INTRODUCTION:
Personalised genomics is a key component of the broader field of personalised medicine. It is concerned with the sequencing, analysis and interpretation of an individual’s genome and its derivatives (RNA, protein, and metabolite) to guide medical decision making {Ginsburg, 2009 #1}(Ginsburg & Willard, 2009). Personalised medicine then integrates this information with environmental and lifestyle data of an individual to achieve highly specific and personalised medical diagnose, treatment and prevention (Steinhausen, 2013).
The need for personalised medicine, and by consequence personalised genomics, arises from the known fact that every individual is fundamentally different. Environmental factors and lifestyle vary throughout the social, economic and geographical spectrum and it is known that approximately 1.6 per cent of the total human genome differs from individual to individual (Naidoo, Pawitan, Soong, Cooper, & Ku, 2011). Hence, it should be well known that the way a certain drug reacts or how a certain disease arises and develops also differs from patient to patient. It is because of this, that personalised medicine has been receiving increasing attention in recent years. With the promise of treatments that take in consideration the specificities of an individual’s environment and genetic makeup, personalised medicine brings hope for a more efficient medical care in which diseases can be treated more efficiently (pharmacogenics) and in some case avoided completely by means of diet changes (nutrigenomics) or gene therapy (Kumar, 2007).
Nearly all conditions and diseases have some genetic component to it (National Library of Medicine, 2018b), which may arise from chromosomal diseases, single-gene disorders, multifactorial disorders or mitochondrial disorders (World Health Organization). Even non-genetic disorders might have some type of genomic intervention as part of their treatment, for example HIV treatments (Mooser & Currat, 2014). This only highlights the importance of personalised medicine, in combination with personalised genomics, in the detection, treatment or prevention of such diseases.
In statistical terms, it is estimated that around 59% of deaths worldwide are caused by some form of genetic disease, reaching 87% in high-income countries (Busse, 2010). In economic terms, the cost of chronic diseases, which by the definition means diseases of genetic origin, and their risk factors can have a considerable impact on a country’s GDP, ranging from 0.02% to 6.77% (Busse, 2010). The prevention of such disease by means of personalised medicine could, then, considerably decrease mortality rates and economic impact.
CONTENT:
The Encyclopaedia Britannica list at least 8 different forms of classifying diseases that range from pathological to juristic (Burrows, Scarpelli, Robbins, & Robbins, 2017). For this report, a classification concerned with how the disease is caused will be used or, in other words, the aetiology classification will be used. In this realm of classification one can find a multitude of subcategories. Aiming at diseases that personalised medicine might have some significance in terms of treatment, in this report a three-type classification that includes infectious diseases (disorders caused by other organisms such as viruses, bacteria or fungi), nutritional diseases (related to up or downregulated vitamins, minerals or other essential elements) and genetic diseases (associated to both hereditary and non-hereditary abnormalities in the genome) will be used.
Each of these types of diseases can be related to a different area of applied personalised medicine, namely pharmacogenomics to infectious diseases, gene therapy to genetic diseases and nutrigenomics to both nutritional diseases and genetic diseases(Kumar, 2007). Overall all these different areas of personalised medicine rely, to different extents, on the same kind of data; which includes a variety of variables related to an individual’s life. Some of the variables might include environmental information about the place where the patients live, their lifestyle, diet, family history, own observations about symptoms and, of course, biomarker information that goes from their genetic makeup to the levels of different metabolites in their body (Tremblay & Hamet, 2013).
On the next few paragraphs, as the different kinds of disease are discussed, further explanation on each of these applications and the technologies involved will be given.
GENETIC DISEASES
Genetic diseases are commonly defined as any sort of disorder rooted on some abnormality in the genome (Mahdieh & Rabbani, 2013). More specifically, genetic disorders can also be sub-classified into three different categories: chromosomal diseases, monogenic diseases and multifactorial disorders (Mahdieh & Rabbani, 2013). In this report, focus will be given to monogenic and multifactorial disorders.
