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International Undiagnosed Diseases Programs (UDPs): components and outcomes


Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.


Rare diseases, although individually uncommon, affect an estimated 1 in 16 people in the general population [1]. Because a large proportion of rare diseases have a genetic basis, obtaining an accurate molecular diagnosis is crucial for appropriate management, family and reproductive counselling and support. However, it has been estimated that at least half of those with rare genetic diseases remain undiagnosed despite ‘standard’ clinical genetics care [1]. The first formal Undiagnosed Diseases Program (UDP), designed to assist diagnosis for rare genetic disorders, was established in 2008 by the United States National Institutes of Health (NIH) in Bethesda, Maryland [2,3,4]. The UDP facilitated integrated clinical evaluations for undiagnosed individuals with the aim of reaching a diagnosis through enhanced clinical and research-driven care. Following the success of the UDP, funding was made available to expand sites across the US through the creation in 2014 of the NIH-funded Undiagnosed Disease Network (UDN) [5,6,7]. As of March 2023, the US UDN has twelve clinical sites, as well as a central biorepository, metabolomics and sequencing cores, two model organism screening centers and a coordinating center [8].

The UDN gained international recognition and informed the development of several other programs worldwide [9]. Reflecting the need to support undiagnosed individuals, the NIH, along with the Wilhelm Foundation, a Swedish patient organization supporting research into undiagnosed diseases [10], sponsored two international conferences (Rome, 2014 and Budapest, 2015) to promote the creation, strengthening, and connection of similar programs worldwide. Representatives from 18 countries attended and this collaborative effort provided the foundation for the Undiagnosed Diseases Network International (UDNI) [11, 12]. The aims of the UDNI align with those of the US UDN and reflect the principles of other UDPs worldwide [12]. Specifically, the UDNI’s objectives are to improve rare disease diagnosis and care, facilitate research and data sharing, and improve scientific understanding through collaboration [12]. As of March 2023, the UDNI had 145 members from 41 countries and meets in annual international conferences, which continue to include close partnership and sponsorship with the Wilhelm Foundation [13].

The US UDN, like most UDPs subsequently created, has a diagnostic pipeline based on several key stages, outlined in Fig. 1 [11, 14,15,16,17,18,19,20,21,22,23]. The process begins with enrollment to the program followed by comprehensive phenotypic evaluation. This is followed by a testing stage – comprised of genomic sequencing or reanalysis of previously obtained genomic data, and the potential use of emerging diagnostic tools [14]. In some cases, data sharing and ‘matchmaking’ across national and international collaborative programs facilitates the identification of similar individuals. This involves connecting researchers with one another based on phenotypic and genotypic similarities in cases, to maximize the potential for diagnosis and aid further research [15]. Genetic findings (e.g., a compelling new gene or gene variant) may also be assessed in model organisms for further validation of pathogenicity. Finally, the results generated by the UDP—either a diagnosis or a plan for follow-up—are returned to the referring clinician or affected individual [14].

Fig. 1
figure 1

Key components of an Undiagnosed Diseases Program. This diagram presents a stepwise process that broadly corresponds to components of the US Undiagnosed Diseases Program [14], as well as how these steps have been adapted or implemented in other Undiagnosed Diseases Programs worldwide [11, 15,16,17,18,19,20,21,22,23, 29, 31, 37]. Green boxes denote elements of each stage that necessitate patient involvement while blue boxes represent research components

The current global landscape of UDPs includes state-wide, national and multi-national initiatives that characterize themselves as continuous programs (often hybrid clinical-research programs) rather than discrete research studies, and offer an ongoing pathway for individuals who have previously received standard clinical care yet remain undiagnosed. What constitutes ‘standard’ clinical care, as well as the components of an individual UDP, varies considerably and is influenced by resources, funding, and staff expertise. However, for the purpose of this review we have defined the key features of a UDP as a regional program that is disease-agnostic—open to undiagnosed individuals with heterogeneous presentations—and has a formalized methodology that incorporates research and advanced testing. This contrasts with the multitude of phenotype-specific research initiatives, such as diagnostic programs for genetic epilepsies [16]. UDPs also differ from general genetics services, which provide clinical care for individuals with a suspected monogenic condition. However, it is acknowledged that the boundary between clinical diagnostics and research in many genetics centers is blurred, and informal ‘ad hoc’ research options for undiagnosed individuals are frequently offered by clinicians.

