the nineteenth century, disagreement among leprologists regarding the hereditary or infectious nature of leprosy was resolved with the identification of the etiological agent, Mycobacterium leprae. However, epidemiological studies maintain the importance of host genetics in leprosy susceptibility. A model free genome-wide linkage scan in multi-case families from Vietnam led to the positional cloning of global genetic risk factors in the PARK2/PACRG and LTA genes. The process of identifying the susceptibility variants provided invaluable insight into the replication of genetic effects, particularly the importance of considering population-specific linkage-disequilibrium structure. As such, these studies serve to improve our understanding of leprosy pathogenesis by implicating novel biological pathways while simultaneously providing a genetic model for common infectious diseases.     

Mapping genes for common diseases: the case for genetic (LD) maps

We examine the current effort to develop a haplotype map of the human genome and suggest an alternative approach which represents linkage disequilibrium patterns in the form of a metric LD map. LD maps have some of the useful properties of genetic linkage maps but have a much higher resolution which is optimal for SNP-based association mapping of common diseases. The studies that have been undertaken to date suggest that LD and recombination maps show some close similarities because of abundant, narrow, recombination hot spots. These hot spots are co-localised in all populations but, unlike linkage maps, LD maps differ in scale for different populations because of differences in population history. The prospects for developing optimized panels of SNPs and the use of linkage disequilibrium maps in disease gene localisation are assessed in the light of recent evidence.     

Leprosy: a paradigm for the study of human genetic susceptibility to infectious diseases

Fifty years ago, the first identification of a non Mendelian genetic contribution to the development of a common infectious disease, i.e. the association between malaria and sickle-cell trait, was shown using a supervised approach which tests a limited number of candidate genes selected by hypothesis. Since then, the few genes that were convincingly associated with susceptibility to human infectious diseases were identified following the same strategy. The study of leprosy has contributed to modifying this way of thinking. In the absence of a satisfying experimental model and because of the impossibility to grow the causative agent in vitro, the candidate gene approach has turned out to be of limited interest. Conversely, positional cloning led to the identification of two major genes involved in the control of the disease, establishing for the first time the oligogenic nature of a human genetic contribution to an infectious disease. It is likely that these major results obtained in leprosy and the recent burst of genomic tools will make the genome-wide screening (functional or positional) the main strategy of dissection of the genetic susceptibility to many common infectious diseases.     

Human genetic susceptibility to tuberculosis and other mycobacterial diseases

The existence of a genetic component in mycobacterial disease susceptibility is no longer in doubt and the investigations now being conducted aim to determine which genes are involved, to what extent, and in which disease phenotype they are relevant. In certain rare instances of susceptibility to poorly pathogenic mycobacteria, the genetic component is clear. The approaches employed to elucidate common disease susceptibility include linkage studies, particularly genome-wide linkage analysis of both tuberculosis and leprosy, and association studies. A number of candidate genes have shown association with tuberculosis, and in many cases, on replication of the study, association has been confirmed in a disparate population, indicating the wider importance of the gene in the disease process. In other instances, associations appear to be particular to a population or a subtype of disease.     

Ethical implications of genetic analysis of individual susceptibility to diseases

Ethics can be regarded as a reflection or reconsideration of existing moral codes in the search of good and goes beyond moral conduct. This means that ethics is a never-ending process, which in science must develop with the development of science itself. Thus, the process of seeking better ethics is as integral within science as the development of new methods. Along these lines of thought it can be argued that (1) poor science cannot be ethically sound, (2) every scientist has a personal responsibility to develop ethics in his area of expertise, (3) the development of solid ethical background in science requires education in ethics as well as in methodology and scientific thinking and (4) research ethics cannot develop in solitude, but needs input from other scientists, other fields (including philosophy) and society. Several burning questions can be identified within genetic analysis for individual susceptibility. These ethical aspects can be viewed from three different perspectives: practice of research, patient/research subject personally and long-term implications in society. This paper tries more to awaken thoughts than give clear answers.     

