Open access peer-reviewed chapter - ONLINE FIRST
Comparison of HLA typing techniques.
Abstract
Next-generation sequencing (NGS) has completely revolutionized the analysis of HLA genes, offering superior resolution and the possibility of identifying previously unknown or rare alleles. NGS technology allows for the complete sequencing of the the HLA locus, the analysis of coding and non-coding regions, and a detailed characterization of haplotypes, with essential benefits in areas such as organ transplantation and in studies of autoimmune diseases. The chapter explores the applications of NGS in personalized medicine, including the identification of neoantigens for oncology immunotherapies and the development of vaccines adapted to the genetic diversity of the population. Bioinformatic and ethical challenges are also discussed. By reducing the limitations of traditional methods and opening up new horizons for research and clinical applications, NGS is redefining the standards in HLA typing and is making a significant contribution to the progress of precision medicine.
Keywords
- NGS
- data analysis
- HLA
- immunogenetics
- MHC
1. Introduction: The significance of HLA genes in Immunogenetics
The human leukocyte antigen (HLA) genes are part of a complex genetic system known as the major histocompatibility complex (MHC) system and are essential for the functioning of the human body’s immune system [1, 2, 3, 4, 5, 6]. These genes are located on chromosome 6 and code for proteins that are found on the surface of cells, especially immune cells such as dendritic cells, macrophages, and T lymphocytes [1, 2, 3]. These HLA proteins have the role of presenting protein fragments (peptides) from inside the cells, as well as fragments of pathogens that infect the body (bacteria, viruses, and fungi) or tumor cells. Thus, they facilitate the recognition and activation of the immune system when pathogens or abnormal cells are detected.
HLA works through the process of “antigen presentation”. This involves the capture of foreign or self-proteins, which are then “broken down” into small fragments (peptides) and bound to HLA proteins [3]. HLA-peptide complexes are presented on the surface of cells for recognition by T cell receptors [2, 3]. T cells have specific receptors that recognize these complexes, and if they identify a foreign fragment, their activation triggers an appropriate immune response, which may include the destruction of infected or abnormal cells [1, 2, 3]. This process is essential for distinguishing between “self” and “non-self,” allowing the immune system to recognize and eliminate pathogens and defective cells without attacking its own cells [3]. The diversity of HLA genes is crucial to the immune response of the human population. HLA is highly polymorphic, meaning that there are many variants of these genes in the population, each encoding slightly different HLA proteins. This diversity allows the body to recognize a wide range of pathogens, which confers an important evolutionary advantage [3]. Essentially, each individual inherits a unique combination of HLA variants, and their diversity ensures that, within a population, there are always individuals capable of recognizing and fighting different types of pathogens. This can provide collective protection, even when some people have a more vulnerable immune system due to less favorable HLA variants [3].
HLA diversity can also influence a person’s susceptibility to certain diseases. For example, some HLA variants are associated with an increased risk of developing autoimmune diseases, such as rheumatoid arthritis, systemic lupus erythematosus, or type 1 diabetes [5, 6, 7, 8, 9, 10, 11, 12, 13]. These variants can alter the way the immune system recognizes its own cells and can lead to them being attacked, causing inflammation and tissue damage [13, 14, 15]. In contrast, other HLA variants can confer protection against certain infections; for example, some variants are associated with better protection against HIV or hepatitis B infections [1, 2, 3, 4, 5, 6].
The interaction between HLA and T cell receptors is essential for the activation of the immune response. T cells recognize peptides presented on HLA molecules using their specific receptors (TCRs). There are two main classes of HLA molecules: class I and class II [1, 2, 3, 4, 5, 6]. HLA class I presents peptide fragments originating from within the cell (e.g., viral or tumor proteins), and HLA class II presents peptides originating from external sources, such as bacteria [1, 2, 3, 4, 5, 6]. Cytotoxic T cell receptors (CD8+) recognize peptides presented on HLA class I, activating the T cell to destroy infected or cancerous cells, while helper T cell receptors (CD4+) recognize peptides presented on HLA class II, stimulating other components of the immune system, such as B cells or macrophages [1, 2, 3, 4, 5, 6]. Thus, by interacting with T cell receptors, HLA plays a crucial role in activating and coordinating the immune response, and the diversity of these genes is essential for the body’s effective defense against a wide variety of pathogens.
