In 2020 Anvi'o [ 1 ]  was available as a multiomics platform that contains pangenomic and metapangenomic analyses as well as visualization workflows. In Anvi'o, genomes are displayed in concentrical circles and each radius represents a gene family, allowing for comparison of more than 100 genomes in its interactive visualization. In 2020, a computational comparison of tools for extracting gene-based pangenomic contents (such as GET_HOMOLOGUES, PanDelos, Roary, and others) has been released. [ 68 ]   Tools were compared from a methodological perspective, analyzing the causes that lead a given methodology to outperform other tools. The analysis was performed by taking into account different bacterial populations, which are synthetically generated by changing evolutionary parameters. Results show a differentiation of the performance of each tool that depends on the composition of the input genomes. Again in 2020, several tools introduced a graphical representation of the pangenomes showing the contiguity of genes (PPanGGOLiN, [ 46 ]  Panaroo [ 65 ] ).
Other software tools for pangenomics include Prodigal, Prokka, PanVis, PanTools, Pangenome Graph Builder (PGGB), PanX, Pagoo, and pgr-tk.  [ 69 ]


The  Personal Genetics Education Project  (pgEd) [ 1 ]  aims to engage and inform a worldwide audience about the benefits of knowing one's genome as well as the ethical, legal and social issues ( ELSI ) [ 2 ]  and dimensions of  personal genetics . [ 3 ]   pgEd was founded in 2006, is housed in the Department of Genetics at  Harvard Medical School  and is directed by  Ting Wu , a professor in that department. It employs a variety of strategies for reaching general audiences, including generating online curricular materials, [ 4 ]  leading discussions in classrooms, workshops, and conferences, developing a mobile educational game (Map-Ed), [ 5 ]  holding an annual conference geared toward accelerating awareness (GETed [ 6 ] ), and working with the world of entertainment to improve accuracy and outreach. [ 7 ]
 [ 8 ]
 [ 9 ]
 [ 10 ]


pgEd develops tools for teachers and general audiences that examine the potential benefits and risks of personalized genome analysis.  These include freely accessible, interactive lesson plans that tackle issues such as  genetic testing  of minors, reproductive genetics, complex human traits and genetics, and the history of eugenics.  pgEd also engages educators at conferences as well as organizes professional development workshops. All of pgEd's materials are freely available online. [ 1 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ]


In 2013, pgEd created a mobile educational quiz called  Map-Ed . [ 5 ]  Map-Ed invites players to work their way through five questions that address key concepts in genetics and then pin themselves on a world map.  Within weeks of its launch, Map-Ed gained over 1,000 pins around the world, spanning across all 7 continents. Translations and new maps linked to questions on topics broadly related to genetics are in development. [ 5 ]


pgEd hosts the annual GETed conference, [ 6 ]  a meeting that brings together experts from across the United States and beyond in education, research, health, entertainment, and policy to develop strategies for accelerating public awareness. Topics covered during these conferences have included reproductive technologies, human behavior and cognition, microbiomes, the intersection of faith and genetics, interplanetary travel, the importance of engaging the political sphere, and the power of entertainment and gaming to reach millions.


pgEd is working with Sandra de Castro Buffington and Hollywood, Health & Society at the  Norman Lear Center ,  University of Southern California  (USC) Annenberg School for Communication, to advance awareness about personal genetics through television. [ 15 ] [ 16 ]  They have also worked with the  Broad Institute  on outreach via fiction. [ 17 ]
 [ 18 ]
 [ 19 ]


pgEd's advisory board includes Sandra de Castro Buffington, Director, Hollywood Health and Society, [ 20 ]   George M. Church , Professor of Genetics, Harvard Medical School,   Juan Enriquez , Managing Director at Excel Venture Management, and  Marc Hodosh , Co-Creator of  TEDMED . [ 1 ]


Personal genomics  or  consumer genetics  is the  branch  of  genomics  concerned with the  sequencing ,  analysis  and interpretation of the  genome  of an individual. The  genotyping  stage employs different techniques, including  single-nucleotide polymorphism  (SNP)  analysis chips  (typically 0.02% of the genome), or  partial  or  full genome sequencing . Once the genotypes are known, the individual's variations can be compared with the published literature to determine likelihood of trait expression, ancestry inference and disease risk.


Automated high-throughput sequencers have increased the speed and reduced the cost of sequencing, making it possible to offer whole genome sequencing including interpretation to consumers since 2015 for less than  $1,000 . The emerging market of direct-to-consumer genome sequencing services has brought new questions about both the medical efficacy and the ethical dilemmas associated with widespread knowledge of individual genetic information.


Personalized medicine  is a medical method that targets treatment structures and medicinal decisions based on a patient's predicted response or risk of disease. [ 1 ]  The  National Cancer Institute  or NCI, an arm of the  National Institutes of Health , lists a patient's genes, proteins, and environment as the primary factors analyzed to prevent, diagnose, and treat disease through personalized medicine. [ 1 ]


There are various subcategories of the concept of personalized medicine such as  predictive medicine ,  precision medicine  and stratified medicine. Although these terms are used interchangeably to describe this practice, each carries individual nuances.  Predictive medicine  describes the field of medicine that utilizes information, often obtained through personal genomics techniques, to both predict the possibility of disease, and institute preventative measures for a particular individual. [ 2 ]   Precision medicine  is a term very similar to personalized medicine in that it focuses on a patient's genes, environment, and lifestyle; however, it is utilized by National Research Council to avoid any confusion or misinterpretations associated with the broader term. Stratified medicine is a version of personalized medicine which focuses on dividing patients into subgroups based on specific responses to treatment, and identifying effective treatments for the particular group. [ 3 ]


Examples of the use of personalized medicine include  oncogenomics  and  pharmacogenomics . Oncogenomics is a field of study focused on the characterization of cancer–related genes. With cancer, specific information about a tumor is used to help create a personalized diagnosis and treatment plan. [ 4 ]  Pharmacogenomics is the study of how a person's genome affects their response to drugs. [ 5 ]  This field is relatively new but growing fast due in part to an increase in funding for the NIH Pharmacogenomics Research Network. Since 2001, there has been an almost 550% increase in the number of research papers in  PubMed  related to the search terms  pharmacogenomics  and  pharmacogenetics . [ 6 ]  This field allows researchers to better understand how genetic differences will influence the body's response to a drug and inform which medicine is most appropriate for the patient. These treatment plans will be able to prevent or at least minimize the adverse drug reactions which are a, "significant cause of hospitalizations and deaths in the United States." Overall, researchers believe pharmacogenomics will allow physicians to better tailor medicine to the needs of the individual patient. [ 5 ]  As of November 2016, the FDA has approved 204 drugs with pharmacogenetics information in its labeling. These labels may describe genotype-specific dosing instructions and risk for adverse events amongst other information. [ 7 ]


Disease risk may be calculated based on genetic markers and  genome-wide association studies  for common medical conditions, which are  multifactorial  and include environmental components in the assessment. Diseases which are individually  rare  (less than 200,000 people affected in the USA) are nevertheless collectively common (affecting roughly 8-10% of the US population [ 8 ] ).  Over 2500 of these diseases (including a few more common ones) have predictive genetics of sufficiently high clinical impact that they are recommended as medical genetic tests available for single genes (and in whole genome sequencing) and growing at about 200 new genetic diseases per year. [ 9 ]


The cost of sequencing a human genome is dropping rapidly, due to the continual development of new, faster, cheaper DNA sequencing technologies such as " next-generation DNA sequencing ".


The National Human Genome Research Institute, an arm of the  U.S. National Institutes of Health , has reported that the cost to sequence a whole human-sized genome has dropped from about $14 million in 2006 to below $1,500 by late 2015. [ 11 ]


There are 6 billion  base pairs  in the diploid human genome. Statistical analysis reveals that a coverage of approximately ten times is required to get coverage of both alleles in 90% human genome from 25 base pair reads with shotgun sequencing. [ 12 ]  This means a total of 60 billion base pairs that must be sequenced. An  Applied Biosystems   SOLiD ,  Illumina  or Helicos [ 13 ]  sequencing machine can sequence 2 to 10 billion base pairs in each $8,000 to $18,000 run. The cost must also take into account personnel costs, data processing costs, legal, communications and other costs. One way to assess this is via commercial offerings.  The first such whole diploid genome sequencing (6 billion bp, 3 billion from each parent) was from  Knome  and their price dropped from $350,000 in 2008 to $99,000 in 2009. [ 14 ] [ 15 ]  This inspects 3000-fold more bases of the genome than SNP chip-based  genotyping , identifying both novel and known sequence variants, some relevant to personal health or  ancestry . [ 16 ]  In June 2009,  Illumina  announced the launch of its own Personal  Full Genome Sequencing  Service at a depth of 30X for $48,000 per genome. [ 17 ]  In 2010, they cut the price to $19,500. [ 18 ]


In 2009,  Complete Genomics  of  Mountain View  announced that it would provide  full genome sequencing  for $5,000, from June 2009. [ 19 ]  This will only be available to institutions, not individuals. [ 20 ]  Prices are expected to drop further over the next few years through economies of scale and increased competition. [ 21 ] [ 22 ]  As of 2014, nearly complete  exome  sequencing was offered by Gentle for less than $2,000, including personal counseling along with the results. [ 23 ]  As of late 2018, over a million human genomes have been nearly completely sequenced for as little as $200 per person, [ 24 ]  and even under certain circumstances ultra-secure personal genomes for $0 each. [ 25 ]  In those two cases, the actual cost is reduced because the data can be monetized for researchers.