This review aims to summarize the published, peer-reviewed literature that is available surrounding UDPs globally to contextualize and inform the implementation of future UDPs.


This narrative review was conducted by using search terms such as ‘undiagnosed disease program’, ‘undiagnosed genetic disease’, ‘research program’, and ‘diagnostic program’ on databases including Medline, Scopus, and PubMed. This allowed for the identification of several publications on UDPs. In addition, countries with UDPs were further identified through membership in the UDNI, and available publications by these UDPs were retrieved. Included articles were English language, and where multiple studies were available for a single UDP, the most seminal was selected. This was a narrative review, without systematic protocol, and as such may reflect biases or perceptions of the authors.

Published literature on Undiagnosed Diseases Programs

The nature of recruiting individuals into a UDP means that literature is restricted to retrospective observational cohort studies, rather than prospective case–control studies. Table 1 provides a summary of the cohort studies available, and includes UDPs in Belgium, Canada, Italy, Japan, Korea, Singapore, South Africa, Spain, Sweden, the US, the UK, Australia (in the state of Victoria) and a Europe-wide program [20, 21, 24,25,26,27,28,29,30,31,32,33,34]. Where multiple studies [17,18,19, 35] are available for a single UDP, this paper aims to reflect the process and results of the most recent iteration of the program [20, 25, 27]. However, the published literature reflects UDPs at varying stages of establishment and maturity. For example Canada’s publication presents the outcomes of three continuous iterations of their UDP [25], while others present results from initial pilot studies [27, 29, 32]. The European Solve-RD study has some features of a collaborative network not unlike the UDNI since many European countries have their own UDP; however, Solve-RD is discussed here as a UDP as it also performs primary analyses through its own diagnostic pipeline [36]. Twelve of the thirteen studies in Table 1 describe the overall outcomes of all components of the UDP over a given period. However, the European UDP only has results available for the diagnostic yield of the reanalysis approach within the program [21]. All thirteen studies are further discussed in the analysis of key components of a UDP. In addition, commentaries are available on the implementation of UDPs in the Australian state of Western Australia (WA) [22] and India [37], and are discussed where relevant.

Table 1 Summary of cohort studies involving individuals enrolled in an Undiagnosed Diseases Program (UDP)

Key components of an Undiagnosed Diseases Program

Although there is no international agreement on the components of a UDP, the steps presented earlier in Fig. 1 reflect the key steps of the diagnostic process as described in fifteen included studies—thirteen cohort studies and two narrative reviews.

  • Step 1: Enrollment

The enrollment process differs among UDPs. For example, some programs accept direct applications from individuals [27, 28], whilst others require referral by a clinician [14, 23, 31, 33]. Most UDPs recruit participants from a range of clinical services [21, 22, 24,25,26,27, 29, 30, 32, 33, 37]. Inclusion and exclusion criteria of the different UDPs are presented in Table 2.

Table 2 Inclusion and exclusion criteria for Undiagnosed Diseases Programs (UDPs)