2. HLA typing: Historical techniques and challenges
Human leukocyte antigen (HLA) typing has evolved significantly over the decades, being essential for the understanding of immune compatibility and for the progress in transplantation and personalized medicine [16]. The history of HLA typing can be divided into several major stages:
Discovery of the HLA system (1960–1970): HLA was first identified in the 1960s as part of the major histocompatibility complex (MHC) [1, 2, 3, 4, 5, 6]. Early research revealed the importance of this system in graft rejection and in immune mechanisms. The term “HLA” was used to describe molecules on the surface of cells that play a key role in the recognition of “self” and “non-self” by the immune system [16].
Serological typing (1970–1990): In the early 1970s, HLA typing techniques were based on serological tests, which involved the use of antibodies to identify HLA antigens on the surface of leukocytes [1, 2, 3, 4, 5, 6]. This was the standard method for determining the HLA types of patients and donors, essential for transplant compatibility [16].
PCR and DNA-based technologies (1990–2000): In the 1990s, with the development of polymerase chain reaction (PCR) technology, it became possible to amplify HLA-specific DNA and analyze it at the molecular level [16]. This allowed for much higher resolution and helped identify rare HLA variants, providing better accuracy in HLA typing [1, 2, 3, 4, 5, 6].
Next-generation sequencing (NGS) (2000–present): NGS technology has completely revolutionized HLA typing, allowing for the complete and detailed sequencing of HLA genes [1, 2, 3, 4, 5, 6]. NGS allows for the identification of variants at a much higher resolution than previous technologies, being able to analyze multiple genes simultaneously and discover rare or unexpected variants. These advances have significantly improved disease diagnosis, personalized treatments, and transplant success [16, 17, 18].
Thus, HLA typing has evolved from simple serological methods to advanced genomic sequencing technologies, contributing significantly to the understanding of human immunity and to advances in the field of transplantation and personalized medicine.
2.1 Traditional HLA typing models: Serology and PCR
HLA typing, essential for transplantation and immunogenetic studies, was initially performed by traditional methods, such as serological tests and polymerase chain reaction (PCR). These techniques, although revolutionary at the time, have significant limitations that have led to the development of more advanced technologies.
2.1.1 Serological compatibility tests
The serological method, also known as microcytotoxicity, involves the use of specific antibodies to identify HLA antigens on the surface of cells [16]. Cell lysis is observed under a microscope after exposure to antibodies and whole serum, indicating a positive reaction [16].
Advantages: Its simplicity and frequent use in laboratories have made this method a standard for decades.
Limitations: Serological tests offer low resolution, unable to distinguish between genetic variants of HLA alleles. They also depend on the availability of specific antibodies, which can limit accuracy.
2.1.2 PCR-based typing
PCR was a step forward, using specific DNA sequences to identify HLA genes. Methods such as SSP-PCR (specific sequence primer PCR) and SSOP (sequence-specific oligonucleotide probes) have increased the resolution of HLA typing.
Advantages: PCR has allowed for more precise detection of genetic variants and direct analysis of DNA, eliminating the reliance on viable cell samples.
Limitations: PCR methods remain laborious and expensive, requiring advanced equipment and skilled personnel. In addition, the resolution, although higher than serology, is still insufficient to analyze complex variants of HLA alleles.
2.1.3 Challenges and limitations of traditional methods
Low resolution: None of the traditional methods can completely differentiate HLA alleles with similar sequences.
Complexity and cost: The tests require laborious processes and technical expertise, making them inaccessible to smaller laboratories.
Limited reproducibility: Results can vary between laboratories, especially in the case of serological tests, making comparisons difficult [16].
These limitations have led to the adoption of modern technologies such as next-generation sequencing (NGS), which offer high resolution, competitive costs, and detailed analysis of the entire spectrum of HLA variations [16].
2.2 Moving to advanced methods: NGS in HLA typing
With technological progress, traditional HLA typing methods such as serological tests and PCR have been gradually replaced by advanced techniques, the most important of which is next-generation sequencing (NGS) [16, 17, 18]. These modern technologies have completely transformed the field of immunogenetics, providing solutions to the limitations and challenges of traditional approaches [16, 17, 18].