The decreasing cost in general of genomic mapping has permitted  genealogical  sites to offer it as a service, [ 26 ]  to the extent that one may submit one's genome to  crowd sourced  scientific endeavours such as  OpenSNP [ 27 ]  or  DNA.land  at the  New York Genome Center , as examples of  citizen science . [ 28 ]  The Corpas family, led by scientist  Manuel Corpas , developed the Corpasome project, [ 29 ]  and encouraged by the low prices in genome sequencing, was the first example of  citizen science   crowd sourced  analysis of  personal genomes . [ 30 ]


The opening of genomic medical clinics at major US hospitals has raised questions about whether these services broaden existing inequities in the US healthcare system, including from practitioners such as  Robert C. Green , director of the Preventive Genomics Clinic at  Brigham and Women's Hospital . [ 31 ] [ 32 ]


Genetic discrimination  is discriminating on the basis of information obtained from an individual's genome. Genetic non-discrimination laws have been enacted in some US states [ 33 ]  and at the federal level, by the  Genetic Information Nondiscrimination Act  (GINA). The GINA legislation prevents discrimination by health insurers and employers, but does not apply to life insurance or long-term care insurance. The passage of the Affordable Care Act in 2010 strengthened the GINA protections by prohibiting health insurance companies from denying coverage because of patient's "pre-existing conditions" and removing insurance issuers' ability to adjust premium costs based on certain factors such as genetic diseases. [ 34 ]  Given the ethical concerns about pre-symptomatic  genetic testing  of minors, [ 35 ] [ 36 ] [ 37 ] [ 38 ]  it is likely that personal genomics will first be applied to adults who can provide consent to undergo such testing, although genome sequencing is already proving valuable for children if any symptoms are present. [ 39 ]


There are also concerns regarding human genome research in developing countries. The tools for conducting whole genome analyses are generally found in high-income nations, necessitating partnerships between developed and developing countries in order to study the patients affected by certain diseases. The relevant tools for sharing access to the collected data are not equally accessible across low-income nations and without an established standard for this type of research, concerns over fairness to local researchers remain unsettled. [ 40 ]


In the United States, biomedical research containing human subjects is governed by a baseline standard of ethics known as  The Common Rule , which aims to protect a subject's privacy by requiring "identifiers" such as name or address to be removed from collected data. [ 41 ]  A 2012 report by the Presidential Commission for the Study of Bioethical Issues stated, however, that "what constitutes 'identifiable' and 'de-identified' data is fluid and that evolving technologies and the increasing accessibility of data could allow de-identified data to become re-identified." [ 41 ]  In fact, research has already shown that it is "possible to discover a study participant's identity by cross-referencing research data about him and his DNA sequence … [with] genetic genealogy and public-records databases." [ 42 ]  This has led to calls for policy-makers to establish consistent guidelines and best practices for the accessibility and usage of individual genomic data collected by researchers. [ 43 ]


There is also controversy regarding the concerns with companies testing individual DNA. There are issues such as "leaking" information, the right to privacy and what responsibility the company has to ensure this does not happen. Regulation rules are not clearly laid out. What is still not determined is who legally owns the genome information: the company or the individual whose genome has been read. There have been published examples of personal genome information being exploited. [ 44 ]  Additional privacy concerns, related to, e.g.,  genetic discrimination , loss of anonymity, and psychological impacts, have been increasingly pointed out by the academic community [ 44 ]  as well as government agencies. [ 41 ]


Additional issues arise from the trade-off between public benefit from research sharing and exposure to data escape and re-identification. The  Personal Genome Project  (started in 2005) is among the few to make both genome sequences and corresponding medical phenotypes publicly available. [ 45 ] [ 46 ]


Full genome sequencing holds large promise in the world of healthcare in the potential of precise and personalized medical treatments. This use of genetic information to select appropriate drugs is known as pharmacogenomics. This technology may allow treatments to be catered to the individual and the certain genetic predispositions they may have (such as personalized chemotherapy).  Among the most impactful and actionable uses of personal genome information is the avoidance of hundreds of severe single-gene  genetic disorders  which endanger about 5% of newborns (with costs up to 20 million dollars), [ 47 ]  for example elimination of  Tay Sachs Disease  via  Dor Yeshorim . Another set of 59 genes vetted by the American College of Medical Genetics and Genomics (ACMG-59) are considered actionable in adults. [ 48 ]


At the same time, full sequencing of the genome can identify  polymorphisms  that are so rare and/or mild sequence change that conclusions about their impact are challenging, reinforcing the need to focus on the reliable and actionable alleles in the context of clinical care. Czech medical geneticist Eva Machácková writes: "In some cases it is difficult to distinguish if the detected sequence variant is a causal mutation or a neutral (polymorphic) variation without any effect on phenotype. The interpretation of rare sequence  variants of unknown significance  detected in disease-causing genes becomes an increasingly important problem." [ 49 ]  In fact, researchers from the Exome Aggregation Consortium (ExAC) project estimated the average person to carry 54 genetic mutations that previously were assumed pathogenic, i.e. having 100% penetrance, but without any apparent negative health presentation. [ 50 ]


As with other new technologies, doctors can order genomic tests for which some are not correctly trained to interpret the results. Many are unaware of how SNPs respond to one another. This results in presenting the client with potentially misleading and worrisome results which could strain the already overloaded health care system. In theory, this might antagonize an individual to make uneducated decisions such as unhealthy lifestyle choices and family planning modifications. Negative results which may potentially be inaccurate, theoretically decrease the quality of life and mental health of the individual (such as increased depression and extensive anxiety).


There are also three potential problems associated with the validity of personal genome kits. The first issue is the test's validity. Handling errors of the sample increases the likelihood for errors which could affect the test results and interpretation. The second affects the clinical validity, which could affect the test's ability to detect or predict associated disorders. The third problem is the clinical utility of personal genome kits and associated risks, and the benefits of introducing them into clinical practices. [ 51 ]


People need to be educated on interpreting their results and what they should be rationally taking from the experience. Concerns about customers misinterpreting health information was one of the reasons for the 2013 shutdown by the FDA of 23&Me's health analysis services. [ 52 ]  It is not only the average person who needs to be educated in the dimensions of their own genomic sequence but also professionals, including physicians and science journalists, who must be provided with the knowledge required to inform and educate their patients and the public. [ 53 ] [ 54 ] [ 55 ]  Examples of such efforts include the  Personal Genetics Education Project  (pgEd), the  Smithsonian  collaboration with  NHGRI , and the MedSeq, BabySeq and MilSeq projects of Genomes to People, an initiative of  Harvard Medical School  and  Brigham and Women's Hospital .


A major use of personal genomics outside the realm of health is that of ancestry analysis (see  Genetic Genealogy ), including evolutionary origin information such as neanderthal content. [ 56 ]


The 1997 science fiction film  GATTACA  presents a near-future society where personal genomics is readily available to anyone, and explores its societal impact.  Perfect DNA [ 57 ]  is a novel that uses Dr  Manuel Corpas ' own experiences and expertise as genome scientist to begin exploring some of these tremendously challenging issues.


In 2018, police arrested  Joseph James DeAngelo , the prime suspect for the Golden State Killer or  East Area Rapist , [ 58 ]  and William Earl Talbott II, the prime suspect in the  murders of Jay Cook and Tanya Van Cuylenborg  in 1987. [ 59 ]  These arrests were based on the personal genomics uploaded to an open-source database,  GEDmatch , which allowed investigators to compare DNA recovered from crime scenes to the DNA uploaded to the database by relatives of the suspect. [ 60 ] [ 58 ]  In December 2018,  Family TreeDNA  changed its terms of service to allow law enforcement to use their service to identify suspects of "a violent crime" or identify the remains of victims. The company confirmed it was working with the FBI on at least a handful of cases. [ 61 ]  Since then, nearly 50 suspects in crimes of assault, rape or murder have been arrested using the same method. [ 62 ]


Personal genomics have also allowed investigators to identify previously unknown bodies using GEDmatch (the  Buckskin Girl , [ 63 ]   Lyle Stevik [ 64 ]  and  Joseph Newton Chandler III ). [ 65 ]


Personalized onco-genomics  ( POG ) is the field of  oncology  and  genomics  that is focused on using  whole genome  analysis to make  personalized  clinical treatment decisions. [ 1 ] [ 2 ]  The program was devised at British Columbia's  BC Cancer Agency  and is currently being led by Marco Marra and Janessa Laskin. [ 3 ]  Genome instability has been identified as one of the underlying hallmarks of cancer. The genetic diversity of cancer cells promotes multiple other cancer hallmark functions that help them survive in their microenvironment and eventually metastasise. The pronounced genomic heterogeneity of tumours has led researchers to develop an approach that assesses each individual's cancer to identify targeted therapies that can halt cancer growth. Identification of these "drivers" and corresponding medications used to possibly halt these pathways are important in cancer treatment. [ 2 ]


With the  oncogenomic databases  that currently recognize mutation and abnormalities in the genomic structure of cancer cells, DNA, RNA, and protein analysis can be used to assess these changes and identify drivers of cancer growth. [ 4 ] [ 5 ]  By decoding the genetic information inside cancer cells, researchers gather information which may help understand factors that promote tumour growth and develop strategies to stop it. Ideally, a catalogue of all  somatic cancer mutations  will be created in the future that can provide insight into the abnormal cellular pathways of cancer cells and the genetic patterns that drive specific cancer phenotypes. This information can help develop effective personalized treatment options for patients with resistant cancers and ideally prevent toxicities related to conventional chemotherapeutics.


The novel approach of comparing a patient's tumour versus normal tissue was first identified in 2010 when assessing the genetic evolution of adenocarcinomas of tongue before and after treatment was received. [ 6 ]  This study presented evidence that the genetic information from these tumours have potential to influence the treatment options for cancers. After identifying the driver mutations of tumour was the RET oncogene, the patient was administered with a RET inhibitor (sunitinib), which stabilized the disease for 4 months. [ 6 ]  A second round of RET inhibitors (sorafenib and sulindac) administration provided an additional 3 months of disease stabilization before the cancer progressed again. [ 6 ]  The observed mutations and amplification in the recurring metastasis were identified to be consistent with resistance to the RET inhibitors. [ 6 ]  The evolution of the adenocarcinoma after treatment with RET inhibitors demonstrated with possibility for personalized cancer therapy. This approach was then successfully replicated in other cancer types and resulted in the formation of the Personalized Onco-Genomics Program at the BC Cancer Agency. This program has now analyzed over 570 adult cancer patients with incurable disease, which were published in a landmark study in 2020. [ 7 ]


Genomic and transcriptomic sequence datasets from the 2020 Nature publication encompassing the first 570 "POG" patients [ 7 ]  have been deposited at the  European Genome-phenome Archive  (EGA,  http://www.ebi.ac.uk/ega/ ) as part of the study EGAS00001001159. Data on mutations, copy changes and expression from tumour samples in the POG program organized by OncoTree classification ( http://oncotree.mskcc.org ) are also accessible from  https://www.personalizedoncogenomics.org/cbioportal/ . The complete small mutation catalog and gene expression TPMs are available for download from  http://bcgsc.ca/downloads/POG570/ .


