Genetic Studies Of Complex Diseases Research Articles

Analysis of the linkage disequilibrium extension in the Azores Islands (Portugal)

The design of genetic studies of complex diseases is dependent on the extension and distribution of linkage disequilibrium (LD) across the genome and populations. Here, we characterize the LD extension in the Azores (Western, Central and Eastern islands groups) and mainland Portugal populations. LD was evaluated in three chromosomic regions: the Xq13.3, the Y-chromosome and the HLA (6p21) for the Azorean population. The results obtained in the Western group for the Xq13.3 markers provide significant evidence of LD in 10 comparisons, after correction. In the São Miguel island population, the assessment of LD for HLA demonstrated a total of 13 out of 36 pairs with significant LD, with the largest genetic distance (2.5 Mb) between HLA-DRB1 and D6S265. The pairwise association between Y-markers in the Azorean population revealed 46% pairs with significant LD. In addition, D′ analysis indicates that the Western group presents higher values when compared with the Central and Eastern groups. Taken together, the data show that the Azorean population presents a lower D′ (0.142) compared with mainland Portugal (0.226). Although, both populations do not show extensive LD, the easy reconstruction of large pedigrees in the Azorean population is a valuable resource for the fine mapping of disease genes.

Prioritize and Select SNPs for Association Studies with Multi-Stage Designs

Large-scale whole genome association studies are increasingly common, due in large part to recent advances in genotyping technology. With this change in paradigm for genetic studies of complex diseases, it is vital to develop valid, powerful, and efficient statistical tools and approaches to evaluate such data. Despite a dramatic drop in genotyping costs, it is still expensive to genotype thousands of individuals for hundreds of thousands single nucleotide polymorphisms (SNPs) for large-scale whole genome association studies. A multi-stage (or two-stage) design has been a promising alternative: in the first stage, only a fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in later stages. Multi-stage designs have also been used in candidate gene association studies, usually in regions that have shown strong signals by linkage studies. To decide which set of SNPs to be genotyped in the next stage, a common practice is to utilize a simple test (such as a chi2 test for case-control data) and a liberal significance level without corrections for multiple testing, to ensure that no true signals will be filtered out. In this paper, I have developed a novel SNP selection procedure within the framework of multi-stage designs. Based on data from stage 1, the method explicitly explores correlations (linkage disequilibrium) among SNPs and their possible interactions in determining the disease phenotype. Comparing with a regular multi-stage design, the approach can select a much reduced set of SNPs with high discriminative power for later stages. Therefore, not only does it reduce the genotyping cost in later stages, it also increases the statistical power by reducing the number of tests. Combined analysis is proposed to further improve power, and the theoretical significance level of the combined statistic is derived. Extensive simulations have been performed, and results have shown that the procedure can reduce the number of SNPs required in later stages, with improved power to detect associations. The procedure has also been applied to a real data set from a genome-wide association study of the sporadic amyotrophic lateral sclerosis (ALS) disease, and an interesting set of candidate SNPs has been identified.

Evaluation of linkage disequilibrium on the Xq13.3 region: Comparison between the Azores islands and mainland Portugal

The design of genetic studies of complex diseases is dependent on the extent and distribution of linkage disequilibrium (LD) across the genome in different populations. Here, we characterize the extent of LD in the Azores (Western, Central, and Eastern island groups) and mainland Portugal populations. LD was evaluated in the Xq13.3 region by genotyping eight STR markers spanning 20.9 Mb. Standardized multiallelic disequilibrium coefficient (D') analysis indicates that the Western group presents higher values when compared with the Central and Eastern groups. However, all island groups show values of D' lower than 0.5 and 0.33, suggesting no extensive LD in these populations. Taken together, the data show that the Azorean population presents a lower D' (0.142) than mainland Portugal (0.226). Although, both populations do not show extensive LD, the easy reconstruction of large pedigrees in the Azorean population is a valuable resource for the fine mapping of disease genes.