2.2.1 Limitations of traditional methods and the need for innovation
Serological tests, although useful in the initial context, cannot distinguish between very closely related alleles, limiting the resolution needed in transplantation or research [16]. PCR, although more accurate, also faces similar difficulties in detecting complex variations and requires multiple laborious steps (Table 1).
| Aspect | Serological methods | PCR-based techniques | NGS |
|---|---|---|---|
| Resolution | Low | Moderate | High |
| Throughput | Low | Moderate | High |
| Allele Detection | Limited to common alleles | Better than serology but limited | Comprehensive, including rare alleles |
| Cost | Moderate | High | Decreasing |
| Reproducibility | Variable | Better than serology | High |
Table 1.
These limitations, such as low resolution, variable reproducibility, and high equipment costs, have created an urgent demand for more robust and efficient methods.
2.2.2 Advantages of NGS in HLA typing
Next-generation sequencing (NGS) has revolutionized genetic analysis with the ability to sequence thousands of DNA fragments simultaneously, providing a complete picture of HLA regions [16, 18].
High resolution: Unlike traditional methods, NGS allows for ultra-detailed typing, precisely identifying alleles variants (at the allele level) of HLA genes. This is essential in transplantation, where an exact HLA match can prevent graft rejection [16].
Complete coverage: NGS analyzes the entire HLA locus, including non-coding regions, providing detailed genetic information about the structure of haplotypes [18].
Efficiency: Processing multiple samples simultaneously reduces analysis time and costs per sample, making NGS a scalable solution [18].
Reliability and reproducibility: Unlike laborious manual methods, NGS produces standardized data, minimizing interlaboratory variation [18].
2.2.3 Impact of NGS on HLA typing
With NGS, researchers can address complex questions about HLA variability and its relationship to autoimmune diseases, susceptibility to infections, and transplantation. In addition, the high resolution allows for the discovery of rare or novel HLA alleles that were previously undetectable [18].
In conclusion, the transition from traditional methods to NGS is not just a technological evolution, but a fundamental change that redefines the boundaries of immunogenetics research. With its combination of sensitivity, precision, and efficiency, NGS has become the gold standard in HLA typing.
3. The rise of next-generation sequencing (NGS)
Next-generation sequencing (NGS) is an advanced technology that allows simultaneous analysis of large DNA sequences, providing a detailed understanding of the genome [19]. Unlike traditional sequencing methods, such as the Sanger method, which sequence small fragments and require a long time, NGS is fast, scalable, and highly accurate.
3.1 How does NGS work?
NGS uses parallel sequencing techniques, in which millions of DNA fragments are read simultaneously. This capability allows the reconstruction of complex regions, such as HLA genes, which are difficult to analyze by traditional methods.
3.1.1 Benefits of NGS
High resolution: NGS can identify genetic variations at the allele level, including rare polymorphisms or non-coding regions, providing a complete picture of DNA.
Processing speed: NGS can analyze thousands of fragments simultaneously, reducing the time required for sequencing compared to classical methods.
Multiplexing: This technology allows the analysis of multiple genes in parallel, which is essential for complex genetic studies, such as those involving multiple HLA loci.
Scalability: NGS is efficient in processing a large volume of samples, making it ideal for research and clinical applications.
3.1.2 Applications in human genetics
NGS has had a profound impact on genetic research, especially in the analysis of the HLA system, where detailed resolution is crucial. In immunogenetics, NGS allows precise HLA typing, which is essential in transplantation, where exact matching reduces the risk of graft rejection [19]. In addition, NGS facilitates the study of autoimmune diseases, susceptibility to infections, and genetic diversity in different populations.
In conclusion, NGS has revolutionized genetic analysis through its efficiency, precision, and ability to explore genomic variability. Its applications, especially in the study of HLA, continue to advance personalized medicine and the understanding of complex diseases [19].
4. Advances in NGS: Unlocking the complexity of HLA genes
Human leukocyte antigen (HLA) genes are essential for the functioning of the adaptive immune system, presenting pathogen-derived peptides to T-cell receptors [6]. These genes are highly polymorphic, reflecting an evolutionary adaptation to diverse selective pressures, such as exposure to specific pathogens. Detailed sequencing of HLA genes is crucial in transplantation, immunotherapy, and understanding susceptibility to autoimmune and infectious diseases [6].
4.1 HLA gene sequencing: Deciphering diversity with NGS
Next-generation sequencing (NGS) is revolutionizing HLA gene analysis with its ability to identify allelic variants (haplotypes) and rare polymorphisms. Unlike traditional methods, which rely on specific probes or targeted sequences, NGS uses massively parallel sequencing to generate comprehensive data, including intronic and non-coding regions [19]. This is essential because many HLA alleles differ by single substitutions or small insertions and deletions, which can have a significant impact on protein function.
