Whole genome sequencing or full genome sequencing is a method of determining an organism’s full genetic sequence. This typically makes use of next-generation sequencing (NGS) technology, also known as massively parallel sequencing. NGS is able to resolve large regions of the genome in one run.
Before the advent of NGS, the primary sequencing technology, Sanger sequencing, could resolve small regions of each genome (800bp) at one time. This made whole genome sequencing difficult, expensive, and time-consuming in comparison. However, new technologies have made this a common tool for researchers and clinicians.
Whole genome sequencing (WGS) is a valuable tool for disease research. In many cases, WGS has found links between certain genes and an increased risk of developing illnesses. For example, mutations in the BRCA genes puts women at a significantly increased risk of breast cancer.
With WGS studies, researchers are able to identify these high-risk genes. In genome-wide association studies (GWAS), many individuals’ genomes are studied to identify commonalities across people and across disease risk.
For example, a study of 200,000 African and African admixed genomes identified a novel genetic variant associated with Parkinson’s disease. This risk factor is uncommon in non-African populations. With access to such large datasets, researchers are able to advance our understanding of disease even in under-researched populations.
Many researchers are also searching for links between genetic indicators and behavior. In these studies, whole genome sequencing is a valuable tool. For example, a 2016 study in Genetics identified genetic factors of anxiety in chickens. Because chickens have a relatively short genome, they are an attractive subject for whole genome studies.
Several of the genes identified in this study were found to be conserved in humans. This showed that chickens are a good subject for genetic behavioral studies. Similar animal models have been used to identify possible genetic indicators of type 2 diabetes, renal fibrosis, colorectal cancer, and countless other diseases.
When researchers identify a new link between a gene and a disease, it makes several things possible. For some diseases, this provides conclusive diagnosis that was previously unavailable. This is especially true in groups of disorders like Parkinsonian diseases, which often have similar symptoms that make it difficult to distinguish among them.
This approach also identifies possible targets for new treatments. If a disease is caused by over- or under-expression of a gene, it then becomes a goal to create therapeutics to address this imbalance.
Pharmacogenomics is the study of the interaction between genetics and drug response. This discipline seeks to explain why some people have drastically different reactions to the same drugs. In cancer treatment, these insights are valuable whether sequencing the patient’s genome or the cancer cells’genomes.
Without a view of the whole genome, researchers sometimes pick suboptimal therapeutic targets. This leads to treatments that are less effective than targeted methods.
For example, radiation is the first treatment for many cancers. However, over 20% of patients find radiation ineffective. The tumor’s metabolism can cause resistance to radiation and complicate treatment. We don’t yet know enough about these factors to implement tumor metabolic testing in routine cancer treatment.
In 2021, researchers from the Georgia Institute of Technology used machine learning on 915 genomic and transcriptomic tumor data sets. This approach successfully identified differences between radiation-sensitive and radiation-resistant tumor cells. It also identified gene targets for inhibiting antioxidant production found in radiation-resistant tumors. Projects like these show the efficacy of genome-scale models for improving therapeutics.
With increased access to next-generation sequencing technologies, personalized medicine will become a key component in treating many diseases. Insights from research projects and genome models are uncovering genetic markers of disease risk and treatment resistance. In pharmacogenomics, WGS technology can make drug selection more targeted and successful.
In the future, we can expect to see more pharmacogenomic approaches applied in drug development. Diagnostic efforts will also benefit from this approach.