There is no ontology term for Phenoconversion in BioPortal

There are two papers ahead of print in Pharmacogenomics both discussing how important phenoconversion is to consider in the implementation of PGx in clinical practice. Phenoconversion in the PGx context is a drug-drug interaction that impacts a drug metabolizing phenotype such that it mimics the effects of a metabolizer genotype. Yet there was no match for phenoconversion in a search of BioPortal (on 10/3/2022) which has over a thousand biomedical ontologies including MeSH, MedDRA, RxNorm and other ones we use for PGx. PharmGKB does collect drug-drug interaction information from drug labels and publications that can potentially be used in the future to help account for phenoconversion. However, while phenoconversion is a well-known phenomenon, the specifics of how phenoconversion affects patient phenotype, especially on top of genotype, has not been quantified (to our knowledge). This makes it difficult to apply drug-drug interaction information to predict how patient genotype-to-phenotype mapping should be altered by information about concomitant drugs the patient takes when using prescribing guidance from CPIC, DPWG or FDA.

Paper 1: Pharmacogenomics in psychiatry - the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing. Mostafa S, Polasek TM, Bousman CA, Müeller DJ, Sheffield LJ, Rembach J, Kirkpatrick CM.Pharmacogenomics. 2022 Sep 28:0. doi: 10.2217/pgs-2022-0104. Online ahead of print. [PMID: 36169629]

This review proposes a model for improved clinical decision support that integrates genomics, co-prescribing information, lifestyle and disease factors into precision dosing. Excerpt from the paper: “In psychiatry, the proposed CDSS (Clinical decision support system) powered by MIPD (model-informed precision dosing) would apply precision dosing of psychotropics by accounting for the influence of genetic variations in CYPs; the presence of CYP phenoconversion; and coexisting lifestyle (smoking), pregnancy or disease (cancer) factors…. In this study, clozapine concentrations were better predicted by MIPD accounting for the CYP1A2 inducing effect in smokers homozygous for the CYP1A2*1F allele. This is an example of where environmental (smoking) and PGx (CYP1A2 genotype) factors were used to optimize the MIPD model, resulting in improved predictions of clozapine plasma concentrations. In principle, this approach can be applied across other psychotropics, especially those with a high risk of toxicity in overdose (e.g., tricyclic antidepressants).”

Paper 2: The importance of phenoconversion when using the CYP2D6 genotype in clinical practice. Cicali EJ, Wiisanen K.Pharmacogenomics. 2022 Sep;23(14):749-752. doi: 10.2217/pgs-2022-0087. Epub 2022 Sep 14. [PMID: 36102178]

This is an editorial with a case study describing a patient with chronic pain taking tramadol (among other medications). The patient is then started on an antidepressant and the pain is no longer relieved even at higher doses. Even though the patient tests as a CYP2D6 normal metabolizer the antidepressant fluoxetine has resulted in phenoconversion and clinically the patient now responds as a CYP2D6 poor metabolizer with respect to tramadol. They discuss options to change the antidepressant or the pain therapies. The authors caution that “CYP2D6 genetic test results should be continually evaluated in the light of concomitant medications throughout a patient’s lifetime.”

Searching PubMed to see the impact of phenoconversion is complicated as this word is also used to describe change or evolution of disease phenotypes, but the results by year tracker shows exponentially increased use. A phenoconversion tag specific for drug interaction related phenoconversion, would help people in PGx research identify the relevant papers.

Maybe phenoconversion could be added as a child term to MedDRA under Drug-drug pharmacokinetic interaction?

Rollout of genotype picker tool on DPWG guideline annotations

The allele pull-down menu (also known as the genotype-picker tool) on PharmGKB annotations of CPIC guidelines is an extremely popular feature among users. We are pleased to now expand this functionality to annotations of guidelines from the Royal Dutch Association for the Advancement of Pharmacy - Pharmacogenetics Working Group (DPWG).

