In recent years, genetic information analysis technology has made great strides, and medical care and health management that utilizes individual genetic information is attracting attention. Genetic information plays an important role in predicting our health conditions and disease risks. This article provides a detailed explanation for experts about genetic information and the reality of risk assessment.
The basics of genetic information
Genes are units of genetic information present on DNA (deoxyribonucleic acid) and play a role in determining the structure and functions of our bodies. The human genome is thought to consist of approximately 3 billion base pairs and contain approximately 20,000 to 25,000 genes. Mutations and polymorphisms in these genes are known to affect an individual’s risk of disease and drug responsiveness.
Risk assessment using genetic information
Risk assessment using genetic information is mainly carried out in the following three areas:
1. Disease risk prediction
Certain genetic mutations and polymorphisms have been shown to be associated with the risk of developing diseases. For example, mutations in the BRCA1 and BRCA2 genes are known to increase the risk of breast and ovarian cancer. Individuals with these genetic mutations are encouraged to consider measures such as regular screening and preventive surgery. amed.go.jp
2. Individualization of drug therapy
Genetic information is important for understanding individual differences in drug effects and side effects. For example, polymorphisms in the CYP2C19 gene affect the metabolism of the antiplatelet drug clopidogrel. In individuals with poor metabolic capacity, the drug’s efficacy may be reduced, and alternative drug options are considered.
3. Diagnosis and management of genetic disorders
Genetic testing is used for diagnosing hereditary diseases and for carrier screening. For example, early diagnosis and appropriate management are important for hereditary diseases such as cystic fibrosis and phenylketonuria. Genetic testing enables early detection and treatment of these diseases.
Interpretation and limitations of genetic information
Risk assessment using genetic information has the following interpretational precautions and limitations:
1. The complexity of multifactorial diseases
Many diseases are multifactorial diseases involving multiple genetic and environmental factors. Therefore, it is difficult to fully predict disease risk with only a single gene mutation. For example, type 2 diabetes involves many genes and lifestyle habits, so assessing risk with genetic information alone is limited.
2. Frequency and effects of gene mutations
When performing whole genome or exome sequencing, unexpected genetic mutations may be discovered. The interpretation and response of these incidental findings involves ethical, legal, and social issues and requires careful handling. amed.go.jp
Ethical and social issues in risk assessment
When performing whole genome or exome sequencing, unexpected genetic mutations may be discovered. The interpretation and response of these incidental findings involves ethical, legal, and social issues and requires careful handling. amed.go.jp
Ethical and social issues in risk assessment
Risk assessment using genetic information involves the following ethical and social issues:
1. Privacy and Data Protection
Genetic information is one of the most private pieces of information an individual has, and inappropriate handling of it can lead to a violation of privacy. Appropriate data management is necessary, including data protection and anonymization, and obtaining consent for sharing information.
2. Risk of discrimination and prejudice
Risk assessment based on genetic information may lead to discrimination in insurance and employment. Legal frameworks such as the Genetic Information Nondiscrimination Act (GINA) have been established to prevent this, but societal understanding and awareness are also important.
3. Informed consent
When undergoing genetic testing, subjects are required to give their consent after fully understanding the purpose, method, meaning of the results, and potential risks of the test. Prior explanation is especially important for tests that may contain incidental findings or undetermined information.
Genetic counseling also plays an extremely important role. In genetic counseling, experts consult with subjects about the significance of the test, how to interpret the results, and future responses. In particular, when a risk of cancer or a hereditary disease is discovered, the psychological burden on patients and their families is large, so appropriate support is required.
4. Commercial Use and Regulation of Genetic Information
In recent years, the spread of DTC (Direct-to-Consumer) genetic testing services has made it possible for consumers to undergo genetic testing without going through a medical institution. This has made it easy for people to learn about their health risks and ancestral roots, but it has also been pointed out that there is a risk that information with poor scientific evidence or misleading results will be provided.
There are also concerns about the commercial use of genetic data. Companies may share the genetic data they collect with third parties, and it may be used for marketing purposes. This has led to calls for greater transparency and stronger regulation of how genetic information is handled.