The mystery of the mystery of common genetic diseases

Common monogenic genetic diseases, ones that have unexpectedly high frequencies in certain populations, have attracted a great number of conflicting evolutionary explanations. This paper will attempt to explain the mystery of why two particularly extensively studied common genetic diseases, Tay Sachs disease and cystic fibrosis, remain evolutionary mysteries despite decades of research. I review the most commonly cited evolutionary processes used to explain common genetic diseases: reproductive compensation, random genetic drift (in the context of founder effect), and especially heterozygote advantage. The latter process has drawn a particularly large amount of attention, having so successfully explained the elevated frequency of sickle cell anemia in malaria-endemic areas. However, the empirical evidence for heterozygote advantage in other common genetic diseases is quite weak. I introduce and illustrate the significance of a hierarchy of genetic disease phenomena found within the genetic disease explanations, which include the phenomena: single mutation variants of a common genetic disease, single genetic diseases, and classes of diseases with related phenotypic effects. I demonstrate that some of the confusion over the explanations of common genetic diseases can be traced back to confusions over which phenomena are being explained. I proceed to briefly evaluate the existing evidence for two common human genetic diseases: Tay Sachs disease and cystic fibrosis. The above considerations will ultimately shed light on why these diseases’ evolutionary explanations remain so deeply unresolved after so such a great volume of research.     

The ubiquitous nature of epistasis in determining susceptibility to common human diseases

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.     

Evolutionary determinants of genetic variation in susceptibility to infectious diseases in humans

Although genetic variation among humans in their susceptibility to infectious diseases has long been appreciated, little focus has been devoted to identifying patterns in levels of variation in susceptibility to different diseases. Levels of genetic variation in susceptibility associated with 40 human infectious diseases were assessed by a survey of studies on both pedigree-based quantitative variation, as well as studies on different classes of marker alleles. These estimates were correlated with pathogen traits, epidemiological characteristics, and effectiveness of the human immune response. The strongest predictors of levels of genetic variation in susceptibility were disease characteristics negatively associated with immune effectiveness. High levels of genetic variation were associated with diseases with long infectious periods and for which vaccine development attempts have been unsuccessful. These findings are consistent with predictions based on theoretical models incorporating fitness costs associated with the different types of resistance mechanisms. An appreciation of these observed patterns will be a valuable tool in directing future research given that genetic variation in disease susceptibility has large implications for vaccine development and epidemiology.     

Genome-wide interaction-based association analysis identified multiple new susceptibility Loci for common diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn''s disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10⁻⁷). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.     

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene–gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identification of common genetic variation that modulates alternative splicing

Alternative splicing of genes is an efficient means of generating variation in protein function. Several disease states have been associated with rare genetic variants that affect splicing patterns. Conversely, splicing efficiency of some genes is known to vary between individuals without apparent ill effects. What is not clear is whether commonly observed phenotypic variation in splicing patterns, and hence potential variation in protein function, is to a significant extent determined by naturally occurring DNA sequence variation and in particular by single nucleotide polymorphisms (SNPs). In this study, we surveyed the splicing patterns of 250 exons in 22 individuals who had been previously genotyped by the International HapMap Project. We identified 70 simple cassette exon alternative splicing events in our experimental system; for six of these, we detected consistent differences in splicing pattern between individuals, with a highly significant association between splice phenotype and neighbouring SNPs. Remarkably, for five out of six of these events, the strongest correlation was found with the SNP closest to the intron–exon boundary, although the distance between these SNPs and the intron–exon boundary ranged from 2 bp to greater than 1,000 bp. Two of these SNPs were further investigated using a minigene splicing system, and in each case the SNPs were found to exert cis-acting effects on exon splicing efficiency in vitro. The functional consequences of these SNPs could not be predicted using bioinformatic algorithms. Our findings suggest that phenotypic variation in splicing patterns is determined by the presence of SNPs within flanking introns or exons. Effects on splicing may represent an important mechanism by which SNPs influence gene function.     