The genotype picker tool relies on determining the following information from each guideline:

1. Which alleles are covered by the guideline

2. The assigned functional status of each allele

3. How allele function combinations are mapped to phenotype groups

The DPWG recommendations that are downloadable from the Dutch Pharmacy Organization’s (KNMP) website and annotated on PharmGKB do not typically contain allele functional status or phenotype group mappings. However, this information is often available in KNMP’s ‘gene background’ files which are posted separately.  While DPWG generally follows the same allele-to-phenotype mappings as CPIC, there are some gene-specific differences. These differences mean that all DPWG gene mappings need to be curated into PharmGKB separately from CPIC’s mappings. Our mapping process for each gene covered by DPWG guidelines, including curator notes, can be found here. As we now have internally curated mappings of allele function to phenotype for DPWG, more DPWG guidelines can now be used to support our Level 1A clinical annotations.

Genotype pickers are now available for all annotations of DPWG guidelines with recommendations for HLA-B, CYP2D6 and CYP2C19 genotypes. Work is ongoing to curate DPWG-assigned allele functions and phenotype groups for other genes into PharmGKB. As each gene is curated, genotype pickers will be released on relevant guideline annotation pages.

PharmGKB introduces scoring system for variant and clinical annotations and updated Levels of Evidence

Since their introduction in 2010, clinical annotations have become one of the most popular features of PharmGKB. Each clinical annotation summarizes a phenotypic association between a drug and genetic variant, shows relevant findings from the curated literature as variant annotations and is assigned a level of evidence to indicate the strength of support for that association in the literature.

As the number of variant and clinical annotations in PharmGKB has increased, it became a challenge to maintain consistency when assessing all the available evidence and assigning a level of evidence to clinical annotations. To address this, PharmGKB began a project at the end of 2019 to improve standardization across clinical annotations by establishing new curator tools and protocols. We are pleased to be able to release the first phase of this project to users today.

Central to this work has been the development of a scoring system which assigns scores to both variant and clinical annotations as a numerical summary of the evidence underlying each annotation. Variant annotations are scored in a five-step process which assesses various attributes found in both the main annotation and in the study parameters. This scoring of variant annotations is not a judgement of study quality. It is a metric used by PharmGKB curators when comparing variant annotations against each other as part of the process of creating and updating clinical annotations.

Annotation Scoring

The scoring process can be described using the following formula, with a list of attributes scored in each step given below. A more detailed description can be found on our Variant Annotation Scoring page.

(Step 1 + Step 2 + Step 3 + Step 4) x (Step 5a x Step 5b)

Step 1 – Phenotype category (toxicity, efficacy, etc.)
Step 2 – Reported p-value
Step 3 – Cohort size
Step 4 – Effect size
Step 5a – Weighting by study type
Step 5b – Weighting by reported association and significance

When a variant annotation is attached to a clinical annotation, the variant annotation’s score contributes to the score for the clinical annotation. Curators can mark any variant annotation which reports an association in the opposing direction to the assertion made in the clinical annotation as a conflicting variant annotation. For example, if a clinical annotation reports that the G allele is associated with decreased response to a drug, a variant annotation which reports that the G allele is associated with increased response would be considered to be conflicting.

Variant annotations which are marked as conflicting are assigned a negative score and receive a tag which can be seen on a clinical annotation’s page. It is important to realize that a variant annotation is only considered to be conflicting within the context of a specific clinical annotation and that the score of the variant annotation only changes in that clinical annotation.
Supporting CPIC and DPWG clinical guidelines or FDA-approved drug labels can also be added to a clinical annotation by curators and can contribute to that annotation’s score.

Assigning a score to guidelines and labels has helped us to better define our Level 1A clinical annotations. Now, any annotation with support from a qualifying CPIC or DPWG guideline or an FDA label is assigned as 1A. More information about clinical annotation scoring, including how a clinical guideline or drug label qualify for addition to a clinical annotation can be found here.

In this first release, only DPWG guidelines which mention specific alleles in the recommendation text have been used to support 1A clinical annotations. We are aware that DWPG provide additional mapping in supporting documents and plan to look at these in more detail in the near future. This will allow more DPWG guidelines to be used to support 1A clinical annotations.

A clinical annotation’s score is used to assign a suitable level of evidence for the annotation. A table with scoring ranges and detailed descriptions of each level can be found on our level of evidence page. We have introduced a separate scoring range for rare variants to account for the fact that these tend to only be reported in small studies, even though there is an underlying pharmacogenomic association. Information on how PharmGKB defines a rare variant can be found here. A clinical annotation’s score is only used to determine the level of evidence and is not intended to be used to rank or compare clinical annotations within a given level of evidence.

