An key goal of genetics research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. Functional Genomics (functional genetics) is the branch of molecular biology that attempts to associate the vast wealth of data produced by genomic projects, such as genome sequencing projects, with genes and their proteins, and their functions. Similarly, personalized medicine seeks to take this information, to understand (specifically) how it affects human health.
Each of us uniquely harbors thousands of small variations in our genomes, including deletions, insertions, duplications and rearrangements of our DNA mutations, insertion elements of various types including transposons and retroviral elements, single-nucleotide polymorphisms (SNPs), epigenetic variation, etc. - that ultimately define our "uniqueness" (height, eye and skin color, etc.), and potentially affect our susceptibility or resistance to metabolic disorders, genetic diseases including cancer, susceptibility to environmental agents (chemicals and pollutants; toxins; pathogens), but also drive the evolution of our species.
Revolutionary in nature, personalized medicine seeks to better understand how our the phenotypic expression of the information encoded in our genomes affects our health at a personal level, with the aim of improving preventative and therapeutic health care. The information required to assess our health at this level of discrimination may be derived from various sources, including detailed family histories and environmental and occupational assessments, proteomic profiling and metabolomic analysis, but much more so through sophisticated genetic testing methods including genetic testing services, "gene chips" (DNA microarrays) and high-speed DNA sequencing.
Functional genomics investigations typically utilize large-scale assays in which many of the genes or proteins of an organism can be measured and tracked under different environmental conditions. Revolutionary technologies, such as microarrays are used, that are capable of high sample throughput and producing vast amounts of data (requiring computational processing for interpretation). The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS).
QTL analysis a statistical method that links two types of information - phenotypic data (trait measurements) and genotypic data (usually molecular markers) - in an attempt to explain the genetic basis of variation in complex traits. QTL analysis allows researchers in fields as diverse as agriculture, evolution, and medicine to link certain complex phenotypes to specific regions of chromosomes. The goal of this process is to identify the action, interaction, number, and precise location of these regions.
GWAS represent a recently developed research technique with many implications on both a global and an individual scale. GWAS seek to identify the single nucleotide polymorphisms (SNPs, pronounced "snips") that are common to the human genome and to determine how these polymorphisms are distributed across different populations. SNPs are the most frequent type of genetic variation: to date, more than 10 million SNPs have been identified in the human genome. As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker for research. DNA microarrays provide an ideal platform for the simultaneous analysis of hundreds of thousands of SNPs. On a broad scale, GWAS studies help scientists uncover associations between individual SNPs and disorders that are passed from one generation to the next in Mendelian fashion. On a small scale, GWAS can be used to determine an individual's risk of developing a particular disorder. Although the impact of GWAS on medical genetics is undeniable, the true usefulness of these studies largely depends upon researchers' understanding of the interacting factors behind common genetic disorders. However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited.
DNA microarrays ("gene chips") are manufactured by robotic machines that precisely arrange microscopic amounts of thousands of different gene sequences a single microscope slide (~1" x 3" in size), generally in duplicate arrays on each slide. To determine which genes are turned on and which are turned off in a given cell, researchers collect the messenger RNA (mRNA) molecules present in that cell. They then label each mRNA molecule by using a reverse transcriptase enzyme that generates a complementary cDNA to the mRNA. During that process fluorescent nucleotides are attached to the cDNA. The sample (e.g. from tumor tissue) and the control (e.g. from nearby, healthy tissue)samples are each labeled with a different fluorescent dye (typically the cyanine dyes Cy3 and Cy5, that fluoresce green and red, respectively). Next, the researcher places the labeled cDNAs onto a DNA microarray slide. The labeled cDNAs that represent mRNAs in the cell will then hybridize - or bind - to their synthetic complementary DNAs attached on the microarray slide, while non-hybridized (non-complementary) cDNA is subsequently washed away. The researcher then uses a special scanner to measure the fluorescent intensity for each of the thousands of spots on the microarray slide. If a particular gene is very active in the original cell sample, it (the cell) produces many molecules of messenger RNA, and thus more labeled cDNAs, which hybridize to the DNA on the microarray slide and generate a very bright fluorescent area. Genes that are somewhat less active produce fewer mRNAs, thus, less labeled cDNAs, resulting in dimmer fluorescent spots on the chip. If there is no fluorescence, none of the messenger molecules would have hybridized to the DNA, indicating that the gene is inactive. Researchers frequently use this technique to examine the activity of various genes at different times. When chips are co-hybridizing with a mix of cDNA from the tumor sample (e.g. labeled with the red Cy5 dye) and the normal sample (labeled with the green Cy3 dye), they will compete for the synthetic complementary DNAs on the microarray slide. As a result, if the spot is red, this means that that specific gene is more expressed in tumor than in normal (i.e., displaying "up-regulated" gene expression in cancer). If a spot is green, that means that that gene is more expressed in the normal tissue (therefore, "down-regulated" in cancer cells). If a spot is yellow that means that that specific gene is equally expressed in normal and tumor. [The source for the preceding summary is DNA Microarray Technology, from the National Human Genome research Institute, www.genome.gov.]