The genetic hypothesis for susceptibility to lepromatous leprosy

Summary Evidence for genetic influence of the host response to infection with Mycobacterium leprae is reviewed. A complex segregation analysis is performed on data for 91 families from Mactan, Philippines, in each of which at least one offspring developed lepromatous leprosy. The data are not found to be inconsistent with an autosomal recessive hypothesis for susceptibility to lepromatous leprosy. Heritability estimates in the range of 80% were calculated for sib-sib pairs under the multifactorial hypothesis for susceptibility. It is argued that the multifactorial hypothesis is more in keeping with available immunologic, epidemiologic, and demographic data than is the single gene hypothesis.     

A search for overlapping genetic susceptibility loci between non-Hodgkin lymphoma and autoimmune diseases

Non-Hodgkin lymphoma (NHL) is a hematological malignancy of the immune system, and, as with autoimmune and inflammatory diseases (ADs), is influenced by genetic variation in the major histocompatibility complex (MHC). Persons with a history of specific ADs also have increased risk of NHL. As the coexistence of ADs and NHL could be caused by factors common to both diseases, here we examined whether some of the associated genetic signals are shared. Overlapping risk loci for NHL subytpes and several ADs were explored using data from genome-wide association studies. Several common genomic regions and susceptibility loci were identified, suggesting a potential shared genetic background. Two independent MHC regions showed the main overlap, with several alleles in the human leukocyte antigen (HLA) class II region exhibiting an opposite risk effect for follicular lymphoma and type I diabetes. These results support continued investigation to further elucidate the relationship between lymphoma and autoimmune diseases.     

Genetic susceptibility to ageing-associated diseases

The constant and rapid increase of life expectancy in western countries is associated with a major ageing of our populations. In these conditions, we can expect an epidemic progression of most chronic diseases, especially cardiovascular, neurodegenerative and metabolic disorders, the main causes of death in the world. The global burden of these diseases will have a dramatic impact on the health and on the socio-economical context of our societies. From a global point of view, the occurrence and progression of these multifactorial diseases rely upon the nature and intensity of the environmental determinants we are exposed to all life long, but also to our individual genetic susceptibility. Through the determination of this higher susceptibility to an environmental risk factor and the understanding of its mechanisms of action, prevention and management efforts will be better focused. In such multifactorial affections, the development and the transmission of the disease do not follow the simple laws of monogenic Mendelian models. The complexity of this transmission is associated with the influence, at various degrees, of several genes and of a close interaction between this particular genetic susceptibility and environmental risk factors. With the recent development of automated and high throughput molecular biology techniques and their use in epidemiological studies, gene expression regulation and post genomic studies, the determination of sub-groups facing a higher individual genetic susceptibility has begun. This determination will offer new clues for a better-targeted disease management.     

Genetic approaches to common diseases

Combined molecular and epidemiological studies are advancing our understanding of the genetic basis of multifactorial diseases. Several of the results obtained during the past year highlight methodological issues associated with these approaches. For example, the affected sib-pair method has been applied successfully to detect linkage between the angiotensinogen gene and susceptibility to hypertension, and a large multi-centre epidemiological study has demonstrated association of a polymorphism of the angiotensin-converting enzyme gene with increased risk of myocardial infarction. The study of Mendelian forms of multifactorial diseases has also led to many new results. These include the characterization of mutations in the glucokinase gene in maturity onset diabetes of the young, localization to chromosome 2 of a gene involved in familial colon cancer, and localization to chromosome 19 of a gene responsible for hemiplegic migraine. New insights have been provided into the genetics of multifactorial disorders such as diabetes and hypertension through the study of animal models. Localization of susceptibility loci in such models has recently led to the identification of new candidate genes that may be implicated in disease.