OMICS technologies are high-throughput methods that help evaluate and unbiased investigate characteristics of the genome, epigenome, transcriptome, proteome, and metabolome.


A  genome  is the complete collection of an organism's DNA sequences, which contains instructions for cellular processes. In human  diploid  cells, 6 billion DNA base pairs of the genome can be found in the nucleus. The development of all cancers starts with a single cell accumulating enough harmful changes in DNA sequence that allow it to proliferate uncontrollably. Subsequently, the origin cell's fast-growing progeny invade surrounding tissues, migrate to other tissues. [ 8 ]


In a typical tumour, several dozens of somatic mutations may be disrupting the normal function of proteins. The majority of somatic mutations are mere byproducts of the unstable cancer genome and do not enhance the tumour cell growth. Usually, between two and eight driver mutations, mutations that gives cancer cells growth advantages, are found amongst all the somatic mutations a tumour sample. [ 9 ]  Driver mutations may be actionable as they serve as  diagnostic  and  prognostic  biomarkers for cancer, and may have therapeutic implications. [ 10 ]  For instance,  kinase inhibitors  can be treating tumours associated with driver mutations in kinases.  Axitinib ,  gefitinib , and  dabrafenib  to are used to treat adult  acute lymphoblastic leukemia ,  non-small-cell lung cancer , and  melanoma  respectively. [ 11 ]


As cancer can arise from countless different genetic mutations and a combination of mutations, it is challenging to develop a cure that is appropriate for all cancers, given the genetic diversity of the human population. To provide and develop the most appropriate genetic therapy for one's cancer, personalized oncogenomics has been developed. By sequencing a cancer patient's genome, a clinical scientist can better understand which genes/part of the genome have been mutated specifically in that patient and a personalized treatment plan can potentially be implemented.


With the advent of next-generation sequencing, the genome-wide sequencing analyses have been made more accessible to thoroughly understand the genetics of each patient's cancer. Genetic material from tumour biopsies can be analyzed by two genome-wide approaches:  whole exome sequencing  (WES) and  whole genome sequencing  (WGS). By developing tools to analyze these extensive sequence data, scientists have begun to comprehend how diseases, including cancer, can be attributed by genes and intergenic variants such as  single nucleotide polymorphisms  (SNPs) and  copy number variants  (CNVs).


Although more expensive than WES, whole genome sequencing allows clinical scientists to understand how cancer can be caused by alterations in a genome including single nucleotide variants (SNVs), SNPs and CNVs. [ 12 ]  Since WES samples only the  exome  (the collection of known  coding regions  of the genome) alterations in the  non-coding region  of the genome will not be detected by WES. Non-coding regions, including  untranslated regions ,  introns ,  promoters ,  regulatory elements , non-coding functional RNA, repetitive regions, and  mitochondrial genomes , make up 98% of the human genome, the function of most regions remains unexplored. [ 13 ]  Although these methods are useful in finding shorter variants, they are limited by read lengths of sequencing technology and are unable to detect large  indels  and structural variants.


Though variants in a non-coding region are not uncovered by WES, both approaches identify all mutations that are known to cause cancer in a given patient and have been helpful in identifying low frequency and rare pathogenic (disease-causing) variants. [ 14 ]  However, the challenge remains in analyzing all the genetic variations in a genome for the  clinical significance  (whether a mutation is disease-causing as not all mutations are harmful) as much of the genome is not thoroughly understood, and new variants are still being discovered.


To better understand the  pathogenicity  of all possible genomic variants to interpret WES/WGS data, researchers are systematically studying genome-wide sequencing data across many tumour genomes. Whole exome sequencing has been the conventional data used by international cancer genome research programs such as  The Cancer Genome Atlas (TCGA)  and The International Cancer Genome Consortium (ICGC). [ 15 ] [ 16 ]  These studies have accumulated WES data for all types of human tumours and have been an open resource for clinical scientists. Systematically analyzing more than 50,000 tumour genomes has elucidated new cancer genes and pathways, opening up new pursuits for pharmaceutical companies. Researchers have also discovered general trends on different types of cancer that may inform public education on proactive preventative measures. For example, more somatic mutations in coding regions are found in carcinogen-exposed cancers than in pediatric tumours and leukemia. Although these systematic studies provide extensive mutational data on coding regions, information is limited on somatic mutations in non-coding regions. [ 16 ] [ 17 ]  Researchers have only recently started to be illuminated on how cancer may be caused by variants in the non-coding regions of the genome. In 2018, Zhang and colleagues analyzed 930 tumour whole genomes with associated transcriptomes (collection of mRNA transcripts) to show mutations in 193 non-coding sequences disrupt normal gene expression. [ 17 ]  Notably, they repetitively found noncoding mutations affected DAAM1, MTG2, and HYI transcription wherein DAAM1 expression initiate invasive cell migration in the tumour. [ 17 ]  Since the core somatic gene expression network is defective in 88% tumours, Zhang et al. suggested that noncoding mutation may have a widespread impact in cancer. [ 17 ]  As the cost of sequencing decreases and sequence analytic pipeline optimizes, researchers are looking to expand cancer genomic knowledge with WGS. Nonetheless, WES may remain effective for clinical diagnosis in the next few years as WES results are obtained faster.


Treatment options for aggressive metastatic cancer typically have a poor response rate to conventional treatments. Through this novel approach, analysis of oncogenic factors is undertaken to identify the unique molecular signatures of the individual's tumour. [ 18 ]  In most cancers, there are multiple pathways that are altered and lead to disease growth and progression. The pathways can vary from person to person, and even between different sites of disease in a single person. These pathways also have the ability to evolve depending on the various treatment options attempted to halt progression. [ 19 ]


The POGs program [ 20 ]  takes biopsies from  metastatic tumours  from patients, sequences both the DNA and RNA, and then research what is driving the person's cancer in an attempt to pick the right drug for the right person at the right time. Recognition of specific genetic abnormalities that are promoting cancer progression can help select non-conventional treatment options to target these abnormalities. [ 1 ]  This approach allows multiple clinicians to work together in a collaborative manner to tackle an aggressive disease. The genetic information collected from the tumour helps health-care professionals make rational clinical decisions regarding treatment strategies. These strategies may be used to target the growth of the tumour, identify potential clinical trials that patients could enrol in, and find more effective and less toxic drug options. [ 1 ]


This approach has been successful in treating small subgroups of cancers with medication commonly used to treat  chronic myeloid leukemia (CML) .  Imatinib  targets a  BCR-ABL   translocation  that appears in more than 95% of CML cases. [ 21 ]  The success of this drug in treating CML has highlighted the importance of personalized treatment options and identification of molecular pathways to be targeted. [ 21 ]  Imatinib is now also used to treat some stomach cancers after identifying the genetic alterations in this cancer type. [ 22 ]  Repurposing current treatment options that are used for other treatments and can halt cancer progression due to similarities in characteristics.


The success of the POGs program is highlighted by the identification of a novel genetic mutation in a 35-year-old recurrent breast cancer patient. Zuri Scrivens was enrolled in the POGs program at the BC Cancer agency to help devise a personalized novel therapy based on her tumour mutations. [ 23 ]  From the results of her tumour genome sequencing analysis, a drug that is most commonly used in the treatment of  type 2 diabetes  was selected to treat her recurrence along with a standard chemotherapy medication. With the combination of these medications, Zuri's cancer quickly returned into remission. [ 23 ]


In the future, the use of this personalized onco-genomics approach may be the first step for the treatment of each person's cancer. There is a great depth of genetic information that can be extracted from each individual's cancer. Genomic data can provide valuable information regarding each individuals’ cancer and guide clinical decision-making from health-care professionals.


There are multiple challenges that are preventing the adoption of POGs. The largest challenge is the identification of genetic factors that are promoting cancer growth. [ 18 ]  These “drivers” need to be understood before any actions can be taken against them. [ 18 ]  Although the costs of  genetic testing  have decreased significantly, [ 24 ]  the results can provide an enormous amount of genetic information that scientists haven't truly understood yet. Through the understanding of these “drivers” of the cancer growth, appropriate drug choices can be made to target them. [ 18 ]  Only a fraction of these drivers have been identified until now and more research is required help uncover many of cancer's characteristics. [ 18 ]


Cancer cells are known for the thousands of genetic changes that are occurring within a single cell. Targeting these genetic changes requires a significant amount of research to identify which changes are drivers and which are “passengers”. [ 18 ]  These passengers do not directly to cancerous growth. Unfortunately, medication is not currently available for every genetic change that may occur within a cancer cell and drive growth. With further research, more medications will be made and identified to target these genetic changes.


Another challenge with POGs is identifying new methods to overcome resistance from drug treatment. [ 25 ]  Some medications that are used to target cancers containing genetic changes cannot provide sustained benefits. [ 26 ] [ 27 ]  Patients may experience either short-lived or long-term benefits from the medication, but since cancer is constantly evolving, it often develops even more genetic changes that allow it to adapt and survive against the drug. This drug resistance allows the cancers to grow back despite previously effective treatment. This has become a major challenge in treating cancers.


The POGs and other new cancer treatments are generally tested on patients with advanced cancers who have failed previous drug therapy. With time, cancer progresses and evolves based on the previous treatments the individual has received. [ 28 ]  These cancers are incredibly difficult to treat due to their drug resistance mechanisms. There is a growing acceptance that these new medications need to be tested in individuals much earlier so cancer does not have time to develop with resistance. As mentioned above, the development of liquid biopsies help provide patients with the right drug at the right time. [ 29 ]  A non-invasive method to help clinicians influence clinical decision making could become the basis of new clinical trials in the future.


The feasibility of undertaking this approach in all cancer centers remains one of the greatest challenges of any personalized program. Until it is feasible for every hospital and cancer center to have the technology required to study the genomics of each individual, it is unlikely that this approach will be adopted. Before many of the tests described above become commonly used, researchers also need to demonstrate that these test can provide benefit when guiding treatment options, and if this approach ultimately increases the survival and prevents recurrence of cancers in patients.


Perturb-seq  (also known as  CRISP-seq  and  CROP-seq ) refers to a high-throughput method of performing  single cell RNA sequencing (scRNA-seq)  on pooled genetic perturbation screens. [ 1 ] [ 2 ] [ 3 ]  Perturb-seq combines multiplexed  CRISPR  mediated gene inactivations with single cell RNA sequencing to assess comprehensive  gene expression   phenotypes  for each perturbation. Inferring a gene’s function by applying genetic perturbations to  knock down  or  knock out  a gene and studying the resulting phenotype is known as  reverse genetics . Perturb-seq is a reverse genetics approach that allows for the investigation of  phenotypes  at the level of the  transcriptome , to elucidate gene functions in many cells, in a massively parallel fashion.