The latest research and technology using genetic information
Genetic information analysis technology is evolving every day, with new research results being announced one after another. Here we will introduce the latest research fields and technologies that are attracting particular attention.
1. Polygenic Risk Score (PRS)
Polygenic Risk Score (PRS) is a method for evaluating disease risk by combining multiple gene polymorphisms. This enables risk assessment of multifactorial diseases that could not be fully captured by conventional analysis of single gene mutations.
For example, multiple genes are involved in the risk of diseases such as cardiovascular disease, diabetes, and Alzheimer’s disease. It is expected that the use of PRS will enable more precise prediction of an individual’s disease risk and contribute to the practice of preventive medicine.
However, there are also issues with the PRS. First, most of the data used for the analysis is biased toward Caucasian populations, and its accuracy has not been fully verified when applied to Asian or African populations. Future research targeting a wider variety of races and ethnicities is required.
2. Gene editing with CRISPR-Cas9
CRISPR-Cas9 technology has attracted attention as a revolutionary gene editing technology, which enables precise modification of specific genes and accelerates the development of treatments for genetic diseases.
For example, CRISPR-Cas9-based treatments are currently in clinical trials for blood disorders such as sickle cell disease and beta-thalassemia, where gene editing can be used to correct the genes that produce abnormal hemoglobin, slowing the progression of the disease.
For example, CRISPR-Cas9-based treatments are currently in clinical trials for blood disorders such as sickle cell disease and beta-thalassemia, where gene editing can be used to correct the genes that produce abnormal hemoglobin, slowing the progression of the disease.
3. Single-cell genomics
Conventional genome analysis involves analyzing DNA extracted from entire tissues, but single-cell genomics makes it possible to analyze the genetic information of individual cells.
This technology is being used to analyze the heterogeneity of cancer cells in detail, as well as in developmental biology and neuroscience research. In particular, in cancer treatment, it is expected that understanding the genetic differences between cells within a tumor will improve the accuracy of personalized medicine.
Additionally, single-cell RNA sequencing (scRNA-seq) can be used to analyze gene expression patterns in individual cells, helping to elucidate the molecular mechanisms of disease.
The future and applications of genetic information
The use of genetic information is bringing about major changes in medicine and lifestyles. It is expected that applications of genetic information will advance further in the following fields in the future.
1. Development of personalized medicine
By utilizing genetic information, precision medicine is advancing, in which optimal treatments and drugs are selected for each individual. In particular, in cancer treatment, the development of molecular targeted drugs based on gene mutations is progressing, making more effective treatment possible.
Additionally, personalized medical care utilizing genetic information is also advancing for lifestyle-related diseases such as diabetes and high blood pressure.
Nutrigenomics, which studies the relationship between genes and nutrition, is also attracting attention. Efforts are being made to maintain health and prevent disease by selecting appropriate foods and supplements based on an individual’s genes.
For example, it is known that the effects of caffeine vary depending on the CYP1A2 gene involved in the metabolism of caffeine. Based on this information, it is possible to minimize health risks by adjusting the amount of caffeine intake.
3. Expansion of gene therapy
Currently, gene therapy is being developed for hereditary and rare diseases, but it is expected to be applied to many more diseases in the future. In particular, research into gene therapy for neurodegenerative and autoimmune diseases is becoming more active.
Additionally, new treatments utilizing epigenetics are being developed, and approaches to suppress disease progression by controlling gene expression are being researched.
4. Combination of artificial intelligence (AI) and genetic analysis
In recent years, advances in artificial intelligence (AI) technology have dramatically improved the analysis of large-scale genetic data. By utilizing AI, it is now possible to predict disease risks and discover new therapeutic targets.
AI-based disease risk prediction
Machine learning algorithms are being used to analyze millions of genetic data and develop models to predict disease risk. For example, the calculation of polygenic risk scores (PRS) using deep learning enables more accurate risk assessment than conventional methods.
AI is also being applied to genetic analysis of cancer to optimize tumor types and treatment choices, potentially leading to more effective personalized medicine.
Discovering new therapeutic targets with AI
In the field of drug discovery, genetic analysis using AI is also progressing. In particular, by combining it with gene editing technologies such as CRISPR-Cas9, the identification of new therapeutic targets is accelerating. Genome-wide association studies (GWAS) using AI can more precisely identify genes associated with diseases, leading to more efficient drug discovery.
