Analysis of Genetic Variations

The generic genome of a species is responsible for all the common traits shared by the members of the species. In this section, we focus on the slight but important differences between these members that promote them to genomic individuals. In humans, these differences are responsible for distinctions in appearance, and, more importantly, for individual predispositions to diseases and responses to certain drugs. In the same context, the individual genomic differences of the infectious agents (bacteria and viruses) play an important role, since they hold the key to understanding the phenomena of resistance to drug treatment. The combined analysis of the genomic variations of pathogen and host leads to an understanding of the problems or benefits of immune response. Therefore, we will briefly remark on genetic variations in both contexts.

3.15.8.1 Genetic Variations in Humans: Analyzing Predispositions

The commonly observed rate of variation in human genomes is about one difference in 1200 base pairs. (This is remarkable small, five times as small as in the chimpanzee and 12 times as small as in the fruitfly.) The length of these differences is usually 1 base pair, and thus they are referred to as single nucleotide polymorphisms (SNPs). However, SNPs come in blocks that are handed down through the generations together, and separated by breakpoints introduced via recombination. These so-called haplotype blocks are the basis for the analysis of genetic predispositions for diseases and individuals' responses to drug therapy. The International HapMap Project provides a database that contains haplotype blocks.281 About 1000 diseases are known to be caused by alterations occurring in a single gene, so-called monogenetic diseases, among them sickle cell anemia (caused by an SNP in the hemoglobin gene), cystic fibrosis (caused by a mutation in an ion transporter), and hemophilia (in which a mutation disables a protein facilitating blood clotting). Several of these diseases have been studied for decades; the OMIM (Online Mendelian Inheritance in Man) database can be queried for information on disease-relevant mutations and the related phenotypes.282

In the case of monogenetic diseases, the genes responsible are identified by statistical genetics methods. Such analyses are based on molecular markers - short, polymorphic stretches of repetitive DNA or SNPs, usually without any apparent function. Such polymorphisms can be located along the genome via genetic comparison, both for healthy people and for those who carry the disease. Statistical procedures are used for investigations into the observed instances of the polymorphisms and if they occur with preference in one of the two groups (healthy and diseased, respectively). If this procedure finds a significant correlation between the disease and one of the markers, this suggests that the region of genome sequence around the marker is involved in the disease, that is, the genes in this region become suspect for playing a role in the disease. Breaking this suggestion down to single genes, the probability of being related to the disease for a gene decreases inversely proportional to the distance of the gene from the marker. This probability is mapped to a statistical score, the so-called LOD (log odds) score, which quantifies the likelihood of a gene being involved in the disease. If a high-scoring genomic region can be found, it is screened with experimental or bioinformatics procedures to identify candidate genes and provide insight into their function.

Methods from statistical genetics can be categorized by the sort of data they analyze: linkage analysis works on familial data (i.e., pedigrees of families in which the disease has occurred with high frequency). This analysis can greatly help to elucidate the genetic basis of the disease, but the required familial data are usually sparse and hard to obtain. Association analysis deals with larger genetic data sets that are retrieved on a population scale, without knowledge of familial relationships between the individuals in the population.283-285 Both kinds of analysis have helped to reveal important parts of the molecular basis of many monogenetic diseases. Even though none of the investigated diseases is curable at present, this knowledge has implications for therapy.

Diseases caused by a defect in a single gene are notoriously rare because, in general, they confer such a severe disadvantage on the affected individual that they are barely propagated in the population. Our common diseases are caused by alterations of multiple genes, influences from the environment, and the behavior of the affected individual, or any combination of these. Therefore, they are referred to as complex diseases. Current genomic research based on the analysis of the genetic variations between individuals mainly focuses on these diseases. The identification of the sjQfL

relevant and responsible genes is much harder in complex diseases than in monogenetic diseases.286,287

The Iceland genotyping program is one of the most ambitious current genotyping efforts, since it aims at genotyping the complete Icelandic population. In concert with the extensive pedigree information that has been collected in Iceland for almost 1000 years, this provides a unique database that allows for detailed studies of the genetic basis of complex diseases.288 The project is carried out by the company deCODE Genetics, Inc.,289 which has been founded for this purpose, and the pharmaceutical company Hoffman-LaRoche. The project provoked controversy,290-294 and required special legislation in Iceland.295 To date, the project has identified 15 genes that are in some way responsible for 12 common diseases.