By using advanced bioinformatic algorithms, NGS can resolve the ambiguity problems caused by the high similarities between HLA genes and their pseudogenes [19, 20]. Thus, the technology allows for precise characterization of haplotypes, including rare or novel alleles, which are critical for transplant compatibility and population studies.
4.2 Complexity of HLA genes and the resolution provided by NGS
The complexity of HLA genes stems from their extreme polymorphism: Each gene can have hundreds of functional alleles. For example, HLA-B has over 5000 known alleles, and this number continues to grow [21]. This diversity plays a key role in the recognition of specific antigens and in determining variable immunological responses between individuals [21].
NGS allows for nucleotide-level resolution, identifying variations even in structurally very similar regions. Furthermore, the ability to analyze the entire HLA locus, including regulatory regions, allows for a more comprehensive understanding of the impact of these genes on immunological and clinical phenotypes [19].
4.3 Clinical applications of detailed HLA sequencing
In the field of transplantation, detailed analysis of HLA genes with NGS ensures perfect compatibility between donor and recipient, reducing the risk of acute or chronic graft rejection [22]. In addition, it allows the identification of the minimal immunogenicity required for improved immunological tolerance.
In oncology, HLA profiling plays a crucial role in the selection of patients for immunotherapies, such as those using immune checkpoint inhibitors [23, 24, 25, 26, 27]. NGS-facilitated HLA studies are also essential in the development of personalized vaccines based on specific tumor neoantigens.
In addition to these applications, HLA analysis performed by NGS improves research on autoimmune diseases (e.g., the association of HLA-B27 with ankylosing spondylitis) and susceptibility to infections (such as the link between HLA and HIV) [23, 24, 25, 26, 27, 28, 29]. The high resolution and processing speed offered by NGS are transforming the understanding and applicability of HLA in medicine, opening new perspectives for diagnosis, treatment, and prevention.
5. Bioinformatics in HLA data interpretation
5.1 NGS data processing in HLA sequencing
The processing of raw data obtained by next-generation sequencing (NGS) for HLA gene analysis involves several crucial steps, each essential for obtaining an accurate profile (Figure 1).
Raw sequence acquisition: NGS generates short or long reads of DNA sequences, which include the coding and non-coding regions of HLA genes. The data are collected as FASTQ files containing the DNA sequences and quality scores [30, 31, 32, 33].
Data preprocessing: This step involves filtering and cleaning the reads to remove artifacts, sequences with low quality scores, or contaminations. Specific tools, such as Trimmomatic or FastQC, are used for quality assessment and optimization of the reads [30, 31, 32, 33].
Read alignment: The reads are mapped to a reference sequence of the human genome using alignment algorithms, such as BWA-MEM or Bowtie2. For HLA, alignment is more complex due to the high homologies between HLA alleles and pseudogenes [30, 31, 32, 33].
HLA haplotype reconstruction: The aligned data is processed to identify haplotypes specific to each individual, using databases such as IMGT/HLA [34]. This step requires specialized algorithms for the analysis of genes with high polymorphism [34].
Variant identification: All-cell variations (SNPs, insertions/deletions) are detected and associated with HLA alleles using advanced bioinformatics tools.
Figure 1.
NGS workflow for HLA typing.
5.2 Algorithms and software used in HLA analysis by NGS
OptiType: An accurate algorithm for HLA typing based on NGS data, using Bayesian methods to reconstruct possible haplotypes [35].
HLA-HD: Optimized software for NGS data analysis, capable of identifying complex haplotypes and detecting rare variants [30].
Seq2HLA: An RNA-Seq-based program that allows expression analysis and HLA typing [36, 37].
ArcasHLA: Advanced HLA typing algorithm that integrates raw data with HLA databases for fast and accurate identification [38].
SAMtools/BCFtools: General tools for variant analysis, useful in calibrating HLA alleles [39].
5.3 Bioinformatics challenges in HLA analysis
Complexity of HLA genes: HLA genes exhibit high homologies between different loci and pseudogenes, which can lead to misalignments and ambiguities in haplotype identification [34].