Disease risk assessment using genetic information
1. Cancer genetic risk assessment
Cancer is a disease in which genetic and environmental factors play a complex role, and it is known that certain genetic mutations increase the risk of developing cancer.
BRCA1 and BRCA2 genes and breast and ovarian cancer
Mutations in the BRCA1 and BRCA2 genes are known to significantly increase the risk of breast and ovarian cancer. It has been reported that women with these gene mutations have a 50-80% chance of developing breast cancer in their lifetime. Therefore, genetic testing can lead to early detection and preventive treatment options.
Lynch syndrome and colorectal cancer
Lynch syndrome (hereditary nonpolyposis colorectal cancer, HNPCC) is an inherited disease that increases the risk of colon and endometrial cancer. The disease is caused by mutations in DNA mismatch repair genes, including MLH1, MSH2, MSH6, and PMS2. Genetic testing can identify individuals at risk for Lynch syndrome, and regular screening can allow for early detection of cancer.
2. Genetic risk assessment for cardiovascular disease
Cardiovascular disease is also a multifactorial disease, with genetic factors playing a major role.
LDLR gene and familial hypercholesterolemia
Mutations in the LDLR gene cause familial hypercholesterolemia (FH), a condition in which the breakdown of low-density lipoprotein (LDL) cholesterol does not function properly, leading to abnormally high blood cholesterol levels and an increased risk of premature atherosclerosis and myocardial infarction.
Identifying the risk of FH through genetic testing and introducing early lifestyle changes and drug therapy such as statins can reduce the risk of cardiovascular disease.
PCSK9 gene and novel treatments
Mutations in the PCSK9 gene are involved in the breakdown of LDL cholesterol, and it has been found that people with certain mutations have a lower risk of cardiovascular disease. Based on this discovery, PCSK9 inhibitors have been developed and are used to treat familial hypercholesterolemia and atherosclerosis.
3. Genetic risk assessment for neurological disorders
Neurodegenerative and psychiatric diseases are also diseases in which genetic and environmental factors play a complex role.
The APOE Gene and Alzheimer’s Disease
The ε4 allele of the APOE gene is known to increase the risk of Alzheimer’s disease. In particular, people with two copies of APOE ε4 have a significantly increased risk of developing Alzheimer’s disease. However, even if you have a genetic risk, it is possible to delay the onset of Alzheimer’s disease by improving environmental factors and lifestyle habits, so preventive measures based on risk assessment are important.
The DISC1 gene and schizophrenia
Schizophrenia is a psychiatric disorder strongly influenced by genetic factors, and the DISC1 gene is thought to be involved in its onset. However, schizophrenia is not caused by a single gene mutation alone, but by many genes and environmental factors, so comprehensive risk assessment is required.
Genetic information and lifestyle
Using genetic information will allow individuals to make lifestyle choices that are best suited to them.
1. Genetic Diet
Genetic diets, which suggest optimal eating methods based on an individual’s genetic information, are attracting attention. For example, polymorphisms in the FTO gene are associated with the risk of obesity, and it is believed that people who have this gene will be able to manage their weight more easily by avoiding high-fat foods.
2. Athletic ability and genes
Mutations in the ACTN3 gene are known to affect muscle contraction ability, and differences in this gene may determine whether an individual is suited to endurance or explosive sports.
3. Sleep and genes
Mutations in the PER3 gene are known to affect sleep quality and morning/evening tendencies. Using genetic testing may help find the optimal sleep schedule for individuals.
4. Stress tolerance and genes
Responses to stress are also influenced by genetic factors. For example, polymorphisms in the COMT gene have been reported to be associated with stress resistance and decision-making ability. This gene plays a role in regulating the breakdown of dopamine, and people with certain mutations may be more sensitive to stress.
In addition, a polymorphism in the serotonin transporter gene called 5-HTTLPR is said to be associated with the risk of developing depression. People with this gene have a low tolerance to psychological stress, so appropriate stress management is important.
