Genetic analysis is a term used to describe the study of a sample of DNA (deoxyribonucleic acid) to look at mutations or changes that may increase an individual's risk for disease or may impact the way that individual responds to treatment. DNA, as well as its counterpart in genetic function, RNA (ribonucleic acid), are studied as components of complex genetic diseases such as depression, cancer, diabetes, autism, asthma, and others. While some genetic diseases, like Huntington disease, are due to single gene changes, most inherited diseases arise from a number of genetic changes that can be compounded by environmental factors, making it difficult for researchers to track down all the related factors.

Although any two people are 99.9% identical at the genetic level, the 0.1 percent difference helps explain why one person is more susceptible to a specific disease than another person. By studying the patterns of genetic variation, or genetic differences, researchers expect to identify which differences are related to diseases.

The tools Illumina develops convert the data that has been generated from major genetics initiatives, such as the Human Genome Project and the International HapMap Project into medically relevant information. This information will correlate genetic variation and function with disease states, improve the ability to discover drugs, and allow diseases to be detected earlier and more specifically.

In recent years, new advancements in technology have made it possible for researchers to scan the entire genome, the composite of a person's genetic material or DNA, to search for patterns of variation that are linked to disease susceptibility or drug response. These tools interrogate single nucleotide (A, G, C, or T) changes in DNA called single nucleotide polymorphisms (SNPs). SNPs are by far the most common source of genetic variation. The human genome is thought to contain over 10 million SNPs, about one in every 300 bases.

It is now possible to analyze patient samples by using whole-genome scans to look at 500,000 or more SNPs across the genome. These patterns of variation derived from whole-genome scans are compared between healthy and diseased patient samples to understand the common SNPs that may contribute to the disease being studied. The use of SNPs in whole-genome association studies is a powerful tool to determine patterns of variation in various disease or treatment populations, and promises to dramatically increase our ability to understand and treat genetic disease.

Follow-on fine-mapping studies may involve analysis of a smaller number of SNPs in defined regions of the genome in an attempt to narrow in on the causative SNPs for the disease in question. They may also involve the study of how the levels of RNA (the messenger of DNA) are affected in normal vs. diseased samples, such as in cancer. The information that is gathered across hundreds or even thousands of samples is analyzed to hunt down the underlying genetic changes and their impact on disease or drug response. These elements, once found, can be used in a genetic test to help identify patients at risk of developing a particular disease, or can be used to predict response or adverse effects to various therapies. Through the analysis of genetic variation and function, researchers hope to determine the underlying causes of and impact the future cures for complex diseases.

Gene expression profiling is the process of determining which genes are active in a specific cell or group of cells. It is accomplished by measuring messenger ribonucleic acid (mRNA), the intermediary between genes and proteins. Variation in gene expression profiles can act as an important indicator of disease or predisposition to disease. By comparing gene expression patterns between cells from different environments, such as normal tissue vs. diseased tissue, researchers can determine which genes are active or inactive in various disease states.

DNA methylation profiling is gaining momentum as an epigenetic approach for understanding the effects of aberrant methylation (either hyper- or hypomethylation) both in basic research and in clinical applications. Quantitative methylation measurement at the single-CpG-site level offers the highest resolution for understanding epigenetic changes.

CpG sites are regions of DNA where a cytosine nucleotide is located next to a guanine nucleotide with the nucleosides linked together by a phosphate. The attachment of a methyl group to the cytosine nucleotide at the CpG site (termed methylation) occurs throughout genes. Methylation is a modification made to DNA that often causes changes in the function of the DNA but not in its primary structure. These epigenetic modifications to the DNA are thought to affect gene transcriptional regulation and can be heritable.

Since faulty DNA methylation is known to be associated with a variety of human diseases including cancer, diabetes, and certain neurological disorders, methylation patterns can help identify and validate biomarkers or support clinical diagnostics. An increasing number of researchers are seeking more cost-effective technology platforms to conduct medium- to high-throughput DNA methylation profiling.

Karyotyping, fluorescence in-situ hybridization (FISH), comparative genomic hybridization (CGH), and loss of heterozygosity (LOH) analyses have been the standards for investigating chromosomal aberrations such as DNA rearrangements and changes in DNA copy number. With Illumina's DNA Analysis solutions, one has the ability to profile, at increased resolution, the genome for DNA copy number, LOH, copy number variants, and other chromosomal aberrations routinely found in cancers, congenital disorders, and samples from phenotypically normal patients.

Copy number variation (CNV) refers to changes in the DNA copy number level of a region when compared to a reference genome. Interestingly, recent research has shown that the level of CNV in the human genome is much higher than previously thought. Their biological impact is just being understood.

Other types of copy number changes occur when a region within a chromosome or an entire chromosome is amplified erratically or deleted altogether. Copy number aberrations are typical of cancer cells and may provide clues to help identify genes, promoter regions, and biomarkers implicated in unchecked growth patterns of cancer cells. In fact, it is now known that in some tumors, high-level amplifications correlate with clinical outcome.

LOH represents the loss of a parent's contribution to part of the cell's genome. LOH is extremely common in cancer, where it often indicates the presence of a tumor suppressor gene in the region. Typically, the remaining copy of the tumor suppressor gene will be inactivated by a point mutation. Interestingly, LOH can sometimes occur without apparent copy number changes (such as gene conversion) and recent publications have documented the critical role of copy-neutral LOH in tumor samples. Events such as these cannot be detected with traditional two color array-CGH.