The Perturb-seq protocol uses  CRISPR  technology to inactivate specific genes and  DNA barcoding  of each guide RNA to allow for all perturbations to be pooled together and later deconvoluted, with assignment of each phenotype to a specific  guide RNA . [ 1 ] [ 2 ]  Droplet-based  microfluidics  platforms (or other cell sorting and separating techniques) are used to isolate individual cells, and then scRNA-seq is performed to generate  gene expression  profiles for each cell. Upon completion of the protocol,  bioinformatics  analyses are conducted to associate each specific cell and perturbation with a transcriptomic profile that characterizes the consequences of inactivating each gene.


In the December 2016 issue of the  Cell  journal, two companion papers were published that each introduced and described this technique. [ 1 ] [ 2 ]  A third paper describing a conceptually similar approach (termed CRISP-seq) was also published in the same issue. [ 4 ]  In October 2016, the CROP-seq method for single-cell CRISPR screening was presented in a preprint on  bioRxiv [ 5 ]  and later published in the  Nature Methods  journal. [ 3 ]  While each paper shared the core principles of combining CRISPR mediated perturbation with scRNA-seq, their experimental, technological and analytical approaches differed in several aspects, to explore distinct biological questions, demonstrating the broad utility of this methodology. For example, the CRISPR-seq paper demonstrated the feasibility of  in vivo  studies using this technology, and the CROP-seq protocol facilitates large screens by providing a vector that makes the guide RNA itself readable (rather than relying on expressed barcodes), which allows for single-step guide RNA cloning. [ 6 ]  A June 2022 paper in  Cell  published results from one of the first genome-scale Perturb-seq screens, which uncovered new perturbations that promote chromosomal instability as well as variations in the expression of mitochondrially encoded transcripts in response to different forms of mitochondrial stress. [ 7 ]


Pooled CRISPR  libraries  that enable gene inactivation can come in the form of either knockout or interference. Knockout libraries perturb genes through double stranded breaks that prompt the error prone  non-homologous end joining  repair pathway to introduce disruptive insertions or deletions.  CRISPR interference  (CRISPRi) on the other hand utilizes a catalytically inactive  nuclease  to physically block  RNA polymerase ,  effectively preventing or halting  transcription . [ 8 ]  Perturb-seq has been utilized with both the knockout and CRISPRi approaches in the Dixit et al. paper  [ 2 ]  and the Adamson et al. paper, [ 1 ]  respectively.


Pooling all guide RNAs into a single screen relies on DNA barcodes that act as identifiers for each unique guide RNA. There are several commercially available pooled CRISPR libraries including the guide barcode library used in the study by Adamson et al. [ 1 ]  CRISPR libraries can also be custom made using tools for sgRNA design, many of which are listed on the  CRISPR/cas9 tools  Wikipedia page.


The sgRNA expression vector design will depend largely on the experiment performed but requires the following central components:


Cells are typically  transduced  with a  Multiplicity of Infection (MOI)  of 0.4 to 0.6  lentiviral particles  per cell to maximize the likelihood of obtaining the most cells which contain a single guide RNA. [ 9 ] [ 10 ]  If the effects of simultaneous perturbations are of interest, a higher MOI may be applied to increase the amount of transduced cells with more than one guide RNA. Selection for successfully transduced cells is then performed using a fluorescence assay or an antibiotic assay, depending on the reporter gene used in the expression vector.


After successfully transduced cells have been selected for, isolation of single cells is needed to conduct scRNA-seq. Perturb-seq and CROP-seq have been performed using droplet-based technology for single cell isolation, [ 1 ] [ 2 ] [ 3 ]  while the closely related CRISP-seq was performed with a microwell-based approach. [ 4 ]  Once cells have been isolated at the single cell level,  reverse transcription , amplification and sequencing takes place to produce gene expression profiles for each cell. Many scRNA-seq approaches incorporate  unique molecular identifiers  (UMIs) and cell barcodes during the reverse transcription step to index individual RNA molecules and cells, respectively. These additional barcodes serve to help quantify RNA transcripts and to associate each of the sequences with their cell of origin.


Read alignment and processing are performed to map quality reads to a reference genome. Deconvolution of cell barcodes, guide barcodes and UMIs enables the association of guide RNAs with the cells that contain them, thus allowing the gene expression profile of each cell to be affiliated with a particular perturbation. Further downstream analyses on the transcriptional profiles will depend entirely on the biological question of interest.  T-distributed Stochastic Neighbor Embedding (t-SNE)  is a commonly used  machine learning  algorithm to visualize the high-dimensional data that results from scRNA-seq in a 2-dimensional scatterplot. [ 1 ] [ 4 ] [ 11 ]  The authors who first performed Perturb-seq developed an in-house computational framework called MIMOSCA that predicts the effects of each perturbation using a linear model and is available on an open software repository. [ 12 ]


Perturb-seq makes use of current technologies in molecular biology to integrate a multi-step workflow that couples high-throughput screening with complex phenotypic outputs. When compared to alternative methods used for gene knockdowns or knockouts, such as  RNAi ,  zinc finger nucleases  or  transcription activator-like effector nucleases  (TALENs), the application of CRISPR-based perturbations enables more specificity, efficiency and ease of use. [ 9 ] [ 13 ]  Another advantage of this protocol is that while most screening approaches can only assay for simple phenotypes, such as cellular viability, scRNA-seq allows for a much richer phenotypic readout, with quantitative measurements of gene expression in many cells simultaneously. Perturb-seq can therefore combine the high throughput of  forward genetics , in terms of the number of genetic perturbations, with the rich phenotype dimension of  reverse genetics . [ 7 ]


However, while a large and comprehensive amount of data can be a benefit, it can also present a major challenge. Single cell RNA expression readouts are known to produce ‘noisy’ data, with a significant number of false positives. [ 14 ]  Both the large size and noise that is associated with scRNA-seq will likely require new and powerful computational methods and bioinformatics pipelines to better make sense of the resulting data. Another challenge associated with this protocol is the creation of large scale CRISPR libraries. The preparation of these extensive libraries depends upon a comparative increase in the resources required to culture the massive numbers of cells that are needed to achieve a successful screen of many perturbations. [ 9 ]


In parallel to these single-cell methods, other approaches have been developed to reconstruct genetic pathways using whole-organism RNA-sequencing. These methods use a single aggregate statistic, called the transcriptome-wide epistasis coefficient, to guide pathway reconstruction. [ 15 ]  In contrast with the statistical framework of the methods described above, this coefficient may be more robust to noise and is intuitively interpretable in terms of Batesonian epistasis. This approach was used to identify a new state in the life cycle of the nematode  C. elegans . [ 16 ]


Perturb-seq or other conceptually similar protocols can be used to address a broad scope of biological questions and the applications of this technology will likely grow over time. Three papers on this topic, published in the December 2016 issue of the Journal Cell, demonstrated the utility of this method by applying it to the investigation of several distinct biological functions. In the paper, “Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens”, the authors used Perturb-seq to conduct knockouts of  transcription factors  related to the  immune response  in hundreds of thousands of cells to investigate the cellular consequences of their inactivation. [ 2 ]  They also explored the effects of transcription factors on cell states in the context of the  cell cycle . In the study led by  UCSF , “A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response” the researchers suppressed multiple genes in each cell to study the  unfolded protein response  (UPR) pathway. [ 1 ]  With a similar methodology, but using the term CRISP-seq instead of Perturb-seq, the paper "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq" performed a proof of concept experiment by using the technique to probe regulatory pathways related to  innate immunity  in mice. [ 4 ]  Lethality of each perturbation and  epistasis  analyses in cells with multiple perturbations was also investigated in these papers. Perturb-seq has so far been used with very few perturbations per experiment, but it can theoretically be scaled up to address the whole genome. Finally, the October 2016 preprint [ 5 ]  and subsequent paper [ 3 ]  demonstrate the bioinformatic reconstruction of the T cell receptor signaling pathway in  Jurkat  cells based on CROP-seq data.


Recently, the Perturb-seq (CROP-seq) workflow has been adapted to enable genome-scale  CRISPRi  (CRISPR interference) screens in Jurkat cells at single-cell resolution. [ 17 ]  The first-of-its-kind genome-scale CRISPRi screen was conducted to verify factors involved in TCR signaling pathways. In more detail, a guide RNA library targeting 18,595 human genes was utilized for CRISPR-based gene knockdowns in Jurkat cells expressing the dCas9- KRAB  fusion endonuclease. In total, one million Jurkat cells were processed for single-cell  RNA sequencing  allowing transcriptomic readouts of a final list of 374 marker genes involved in TCR signaling. The bioinformatic analysis confirmed more than 70 known activators and repressors of TCR signaling cascades, hence showcasing the potential of Perturb-seq (CROP-seq) screens to support translational research.


While these publications used these protocols for answering complex biological questions, this technology can also be used as a validation assay to ensure the specificity of any CRISPR based knockdown or knockout; the expression levels of the target genes as well as others can be measured with single cell resolution in parallel, to detect whether the perturbation was successful and to assess the experiment for off target effects. Furthermore, these protocols make it possible to perform perturbation screens in heterogeneous tissues, while obtaining cell type specific gene expression responses.


Pharmacodiagnostic testing  is  pre-treatment testing  performed in order to determine whether or not a  patient  is likely to  respond  to a given  therapy . This type of test is  classified  as a  predictive test  and is a prerequisite for the implementation of  stratified  and  personalized medicine . [ 1 ]


This  medical treatment –related article is a  stub . You can help Wikipedia by  expanding it .


This  pharmacology -related article is a  stub . You can help Wikipedia by  expanding it .


This  medical  diagnostic article is a  stub . You can help Wikipedia by  expanding it .