Large-Scale SNP Genotyping with Canine Buccal Swab DNA

The dog is an attractive model for genetic studies of complex disease. With drafts of the canine genome complete, a large number of single-nucleotide polymorphisms (SNPs) that are potentially useful for gene-mapping studies and empirical estimations of canine diversity and linkage disequilibrium (LD) are now available. Unfortunately, most canine SNPs remain uncharacterized, and the amount and quality of DNA available from population-based samples are limited. We assessed how these real-world challenges influence automated SNP genotyping methods such as Illumina's GoldenGate assay. We examined 384 SNPs on canine chromosome 9 and successfully genotyped a minimum of 217 and a maximum of 275 SNPs using buccal swab samples for 181 dogs (86 beagles, 76 border collies, and 15 Australian shepherds). Call rates per SNP and sample averaged 97%, with reproducibility within and between analyses averaging 98%. The majority of these SNPs were polymorphic across all 3 breeds. We observed extensive LD, albeit less than reported for surveys using fewer dogs, consistent between breeds. Analyses of population substructure indicated that beagles are distinct from border collies and Australian shepherds. These results demonstrate the suitability of amplified canine buccal samples for high-throughput multiplex genotyping and confirm extensive LD in the dog.

Genetics of sarcoidosis

The predisposition to sarcoidosis is genetically determined, and genetics appears also to account for the variability in clinical phenotype and behaviour. Many genetic loci have been investigated to date, mainly in case-control association studies. However, only a small number of human leukocyte antigen (HLA) alleles have been consistently associated with sarcoidosis susceptibility/phenotype. In this regard, the association between Löfgren's syndrome and the extended HLA-DRB1*0301/DQB1*0201 haplotype is probably the most extensively reproduced. Several, generally less convincing, associations have been also reported. Of these, the chemokine receptor and butyrophilin-like 2 (BTNL2) associations are most promising. However, two major limitations of genetic studies are that the understanding of the biological relevance of gene variations in the genome is still incomplete and that the reported associations need to be verified in populations of different ethnicities. Despite a number of intriguing hypotheses, what causes sarcoidosis remains obscure. Genetic studies and, importantly, functional analysis of candidate genes will provide insight into pathogenesis and disease risk. However, if, as many believe, sarcoidosis is a heterogeneous collection of disorders, a critical step will be to carefully refine the clinical phenotype, as genetic studies of complex diseases are more rewording if a very specific disease subset is addressed.

Test for Interaction between Two Unlinked Loci

Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.

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

BackgroundAlcoholism is a complex disease. There have been many reports on significant comorbidity between alcoholism and schizophrenia. For the genetic study of complex diseases, association analysis has been recommended because of its higher power than that of the linkage analysis for detecting genes with modest effects on disease.ResultsTo identify alcoholism susceptibility loci, we performed genome-wide single-nucleotide polymorphisms (SNP) association tests, which yielded 489 significant SNPs at the 1% significance level. The association tests showed that tsc0593964 (P-value 0.000013) on chromosome 7 was most significantly associated with alcoholism. From 489 SNPs, 74 genes were identified. Among these genes, GABRA1 is a member of the same gene family with GABRA2 that was recently reported as alcoholism susceptibility gene.ConclusionBy comparing 74 genes to the published results of various linkage studies of schizophrenia, we identified 13 alcoholism associated genes that were located in the regions reported to be linked to schizophrenia. These 13 identified genes can be important candidate genes to study the genetic mechanism of co-occurrence of both diseases.

A systems biology approach to genetic studies of complex diseases

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

Linkage disequilibrium in young genetically isolated Dutch population

The design and feasibility of genetic studies of complex diseases are critically dependent on the extent and distribution of linkage disequilibrium (LD) across the genome and between different populations. We have examined genomewide and region-specific LD in a young genetically isolated population identified in the Netherlands by genotyping approximately 800 Short Tandem Repeat markers distributed genomewide across 58 individuals. Several regions were analyzed further using a denser marker map. The permutation-corrected measure of LD was used for analysis. A significant (P<0.0004) relation between LD and genetic distance on a genomewide scale was found. Distance explained 4% of the total LD variation. For fine-mapping data, distance accounted for a larger proportion of LD variation (up to 39%). A notable similarity in the genomewide distribution of LD was revealed between this population and other young genetically isolated populations from Micronesia and Costa Rica. Our study population and experiment was simulated in silico to confirm our knowledge of the history of the population. High agreement was observed between results of analysis of simulated and empirical data. We conclude that our population shows a high level of LD similar to that demonstrated previously in other young genetic isolates. In Europe, there may be a large number of young genetically isolated populations that are similar in history to ours. In these populations, a similar degree of LD is expected and thus they may be effectively used for linkage or LD mapping.

