An ultimate goal of genetic 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 a field of molecular biology that attempts to make associate the vast wealth of data produced by genomic projects, such as genome sequencing projects, with genes and their proteins. Unlike genomics and proteomics, functional genomics focuses on dynamic (functional) aspects of genetics such as gene transcription (including genetic regulation by non-coding RNAs such as miRNA, and epigenetic regulation), translation, and protein-protein interactions and and their translation into the complex organization of cells, tissues and organisms, as opposed to the static aspects of the genomic information such as DNA sequence or structures. Functional genomics attempts to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products. The goal of functional genomics is to understand the relationship between an organism's genome and its phenotype. Functional genomics involves studies of natural variation in genes, RNA, and proteins as well as studies of natural or experimental disruptions affecting the function of genes, chromosomes, RNAs, or proteins. The promise of functional genomics is to provide a more complete picture of how biological function arises from the information encoded in an organism's genome. For example, understanding how a particular mutation leads to a given phenotype has important implications for human genetic diseases and their treatment. [Source: This description was excerpted from Wikipedia, with subsequent edits.]
The discoveries from which functional genomics arise are largely drawn from genome-wide studies of human populations and individuals, that attempt to identify specific genes, genetic regulatory mechanisms with observable phenotypes such as diseases (e.g. specific cancers; autism; diabetes; osteoporosis; susceptibility to pathogens such as HIV, the West Nile virus; etc.), metabolic conditions (e.g. auto-immune disorders; diabetes; etc.) or other traits (height; susceptibility to psychiatric disorders, psychological effects; athletic endurance; etc.). These studies include the analysis of single-nucleotide polymorphisms (SNPs, pronounced "snips") that are prevalent throughout and unique to each of our genomes, that at times result in changes in genes that result in functional consequences, for example, how well we can metabolize an anticancer drug.
Functional genomics investigations typically utilize large-scale assays in which many of the genes or proteins of an organism can be measured and tracked in parallel through space and time or under different environmental conditions. Revolutionary technologies, such as microarrays, are used, 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) that are common to the human genome and to determine how these polymorphisms are distributed across different populations. On a broad scale, these 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.
GWAS use gene chips in automated systems that analyze about 500,000 to over one million sites where single-letter differences in DNA (single-nucleotide polymorphisms, or SNPs) tend to occur. SNP chips 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 and phenotypes (height; ageing, etc.) are complex traits, resulting from the combined action of dozens or hundreds of genes.
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