5. Caffeine sensitivity and genes
The ability to metabolize caffeine also differs depending on genes. Depending on the polymorphism of the CYP1A2 gene, people are divided into “rapid metabolizers” who can metabolize caffeine quickly, and “slow metabolizers” whose effects of caffeine last longer.
It has been pointed out that excessive caffeine intake by slow metabolizers may place a greater burden on the cardiovascular system, whereas fast metabolizers are less susceptible to the effects of caffeine, and moderate amounts are expected to help improve concentration and athletic performance.
Current status and issues of genetic testing
Health management using genetic information is advancing rapidly, but many challenges remain.
1. Accuracy and reliability of genetic testing
Currently, many companies offer direct-to-consumer (DTC) genetic testing, but there are challenges with the accuracy of the tests and interpreting the results.
Test-to-test variability
If the same person undergoes genetic testing from different companies, different results may be obtained. This is because each company uses different databases and analysis algorithms. In particular, when assessing disease risk, it is necessary to carefully consider whether the scientific basis is established.
Difficulty in interpreting the results
The results of genetic testing are probabilistic information, and do not necessarily mean that “if you have this gene, you will definitely get a disease.” Therefore, it is necessary for experts to provide a proper explanation of how to use genetic information in health management.
2. Data Privacy and Security
Genetic information is highly personal data and requires careful handling.
Third-party use of genetic data
Some DTC genetic testing companies share the genetic data they collect with pharmaceutical companies and research institutions, which can help accelerate the development of new treatments, but also creates the risk that the data may be used without an individual’s consent.
Cybersecurity Vulnerabilities
Once genetic data is leaked, it can potentially be misused as information about an individual’s entire life. Therefore, data encryption and strict access management are required.
3. Ethical and legal issues
The use of genetic information raises ethical and legal challenges.
Genetic discrimination in insurance and employment
There are concerns that people may be denied insurance or suffer disadvantages in employment based on their genetic information. To prevent such discrimination, the Genetic Information Nondiscrimination Act (GINA) was enacted in the United States, which prohibits discrimination in insurance enrollment or employment based on genetic information.
Issues with reproductive medicine and genome editing
Advances in gene editing techniques such as CRISPR-Cas9 have made it technically possible to modify genetic characteristics at the embryonic stage, but due to ethical concerns, germline gene editing is prohibited in many countries.
There is also ongoing debate about the appropriateness of genetic selection, such as for “designer babies.” Social acceptance may differ if gene editing is used for medical purposes versus if it is used to intentionally create children with superior physical abilities or intelligence.
Future outlook for genetic information
The use of genetic information will continue to evolve in the future, and new applications such as the following are expected.
1. Health management through epigenetics
Epigenetics refers to the phenomenon in which gene expression changes due to environmental factors, even though the base sequence of DNA does not change. For example, gene expression can be regulated by diet, exercise, and stress management, preventing disease and maintaining health.
In particular, as analysis of DNA methylation and histone modifications progresses, it is possible that new services will emerge that suggest optimal health management methods for individuals.
2. Microbiome-gene interactions
The gut bacteria (microbiome) have a profound impact on our health, and recent studies have demonstrated that the composition of our gut bacteria is in part determined by genetic factors.
In the future, it is believed that combining genetic information with microbiome analysis will enable more precise health management and disease prevention.
3. Mental health measures using genetic information
It is known that mental illnesses such as depression and anxiety disorders develop as a result of interactions between genetic and environmental factors. It is expected that genetic testing can be used to evaluate an individual’s mental health risk, aiding in early intervention and the selection of appropriate treatment methods.
For example, based on an analysis of serotonin-related genes, it may be possible to predict the effectiveness of specific antidepressants and select the most appropriate treatment.
Summary
Risk assessment using genetic information is a technology that predicts an individual’s risk of disease and is useful for prevention and early detection. It has been shown that certain gene mutations increase the probability of developing a disease, and this technology is being applied to risk assessment of cancer, diabetes, and other diseases. However, since environmental factors and lifestyle habits also have an impact, a reliable diagnosis cannot be made based on genetic information alone. Privacy protection and ethical issues are also being debated. This is a field that requires accurate understanding and appropriate use, and must be handled with care.