Genetic studies in complex disease

Since the introduction of user-friendly and high-throughput techniques for performing genetic studies in complex diseases, a large number of (published and unpublished) studies have been carried out in an attempt to find polymorphisms or variants of putative genes and establish their relationships with myocardial infarction. However, as none of the single nucleotide polymorphisms (SNPs) of genes encoding proteins involved in atherothrombosis has yet been consistently associated with an increased or decreased risk of myocardial infarction or stroke, it is not surprising that doubts are beginning to arise as to whether the main reason for this lack of success is methodological [1]. Are we using an inadequate tool? Association studies are appealingly quick and cheap, have successfully identified traditional risk factors and, Rosendaal points out [2], have been extensively used in genetic research—but extensively is not the same as successfully. On the other hand, although linkage studies have been successfully used in the study of monogenic diseases, their value in identifying new culprit genes associated with myocardial infarction has not yet been proved. Rosendaal makes a very good point that should not be forgotten: it is not a question of deciding between association and linkage studies, but between ‘proof of concept’ studies and ‘fishing expeditions’. We know quite a lot about the pathophysiology of myocardial infarction, and have identified a number of factors that are almost certainly involved, such as inflammation, thrombosis and lipid metabolisms. However, the proteins involved in these mechanisms are encoded by thousands of genes, and we still do not know enough about other mechanisms involving an equally large number of genes. The result is that, although there are many pathophysiological candidates, we need to identify the most important and complete our understanding of them. Positional candidates are welcome. Linkage studies can help us to focus on the most likely regions and thus enable us to concentrate on what our knowledge indicates as being the most likely candidates; the epidemiological relevance of such candidates can then be confirmed (or not) by association studies. Linkage and association approaches are therefore not mutually exclusive but complementary, and have been recently integrated with other approaches such as gene expression profiling (transcriptome analysis) and proteomics. By studying the tissues or cells (atherosclerotic plaques, circulating blood cells) directly involved in both normal and diseased states, as well as at different time points during the course of disease, we can identify new candidates deserving further investigation in functional and epidemiological studies; furthermore, the availability of new databases correlating gene function and known SNPs could lead to a major breakthrough. In our opinion, we are now beginning to come out of the fog. Rather than being simply a means of finding genes, genetic studies are much more complex and require the integration of different methodologies and approaches, in the same way that the development of a new drug does not depend on Phase III trials alone but has to go through many other stages before reaching the clinical arena.

Genetic studies in complex disease

Genetic studies in complex disease

Genetic studies in complex disease

Genetic studies in complex disease

Genetic studies in complex disease

Genetic studies in complex disease

Genetic studies in complex disease: the case pro association studies

Genetic studies in complex disease: the case pro association studies

Genetic studies in complex disease: the case pro linkage studies

Complex diseases are usually the result of the interactions between multiple genes and multiple environmental factors. The identification of genes and genetic variants (i.e. polymorphisms) affecting the risk of suffering from a complex disease is not an easy task, even if the more sophisticated genetic epidemiology methods are used [1]. For this reason, study design and appropriate methods of analysis are critically important. The primary tools for the identification, localization, and functional analysis of genes are linkage and association studies [2].

Association of NOD2 with Crohn's disease in a homogenous Irish population.

Linkage of IBD to the pericentromeric region of chromosome 16 has been widely confirmed by analyses of multiple populations. The NOD2 gene is located in the peak region of linkage on chromosome 16 and thought to be involved in the activation of nuclear factor (NF) kappaB in response to bacterial components. Mutations in the NOD2 gene are found to be strongly associated with susceptibility to Crohn's disease (CD). A total of 65 Irish CD families were genotyped to determine if NOD2 mutations conferred susceptibility to CD and the prevalence of these mutations in sporadic and familial forms of the disease. The Irish population is relatively homogenous and thus may provide advantages in genetic studies of complex diseases. We confirmed the IBD1 locus as a susceptibility locus for IBD within the Irish population by linkage analysis followed by linkage disequilibrium studies. No significant evidence of linkage was observed to the previously identified regions on chromosomes 1, 12 and 14. In all, 131 CD affected families were then genotyped for seven of the previously published NOD2 single-nucleotide polymorphisms (SNPs). Allelic transmission distortion was investigated using the pedigree disequilibrium test (PDT). SNP13 (3020insC) was found to be associated with CD (P=0.0186). Patients who possessed a rare allele of SNP8, 12 or 13 presented earlier when compared to patients without rare variants (mean age, 20.1 vs 24 years, P=0.011) and the rare allele of SNP13 was observed to be predominantly linked to ileal disease (P=0.02). This report confirms the importance of NOD2 as a susceptibility gene for CD within the Irish population.