Genes that are shown to be linked to a disease can be suitable targets for the design of drugs against the disease, or they can provide stepping stones to finding such drug targets. The field of pharmacogenomics uses genomics, be it on the basis of individual genetic variants or not, to search for drug targets and the corresponding drugs (see 3.03 Pharmacogenomics).296,297 Often, the term 'pharmacogenetics'298 is used synonymously with 'pharmacogenomics,' but it can also point specifically to research that deals with individual genomics differences. (There is some controversy about the meaning of 'pharmacogenomics' and 'pharmacogenetics' in the literature.299,300) Pharmacogenomics can benefit from all the technological achievements presented in this chapter, be it experimental or bioinformatics technologies; mRNA microarrays are of special interest (see 3.03 Pharmacogenomics).301 Experimental target finding and drug design is often easier when it is performed in some model organism. Comparative genomics analysis can thus help to transfer the knowledge gained on models to human research.302,303

The special challenges that pharmacogenomics poses for bioinformatics are reviewed by Altman and Klein.304 This group maintains and collects data relevant for pharmacogenomics (genomic, phenotypical, and clinical information) for the PharmGKB database.305 The associated project addresses interlinking and querying the data, visualizing the query results, and maintaining both the confidentiality and privacy of critical data. Pharmacogenetics also covers the bioinformatics analysis of structural and functional consequences of the so-called 'nonsynonymous' SNPs, which occur in the coding region of a gene and change a residue, and therefore have an effect on the structure and function of the gene product.306 The transcriptional effects of SNPs in the regulatory regions of genes are more indirect than those of nonsynonymous SNPs in coding regions of genes, and these effects are therefore more difficult to analyze with bioinformatics methods.

3.15.8.2 Genetic Variations in Pathogens: Analyzing Resistance

The analysis of the genome of a pathogen and its interaction with the host can help reveal the molecular basis of pathogenicity. Analyzing the genomic variation in the pathogen that is the result of selective pressure imposed on the pathogen by the immune system of the host or by a given drug therapy can help reveal mechanisms by which the pathogen acquires resistance. We will discuss this issue using the example of the drug treatment resistance of HIV/AIDS (for further reading see recent reviews307-311).

Upon infection with a pathogen, the immune system of the host mounts an attack on the intruder, with the goal of eradicating it. A drug can influence the subsequent battle in two ways: it either targets the pathogen directly or it assists the immune system. As the therapy continues, the pathogen experiences a selective pressure from the drug treatment, and the pathogen reacts to it by evolving into resistant variants. This defensive action of the pathogen has to be analyzed at the genomic level, at best also involving the genotype of the host. Bioinformatics can support this analysis.

HIV is a rapidly mutating virus that attacks the immune system of the host. Currently, more than 20 drugs are available that target viral proteins, but the high mutational rate of the virus enables it to rapidly become resistant to each drug. Therefore, according to the highly active antiretroviral therapy (HAART) scheme, several drugs with different target proteins and different modes of activity are administered simultaneously. The development of a resistant strain of the virus usually requires months to a year or two, and then the drug combination must be adapted to the new dominant strain. Since the virus develops multiple resistances over time during such a treatment, every drug combination should be effective against the current viral strain and should also lower the chance for the virus to become resistant again. The combination of drugs can be selected with the help of bioinformatics methods. This requires two kinds of data: (1) genotypic resistance data relating the genotype of the dominant viral strain inside the patient to clinical disease measures (here the so-called viral load, the number of free virus particles in 1 ml of blood serum) and (2) phenotypic resistance data originating from laboratory experiments, in which viral strains are exposed to HIV drugs, and the so-called resistance factor is measured. The resistance factor is the quotient of the drug concentration that halves the growth of the tested virus strain divided by the respective concentration for the wild type. Several servers provide resistance and mutation data information.312,313 These data can be analyzed with bioinformatics methods that use (parts of) the sequences of the viral protein targets as input, and predict the effectiveness of single drugs314,315 and drug combinations as well as a the possible shortest mutational path to resistance.316

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