A clinical annotation’s score is now displayed on clinical annotation pages. This includes a score breakdown to indicate how different types of evidence are contributing to the annotation’s score. In rare cases, the team may feel that a clinical annotation’s score is not reflective of the underlying evidence and can, after group discussion and consensus, choose to override the scoring system. When an annotation’s level has been overridden, it is displayed on the annotation page along with a written justification for the override.

This scoring system minimizes subjectivity in the assessment of clinical annotations and makes the assignment of levels of evidence more consistent, reproducible and transparent to users. To complement the scoring system, PharmGKB clinical annotations are now being written to new standards as well as displaying additional information for users.

New Clinical Annotation Features

Clinical annotations will now begin to use a new template and set of standardized sentences to highlight caveats and other considerations to users. They are also now written only on a single drug, drug combination or drug class and a single phenotype category (e.g. dosage, efficacy, etc.). Additionally, we have introduced extra checks to ensure that all level 1 and 2 clinical annotations are supported by two independent pieces of evidence.

PharmGKB now offers two distinct types of clinical annotation: gene-level and variant-level. Variant-level annotations provide genotype-based summaries for a specific rsID (example), while gene-level clinical annotations display summaries for one of more star alleles of a gene (example). These formats have always been part of our clinical annotations, but have now been formalized with templates to standardize annotation writing.

The score and level of evidence assigned to gene-level clinical annotations represents the strength of evidence underlying the association at the level of the gene rather than at the level of the individual variant. A ‘Limited Evidence’ tag is used to highlight alleles which are supported by substantially less evidence than the overall level indicated by the level of evidence. Where possible, we also now display allele function as assigned by CPIC.

These changes mean that clinical annotations are easier to compare against each other and that the level of evidence is more representative of the evidence supporting a phenotypic association for the variant-drug pair. We acknowledge that this has reduced the number of clinical annotations at levels 1B, 2A and 2B however, these annotations are now more consistent and based on quantitative criteria.

This project has entailed detailed review of over 350 clinical annotations by our curation team and, as part of this release, all level 1 and 2 clinical annotations have been rewritten to our new standards and reviewed by at least two curators. Updating clinical annotations at levels 3 and 4 to our new standards will continue as part of our regular curation activities. Users can check the history section of each annotation to see if there has been a recent update.
We are excited to bring the first phase of this project to PharmGKB users and welcome user comments or suggestions sent to feedback@pharmgkb.org.

PharmGKB releases automated annotations

We are excited to announce that automated annotations of pharmacogenomic information in the scientific literature are now available from PharmGKB. These annotations have been produced using the PGxMine project, the result of a collaboration with Dr. Jake Lever at Stanford University.

PGxMine uses a supervised machine learning algorithm to carry out text mining of PubMed abstracts and full-text articles from PubMed Central. Sentences which contain a chemical and a variant are found using the PubTator Central resource and subsequently identified as being highly likely to contain PGx information are highlighted as an automated annotation. Automated annotations will also be used by PharmGKB curators to identify papers for manual curation.

The new automated annotations tab can now be found on drug, gene, variant and haplotype pages on the PharmGKB website. Each automated annotation displays the relevant sentence identified by PGxMine as well as information about the article where the sentence was found. PGxMine was deliberately designed to have a high level of precision at the expense of a lower recall rate. This means that PGx associations that are mentioned in multiple papers should be captured by the algorithm while associations mentioned in only one paper may be missed.

Unlike variant annotations or clinical annotations, which are manually curated by PharmGKB curators, automated annotations are found using computational methods only. The accuracy or relevance of these annotations has not been checked by PharmGKB staff. Users should therefore be aware that there is some noise associated with these annotations. Users should also note that this is not a comprehensive annotation of all published articles. Articles which are only accessible through a journal subscription cannot be annotated by PGxMine and will not be displayed in the automated annotations section.

A paper describing PGxMine in greater detail has been accepted by the Pacific Symposium on Biocomputing and will be available online soon. We will add the URL as a comment to this blog post as soon as it is available. An FAQ page about automated annotations and the PGxMine project can be found on the PharmGKB website.

Future updates of our automated annotations will be tied to the update schedule of PubTator Central. The PGxMine code is open source and can be accessed at GitHub while a full data dump can be accessed at Zenodo.