Monogenic disorders
Monogenic or Mendelian disorders, are relatively rare disorders that are related to a mutation in a single gene. The mutation may be present on one or both chromosomes (one chromosome inherited from each parent) and it can be classified as either dominant or recessive, depending on whether the disease gene is on both chromosomes (recessive) or on a single chromosome (dominant) (National Human Genome Research Institute, 2015). Recently it has been found that some diseases that were initially characterised as monogenic might be influenced by a relatively small number of additional genes, as is the case of cystic fibrosis (Chial, 2008).
Because of Mendel’s early discovery of monogenic inheritance patterns, classical linkage studies have been used to characterise monogenic disorders for a long time(Naidoo et al., 2011). With the development of new technologies, more effective approaches started to arise. PCR based detection methods were the first to be extensively used (Taylor & Taylor, 2004). Microarrays, due to their relatively easy handling and high throughput are now the common choice in routine genotyping (Hoheisel, 2006). Homozygosity mapping, for example, is widely used in the detection of recessive disorders. The main idea behind this approach is that recessive disorders can be narrowed down to homozygosity regions, this is accomplished using SNP arrays followed by computational analysis (Alkuraya, 2010). Diseases that cannot be detected through these techniques might also be subjected to exome sequencing or whole-genome sequencing coupled with genome-wide association studies (GWAS), where two or more individuals’ genetic variants are compared in the search for variants associated with a specific trait (Naidoo et al., 2011).
In 2011, a Centre for Mendelian Disorders was created at the National Human Genome Research Institute (USA) with the promise of sequencing 40 to 50 Mendelian disorders per year (Naidoo et al., 2011). This promise was not only successful as the results greatly exceeded the expectations reaching 4000 determined Mendelian disorders at the current year of 2018 (National Human Genome Research Institute, 2018). This is great news, considering that of the more than 14000 known diseases, more than 10000 are monogenic (Moss, 2014).
Multifactorial disorders
Multifactorial disorders occur as the result of mutations in multiple genes, often coupled with environmental factors (World Health Organization). These disorders comprehend both diseases like diabetes or heart disease and behaviours such alcoholism or mental illnesses (National Human Genome Research Institute, 2015).
Despite being more common than monogenic diseases, multifactorial disorders are not so easily detected. Methods such as linkage analysis do not seem to be so effective due to the polygenic nature of multifactorial diseases. This suggest that a shift from family-based genetic studies to population-based studies should lead to better results in multifactorial disorders research (Bush & Moore, 2012). GWAS, which has already been successful at identifying a gene as major risk factor for age-related macular degeneration (AMD) (Bush & Moore, 2012), is currently the most promising method that relies on population comparison for the identification of new gene-disease correlates (Lobo, 2008).
The main concepts underlying GWAS functioning are single nucleotide polymorphisms (SNPs). Due to the high frequency of SNPs in the human genome, SNPs are used as markers for different genomic regions in the search for disease causing genes (Naidoo et al., 2011).
Although typically SNPs have minimal impact on biological systems, they can also present functional consequences (Bush & Moore, 2012). Because of this, GWAS findings are distinguished between direct association (when the SNPs cause the disease) and indirect association (when the SNPs are only associated to the gene causing the disease) (Bush & Moore, 2012). As there are roughly 10 million SNPs in the human genome (National Library of Medicine, 2018c), multiple SNPs might be associated to the same mutation. To avoid redundancy, HapMap (an international project designed to identify variations and characterize correlations across the genome) makes use of a characteristic of SNPs (linkage disequilibrium) to reduce the number of relevant SNPs (Bush & Moore, 2012).
Linkage disequilibrium (LD) is the idea that some SNPs are inherited together within a population. Based on this, HapMap project calculates that 80% of commonly occurring SNPs in European descent populations can be detected using a subset of 50000 to 1 million SNPs (Bush & Moore, 2012). Because linkage disequilibrium rate of decay depends on multiple factors, it varies from population to population. African descent populations, for instance, have smaller regions of LD due to accumulation of recombination events (Bush & Moore, 2012).
The findings that GWAS have evoked lead to the proposition of a hypothesis on the mechanisms of multifactorial diseases, the Common Disease, Common Variant hypothesis. The hypothesis proposes that multifactorial diseases are caused by common allele variants of low penetrance (Schork, Murray, Frazer, & Topol, 2009). If this proves to be correct, the chances of developing clinical applications such as therapeutic interventions are more likely (Iyengar & Elston, 2007). Recent discussion, however, point out the inefficacy of the hypothesis to stablish hereditability patterns, which puts the mechanisms of CD/CV at stake (Naidoo et al., 2011).