Pharmacogenomics , often abbreviated "PGx," is the study of the role of the  genome  in  drug  response. Its name ( pharmaco-  +  genomics ) reflects its combining of  pharmacology  and  genomics . Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs. [ 1 ]  It deals with the influence of  acquired  and  inherited  genetic variation on drug response, by correlating DNA  mutations  (including  point mutations ,  copy number variations , and  structural variations ) with  pharmacokinetic  (drug  absorption ,  distribution ,  metabolism , and  elimination ),  pharmacodynamic  (effects mediated through a drug's  biological targets ), and/or  immunogenic  endpoints. [ 2 ] [ 3 ] [ 4 ]


Pharmacogenomics aims to develop rational means to optimize  drug therapy , with regard to the patients'  genotype , to achieve maximum efficiency with minimal  adverse effects . [ 5 ]  It is hoped that by using pharmacogenomics,  pharmaceutical drug  treatments can deviate from what is dubbed as the "one-dose-fits-all" approach. Pharmacogenomics also attempts to eliminate trial-and-error in prescribing, allowing physicians to take into consideration their patient's genes, the functionality of these genes, and how this may affect the effectiveness of the patient's current or future treatments (and where applicable, provide an explanation for the failure of past treatments). [ 6 ] [ 7 ]  Such approaches promise the advent of  precision medicine  and even  personalized medicine , in which drugs and drug combinations are optimized for narrow subsets of patients or even for each individual's unique genetic makeup. [ 8 ] [ 9 ]


Whether used to explain a patient's response (or lack of it) to a treatment, or to act as a predictive tool, it hopes to achieve better treatment outcomes and greater efficacy, and reduce drug toxicities and adverse drug reactions (ADRs). For patients who do not respond to a treatment, alternative therapies can be prescribed that would best suit their requirements. In order to provide pharmacogenomic recommendations for a given drug, two possible types of input can be used:  genotyping , or  exome  or whole  genome   sequencing . [ 10 ]  Sequencing provides many more data points, including detection of mutations that prematurely terminate the synthesized protein (early  stop codon ). [ 10 ]


The term  pharmacogenomics  is often used interchangeably with  pharmacogenetics . Although both terms relate to drug response based on genetic influences, there are differences between the two.  Pharmacogenetics  is limited to  monogenic  phenotypes (i.e., single gene-drug interactions).  Pharmacogenomics  refers to polygenic drug response phenotypes and encompasses  transcriptomics ,  proteomics , and  metabolomics .


Pharmacokinetics  involves the absorption, distribution, metabolism, and elimination of pharmaceutics. These processes are often facilitated by enzymes such as drug transporters or drug metabolizing enzymes (discussed in-depth below). Variation in DNA loci responsible for producing these enzymes can alter their expression or activity so that their functional status changes. An increase, decrease, or loss of function for transporters or metabolizing enzymes can ultimately alter the amount of medication in the body and at the site of action. This may result in deviation from the medication's  therapeutic window  and result in either toxicity or loss of effectiveness.


The majority of clinically actionable pharmacogenetic variation occurs in genes that code for drug-metabolizing enzymes, including those involved in both  phase I  and  phase II  metabolism. The  cytochrome P450  enzyme family is responsible for metabolism of 70-80% of all medications used clinically. [ 11 ]   CYP3A4 ,  CYP2C9 ,  CYP2C19 , and  CYP2D6  are major CYP enzymes involved in drug metabolism and are all known to be highly polymorphic. [ 11 ]  Additional drug-metabolizing enzymes that have been implicated in pharmacogenetic interactions include  UGT1A1  (a  UDP-glucuronosyltransferase ),  DPYD , and  TPMT . [ 12 ]


Many medications rely on  transporters  to cross cellular membranes in order to move between body fluid compartments such as the blood, gut lumen, bile, urine, brain, and cerebrospinal fluid. [ 13 ]  The major transporters include the  solute carrier ,  ATP-binding cassette , and  organic anion transporters . [ 13 ]  Transporters that have been shown to influence response to medications include  OATP1B1  ( SLCO1B1 ) and breast cancer resistance protein (BCRP) ( ABCG2 ). [ 14 ]


Pharmacodynamics  refers to the impact a medication has on the body, or its mechanism of action.


Drug targets are the specific sites where a medication carries out its pharmacological activity. The interaction between the drug and this site results in a modification of the target that may include inhibition or potentiation. [ 15 ]  Most of the pharmacogenetic interactions that involve drug targets are within the field of  oncology  and include  targeted therapeutics  designed to address  somatic mutations  (see also  Cancer Pharmacogenomics ). For example,  EGFR  inhibitors like  gefitinib  (Iressa) or  erlotinib  (Tarceva) are only indicated in patients carrying specific mutations to  EGFR . [ 16 ] [ 17 ]


Germline mutations  in drug targets can also influence response to medications, though this is an emerging subfield within pharmacogenomics. One well-established gene-drug interaction involving a germline mutation to a drug target is  warfarin  (Coumadin) and  VKORC1 , which codes for  vitamin K epoxide reductase (VKOR) . Warfarin binds to and inhibits VKOR, which is an important enzyme in the vitamin K cycle. [ 18 ]  Inhibition of VKOR prevents  reduction  of  vitamin K , which is a  cofactor  required in the formation of  coagulation  factors  II ,  VII ,  IX  and  X , and inhibitors  protein C  and  S . [ 18 ] [ 19 ]


Medications can have off-target effects (typically unfavorable) that arise from an interaction between the medication and/or its metabolites and a site other than the intended target. [ 20 ]  Genetic variation in the off-target sites can influence this interaction. The main example of this type of pharmacogenomic interaction is  glucose-6-phosphate-dehydrogenase (G6PD) . G6PD is the enzyme involved in the first step of the  pentose phosphate pathway  which generates NADPH (from NADP). NADPH is required for the production of reduced  glutathione  in  erythrocytes  and it is essential for the function of  catalase . [ 21 ]  Glutathione and catalase protect cells from oxidative stress that would otherwise result in cell  lysis . Certain variants in  G6PD  result in  G6PD deficiency , in which cells are more susceptible to oxidative stress. When medications that have a significant oxidative effect are administered to individuals who are G6PD deficient, they are at an increased risk of erythrocyte lysis that presents as  hemolytic anemia . [ 22 ]


The  human leukocyte antigen  (HLA) system, also referred to as the  major histocompatibility complex  (MHC), is a complex of genes important for the  adaptive immune system . Mutations in the HLA complex have been associated with an increased risk of developing hypersensitivity reactions in response to certain medications. [ 23 ]


The  Clinical Pharmacogenetics Implementation Consortium  (CPIC) is "an international consortium of individual volunteers and a small dedicated staff who are interested in facilitating use of pharmacogenetic tests for patient care. CPIC’s goal is to address barriers to clinical implementation of pharmacogenetic tests by creating, curating, and posting freely available, peer-reviewed, evidence-based, updatable, and detailed gene/drug clinical practice guidelines. CPIC guidelines follow standardized formats, include systematic grading of evidence and clinical recommendations, use standardized terminology, are peer-reviewed, and are published in a journal (in partnership with Clinical Pharmacology and Therapeutics) with simultaneous posting to cpicpgx.org, where they are regularly updated." [ 12 ]


The CPIC guidelines are "designed to help clinicians understand HOW available genetic test results should be used to optimize drug therapy, rather than WHETHER tests should be ordered. A key assumption underlying the CPIC guidelines is that clinical high-throughput and pre-emptive (pre-prescription) genotyping will become more widespread, and that clinicians will be faced with having patients’ genotypes available even if they have not explicitly ordered a test with a specific drug in mind. CPIC's guidelines, processes and projects have been endorsed by several professional societies." [ 12 ]


In February 2020 the  FDA  published the Table of Pharmacogenetic Associations. [ 24 ]  For the gene-drug pairs included in the table, "the FDA has evaluated and believes there is sufficient scientific evidence to suggest that subgroups of patients with certain genetic variants, or genetic variant-inferred phenotypes (such as affected subgroup in the table below), are likely to have altered drug metabolism, and in certain cases, differential therapeutic effects, including differences in risks of adverse events." [ 25 ]


"The information in this Table is intended primarily for prescribers, and patients should not adjust their medications without consulting their prescriber. This version of the table is limited to pharmacogenetic associations that are related to drug metabolizing enzyme gene variants, drug transporter gene variants, and gene variants that have been related to a predisposition for certain adverse events. The FDA recognizes that various other pharmacogenetic associations exist that are not listed here, and this table will be updated periodically with additional pharmacogenetic associations supported by sufficient scientific evidence." [ 25 ]


The FDA Table of Pharmacogenomic Biomarkers in Drug Labeling lists FDA-approved drugs with pharmacogenomic information found in the drug labeling. "Biomarkers in the table include but are not limited to germline or somatic gene variants (polymorphisms, mutations), functional deficiencies with a genetic etiology, gene expression differences, and chromosomal abnormalities; selected protein biomarkers that are used to select treatments for patients are also included." [ 26 ]


The  Pharmacogenomics Knowledgebase (PharmGKB)  is an " NIH -funded resource that provides information about how human genetic variation affects response to medications. PharmGKB collects, curates and disseminates knowledge about clinically actionable gene-drug associations and genotype-phenotype relationships." [ 27 ]


There are many commercial laboratories around the world who offer pharmacogenomic testing as a  laboratory developed test (LDTs) . The tests offered can vary significantly from one lab to another, including genes and alleles tested for, phenotype assignment, and any clinical annotations provided. With the exception of a few direct-to-consumer tests, all pharmacogenetic testing requires an order from an authorized healthcare professional. In order for the results to be used in a clinical setting in the  United States , the laboratory performing the test much be  CLIA -certified. Other regulations may vary by country and state.


Direct-to-consumer  (DTC) pharmacogenetic tests allow consumers to obtain pharmacogenetic testing without an order from a prescriber. DTC pharmacogenetic tests are generally reviewed by the FDA to determine the validity of test claims. [ 28 ]  The FDA maintains a  list of DTC genetic tests  that have been approved.


There are multiple ways to represent a pharmacogenomic  genotype . A commonly used  nomenclature  system is to report  haplotypes  using a star (*) allele (e.g.,  CYP2C19  *1/*2).  Single-nucleotide polymorphisms  (SNPs) may be described using their assignment reference SNP cluster ID (rsID) or based on the location of the  base pair  or  amino acid  impacted. [ 29 ]


In 2017 CPIC published results of an expert  survey  to standardize terms related to clinical pharmacogenetic test results. [ 30 ]  Consensus for terms to describe allele functional status,  phenotype  for drug metabolizing enzymes, phenotype for drug transporters, and phenotype for high-risk genotype status was reached.