Combining large PGx datasets from cancer cell lines

Testing cancer cell lines in vitro for drug sensitivity is a cornerstone of preclinical drug development. Large publically available datasets can be found at The Genomics of Drug Sensitivity in Cancer Project (GDSE) and The Cancer Cell Line Encyclopedia (CCLE).

Studies attempting to combine large public datasets and analyzing for correlation questioned the reliability of the data due to limited concordance, reported in [PMID: 24284626], discussed in [PMID:24284624] and a confirmation study here.

A new report in Nature describes different methods to analyze the data from CCLE and GDSE and concludes that “data from either study yields similar predictors of drug response” [PMID:26570998].

These papers demonstrate the continuing difficulty trying to compare across large datasets. Such problems include comparing different experimental protocols and measurements for drug sensitivity across studies, trouble matching the drug and cell line names to ensure like comparison, discordance in the genotyping data, and drugs that had few examples of cell lines that were drug sensitive.  As always, attention to detail in the documentation and description of the experiments can help mitigate some of these difficulties. While development of standard testing protocols and data curation and reporting frameworks may lead to better validation of drug response predictors going forward there will always be the need for methods to filter the noise that is inevitable in large datasets.

Concordance of Drug Labels and Clinical Annotations

Some drug labels have been identified by the FDA as containing pharmacogenetic biomarker information, but occasionally these variants are not listed in the Clinical Annotations on PharmGKB. The Clinical Annotations are based on literature available in peer reviewed journals and focus on germline variants. As a result, there are a few reasons why variants identified in drug labels may not be found in PharmGKB Clinical Annotations.

1. Data used to create the drug labels are not publicly available. Research conducted by pharmaceutical companies, such as trials done for drug approval, may be proprietary information and/or not be published, but are used by the FDA. Only published literature is curated in PharmGKB.  

2. FDA labels may be based on drug classes but Clinical Annotations are drug specific. For example, the label of Protriptyline contains information about CYP2D6 but it is unknown whether proprietary information or drug class generalizations were used for this associations. Protriptyline is a tricyclic antidepressant. While no literature is available in a PubMed search for pharmacogenetics of protriptyline itself, the protriptyline drug label has a precaution for all tricyclic antidepressants, warning that poor metabolizers via CYP2D6 have higher plasma concentrations of tricyclic antidepressants generally.

3. Data are based on studies that do not identify specific variants or that are done on functional protein assays. Clinical Annotations are written about specific SNPs or haplotypes, not for genes or pathways generally. For example, Tetrabenazine is used to treat hyperkinetic movement disorder, such as in Huntington’s Disease, and CYP2D6 testing is required by the FDA. The study describing altered metabolism of tetrabenazine based on CYP2D6 metabolizer status (Mehanna, 2013) classifies CYP2D6 metabolizer status by phenotypes but does not include any specific genotyping information. As a result, there are no variants to annotate in the case of tetrabenazine, though altered CYP2D6 activity has been associated with tetrabenazine response. Another example is valproic acid, which is contraindicated in patients with Urea Cycle Disorders (UCD). Therefore, genetic variation in any of the genes involved in the urea cycle is actionable, but not specifically annotatable.

4. Relevant variation is in tumor cells and not germline. Cancer drugs that target tumors may be active or inactive based on mutations in the tumor cells. These drugs include afatinib, which is used to treat non-small cell lung cancer with EGFR mutations. Because these mutations occur in the tumor cells and are not part of the germline DNA, they are not covered extensively in PharmGKB, though we are working on ways to expand coverage.

5. PharmGKB does not have the resources to cover all the articles of PubMed, and some annotations may not be curated yet. For routine curation, we focus on journals with a high volume of pharmacogenetics articles. Articles published in other journals may lag behind in curation. If you know articles that you think should be added to curation, please send them to feedback@pharmgkb.org.

ABCB5 and haloperidol-induced toxicity: Results from a new study

Approximately 50% of patients treated with the antipsychotic drug haloperidol will develop extrapyramidal symptoms, a category that includes tremors, parkinsonism and decreased spontaneous movement. However, studies looking into the genetic variations associated with the development of these symptoms have been limited.