Variant calibration: Detection of rare alleles and subtle differences between alleles requires precise calibration, and technical variations in NGS data can complicate this process.
Large volumes of data: NGS generates a massive amount of data that requires robust computational infrastructure for storage and analysis [40].
Database limitations: HLA databases (such as IMGT/HLA) are continuously updated, but do not include all possible variants, which may limit complete identification [34].
Computational costs: HLA analysis requires specialized bioinformatics algorithms, which involve high computational power and associated costs.
Despite these challenges, advances in bioinformatics algorithms and the continuous updating of HLA databases allow for increasingly accurate and efficient interpretation of NGS data, contributing to clinical and research applicability.
6. Clinical applications of HLA sequencing: From transplantation to autoimmune diseases
See Table 2.
| Application | Importance of HLA typing |
|---|---|
| Organ Transplantation | Ensures donor–recipient compatibility, reducing rejection risk |
| Autoimmune Disease Diagnosis | Identifies genetic predisposition to diseases like lupus, rheumatoid arthritis |
| Personalized Medicine | Customizes therapies based on HLA profile, minimizing adverse drug reactions |
| Vaccine Development | Designs vaccines targeting diverse genetic populations for robust responses |
Table 2.
Clinical applications of HLA sequencing.
6.1 Transplants: The role of HLA sequencing in donor–recipient compatibility
HLA sequencing plays a critical role in determining immunological compatibility between donor and recipient in organ, tissue, and bone marrow transplantation [1, 2]. HLA genes are responsible for presenting cellular antigens to the immune system, so significant differences between the HLA alleles of the donor and recipient can trigger an aggressive immune response, resulting in transplant rejection [1, 2].
Advanced technologies, such as NGS sequencing, allow for the high-resolution identification of HLA alleles, including rare variants. This level of detail is crucial for selecting compatible donors and minimizing the risk of immune reactions, such as graft-versus-host disease (GvHD) in bone marrow transplantation. By using HLA sequencing, the success rate of transplants can be significantly increased, and the duration of graft survival can be prolonged.
6.2 Autoimmune diseases: Understanding genetic predisposition through HLA sequencing
HLA sequencing provides valuable insights into genetic susceptibility to autoimmune diseases such as systemic lupus erythematosus, rheumatoid arthritis, and type 1 diabetes [6, 7, 8, 9, 10]. Many autoimmune diseases are closely associated with specific HLA alleles. For example:
HLA-DR4 is frequently associated with rheumatoid arthritis.
HLA-DQ8 and HLA-DQ2 are implicated in celiac disease.
NGS helps identify alleles involved in these diseases with high accuracy, contributing to understanding the molecular mechanisms underlying autoimmunity. This may facilitate early diagnosis and the development of targeted treatments.
6.3 Personalized medicine: Personalizing treatments through HLA analysis
HLA sequencing is an essential component of personalized medicine, especially in the context of immunotherapy and vaccine development [41, 42, 43]. By analyzing patients’ HLA profiles in detail:
Cancer immunotherapy: HLA sequencing can guide the selection of neoantigens for T-cell-based therapies, ensuring a tumor-specific immune response.
Adverse drug reactions: Some HLA alleles, such as HLA-B*57:01, are associated with severe drug reactions, such as hypersensitivity to abacavir [43]. Identifying these alleles allows doctors to adjust treatments to minimize risks.
Vaccine development: Understanding HLA diversity helps create vaccines that can generate robust immune responses in diverse populations [44].
Thus, HLA sequencing is a cornerstone in targeting treatments and optimizing medical care based on individual genetic characteristics.
7. The future of HLA sequencing: Personalized medicine and beyond
The future of HLA sequencing is closely linked to technological innovations and the integration of genetic data in personalized medicine. Advances in NGS will allow for increasingly higher resolution and faster and more accessible analysis of the complex diversity of HLA genes [45]. This will facilitate the development of personalized treatments, optimization of transplant compatibility, and a better understanding of genetic predispositions to autoimmune or infectious diseases.
At the same time, the integration of artificial intelligence and advanced bioinformatics will revolutionize the interpretation of HLA data, paving the way for personalized immuno-oncology therapies and vaccines tailored to human genetic diversity [46]. In the long term, full genome sequencing, including HLA analysis, will become standard practice, providing clearer insights into individual and collective health.