The list below provides a few more commonly known applications of pharmacogenomics: [ 31 ]


Pharmacogenomics may be applied to several areas of medicine, including  pain management ,  cardiology ,  oncology , and  psychiatry . A place may also exist in  forensic pathology , in which pharmacogenomics can be used to determine the cause of death in drug-related deaths where no findings emerge using  autopsy . [ citation needed ]


In  cancer treatment , pharmacogenomics tests are used to identify which patients are most likely to respond to certain  cancer drugs . In behavioral health, pharmacogenomic tests provide tools for physicians and care givers to better manage medication selection and side effect amelioration. Pharmacogenomics is also known as companion diagnostics, meaning tests being bundled with drugs. Examples include  KRAS  test with  cetuximab  and  EGFR  test with  gefitinib . Beside efficacy, germline pharmacogenetics can help to identify patients likely to undergo severe toxicities when given cytotoxics showing impaired detoxification in relation with genetic polymorphism, such as canonical 5-FU. [ 32 ]  In particular, genetic deregulations affecting genes coding for  DPD ,  UGT1A1 ,  TPMT ,  CDA  and  CYP2D6  are now considered as critical issues for patients treated with 5-FU/capecitabine, irinotecan, mercaptopurine/azathioprine, gemcitabine/capecitabine/AraC and tamoxifen, respectively. [ 33 ]


In  cardiovascular disorders , the main concern is response to drugs including  warfarin ,  clopidogrel ,  beta blockers , and  statins . [ 10 ]  In patients with CYP2C19, who take clopidogrel, cardiovascular risk is elevated, leading to  medication package insert  updates by regulators. [ 34 ]  In patients with  type 2 diabetes ,  haptoglobin  (Hp) genotyping shows an effect on cardiovascular disease, with Hp2-2 at higher risk and supplemental vitamin E reducing risk by affecting  HDL . [ 35 ]


In psychiatry, as of 2010, research has focused particularly on  5-HTTLPR  and  DRD2 . [ 36 ]


Initiatives to spur adoption by clinicians include the  Ubiquitous Pharmacogenomics (U-PGx)  program in  Europe  and the Clinical Pharmacogenetics Implementation Consortium (CPIC) in the United States. [ 37 ]  In a 2017 survey of European clinicians, in the prior year two-thirds had not ordered a pharmacogenetic test. [ 38 ]


In 2010,  Vanderbilt University Medical Center  launched Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT); [ 39 ]  in 2015 survey, two-thirds of the clinicians had ordered a pharmacogenetic test. [ 40 ]


In 2019, the largest private health insurer,  UnitedHealthcare , announced that it would pay for genetic testing to predict response to psychiatric drugs. [ 41 ]


In 2020,  Canada's  4th largest health and dental insurer,  Green Shield Canada , announced that it would pay for pharmacogenetic testing and its associated clinical decision support software to optimize and personalize mental health prescriptions. [ 42 ]


A potential role for pharmacogenomics is to reduce the occurrence of  polypharmacy : it is theorized that with tailored drug treatments, patients will not need to take several medications to treat the same condition. Thus they could potentially reduce the occurrence of  adverse drug reactions , improve treatment outcomes, and save costs by avoiding purchase of some medications. For example, maybe due to inappropriate prescribing,  psychiatric  patients tend to receive more medications than age-matched non-psychiatric patients. [ 43 ]


The need for pharmacogenomically tailored drug therapies may be most evident in a survey conducted by the Slone Epidemiology Center at  Boston University  from February 1998 to April 2007. The study elucidated that an average of 82% of adults in the United States are taking at least one medication (prescription or nonprescription drug, vitamin/mineral, herbal/natural supplement), and 29% are taking five or more. The study suggested that those aged 65 years or older continue to be the biggest consumers of medications, with 17-19% in this age group taking at least ten medications in a given week. Polypharmacy has also shown to have increased since 2000 from 23% to 29%. [ 44 ]


Case A – Antipsychotic adverse reaction [ 45 ]


Patient A has schizophrenia. Their treatment included a combination of ziprasidone, olanzapine, trazodone and  benztropine . The patient experienced dizziness and sedation, so they were tapered off ziprasidone and olanzapine, and transitioned to quetiapine. Trazodone was discontinued. The patient then experienced excessive sweating, tachycardia and neck pain, gained considerable weight and had hallucinations. Five months later, quetiapine was tapered and discontinued, with ziprasidone re-introduced into their treatment, due to the excessive weight gain. Although the patient lost the excessive weight they had gained, they then developed muscle stiffness,  cogwheeling , tremors and night sweats. When benztropine was added they experienced blurry vision. After an additional five months, the patient was switched from ziprasidone to aripiprazole. Over the course of 8 months, patient A gradually experienced more weight gain and sedation, and developed difficulty with their gait, stiffness, cogwheeling and dyskinetic ocular movements. A pharmacogenomics test later proved the patient had a CYP2D6 *1/*41, which has a predicted phenotype of IM and CYP2C19 *1/*2 with a predicted phenotype of IM as well.


Case B – Pain Management [ 46 ]


Patient B is a woman who gave birth by caesarian section. Her physician prescribed codeine for post-caesarian pain. She took the standard prescribed dose, but she experienced nausea and dizziness while she was taking codeine. She also noticed that her breastfed infant was lethargic and feeding poorly. When the patient mentioned these symptoms to her physician, they recommended that she discontinue codeine use. Within a few days, both the patient's and her infant's symptoms were no longer present. It is assumed that if the patient had undergone a pharmacogenomic test, it would have revealed she may have had a duplication of the gene CYP2D6, placing her in the Ultra-rapid metabolizer (UM) category, explaining her reactions to codeine use.


Case C – FDA Warning on Codeine Overdose for Infants [ 47 ]


On February 20, 2013, the  FDA  released a statement addressing a serious concern regarding the connection between children who are known as CYP2D6 UM, and fatal reactions to codeine following  tonsillectomy  and/or  adenoidectomy  (surgery to remove the tonsils and/or adenoids). They released their strongest Boxed Warning to elucidate the dangers of CYP2D6 UMs consuming codeine. Codeine is converted to morphine by CYP2D6, and those who have UM phenotypes are in danger of producing large amounts of morphine due to the increased function of the gene. The morphine can elevate to life-threatening or fatal amounts, as became evident with the death of three children in August 2012.


Although there appears to be a general acceptance of the basic tenet of pharmacogenomics amongst physicians and healthcare professionals, [ 49 ]  several challenges exist that slow the uptake, implementation, and standardization of pharmacogenomics. Some of the concerns raised by physicians include: [ 50 ] [ 49 ] [ 51 ]


Issues surrounding the availability of the test include: [ 48 ]


Although other factors contribute to the slow progression of pharmacogenomics (such as developing guidelines for clinical use), the above factors appear to be the most prevalent. Increasingly substantial evidence and industry body guidelines for clinical use of pharmacogenetics have made it a population wide approach to precision medicine. Cost, reimbursement, education, and easy use at the point of care remain significant barriers to widescale adoption.


There has been call to move away from race and ethnicity in medicine and instead use genetic ancestry as a way to categorize patients. [ 52 ]  Some  alleles  that vary in frequency between specific populations have been shown to be associated with differential responses to specific  drugs . As a result, some disease-specific guidelines only recommend pharmacogenetic testing for populations where high-risk alleles are more common [ 53 ]  and, similarly, certain insurance companies will only pay for pharmacogenetic testing for beneficiaries of high-risk populations. [ 54 ]


In the early 2000s, handling genetic information as exceptional, including legal or regulatory protections, garnered strong support. It was argued that genomic information may need special policy and practice protections within the context of electronic health records (EHRs). [ 55 ]  In 2008, the  Genetic Information Nondiscrimination Act (GINA)  was enacted to protect patients from health insurance companies discriminating against an individual based on genetic information. [ 56 ] [ 57 ]


More recently it has been argued that  genetic exceptionalism  is past its expiration date as we move into a blended genomic/big data era of medicine, yet exceptionalism practices continue to permeate clinical healthcare today. [ 58 ] [ 59 ]  Garrison et al. recently relayed a call to action to update verbiage from genetic exceptionalism to genomic contextualism in that we recognize a fundamental duality of genetic information. [ 60 ]  This allows room in the argument for different types of genetic information to be handled differently while acknowledging that genomic information is similar and yet distinct from other health-related information. [ 60 ]  Genomic contextualism would allow for a case-by-case analysis of the technology and the context of its use (e.g., clinical practice, research, secondary findings).


Others argue that genetic information is indeed distinct from other health-related information but not to the extent of requiring legal/regulatory protections, similar to other sensitive health-related data such as HIV status. [ 61 ]  Additionally, Evans et al. argue that the EHR has sufficient privacy standards to hold other sensitive information such as social security numbers and that the fundamental nature of an EHR is to house highly personal information. [ 58 ]  Similarly, a systematic review reported that the public had concern over privacy of genetic information, with 60% agreeing that maintaining privacy was not possible; however, 96% agreed that a direct-to-consumer testing company had protected their privacy, with 74% saying their information would be similarly or better protected in an EHR. With increasing technological capabilities in EHRs, it is possible to mask or hide genetic data from subsets of providers and there is not consensus on how, when, or from whom genetic information should be masked. [ 55 ] [ 62 ]   Rigorous protection and masking of genetic information is argued to impede further scientific progress and clinical translation into routine clinical practices. [ 63 ]


Pharmacogenomics was first recognized by  Pythagoras  around 510 BC when he made a connection between the dangers of  fava bean  ingestion with  hemolytic anemia  and  oxidative stress . In the  1950s , this identification was validated and attributed to deficiency of  G6PD  and is called  favism . [ 64 ] [ 65 ]  Although the first official publication was not until 1961, [ 66 ]  the unofficial beginnings of this science were around the 1950s. Reports of prolonged paralysis and fatal reactions linked to genetic variants in patients who lacked  butyrylcholinesterase  ('pseudocholinesterase') following succinylcholine injection during anesthesia were first reported in 1956. [ 2 ] [ 67 ]  The term pharmacogenetics was first coined in 1959 by  Friedrich Vogel  of  Heidelberg ,  Germany  (although some papers suggest it was 1957 or 1958). [ 68 ]  In the late 1960s,  twin studies  supported the inference of genetic involvement in drug metabolism, with identical twins sharing remarkable similarities in drug response compared to fraternal twins. [ 69 ]  The term pharmacogenomics first began appearing around the 1990s. [ 64 ]


The first FDA approval of a pharmacogenetic test was in 2005 [ 9 ]  (for  alleles  in  CYP2D6  and  CYP2C19 )


Computational advances have enabled cheaper and faster sequencing. [ 70 ]  Research has focused on  combinatorial chemistry , [ 71 ]  genomic mining, omic technologies, and  high throughput screening .