Most UDPs require individuals to have high suspicion for a monogenic condition in the face of a lack of diagnosis following standard clinical investigations. An important limitation of these criteria is the lack of a quantifiable metric for the extent of these investigations, aside from the Japanese inclusion criteria which specifies the lack of a diagnosis for at least 6 months [34] and the Korean criteria, one of which requires a diagnostic journey of more than 5 years [27]. Some do not require prior investigations at all, notably the South African program that runs standard clinical testing in parallel with exome sequencing (ES), given that genetic testing was entirely unavailable to some participants [29]. The Canadian criteria have varied over iterations of the program and general inclusion criteria are presented [25]. The US, Belgian, and Swedish methodologies involve a review of records by experts [14, 31, 33], meaning that inclusion criteria are not strictly defined, although the US UDN has recommended criteria for an ‘ideal’ applicant. These criteria include objective findings pertinent to the phenotype, a lack of diagnosis despite review by at least two specialists, consent for inclusion by the patient or their guardian and agreement to share identified information and data between UDN centres, and deidentified data internationally [38]. A systematic review of applications for the US UDN found that accepted applications differed in a statistically significant manner from those not accepted in several measures: enrolled participants were younger, had more objective and fewer subjective findings, a longer period of illness, and higher rates of referral from specialists as opposed to primary care physicians [39]. Similarly, the Belgian program found that those with multiple objective signs and symptoms, and those referred by specialists were more likely to be accepted [31]. The European study delineates the cohorts that individuals were grouped into but does not provide inclusion criteria [36].

  • Step 2: Comprehensive clinical phenotyping

All fifteen UDPs recognize the use of phenotypic information as a key component of the UDP, consistent with the identification of deep phenotyping as a critical step for many diagnostic approaches to rare disease [1]. However, UDPs differ in how the individual is phenotyped. Phenotypic profiling occurs after enrolment in the US [14], UK [32], Canadian [25], Spanish [28], Belgian [31] and Australian (WA) [22] programs, and is generally performed via inpatient admission or outpatient appointments. Other UDPs rely on phenotyping by the referring physician or within the existing clinical framework and medical records [24, 26, 30, 33]. Most UDPs refer to the utility of bringing in multidisciplinary experts to ensure accurate and in-depth phenotyping, and Table 3 shows the range of specialists, involved in phenotyping and throughout the diagnostic process of different UDPs.

Table 3 Multidisciplinary team members involved in Undiagnosed Diseases Programs. Information in table from [20,21,22, 24,25,26, 28, 30,31,32,33]
  • Step 3: Research diagnostics

The diagnostic process within a UDP is also variable, but typically begins with the completion and review of prior testing, and comprehensive unbiased genomic sequencing (ES or whole genome sequencing [WGS]), if not recently completed. This is followed by reanalysis of the genomic data and, if required, the application of advanced technologies such as RNA sequencing [1]. Commonly used diagnostic tools are defined in Table 4.

Table 4 Key analytic techniques and their uses in diagnosing undiagnosed genetic conditions. Information in table adapted from [1, 72]

One common feature of all UDPs examined is the option of unbiased genomic sequencing, either ES [14, 24,25,26,27,28,29,30,31, 34], or WGS [14, 22, 24,25,26,27, 32, 33, 36, 37]. Whilst enrolled individuals have often previously received screening for chromosomal copy number variants or targeted testing of a small number of genes [40], ES and WGS offer a more comprehensive examination of the genome. ES covers approximately 98% of the exome, which is 1–1.5% of the genome that is protein-coding. WGS covers approximately 90% of the whole genome and may provide a molecular diagnosis where ES cannot via improved coverage of exons, interrogation of the mitochondrial genome, and better detection of structural, splicing, and regulatory variants [41]. Nonetheless, it remains important to rationalize the use of unbiased testing, as broad clinical availability of low-cost genetic testing is variable [29] and first-tier tests such as chromosomal microarray remain important in the identification of copy number variants [1].