In a study recently published in PLOS Medicine, Zheng et al. used murine models and a human genetic association study to show a link between the ABCB5 gene and haloperidol-induced extrapyramidal symptoms (referred to as haloperidol-induced toxicity (HIT), and indicated in the murine models by "latency", or the time required for a mouse to move all four paws after being placed on an inclined wire-mesh screen). In the human genetic association study, it was the missense SNP rs17143212 in particular that was associated with haloperidol toxicities during the first 7 days of treatment, both before and after correcting for multiple testing using a permutation test.

ABCB5 is a member of the ATP-binding cassette (ABC) transporter family, and is responsible for the movement of substrates across cell membranes. Zheng et al. also used murine models to show that ABCB5 mRNA is expressed in brain capillaries, the location of the blood-brain barrier. This provides a possible mechanistic explanation for the association between the gene and HIT in mice - mouse strains with genetic variations that result in reduced ABCB5 activity may be more susceptible to HIT due to increased haloperidol concentrations in the brain. Furthermore, the authors suggest that this toxicity may actually be due to a metabolite of haloperidol, HPP+, which can induce mitochondrial toxicity that results in Parkinsonian-like symptoms.

While the authors conclude the paper by noting that other genetic factors are likely involved in the development of HIT in humans, the results from this study shed further light on the pharmacogenetics behind haloperidol-induced toxicity.

Read the original article:
The role of abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humans.
Zheng M, Zhang H, Dill DL, Clark JB, Tu S, Yablonovitch AL, Tan MH, Zhang R, Rujescu D, Wu M, Tessarollo L, Vieira W, Gottesman MM, Deng S, Eberlin LS, Zare RN, Billard JM, Gillet JP, Li JB, Peltz G. PLoS Medicine. 2015 Feb 3;12(2):e1001782. PMID 25647612.

See the annotation for this paper on PharmGKB:
https://www.pharmgkb.org/pmid/25647612

PharmGKB PGx summaries now available in the Genetic Testing Registry

The Genetic Testing Registry (GTR) now contains summaries written by PharmGKB Curators regarding the association between genes and drug responses that have CPIC therapeutic recommendations. These can be found on the GTR website under Conditions/Phenotypes and links to relevant clinical tests are provided on each page:

• Warfarin response
• Codeine response
• Clopidogrel response
• Abacavir hypersensitivity 
• Simvastatin response
• Allopurinol response 

Visualizing Levels of Evidence

PharmGKB Clinical Annotations provide a summary of an association between a genetic variant and a drug response. Each is assigned a level of evidence by our curators, based on established criteria.

We have created an illustration to help users understand the 4 levels of evidence:

PharmGKB Clinical Annotations are rated based on the level of evidence for the association 

Tailored therapy in asthma? ADRB2 genotype in the context of salmeterol therapy

In a recent trial, asthmatic children homozygous for the beta2-adrenergic receptor Arg16 variant (ADRB2 gene, rs1042713 genotype AA) were randomized and treated with either salmeterol (a beta2-adrenergic receptor agonist) or montelukast (a leukotriene receptor antagonist), both combined with fluticasone. Those in the montelukast treatment group displayed significantly better responses in several symptom categories, had fewer school absences, and overall had significantly higher quality of life scores over the 1 year period compared to those treated with salmeterol. [Click here for Further details]

Tailored second line therapy in asthmatic children with the arginine-16 genotype. Lipworth BJ, Basu K, Donald HP, Tavendale R, Macgregor DF, Ogston SA, Palmer CN, Mukhopadhyay S. Clinical Science (2013) 124, (517-519).

This trial was based on previous studies from the same group showing an increased risk of asthma exacerbations in children with daily exposure to beta2-adrenergic receptor agonists who have the ADRB2 Arg16 variant: 

• "Arginine-16 beta2 adrenoceptor genotype predisposes to exacerbations in young asthmatics taking regular salmeterol" Palmer et al, (2006). 
• "Adrenergic beta(2)-receptor genotype predisposes to exacerbations in steroid-treated asthmatic patients taking frequent albuterol or salmeterol" Basu et al, (2009).

These studies suggest that alternative treatments may be more beneficial in children with the Arg16/Arg16 genotype compared to using beta2 adrenergic receptor agonists.