As the cost per genetic test decreases, the development of personalized drug therapies will increase. [ 72 ]  Technology now allows for genetic analysis of hundreds of target genes involved in medication metabolism and response in less than 24 hours for under $1,000.  This a huge step towards bringing pharmacogenetic technology into everyday medical decisions. Likewise, companies like  deCODE genetics , MD Labs Pharmacogenetics,  Navigenics  and  23andMe  offer genome scans. The companies use the same  genotyping  chips that are used in GWAS studies and provide customers with a write-up of individual risk for various traits and diseases and testing for 500,000 known SNPs. Costs range from $995 to $2500 and include updates with new data from studies as they become available. The more expensive packages even included a telephone session with a genetics counselor to discuss the results. [ 73 ]


Pharmacogenetics has become a controversial issue in the area of  bioethics . Privacy and confidentiality are major concerns. [ 74 ]  The evidence of benefit or risk from a genetic test may only be suggestive, which could cause dilemmas for providers. [ 74 ] : 145   Drug development may be affected, with rare genetic variants possibly receiving less research. [ 74 ]  Access and patient autonomy are also open to discussion. [ 75 ] : 680


Journals:


In genetics and  genetic epidemiology , a  phenome -wide association study , abbreviated  PheWAS , is a study design in which the association between  single-nucleotide polymorphisms  or other types of DNA variants is tested across a large number of different  phenotypes . [ 1 ]  The aim of PheWAS studies (or PheWASs) is to examine the causal linkage between known sequence differences and any type of trait, including molecular, biochemical, cellular, and especially clinical diagnoses and outcomes. [ 2 ] [ 3 ] [ 4 ]  It is a complementary approach to the  genome-wide association study , or GWAS, methodology. [ 5 ]  A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk. In a GWAS, the polarity of analysis is from one or a few phenotypes to many possible DNA variants. [ 3 ]  The approach has proven useful in rediscovering previously reported genotype-phenotype associations, [ 2 ] [ 5 ]  as well as in identifying new ones. [ 6 ]


The PheWAS approach was originally developed due to the widespread availability of both anonymized human clinical  electronic health record  (EHR) data and matched genotype data, using phenotypes defined by groupings of ( ICD ) codes called phecodes. [ 7 ]   Massive genome and phenome data sets for model organisms were being assembled have also proved effective for PheWAS. [ 8 ]  PheWASs have also been conducted using data from existing  epidemiological  studies. In 2010, a  proof-of-concept  PheWAS study was published based on EHR billing codes from a single study site. [ 9 ]  Though this study was  generally underpowered, its results suggested the potential existence of new associations between multiple phenotypes, possibly due to a common underlying cause. This paper also coined the abbreviation "PheWAS". [ 10 ]  As of 2019, PheWAS in the EHR has been conducted using  ICD-9-CM , [ 11 ]   ICD-10 , and  ICD-10-CM [ 12 ]  diagnosis codes.


PheWAS initially started from the growing use of  EMR  (electronic medical record) for clinical practice and patient care. [ 9 ]  One of the main components of EMR system is the International Classification of Disease version 9-CM ( ICD9 ) codes, used as a tool for medical billing record. [ 9 ]  This system includes information of 14,000 diseases binned into different hierarchy codes. [ 9 ]  These phenotypic information is the basis of the PheWAS study, which associates a genetic variant (or a combination of variants) with a wide range of  phenotypes . [ 5 ]


Most common PheWAS studies would divide its cohort into two groups: individuals who did not have a specific ICD9 code are treated as “controls” while individuals who has an ICD9 code associated with them are considered “cases”. [ 13 ]  Starting from the given genetic variant, a PheWAS would systematically perform genetic variant (typically a  SNP ) analysis to identify how a particular genotype would be associated to a phenotype. [ 13 ]  From the variant data, PheWAS calculates their genotype distribution and the  chi-squared distribution , followed by  Fisher's exact test  to calculate the P-value, identifying how relevant a genotype would be to a certain phenotype of interest from the EMR. [ 9 ] [ 14 ]  Often times,  Bonferroni  correction is then applied to take into consideration the multiple comparisons done while calculating the P-value.


The first study of PheWAS was done on 6000 European-American population with 5  SNPs  of interest picked for validation: rs1333049, rs2200733, rs3135388, rs6457620, and rs1333049. [ 9 ]  Quality control was done by examining marker and sample genotyping efficiency,  allele  frequency calculations, and  Hardy-Weinberg  equilibrium tests. [ 9 ]


This initial PheWAS aim to examine the impact of genetic variants across various phenotypes. [ 3 ]  Since the  ICD9  was not specifically designed for research purposes, this PheWAS devised a new way to simplify the code for genetic studies. Specifically, three modifications were made to the ICD9:


As one example of its successes, this PheWAS show evidence of strong association between rs3135388 and  multiple sclerosis  (MS), which was a previously studied association. [ 9 ]  Twenty-two other diseases also demonstrated significant associations with P < 0.05. [ 9 ]


One of the main advantages of the PheWAS study is its potential to identify genomic variants with  pleiotropic  properties. [ 5 ]  Understanding cross-phenotype (CP) associations, where one genetic variation can affect two or more independent phenotypes, is the key to understanding the pleiotropic effect. [ 13 ]  The pleiotropic effect study was done by first obtaining the summary of genotype and phenotype data from the Population Architecture using Genomics and Epidemiology (PAGE) study sites. [ 1 ]  After several quality control and data organization steps, either the standard  logistic  or  linear regression  analysis is performed depending on the phenotypic information. [ 1 ]  Subsequently, all continuous phenotypes are log-transformed before the association between the SNPs and the transformed phenotypes is finally calculated. [ 1 ]


Generally, there are two types of results from a PheWAS study:


Even though novel associations between phenotype is discovered, further biological studies is necessary to determine whether it actually reflects the system. [ 15 ]


A PheWAS has also successfully highlights discrepancies in drug response among individuals. A quantitative PheWAS study was done to identify variation in  thiopurine  response. [ 16 ]  The EMR stores quantitative value of  IBD  patient's  TPMT  (thiopurine S-methyltransferase) activity, which then allow researchers to split the patients it into three categories: low TPMTa, normal TPMTa, and very high TPMTa. [ 16 ]  It was found that cohorts with very high TPMTa level are associated with  diabetes mellitus  and iron-deficiency  anemia , which further shows that thiopurine therapy are three times more likely to fail in patients with very high TPMTa. [ 16 ] [ 14 ]  Performing thiopurine therapy on patient with very high TPMTa level may increase the frequency of anemia episode. [ 16 ]  This PheWAS finding may further the progress of  personalized treatment  based on patient's measurement. Instead of treating IBD patients with the conventional thiopurine treatment, patient may benefit more from more intensive therapy or other approaches. [ 16 ]


A clinical test has been done by utilizing PheWAS on  HIV  patients, obtained from the AIDS Clinical Trial Group (ACTG) datasets from 27 different laboratories. [ 15 ]  Identifying accuracy between PheWAS and clinical trials is important before pushing PheWAS further for making clinical decisions. Forty-seven percent of the previously-reported associations were successfully reproduced in this study, demonstrating this PheWAS' capability for working with clinical data. [ 15 ]  Additionally, several  pleiotropic  effects were discovered using this clinical data. Specifically, a block of  SNPs  on chromosome 7 were associated to both  LDL-C  phenotypes and the total  cholesterol  level according to this study. [ 15 ]  For clinical relevance, more research need to be done to validate the  pleiotropic  effect obtained from PheWAS. [ 15 ]


Despite the promising potentials, PheWAS has some potential limitations:


The  phosphatome  of an organism is the set of  phosphatase  genes in its  genome . Phosphatases are  enzymes  that catalyze the removal of   phosphate  from  biomolecules . Over half of all cellular proteins are modified by phosphorylation which typically controls their functions. Protein phosphorylation is controlled by the opposing actions of  protein phosphatases  and  protein kinases .
Most phosphorylation sites are not linked to a specific phosphatase, so the phosphatome approach allows a global analysis of dephosphorylation, screening to find the phosphatase responsible for a given reaction, and comparative studies between different phosphatases, similar to how protein kinase research has been impacted by the  kinome  approach.


Protein phosphatases remove phosphates from proteins, usually on Serine, Threonine, and Tyrosine residues, reversing the action of protein kinases. The PTP family of protein phosphatases is tyrosine-specific, and several other families (PPPL, PPM, HAD) appear to be serine/threonine specific, while other families are unknown or have a variety of substrates (DSPs dephosphorylate any amino acid, while some protein phosphatases also have non-protein substrates). In the human genome, 20 different folds of protein are known to be phosphatases, of which 10 include protein phosphatases. [ 1 ]


Protein phosphatomes have been cataloged for human and 8 other key eukaryotes, [ 1 ]  for Plasmodium and Trypanosomes
 [ 2 ]
 [ 3 ]
 [ 4 ]
and phosphatomes have been used for functional analysis, by experimentally investing all known protein phosphatases, in the yeast Fusarium, [ 5 ]  in Plasmodium  [ 6 ]  and in human cancer
 [ 7 ] [ 8 ]


Large scale databases exist for human and animal phosphatomes  Phosphatome.net , parasitic protozoans  ProtozPhosDB  and for the substrates of human phosphatases  DEPOD .


Non-protein phosphorylation has three general forms


The human non-protein phosphatome has been cataloged, [ 1 ]  but most phosphatome analyses are restricted to protein and lipid phosphatases that have regulatory functions.


The phosphatome includes proteins that are structurally closely related to phosphatases but lack catalytic activity. These retain biological function, and may regulate pathways that involve active phosphatases, or bind to phosphorylated substrates without cleaving them. [ 1 ] [ 9 ]  Examples include  STYX , where the phosphatase domain has become a phospho-tyrosine binding domain, and  GAK , whose inactive phosphatase domain instead binds phospholipids.