As of 2018, the European Bioinformatics Institute (EMBL-EBI) had over 14000 catalogued SNPs related to different disease and traits (European Bioinformatics Institute, 2017). The relevance of such number in terms of development of new clinical applications ultimately lies on the better understanding of the heritability mechanisms of these diseases and further high coverage studies (Naidoo et al., 2011).
Both monogenic and multifactorial disorders can be subjected to gene therapy. The concept behind this approach is that genetic diseases can be treated and eventually cured by artificially modifying the genome (Dunbar et al., 2018). Acquired, non-inherited, genetic diseases such as some forms of cancer or HIV/AIDS can also benefit from this type of treatment (Moss, 2014). Currently there are two main approaches to gene therapy: viral vectors (gene addition) and engineered or bacterial nucleases (gene addition, ablation, correction and other types of modification) (Dunbar et al., 2018). This field of research has been making astounding progress since its second wave of trials in the 2000s and seems to promise even better results with the recently discovered CRISPR-based approaches (Dunbar et al., 2018). In 2017, the FDA approved the first gene therapy products, including AAV vectors for in vivo treatment of congenital blindness (Dunbar et al., 2018).
For those genetic diseases where gene therapy might not be viable, nutrigenomics offers an alternative. By identifying susceptibility to certain diseases, nutrigenomics might help by providing personalised dietary advice that halts the onset of the disease in question (Hesketh, 2013).
NUTRITIONAL DISEASES
Nutritional diseases are any sort of disease related to either the immediate effect of nutrients and other products in the body (related to metabolic pathways such as glycolysis) (Gupta & Gupta, 2014) or the after effect of those nutrients on factors such as epigenetics (Choi & Friso, 2010).
A variety of different techniques are currently used to detect and treat these types of diseases. Detection techniques range from detection of specific markers in body fluids (such as levels of essential minerals) (Gupta & Gupta, 2014) to correlational studies between epigenetic alterations and diet (Choi & Friso, 2010). As nutritional diseases involve many different levels of the human biology and can be influenced by a multitude of variables such as environment, exercise and lifestyle, developing a nutritional phenotype becomes a difficult task (Zeisel et al., 2005). Despite of this, nutrigenomics proposes to do exactly that through a combination of different –omics technologies such as genomics, proteomics and metabolomics (Whitfield, German, & Noble, 2004) and other sorts of data such as family history and environment (Hesketh, 2013).
Although nutrigenomics is still under development and meta-analytical studies have shown no definite association between commonly studied genes in nutrigenomics and diet-related diseases (Pavlidis, Patrinos, & Katsila, 2015), information obtains from these studies might still prove to be useful for further research.
INFECTIOUS DISEASES
Infectious diseases include those caused by pathogenic microorganisms such as bacteria, viruses, parasites or fungi (World Health Organization). Even though these diseases might be caused by the same pathogens, the reaction to the pathogens varies from individual to individual because of genetic variation in both the pathogen and the host (Al-Mozaini & Mansour, 2016).
Studies attempting to connect infectious diseases to personalised medicine mainly focus on susceptibility to pathogens. Certain genetic polymorphisms are associated to higher risk of some infectious diseases, an example is the association of the HBB gene and severe malaria (Chapman & Hill, 2012). It is expected that by identifying these susceptibility genes, new clinical applications, based on the susceptibility genes, can be designed (Chapman & Hill, 2012).
Coupled to the studies of the mechanisms of infectious disease themselves is the study of the drugs used to fight against them. Pharmacogenomics is the field that studies how genetic variants affect the pharmacokinetics and pharmacodynamics of a certain drug (Relling & Evans, 2015).
In the field of personalised medicine, pharmacogenomics proves its usefulness in the determination of the right dosage for each patient. It is known that the rate at which patients metabolise drugs varies according to polymorphisms on CYP450 genes. Simple screening of patients for these genes might aid in the determination of dosages that deliver more efficient treatments without risking toxicity (Tremblay & Hamet, 2013).