Gene chips used in GWAS and other studies are employed in automated systems that analyze about 500,000 to over one million sites where single-letter differences in DNA (SNPs) tend to occur. For example, Affymetrix's current Genome-Wide Human SNP Array 6.0 features 1.8 million genetic markers, including more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number variation. "SNP chips" (arrays) have been used over the past decade to comparing DNA samples between healthy subjects and patients, enabling scientists to identify thousands of SNPs associated with common, complex diseases. However, geneticists believe that the SNPs investigated by the gene chips do not themselves cause a disease, but instead serve as a "biomarkers" that are linked to the actual causal mutations that may reside in a nearby region of the genome. Consequently, after researchers find SNPs linked to a disease using GWAS they then perform "fine-mapping" studies - for example, sequencing the DNA near the SNP - to uncover altered genes or regulatory sequences that harbor a mutation responsible for the disease. While this is a powerful approach with many successes, unfortunately many of these GWAS results (SNP-disease associations) have been unimpressive, yielding causal variants with very small effects. This likely reflects that fact that many genetic diseases (e.g. autism, cancer, diabetes, etc.) and phenotypes (e.g. height, skin color, ageing, etc.) are complex (polygenic) traits, resulting from the combined action of dozens or even hundreds of genes.
In addition to those tests / chips that focus on DNA polymorphisms in our nuclear DNA, perturbations of our smaller yet essential mitochondrial genome also result in ageing, metabolic disorders, and other disease, and chips that examine SNPs in our mitochondrial genome (mtDNA) are also available, e.g. the "MitoChip" (v.2.0), commercially available from Affymetrix as the GeneChip Human Mitochondrial Resequencing Array 2.0.
The International HapMap Project is a partnership of scientists and funding agencies from Canada, China, Japan, Nigeria, the United Kingdom and the United States to develop a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals. The International HapMap Project is a multi-country effort to identify and catalog genetic similarities and differences in human beings. Using the information in the HapMap, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. The Project is a collaboration among scientists and funding agencies from Japan, the United Kingdom, Canada, China, Nigeria, and the United States. [See Participating Groups and Initial Planning Groups.] All of the information generated by the Project will be released into the public domain. The goal of the International HapMap Project is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. [See What is the HapMap?] By making this information freely available, the Project will help biomedical researchers find genes involved in disease and responses to therapeutic drugs. [See How Will the HapMap Benefit Human Health?] In the initial phase of the Project, genetic data are being gathered from four populations with African, Asian, and European ancestry. Ongoing interactions with members of these populations are addressing potential ethical issues and providing valuable experience in conducting research with identified populations. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. [See Data Release Policies.] The Project officially started with a meeting in October 2002 (http://genome.gov/10005336) and is expected to take about three years.
Genetic testing can provide information on sequence variation - mostly SNPs - in specific genes (e.g. the breast cancer-associated genes BRCA1 and BRCA2) or genomic regions. While genetic testing services are available at large hospitals, health centers and cancer agencies (including, locally, the BC Cancer Agency), companies that sell genetic tests directly to consumers include 23andMe, deCODEme, Navigenics, and Pathway Genomics. The tests, which cost upwards of $1,000, are supposed to assess genetic variations in your genome, or DNA, and tell you whether you are at a higher or lower risk of getting a handful of diseases, such as diabetes or prostate cancer. While a natural evolution of the field, commercial genetic testing labs (and the results that they provide) are not without significant controversy and concern - highly critiqued, for example, in this article.
Advances in high-speed DNA sequencing also offer tremendous opportunity for directly providing genomic information for individuals, as indicated in this article ("Genome advances promise personalized medical treatment"):
This overview provides an indication of the complexity and issues associated with genetic testing and personalized medicine.
The results from these tests are complex and largely esoteric to patients and other end-users of such services. With expertise in molecular genetics and the ability to analyze and augment genetics testing / personalized medicine results (including medical results that identify specific, affected genes or genetic regions), I can assist you both in the interpretation of such data, and augment it with up-to-date information from the scientific and medical literature.
For additional information regarding better understanding the applications and implications of functional genomics / personalized medicine (e.g. personalized medical and genetic test results; etc.) please contact me at mail @ persagen.com. Complete confidentiality is assured!