Learn more about Beta-agonist action on ADRB2:

PharmGKB Beta-agonist/Beta-blocker Pathway, Pharmacodynamics

Learn more about the ADRB2 gene:

ADRB2 Very Important Pharmacogene summary

PharmGKB Clinical Annotations Update and New Levels of Evidence

We have launched an update of our Clinical Annotations, assessing new evidence available for each gene variant - drug association. Each Clinical Annotation is written by a PharmGKB curator and assigned a level of evidence. We have recently revised our criteria to provide 6 levels of evidence, from the highest (1A) to the lowest (4), detailed below:

Level 1A - Annotation for a variant-drug combination in a CPIC or medical society-endorsed PGx guideline, or implemented at a PGRN site or in another major health system.

Level 1B - Annotation for a variant-drug combination where the preponderance of evidence shows an association. The association must be replicated in more than one cohort with significant p-values, and preferably will have a strong effect size.

Level 2A - Annotation for a variant-drug combination that qualifies for level 2B where the variant is within a VIP (Very Important Pharmacogene) as defined by PharmGKB. The variants in level 2A are in known pharmacogenes, so functional significance is more likely.

Level 2B - Annotation for a variant-drug combination with moderate evidence of an association. The association must be replicated but there may be some studies that do not show statistical significance, and/or the effect size may be small.

Level 3 - Annotation for a variant-drug combination based on a single significant (not yet replicated) association or annotation for a variant-drug combination evaluated in multiple studies but lacking clear evidence of an association.

Level 4 - Annotation based on a case report, non-significant study or in vitro, molecular or functional assay evidence only.

Clinical Annotations can be found on PharmGKB:

• on our homepage by clicking "Clinically relevant PGx summaries'
• on gene, drug and variant pages under the 'Clinical PGx' tab
• made available as a download under the PharmGKB Data Usage Agreement 

We describe these new level of evidence criteria in the new published article:

Pharmacogenomics Knowledge for Personalized Medicine 

M Whirl-Carrillo, E M McDonagh, J M Hebert, L Gong, K Sangkuhl, C F Thorn, R B Altman and T E Klein. Clinical Pharmacology & Therapeutics (2012) 92: 414-417; doi:10.1038/clpt.2012.96

Click here to download the PDF

New sortable PGx Research tables on PharmGKB

Tables of our Variant Annotations found on the PGx Research tab (view on gene, drug, variant and haplotype pages) can now be sorted and filtered to allow you to see the most relevant information you want to see.

example: If you want to see all variant annotations between the genetic variant rs1800462 (TPMT*2) and the drug mercaptopurine.

• From our homepage search for 'rs1800462' and click on the PGx Research tab to see the table of Variant Annotations.  
• Add filter: select 'Drug'/ pick filter select 'contains'/ type mercaptopurine/ click add (pictured). 
• The list can then be sorted by each column e.g. significance, p value. 
• Click the configure icon (pictured) in the right hand corner above the table to add, remove or rearrange columns and save your desired settings to keep them for the next time you come to the website.  

 

New Relationships File Available

PharmGKB has a new relationships file available for download. This file catalogs all the current relationships between drugs, genes, diseases, variants, and haplotypes in PharmGKB. These relationships are based on annotations generated by our curators.

The new relationships file replaces and improves on the old relationships file. The entries in the old relationships file were based on co-occurence of gene, drug, and disease names within literature (primarily abstracts) and did not necessarily represent direct, curated relationships between entities.

The relationships file has quite a few new features.

• Includes relationships from our new annotations like clinical annotations, dosing guidelines, and drug label annotations
• Each relationship indicates what types of annotation it comes from in the “Evidence” field.
• PMIDs are now listed for relationships when available in the “PMIDs” field. This is a semi-colon(;) delimited list of PMIDs used to support the annotation.
• The “Association” field gives a sense of whether an association is positive, negative, or ambiguous. Please see note below regarding associations in disease relationships.
• Haplotype annotations are now included.
• PK/PD flags for pharmacokinetic or pharmacodynamic relationships have been added back in.

To get a copy of this file please sign into the site, agree to the PharmGKB Relationship Agreement on the Downloads page, and a copy of the file with supporting documentation will be sent to you.

NOTE: Disease associations derived from a VariantAnnotation or ClinicalAnnotation are really referring to a variant that has an annotation with that “Disease” tag. These associations can be misleading because they are not necessarily indications that a variant is directly associated with a disease phenotype.