Ascertainment adjustment in complex diseases.

Genetic studies of complex diseases must confront two statistically difficult issues simultaneously. First, in many settings, to minimize the number of individuals to be genotyped, families enriched for disease must be oversampled. Also, statistical models in family studies should allow for residual association. This association will represent unmeasured genetic and environmental factors influencing disease risk. Dealing with these features simultaneously is both compelling and challenging. Burton et al. [2000] (Am. J. Hum. Gen. 69: 1505-14) recently discussed this issue and suggested that ascertainment corrections may lead to problematic parameter estimation. We revisit the issues and examples of Burton et al. [2000] (Am. J. Hum. Genet. 69: 1505-14) and present a more optimistic assessment. Estimation in this context is conceptually straightforward, but may be more problematic in practice. Specifically, we find that even slight misspecification of the random effects distribution in ascertainment-adjusted likelihood can yield severely biased parameter estimates. This result should make scientists wary when interpreting results from ascertainment-adjusted variance-component models. .

Use of Closely Related Affected Individuals for the Genetic Study of Complex Diseases in Founder Populations

We propose a method, the maximum identity length contrast (MILC) statistic, to locate genetic risk factors for complex diseases in founder populations. The MILC approach compares the identity length of parental haplotypes that are transmitted to affected offspring with the identity length of those that are not transmitted to affected offspring. Initially, the statistical properties of the method were assessed using randomly selected affected individuals with unknown relationship. Because both nuclear families with multiple affected sibs and large pedigrees are often available in founder populations, we performed simulations to investigate the properties of the MILC statistic in the presence of closely related affected individuals. The simulation showed that the use of closely related affected individuals greatly enhances the power of the statistic. For a given sample size and type I error, the use of affected sib pairs, instead of affected individuals randomly selected from the population, could increase the power by a factor of two. This increase was related to an increase of kinship-coefficient contrast between haplotype groups when closely related individuals were considered. The MILC approach allows the simultaneous use of affected individuals from a founder population and affected individuals with any kind of relationship, close or remote. We used the MILC approach to analyze the role of HLA in celiac disease and showed that the effect of HLA may be detected with the MILC approach by typing only 11 affected individuals, who were part of a single large Finnish pedigree.

Approaches to the genetics of cardiovascular disease through genetic field work

Successful molecular genetic studies of complex disease require exact, careful phenotypization, which is more difficult than that performed for monogenic diseases. We have developed a family-oriented field working approach, which relies on index patients, their primary care physicians, and a minimum number of field working staff. The patients are responsible for recruiting their family members. Packets containing an explanatory pamphlet, an informed consent statement, a questionnaire, and blood cuvettes are provided. Data are transferred from questionnaire and from the laboratory into a computer program that facilitates construction of the family tree. We have applied this genetic field working approach primarily to patients with lipid disorders. Coupling results from genetic field working with modern DNA diagnostic tests such as the oligonucleotide ligation assay, has enabled us to effectively identify patients with familial hypercholesterolemia in the German population. We are now extending genetic field working to hypertension. Hypertension is much more difficult to study, because the phenotype is more difficult to discern and document. Both complex diseases have the disadvantage that the parents of the index patients are likely to already be dead. Nevertheless, we concentrate on the recruitment of large pedigrees, sibling pairs with parents whenever possible, and trios consisting of index patient and both parents or index patient, parent and sibling. With these constellations we can conduct association studies, linkage analysis, and novel combinations of both approaches.

Do genetics play a role in the pathogenesis of asthma?

Asthma is a phenotypically heterogeneous disorder associated with intermittent respiratory symptoms, bronchial hyperresponsiveness (BHR), and reversible airflow obstruction. 1, 2 Most individuals with asthma have evidence of BHR and atopy, with elevated total serum IgE, allergen-specific IgE, or skin-test reactivity to common allergens. At present, it is not clear whether asthma, BHR, and atopy are clinical manifestations of the same underlying primary defect or whether they share common pathways with distinct primary molecular origins. A genetic component to asthma has long been suggested by studies demonstrating increased prevalences of asthma among first-degree relatives of asthmatic subjects and greater concordance rates among monozygotic compared with dizygotic twins. 3-7 Such “clustering” in families is consistent with a genetic cause but may also reflect the effects of shared environmental exposures. Thus familial clustering does not prove a genetic cause. Although segregation analyses in populations of asthmatic families have suggested the likelihood of a major