Single-Nucleotide Polymorphisms and Lung Disease: Finding SNPs in the Human Genome

March 29, 2016 Category: Lung

SNP GenotypingAn important aspect of the Human Genome Project was the massive governmental and industry-sponsored effort to develop a dense set of SNP markers throughout the human genome. This effort was spurred on by the realization that a dense set of SNP markers could yield critical information to determine specific functional SNPs and combinations of SNPs that form the genetic basis of complex diseases. The SNP Consortium and the International HapMap Project (, as well as research conducted by individual laboratories throughout the world, have generated enormous SNP-based resources to allow biologists to better investigate complex genetic diseases.

SNP Genotyping

Determination of the base sequence of DNA at a specific SNP site is called genotyping. For research discovery purposes, there are a number of high through-put technologies available to optimize the genotyping of large numbers of individuals for one SNP at a time. Genotyping by microarray allows the opposite approach—the simultaneous determination of multiple SNPs from an individual—and it is this strategy that promises to influence the practice of medicine. Microarrays allow the fixation of hundreds or thousands of specific oligonucleotide probes in a precise configuration or array onto a small-format solid support, such as a microscope slide, where they can be identified.

New technologies have recently been described that will allow the complete sequencing of an individual’s DNA. Ultimately, such an approach would eliminate the need for genotyping of hundreds or thousands of SNPs across the genome; however, the cost-effectiveness and bioinformatic challenge of this approach to clinical genomics is still unclear.

General Approaches to Gene Mapping

Two major strategies have been employed to identify the genes and the mutations/polymorphisms that contribute to the development of pulmonary diseases: linkage analysis and candidate gene association studies. Linkage analysis requires recruitment of affected families, whereas candidate genes are tested by association studies of unrelated subjects approved by Canadian Health&Care Mall.

Linkage Analysis

Linkage Analysis

Linkage analysis (sometimes referred to as positional cloning or genomic scanning) is the classical method for randomly searching the entire human genome for disease-causing genes. It usually requires affected families of at least two generations, although single-generation sibling-pairs can also be used, Each family member is genotyped for DNA markers (SNPs) that are scattered throughout the genome, Linkage analysis determines whether any of the markers are inherited with the disease more often than would be predicted by chance. The genes are identified solely on the basis of their position in the genome (thus “positional cloning”). The CF transmembrane conductance regulator gene and the mutations within this gene that are the cause of CF were the first severe disease-causing gene and mutations to be identified using positional cloning. An advantage of this approach is that completely novel genes can be implicated in disease pathogenesis; one is not limited to a search for disease-causing polymorphisms in candidate genes that are known to be, or suspected to be, involved in the disorder. However, once an approximate position in the genome is identified, a major challenge of this approach is the painstaking research necessary to identify the functional mutations responsible for the phenotype. This work has been made much easier by the open-source publication of the human genome sequence.

Genetic Association Studies

The second major gene hunting strategy is the candidate gene-association approach in which polymorphisms in individual genes thought to be important in disease pathogenesis are tested for their involvement in a disease. One first identifies candidate genes that are hypothesized or known to be important in the pathogenesis of a condition. Improve your health condition with Canadian Health&Care Mall. Such genes might be suggested by studying the biology of the disease and/or by comparing gene expression in normal and diseased tissues (for example, by using messenger RNA microarrays). The next step is to identify polymorphisms within the gene that could affect its regulation or function. Finally, one examines whether the specific polymorphisms occur more frequently in individuals who have a disease than in an appropriate control population, or if they predict the development of disease in a cohort study. In an attempt to increase the “hit” rate for candidates, a “positional candidate” approach is an option; biologically plausible candidates that are located in regions previously implicated by linkage analysis are given precedence.

Publication and on-line access to comprehensive “directories” of SNPs in different ethnic groups as part of the HapMap and other projects have greatly facilitated association studies. Rather than testing all of the SNPs within a gene for association, one strategy is to select “Tag” SNPs. Since some SNPs are not independent of each other, and display an inter-SNP correlation that is called linkage disequilibrium, typing of Tag SNPs provides a reliable interrogation of additional SNPs.

COPDOne of the major advantages of association studies is that one uses knowledge of biologically plausible pathogenic mechanisms to focus the search for genes on relatively few candidates, although obviously only genes of known function can be examined. Another advantage is that the study subjects are usually unrelated individuals, so that genotypic and phenotypic data from multiple generations are not required. This is especially important in diseases such as COPD, in which the late age of onset makes it very difficult to obtain DNA and phenotypic data from parents of affected individuals. It should be pointed out that a major limitation of association studies is that a positive association may not always be due to a causative role for the polymorphism in disease pathogenesis. For example, false-positive associations can occur if a different ethnic group (with different SNP frequencies) is overrepresented in the case or control groups. Such population admixture is just one of the reasons for some apparently contradictory results of association studies.

The results of association studies may differ between different populations due to a number of factors: variations in the frequency of SNPs in different populations; the modulating effects of other SNPs or mutations within individuals; and variation in the penetrance of the effects of an SNP due to environmental factors such as age and exposures. Testing of gene/environment interaction is critical for interpretation of genetic testing in disease. A striking example is the effect of pollutant exposure on children with asthma. Children with polymorphisms in specific genes involved in the metabolism of oxidant pollutants (glutathione transferases) are selectively affected by environmental tobacco smoke and pollution. Many studies of complex genetic disease have been plagued by failure to replicate reported associations. Although there are a variety of possible reasons for nonreplication, many false-positive associations are due to population heterogeneity and small sample size. Increasingly, very large sample sizes and metaanalytic approaches are being employed.

Despite these caveats, consistent patterns are starting to emerge as more and more genetic linkage and association studies are undertaken. For example, in asthma, the most thoroughly studied, complex pulmonary disease, genome-wide linkage screens have been performed in 11 different populations and have identified 18 genomic regions that contain asthma/atopy genes, with consistently replicated regions on chromosomes 5q, 2q, 13q, 6q, and 12q. In studies of unrelated individuals, > 100 genes have been associated with allergy/asthma, and 79 of these associations have been replicated in a second study. Among these candidates, six are completely novel genes that were identified by positional cloning.

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