ETHICAL ISSUES
Despite the fancy terminology, personalised medicine is not a completely new concept. The understanding that individuals are different and that some treatments might have different reactions for every patient has been around for a long time. Some examples where this patient to patient difference leads to ‘personalised’ treatments are antibiotic choices in serious bacterial infections or the choice for a certain antiviral in HIV therapy (Crommelin, Storm, & Luijten, 2011). Rather than a concept barrier, the most probable reasons for the non-universality of personalised medicine are the current high costs and the technological and ethical challenges it presents.
The so acclaimed $1000 whole-genome sequencing has only recently come out (National Human Genome Research Institute, 2016) and even if it became a common practice in the detection of genetic diseases, there would still be the costs for drug development. The first FDA approved gene therapy, Luxturna, for example, has already set its price on $850000 per treatment (MIT Technology Review, 2018).
In technological terms, there is also much improvements to be made. Despite the advances in the realm of genomics, fields such as metabolomics, have yet a long path towards high-throughput technologies for quantification and identification of metabolites (Whitfield et al., 2004). The unification of all the different –omics technologies, as well, is still in its first steps and discussions about possible algorithms that could aid in the management of such big data are only now starting to come out (Huang, Chaudhary, & Garmire, 2017).
However, despite all the current drawbacks of personalised medicine, the field maintains itself as one of the most promising. Given the astoundingly rapid advances in genomics since it started (Naidoo et al., 2011), there is no reason to believe that the same could not happen with the other –omics technologies, opening the possibilities for cheaper and more accessible treatments.
If all the challenges that currently exist where to be overcome, the possibilities that personalised medicine could offer could range from highly specific drug delivery (Florence & Lee, 2011) to highly specific lifestyle guidelines that guarantee optimum health (Maher, Pooler, Kaput, & Kussmann, 2016). Screening every individual’s genome in search for genetic diseases and editing the disease-related genes could become a routine practice in hospitals. Just as genetic amniocentesis tests are offered today during prenatal practices, screening the genome of embryos could also become routine (Stankovic, 2005). With the advances in GWAS research and the better understanding of polygenetic traits, not only diseases could be detected but also other traits such as height or eye colour and even behaviours such as alcoholism (Visscher, 2016).
In the year 2000, the first baby to be artificially selected was born. To save their daughter, Molly, who needed a bone marrow transplant, the parents of the child decided to have another baby who would not carry the disease-causing gene and that could also become a donor (Nerlich, Johnson, & Clarke, 2003). In 2017, a significantly successful study on editing an embryo by means of CRISPR was published (Ledford, 2017). The team involved in the study were able to correct a genetic mutation that is responsible for approximately 40% of the genetic defects involved in the Hypertrophic cardiomyopathy (HCM) condition (Ma et al., 2017). In 2015, the United Kingdom became the first country to legalise mitochondrial replacement techniques in clinics (Callaway, 2015) and in 2018 the first woman to have permission to undergo such a treatment arose (Sample, 2018). Given all such developments in the realm of clinical detection and editing of disease-causing genes one cannot but wonder when would the headlines on the news shift from ‘disease-free baby’ to ‘artificially enhanced baby’.
In a utopian world free of genetic diseases, could modifications in the genome that lead to an ‘average’ baby be considered a treatment? What would ‘average’ mean? In a reinterpretation of Nietzsche’s ‘übermensch’, Peter Sloterdijk, in ‘Rules for the Human Zoo’, proposes the idea of a new wave of anthropotechnologies (all techniques, from training and education to genome-based technologies, that may be used in pro of human enhancement) that would lead to the ultimate conflict between those who wish to breed for minimization and those who wish to breed for the maximization of human function (Sloterdijk, 2009). This conflict, rooted on the question of to whom belongs the power to choose the future of the human race, brings up an even more important question concerning how would such decisions be made.
Some authors, as is the case of Hub Zwart, believe that the establishment of what ‘average’ means and what are the boundaries to genome modification would somehow come to existence ‘naturally’. They argue that the desire of enhancement is embedded in human culture and that the steps from enhanced athletes to pre-employment genetic screening (PEGS) and ordinary people willingly applying for genetic modification and tailored lifestyles that suit best their job pursuit are not so far apart (Zwart, 2009). What is not discussed is how social and economic factors would influence these decisions.