7.1 Personalized medicine: The revolution brought by HLA sequencing
HLA sequencing technology, especially through next-generation sequencing (NGS), plays a central role in the transformation of personalized medicine. By analyzing a patient’s HLA profile in detail, doctors can tailor treatments to address their unique genetic characteristics. For example, cancer immunotherapy, which relies on stimulating the immune system to recognize and eliminate tumor cells, benefits enormously from the information provided by HLA sequencing [45]. This allows the identification of tumor-specific neoantigens, which are then used to personalize therapeutic vaccines or T-cell therapies.
In addition, HLA sequencing helps prevent adverse drug reactions. For example, the HLA-B*57:01 allele is associated with severe reactions to abacavir, a drug used to treat HIV [43]. Early identification of this allele ensures that safer treatment is chosen for the patient.
7.2 Whole genome sequencing: A new frontier in personalized medicine
As technologies advance, whole genome analysis is becoming increasingly accessible. Unlike targeted HLA gene sequencing, whole genome analysis provides a holistic picture of a patient’s genetic predispositions. This allows the identification of variants associated not only with the immune response, but also with susceptibility to other chronic diseases, such as diabetes, cardiovascular disease, and cancer [45].
By integrating HLA sequencing with complete genomic data, researchers can develop new diagnostic and treatment strategies. This approach can detect hidden genetic risks and guide early interventions, ensuring more effective prevention.
7.3 Future possibilities in treatments: The impact of NGS and HLA on tomorrow’s medicine
NGS technology, combined with detailed HLA analysis, will open up new opportunities in the treatment of complex diseases. In oncology, the discovery of HLA-associated neoantigens will facilitate the development of more effective therapies, such as personalized vaccines and CAR-T therapies [45, 47].
In the case of rare diseases, where genetic diversity plays a major role, NGS can identify HLA variants that contribute to the manifestation of pathology, thus providing targets for new treatments. Also, in infectious diseases, HLA analysis can guide the development of vaccines tailored to the genetic diversity of global populations.
Thus, the future of medical treatments will be increasingly guided by advanced genomics, revolutionizing the way doctors approach the prevention, diagnosis, and treatment of diseases.
8. NGS in HLA research: New horizons in immunogenetics
Next-generation sequencing (NGS) has revolutionized HLA research, enabling detailed, high-resolution analysis of HLA gene variability (Figure 2).
Figure 2.
Applications of NGS in HLA typing.
NGS facilitates the identification and characterization of rare HLA gene variants that were not detectable by traditional typing methods. This advanced technology contributes to the understanding of HLA diversity in diverse populations, allowing researchers to explore how these variants influence disease susceptibility and immune responses. In addition, NGS allows the study of complex interactions between HLA and pathogens, providing valuable information for the development of personalized therapies and vaccines. This approach opens up new possibilities for precision medicine and improved treatments, including in the fields of transplantation and autoimmune diseases (Figure 1).
8.1 The new frontier of HLA research
Next-generation sequencing (NGS) is redefining the exploration of HLA variability, providing an unprecedented level of detail. In population studies, NGS allows for the precise analysis of rare and common HLA alleles, contributing to the understanding of genetic adaptations and selective pressures throughout human history. This opens new perspectives on HLA diversity in isolated or under-represented populations, with applications in precision medicine and in studies of migration and evolution.
8.2 Immunogenetics of the future
Future research, facilitated by NGS, will deepen the understanding of immunological mechanisms by comprehensively analyzing HLA regions and their interactions with other genes. Identification of genetic variations associated with autoimmune and infectious diseases will contribute to prevention and personalized therapies [48]. Furthermore, emerging technologies can integrate HLA analysis with other omics data (transcriptomics and proteomics), building a more complete picture of the immune response [48].
8.3 HLA in interaction with pathogens
The HLA system plays a key role in the immune response by presenting pathogenic antigens. Recent studies, supported by NGS, analyze how certain HLA alleles influence resistance or susceptibility to infections such as malaria, HIV, or tuberculosis [27, 29, 49, 50]. This technology allows the rapid identification of HLA variants involved in effective responses to specific pathogens. These discoveries are fundamental for the development of vaccines and treatments tailored to individual genetic diversity, contributing to the fight against emerging and pandemic diseases [29].
