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Genetic Linkage & Association Studies

Designating a specific gene to disease is a meticulous and methodical process as the human genome is made of tens of thousands of individual genes. Gene discovery typically begins with an analysis of the genetic linkage which assesses diseases within families. Genetic association studies follow linkage analyses in order to assess diseases across families and unrelated individuals. 

Genetic Linkage

The tendency of genes located near the same chromosome to be inherited together is genetic linkage. Linkage analysis is a crucial piece in the effort to identify disease susceptibility in genes. 

One of the main goals of gene linkage analysis is to identify the approximate location of a disease gene relative to an established genetic marker and apply this knowledge of patterns of linkage. Traditional linkage analysis traces patterns of heredity based on both disease phenotype and genetic markers. This is typically based on high-risk families and has been used to locate disease causing gene mutations. One of the most common examples of this is the BRCA1 breast cancer gene linked to chromosome 17. 

However, since the method of inheritance is not always clear for common diseases, alternative approaches to genetic linkage have been developed. One of the alternative approaches to the traditional linkage analysis that has been developed focuses on the basic genetic principle that on average siblings share half of their alleles. Through the investigation and examination of allelic sharing across genomes, pairs of siblings with the same disease can be used to identify chromosomal regions that may contain genes whose variations can be linked to the disease being studied. 

Genetic linkage can be identified when multiple sibling pairs affected by the same disease demonstrate greater sharing of alleles of the polymorphic genetic marker. The marker is more likely to be linked to the gene responsible for the disease that is being examined. Using the affected sibling pairs method usually requires typing multiple affected groups of siblings with hundreds of polymorphic markers which are uniformly positioned along the human genome in order to find the chromosomal regions that have evidence of linkages.

This practice of genetic linkage is commonly used to identify regions of genomes that are believed to contribute to common chronic diseases. However, results of genetic linkage analyses are not always consistently replicated. Insufficient statistical power or an inadequate number of sibling pairs with the same disease that is being studied leads to an inability to successfully replicate linkage findings. 

Another issue with this type of genetic linkage and association studies are false positive results. In addition to these problems, different populations are also affected by different susceptibility genes than those in previous studies. Due to the lack of consistency, replication of results are inadequate and researchers are unable to draw conclusions regarding the contribution of gene locus to specific diseases. 

After confirmation of genetic linkage, researchers are able to start searching the region for a candidate with specific susceptibility genes. The quest for a single susceptibility gene for common diseases typically involves the examination of large regions. These regions can contain anywhere from 20 to 30 million base pairs and potentially hundreds of genes. 

Another important fact is that while genetic linkage mapping is a powerful tool for identifying Mendelian disease genes, it can also produce inconsistent and weak signals in studies of more complex diseases which may have multiple factors. The best genetic linkage studies are performed when there is a single susceptibility allele at a particular locus. This typically performs poorly when there is more substantial genetic heterogeneity.

Genetic Association

Genetic association occurs when one or more genotypes within a population occur at the same time with a phenotypic trait that is more frequent than a chance occurrence.

The direct examination of specific genetic differences among numerous people has been made possible with technological advances in high-throughput genotyping. 

These genetic association techniques are typically the most efficient approach in examining and identifying how specific genetic variations can impact the risk of diseases. 

Over the last several decades, genetic association studies have been used in the development of new study designs. These designs include case-only and family-based designs. Additionally, new genotyping systems and other methods are used for addressing biases like populations. 

The analysis of the effects of genetic association and variation usually involves the discovery of single nucleotide polymorphism (SNPs) in addition to the analysis of variations of samples from populations. On average, SNPs occur every 500 to 2,000 bases in the human genome. The sequence of genes of interest in sample individuals is one of the most common approaches to SNP discoveries. The sequencing of entire genes on 25 to 50 individuals can detect polymorphisms which occur in 1 to 3 percent of the population. This is also done with nearly 95 percent accuracy. 

One example of genetic association studies with this application of automated DNA sequencing can be found in the Human DNA Polymorphism Discovery Program of the National Institute of Environmental Health Sciences’ Environmental Genome Project. This has allowed the identification of SNPs in human genes which can be linked to disease susceptibility as well the response to their environment. 

Genetic Association and SNP Analysis Advancements

In addition to these genetic association studies, the National Heart, Lung, and Blood Institute’s Programs in Genomic Applications has identified vital increases in knowledge regarding the distribution of SNPs in key genes. These have been believed to be biologically implicated in risk of disease susceptibility. 

New advances in SNP analysis technology have been rapidly changing and redefining the scope of SNP discovery. This includes both mapping and genotyping to identity genetic association. Whole genome association between individuals and strains of laboratory animal species has been possible with new array-based technology. 

Arrays used for these genetic association studies represent hundreds of thousands of SNPs mapped across genomes. This approach has allowed for more rapid identification of SNPs which are associated with not only susceptibility of diseases, but also environmental factors. 

This genetic association technology and the large amount of easily measurable genetic variation offers researchers more cost effective options. They typically cost $500- $1,000 per chip. The criteria for SNP selection to be included in arrays are critical as they affect the inferences drawn from using these platforms. Although it is not currently feasible, whole genome sequencing is the ultimate tool for SNP discovery and genotyping. 

These new and quickly developing advancements in technology are being stimulated by the National Human Genome Research Institute’s “$1,000 genome” project. This will make this approach to genetic association the most ideal one for SNP discoveries and future genotyping. 

Researchers are focusing more genetic association while they move away from investigating single genes as their ability to examine larger quantities of genetic variations becomes more readily available. 

This has also allowed for the examination of entire pathways or physiological systems which include information from genomic, proteomic, transcriptomic, and metabonomic levels. However, these are also all subject to different environmental factors. 

While these advancements in genetic association studies have led the way for more advanced and accurate studies, the genome- and pathway-driven study procedures and techniques of analysis are still in the early stages of development. These advancements require the efforts of multiple disciplines ranging from clinicians and social scientists to molecular biologists and bioinformaticians. These joint efforts are necessary in order to make the most effective use of this multitude of data.

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