Phylogenomics  is the intersection of the fields of  evolution  and  genomics . [ 1 ]  The term has been used in multiple ways to refer to analysis that involves  genome  data and evolutionary reconstructions. [ 2 ]  It is a group of techniques within the larger fields of  phylogenetics  and genomics. Phylogenomics draws information by comparing entire genomes, or at least large portions of genomes. [ 3 ]  Phylogenetics compares and analyzes the sequences of single genes, or a small number of genes, as well as many other types of data. Four major areas fall under phylogenomics:


The ultimate goal of phylogenomics is to reconstruct the evolutionary history of species through their genomes. This history is usually inferred from a series of genomes by using a genome evolution model and standard statistical inference methods (e.g.  Bayesian inference  or   maximum likelihood estimation ).  [ 4 ]


When  Jonathan Eisen  originally coined  phylogenomics , it applied to prediction of gene function. Before the use of phylogenomic techniques, predicting gene function was done primarily by comparing the gene sequence with the sequences of genes with known functions. When several genes with similar sequences but differing functions are involved, this method alone is ineffective in determining function. A specific example is presented in the paper "Gastronomic Delights: A movable feast". [ 5 ]  Gene predictions based on sequence similarity alone had been used to predict that  Helicobacter pylori  can repair mismatched  DNA . [ 6 ]  This prediction was based on the fact that this organism has a gene for which the sequence is highly similar to genes from other species in the "MutS" gene family which included many known to be involved in mismatch repair. However, Eisen noted that  H. pylori  lacks other genes thought to be essential for this function (specifically, members of the MutL family). Eisen suggested a solution to this apparent discrepancy – phylogenetic trees of genes in the MutS family revealed that the gene found in  H. pylori  was not in the same subfamily as those known to be involved in mismatch repair. [ 5 ]   Furthermore, he suggested that this "phylogenomic" approach could be used as a general method for prediction functions of genes.  This approach was formally described in 1998. [ 7 ]  For reviews of this aspect of phylogenomics see Brown D, Sjölander K. Functional classification using phylogenomic inference. [ 8 ] [ 9 ]


Traditional phylogenetic techniques have difficulty establishing differences between genes that are similar because of lateral gene transfer and those that are similar because the organisms shared an ancestor. By comparing large numbers of genes or entire genomes among many species, it is possible to identify transferred genes, since these sequences behave differently from what is expected given the  taxonomy  of the organism. Using these methods, researchers were able to identify over 2,000 metabolic enzymes obtained by various eukaryotic parasites from lateral gene transfer. [ 10 ]


The comparison of complete gene sets for a group of organisms allows the identification of events in gene evolution such as  gene duplication  or  gene deletion . Often, such events are evolutionarily relevant. For example, multiple duplications of genes encoding degradative enzymes of certain families is a common adaptation in microbes to new nutrient sources. On the contrary, loss of genes is important in  reductive evolution , such as in intracellular parasites or symbionts.  Whole genome duplication  events, which potentially duplicate all the genes in a genome at once, are drastic evolutionary events with great relevance in the evolution of many clades, and whose signal can be traced with phylogenomic methods.


Traditional single-gene studies are effective in establishing phylogenetic trees among closely related organisms, but have drawbacks when comparing more distantly related organisms or microorganisms. This is because of  lateral gene transfer ,  convergence , and varying rates of evolution for different genes. By using entire genomes in these comparisons, the anomalies created from these factors are overwhelmed by the pattern of evolution indicated by the majority of the data. [ 11 ] [ 12 ] [ 13 ]  Through  phylogenomics , it has been discovered that most of the photosynthetic eukaryotes are linked and possibly share a single ancestor. Researchers compared 135 genes from 65 different  species  of photosynthetic organisms. These included  plants ,  alveolates ,  rhizarians ,  haptophytes  and  cryptomonads . [ 14 ]  This has been referred to as the  Plants+HC+SAR megagroup .  Using this method, it is theoretically possible to create fully resolved phylogenetic trees, and timing constraints can be recovered more accurately. [ 15 ] [ 16 ]  However, in practice this is not always the case. Due to insufficient data, multiple trees can sometimes be supported by the same data when analyzed using different methods. [ 17 ]


Phylomedicine  is an emerging discipline at the intersection of  medicine ,  genomics , and  evolution . It focuses on the use of evolutionary knowledge to predict functional consequences of  mutations  found in personal  genomes  and populations. [ 1 ] [ 2 ]


Modern technologies have made  genome sequencing  accessible, and biomedical scientists have profiled genomic variation in apparently healthy individuals and individuals diagnosed with a variety of diseases. This work has led to the discovery of thousands of  disease-associated genes  and genetic variants, elucidating a more robust picture of the amount and types of variations found within and between humans. [ 3 ] [ 4 ]


Proteins are encoded in genomic DNA by  exons , and these comprise only ~1% of the human genomic sequence (aka the  exome ). The exome of an individual carries about 6,000–10,000  amino-acid -altering nSNVs ,  and many of these variants are already known to be associated with more than 1000 diseases. [ 5 ]  Although only a small fraction of these personal variants are likely to impact health, the sheer volume of known genomic and exomic variants is too large to apply traditional laboratory or experimental techniques to explore their functional consequences. Translating a personal genome into useful  phenotypic  information (e.g. relating to predisposition to disease, differential drug response, or other health concerns), is therefore a grand challenge in the field of  genomic medicine .


Fortunately, results from the natural experiment of  molecular evolution  are recorded in the genomes of humans and other living species. All genomic variation is subjected to the process of natural selection which generally reduces mutations with negative effects on phenotype over time. With the availability of a large number of genomes from the tree of life, evolutionary conservation of individual genomic positions and the sets of mutations permitted among species informs the functional and health consequences of these mutations.


Consequently, phylomedicine has emerged as an important discipline at the intersection of  molecular evolution  and genomic medicine with a focus on understanding the inherited component of human disease and health. Examples include studies of retinal disease, auditory diseases, and common diseases more generally. [ 6 ] [ 7 ] [ 8 ]  Phylomedicine expands the purview of contemporary evolutionary medicine to use evolutionary patterns beyond short-term history (e.g. populations within a species) to the long-term evolutionary history of multispecies genomics .


Population genomics  is the large-scale comparison of  DNA  sequences of populations. Population genomics is a  neologism  that is associated with  population genetics . Population genomics studies  genome -wide effects to improve our understanding of  microevolution  so that we may learn the  phylogenetic  history and  demography  of a population. [ 1 ]


Population genomics has been of interest to scientists since Darwin. Some of the first methods used for studying genetic variability at multiple loci included gel electrophoresis and restriction enzyme mapping. [ 2 ]  Previously  genomics  was restricted to only the study of a low amount of  loci . However recent advancements in sequencing and computer storage and power have allowed for the study of hundreds of thousands of loci from populations. [ 3 ]  Analysis of this data requires identification of non-neutral or outlier loci that indicate selection in that region of the genome. This will allow the researcher to remove these loci to study genome wide effects or to focus on these loci if they are of interest.


In the study of  Schizosaccharomyces pombe  (more commonly known as fission yeast), a popular model organism, population genomics has been used to understand the reason for the phenotypic variation within a species. However, since the genetic variation within this species was previously poorly understood due to technological restrictions, population genomics allows us to learn about the species' genetic differences. [ 4 ]  In the human population, population genomics has been used to study the genetic change since humans began to migrate away from Africa approximately 50,000-100,000 years ago. It has been shown that not only were genes related to fertility and reproduction highly selected for, but also that the further humans moved away from Africa, the greater the presence of lactase. [ 5 ]


A 2007 study done by Begun  et al.  compared the whole genome sequence of multiple lines of  Drosophila simulans  to the assembly of  D. melanogaster  and  D. yakuba . This was done by aligning DNA from whole genome shotgun sequences of  D. simulans  to a standard reference sequence before carrying out whole genome analysis of polymorphism and divergence. This revealed a large number of proteins that had experienced  directional selection . They discovered previously unknown, large scale fluctuations in both polymorphism and divergence along chromosome arms. They found that the X chromosome had faster divergence and significantly less polymorphism than previously expected. They also found regions of the genome (e.g.  UTRs ) that signaled adaptive evolution. [ 6 ]


In 2014 Jacquot  et al.  studied the diversification and epidemiology of endemic bacterial pathogens by using the  Borrelia burgdorferi  species complex (the bacteria responsible for Lyme disease) as a model. They also wished to compare the genetic structure between  B. burgdorferi  and the closely related species  B. garinii  and  B. afzelii . They began by sequencing samples from a culture and then mapping the raw read onto reference sequences.  SNP  based and phylogenetic analyses were used on both intraspecific and interspecific levels. When looking at the degree of genetic isolation, they found that intraspecific recombination rate was ~50 times higher than the interspecific rate. They also found that by using most of the genome conspecific strains didn’t cluster in clades, raising questions about previous strategies used when investigating pathogen epidemiology. [ 7 ]


Moore  et al  conducted a study in 2014 in which a group of  Atlantic Salmon  populations which were previously analyzed with traditional population genetic analyses ( microsatellites ,  SNP-array genotyping , BayeScan (which uses the  Dirichlet-multinomial distribution )) to place them into defined  conservational units .  This genomic assessment mostly agreed with previous results, but did identify more differences between regionally and genetically discrete groups, suggesting there were potentially even greater number of conservation units of salmon in those regions.  These results verified the usefulness of genome-wide analysis in order to improve the accuracy of future designation of conservation units. [ 8 ]


In  highly migratory marine species , traditional population genetic analyses often fail to identify population structure. In tunas, traditional markers such as short-range PCR products, microsatellites and SNP-arrays have struggled to distinguish  fish stocks  from separate ocean basins. However, population genomic research using  RAD  sequencing in  yellowfin tuna [ 9 ] [ 10 ]  and  albacore [ 11 ] [ 12 ]  has been able to distinguish populations from different ocean basins and reveal fine-scale population structure. These studies identify putatively adaptive loci that reveal strong population structure, even though these sites represent a relatively small proportion of the overall DNA sequence data. In contrast, the majority of sequenced loci that are presumed to be selectively neutral do not reveal patterns of population differentiation, matching results for traditional DNA markers. [ 9 ] [ 10 ] [ 11 ] [ 12 ]  The same pattern of putatively adaptive loci and RAD sequencing revealing population structure, compared to limited insight provided by traditional DNA markers is also observed for other marine fishes, including  striped marlin [ 13 ]  and  lingcod . [ 14 ]