9. Ethical and practical considerations in HLA sequencing
Ethical issues in HLA typing include issues such as confidentiality and use of genetic data, informed consent, and genetic discrimination [51]. Because HLA information can reveal genetic predispositions to autoimmune diseases or risks to transplant success, there is a risk that it could be misused by employers or insurance companies (Table 3).
| Issue | Explanation |
|---|---|
| Privacy and Confidentiality | Risks of misuse of sensitive genetic data in employment or insurance |
| Informed consent | Ensuring participants understand usage and implications of genetic information |
| Equity in access | Disparity in availability of advanced technology in low-resource settings |
| Genetic Discrimination | Potential misuse of genetic predisposition information by third parties |
Table 3.
Ethical considerations in HLA sequencing.
In addition, the typing process must be carried out with the patient’s clear consent, ensuring that they understand how their genetic data will be used. It is also important that access to these advanced technologies is equitable and does not discriminate against certain population groups.
9.1 Confidentiality of genetic data
The confidentiality of genetic data obtained through HLA sequencing is a major concern, given its highly personal nature [51]. Although this information can bring significant benefits for personalized diagnosis and treatment, the risks associated with its disclosure may include genetic discrimination and violation of individual privacy. Thus, there is a need for strict regulations and clear data protection policies, including anonymization and encryption. Protecting patient rights and ensuring informed consent are also essential to prevent the misuse of genetic information, especially in the fields of insurance and employment.
9.2 Equity in access to technology
Access to advanced sequencing technologies, including NGS for HLA analysis, remains an important topic of discussion, especially in resource-limited regions. While these technologies have the potential to revolutionize genetic diagnosis and treatment, the high costs and required technological infrastructure can be barriers for many populations. Lack of access to advanced sequencing can contribute to disparities in health care and disease prevention, and it is essential that governments and international organizations promote equity in access to these innovative technologies.
9.3 Impact of medical decisions
HLA analysis plays a crucial role in medical decision-making, especially in the fields of transplantation, autoimmune diseases, and personalized medicine. However, errors in the interpretation of genetic data or wrong predictions can lead to incorrect diagnoses or wrong therapeutic decisions, directly affecting the patient’s health. For example, incorrect HLA typing can lead to the rejection of an organ transplant or the administration of a treatment that is not optimal for the patient. Thus, it is necessary that HLA analysis is supported by well-trained professionals and is accompanied by a rigorous validation system to minimize the associated risks.
10. Conclusions
In conclusion, NGS represents a revolution in HLA typing and will continue to deepen our understanding of human genetics and immune interactions, providing new insights and solutions for a wide range of clinical and research applications.
We can draw several important conclusions regarding the evolution and impact of HLA typing technologies, especially in the context of next-generation sequencing (NGS) technologies.
Evolution of HLA typing technologies: From the first serological techniques, which were limited in resolution, to the advent of PCR and, more recently, next-generation sequencing (NGS), progress in HLA typing has been essential for the success of transplants, the understanding of autoimmune diseases, and the development of personalized medicine [16, 19]. NGS represents a major leap forward, allowing detailed analysis of genetic variants and the identification of rare variants, with a significant impact on diagnosis and treatment.
Impact of NGS on research and clinical applications: NGS technology has revolutionized immunogenetics research, allowing a much more detailed understanding of HLA variability at the population and individual level [16, 19]. This contributes not only to the identification of compatibility in transplants, but also to a better understanding of predispositions to autoimmune, infectious diseases and cancer, providing an essential tool for personalized medicine.
Traditional challenges and limitations: Despite advances, traditional HLA typing methods, such as serological tests and PCR, had significant limitations, including low resolution, high cost, and technological complexity. NGS has overcome these challenges, but this technology also comes with its own challenges, including the need for complex bioinformatic algorithms and the correct interpretation of genetic variants [16, 18, 19].
The future of HLA sequencing and its applications: As NGS technology continues to evolve, its potential to transform personalized medicine is immense. Whole genome sequencing and detailed analysis of HLA profiles will play a key role in the development of more precise treatments, including in the fields of cancer, rare diseases, and vaccinology [48]. At the same time, NGS will support research on HLA-pathogen interactions, providing vital information for the development of more effective therapies.
Ethical and social challenges: Despite technological advances, important challenges remain related to the privacy of genetic data, equitable access to advanced technologies, and the potential impact of medical decisions influenced by HLA analysis. Ensuring a robust and equitable ethical framework will be essential as these technologies become more accessible [50, 51].
Conflict of interest
The authors declare no conflict of interest.
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