Ongoing research is needed to better understand the diagnostic yield of ES/WGS, which a 2021 systematic review found has a wide range between 13 and 70% in a cohort with suspected monogenic disease, with a slight increase in yield from WGS [42]. Much of the variability in diagnostic yield is likely due to the phenotype of the tested cohort, with cohorts including more severe presentations (e.g., early-onset, multisystem and complex neurological presentations) having a higher likelihood of underlying monogenic diagnoses [40]. Cohorts with more extensive prior testing—and as such presenting with a prior negative ES/WGS—also have a lower yield, and this may further vary depending on the threshold applied for reporting a diagnosis [40]. As both ES and WGS are important technologies in the diagnosis of rare diseases [43, 44], comprehensive reporting of outcomes by UDPs allows for continuous evaluation of their utility in an undiagnosed cohort [19]. ES is globally the more accessible of the two due to earlier availability and lower costs, hence is increasingly incorporated into ‘standard’ clinical pathways as a cost-effective screening test [45]. For example, in Australia, ES has been federally funded through the Medicare system since 2020 for a subgroup of children under the age of ten [46], with chromosomal microarray also being funded via Medicare. It is hoped this funding will make ES more accessible in Australia and improve inequities in access to genomic diagnostics for those with suspected rare genetic disease, with government funding for unbiased sequencing a high priority globally.

If ES is non-diagnostic, the alternative pathways UDPs may take to increase the diagnostic yield are summarized in Fig. 2. One pathway is the reanalysis of past genomic data obtained through ES. Reanalysis over time can increase diagnostic yield from initial analysis via access to new knowledge, such as new disease-gene associations, improved analytic technology, refined information on the significance of variants previously classified as of uncertain clinical significance through improved global data collection and/or functional studies, or because novel phenotypic information about the individual becomes available [47]. Studies of heterogenous cohorts have shown that reanalysis results in an improved diagnostic yield of 4–32% [48,49,50,51,52,53]; a recent narrative review of 27 articles found a median improvement in diagnostic yield of 15% [54]. The potential utility of periodic reanalysis has been recognized by many UDPs, such as the Swedish program in which renewed referral for reanalysis is recommended every 3–5 years for individuals with negative ES/WGS [33]. Given the relatively low-cost and high utility of reanalysis [45], further pathways such as automated reanalysis methodologies may become more broadly implemented [55]. Such technologies may enable UDPs to efficiently increase diagnostic yield in an undiagnosed cohort [48, 56].

Fig. 2
figure 2

Possible research diagnostic pathways in an Undiagnosed Diseases Program. Figure adapted from [1, 70]. Multiomics refers to the integrated analysis of various sources of information, such as the genomic sequence, transcriptomic datasets, and metabolomic datasets. ES, exome sequencing; GUS, gene of uncertain significance; LP/P, likely pathogenic/pathogenic; RNA, ribonucleic acid; VUS, variant of uncertain significance; WGS, whole genome sequencing.

There is also a role for ‘multiomic’ technologies in diagnosis, such as RNA sequencing, methylation profiling, and metabolomics. These can be beneficial in assessing the pathological consequences of genomic variants [1]. RNA sequencing is incorporated into the Canadian [25], US [14], European [36], Australian (Victoria) [26], Korean [27], and Indian [37] UDPs, and metabolomics in the US UDN, Belgian, and European UDPs. Studies of model organisms such as zebrafish are also important in gene discovery and variant assessment, allowing scientists to examine equivalent genes and pathways to validate pathogenic mechanisms [57]. The US, Korean, Belgian, and Canadian UDPs have corresponding networks that undertake research into model organisms [14, 27, 31, 57], and Japan, Europe and Australia have links to collaborative networks [57].

  • Step 4: Data sharing and matchmaking

During research genomics, deleterious variants in candidate genes may be identified, suggestive of a novel disease-gene association. In this setting, data sharing is vitally important in determining whether there are additional individuals with variants in the same gene and an overlapping phenotype [40]. Matchmaker Exchange is a key data sharing network that enables connections between various matchmaking ‘nodes’ that contain different types of information to create an overarching large dataset that facilitates genomic discovery [15]. The UDNI is a global partnership of UDPs that also aims to facilitate data sharing by allowing individual UDPs to share data on unsolved individuals so that knowledge and expertise can be exchanged among UDPs [12], with the aim to increase diagnoses for those with suspected rare genetic disease.