As an up to date example of the social influences over genome editing, the ethical discussions surrounding sex selection can be used. Sex selection has always been an issue throughout different cultures. Whether male preference is given due to inheritance issues (Eftekhaari et al., 2015) or female preference is given due to gender imbalance (Watts, 2004), different techniques for influencing the sex of the baby have long been developed in different cultures (Eftekhaari et al., 2015). Nevertheless, discussions over the ethics of artificially selecting the sex of a baby in the laboratory are far from being solved.
As should be known, sex discrimination is an incursion of the Human Rights (United Nations) and, therefore, any attempt to prevent the birth of a certain sex over another due to cultural preferences should not be overlooked. One of the main sources of controversy in this debate, for example, lies in situations where the option for a certain sex lies on the protection of the child against cultural discrimination itself (Wertz, Fletcher, & Berg, 2003): could a mother choose not to have a daughter based on the knowledge that the daughter would suffer discrimination from the family because of cultural believes? These types of questions lead to a debate over the mechanisms of culture and the inter and intra influences between the public and the private life and the solutions for these are far from being found inside the laboratory.
What examples like these show, are the intricately complex mechanisms of culture and how the addition of genome editing technologies, if given without pre-meditation, could only serve as perpetuators of already established cultural biases. Attempts to formalise proper guidelines towards the ethics of genome editing and its boundaries such as the World Health Organization’s ‘Review of Ethical Issues in Medical Genetics’ have been written, but those lack firmness and seem to fail to comprehend the specificities of the multiple cultures.
Complementary to the debate about the boundaries of gene editing, there is also the debate about whom would profit from it. This debate can be divided into two different parts. First there is the funding of genome editing research and second there is the commercialisation of such treatments.
As was mentioned previously, not all ethnical groups have the same SNPs organisation (Naidoo et al., 2011) and as result of this, research on mechanisms for identification of mutations responsible for diseases and traits should be specific for each ethnical group. This is reasonably worrying, considering how research funding works nowadays. Where funding is given only to research that might provide future economic gains (commercialisation of a new drugs, for example) (Thanukos, Skene, Gilet, Stuart, & Casazza, 2018); investment on minor ethnical groups that would not necessarily be commercially profitable, would be threatened (Thanukos et al., 2018). Currently, the 1000 Genome Project studies 26 different populations originating from nearly all continents (Oceania excluded) (The International Genome Sample Resource, 2018), how the information gathered in the project will be used in future research for personalised treatments should be a matter of discussion.
Adding to the problem of whether all ethnical groups would benefit equally from personalised treatment techniques, there is the discussion over how these treatments should be commercialised. Gene patent, for instance, has been an issue that has evoked many controversies. As from 2013, the Supreme Court of the United States ruled that natural human genes were not to be patented. Lab manipulated DNA, however, would still be eligible (National Library of Medicine, 2018a). In Europe, there is not even such regulation and both natural and lab manipulated genes are eligible (Cole, 2015). With the advent of gene targeted treatments, such regulations prompt an obvious question: could it be that, at any given time, genome modification became another social gap where genes for certain traits such as intelligence, for example, would be sold at high prices only to those who can afford it? It seems quite clear that, to avoid such outcomes, modification to these regulations should be made.
All the different treatments discussed up to now could only be developed because of donations of genetic and medical information from patients. Currently there is no established procedure as to how to obtain this type of data but discussions are taking place. The main ethical issues involved in this type of data gathering are those related to medical privacy and research participant exploitation (in cases where financial benefit is obtained from the use of data given by the participants) (Ursin, 2010).
As of 2015, the first directory gathering information from 515 biobanks in Europe was launched (Holub et al., 2016). By the name of Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC), the directory aim is to aid in the storage and sharing of biological data ranging from DNA to body tissues (BBMRI-ERIC, 2018). Even though the directory gathers data from biobanks across Europe, the ethical regulations regarding each biobank are not unified. On the 20th of February of 2018 a conference organised jointly by Uppsala University, BBMRI-ERIC, EURORDIS-Rare Diseases Europe, and RD-Connect will be held with the goal of setting common ethical standards amongst institutions (Uppsala University, 2018).
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