Data sharing plays a role in most UDPs examined, and methods of sharing variant and phenotypic data are summarized in Table 5. Although the South African UDP uploaded variants to ClinVar, data-sharing was not mentioned as part of their diagnostic pipeline [29].

Table 5 Summary of data sharing methods used in Undiagnosed Diseases Programs

In evaluating the role of data-sharing in UDPs, guidelines such as the FAIR principles should be considered [58]. These guiding principles of ‘findability,’ ‘accessibility,’ ‘interoperability’ and ‘reusability’ play a pivotal role in ensuring that shared data effectively support UDP research. While many UDPs share phenotypic and demographic data in line with these principles, sharing genomic data poses more significant challenges.

Most UDPs share genomic data at the variant level, but standardizing the interpretation of these variants can be a complex task. To mitigate inconsistencies in variant interpretation, sharing platforms like ClinVar can prove to be valuable [59]. When dealing with genome-wide data, the challenges are even more substantial. Beyond the necessity of ensuring privacy and ethical data sharing [60], standardization and harmonization on an international scale can be challenging.

An illustrative example of effective data-sharing can be found in the European UDP. With numerous European Reference Networks involved, the UDP integrates data from multiple sites and employs a standardized pipeline and a common workflow for the analysis of NGS data. These data are then shared through platforms such as the European Genome-Phenome Archive and the RD-Connect Genome-Phenome Analysis Platform, with controlled access measures in place [36]. The successes achieved in broad data-sharing and analysis underscore the potential scope of data-sharing by UDPs.

Data and metadata are also important more generally in research, since population level genomic data from a diverse range of the global population assists in the interpretation of test results and understanding of the clinical validity of variants [60]. UDPs may offer an avenue to improve data sharing internationally, especially by providing access to genomic sequencing where this may not be available through the health system. The South African UDP is an example of the potential that UDPs have to broaden access to genomic data; it is one of few studies evaluating next-generation sequencing (NGS) in sub-Saharan Africa, and as such serves as a starting point in improving the equity of genomic research in historically understudied populations [29].

  • Step 5: Results and follow-up

Ultimately, the UDP provides a diagnosis or an inconclusive result. As UDPs focus on the diagnosis of a broad range of conditions, treatment is generally not integrated into the program. In eight of the thirteen UDPs examined, results and recommendations are provided to the referring clinician or clinical center responsible for follow-up [19,20,21, 24, 27, 30, 32, 33]. The program in Victoria, Australia, is fully integrated with the state’s outpatient genetics service [26], and the return of results is integrated directly into the South African and Japanese UDPs [29, 34]. The process of returning is not explicitly mentioned in the Belgian [31] or Spanish [28] studies. Description of the follow-up process is limited overall, and mixed methods research is needed to inform the preferences of families enrolled in UDPs regarding the optimal manner of disclosing progress updates and diagnostic outcomes to families. The UDNI has a Diagnostic Working Group and a Genetic Counseling Group [13], both considering best approaches for how to guide UDPs with a ‘second opinion’ and how to best support families who receive an inconclusive result.

Possible outcomes of an Undiagnosed Diseases Program

The primary outcome of all UDPs examined was molecular diagnosis, with diagnostic yield ranging between 3 and 53% and summarized in Table 6. In part, this range reflects the different inclusion and exclusion criteria of each UDP. Those UDPs that enroll individuals who have already had comprehensive diagnostic genomic studies (i.e., ES/WGS) would be expected to have lower diagnostic yields than programs that require less extensive pre-program investigations. For example, the high diagnostic yield of the Korean UDP in part reflects the diagnoses of ‘Group I’, a cohort referred with clinical diagnoses that had not yet had genetic testing for the specific predicted condition. Global inequity of access to genomic technologies is also a factor and there are many countries where NGS is not readily available. For example, the South African UDP, which has a comparably high diagnostic yield, notes the paucity of access to NGS for most patients in their country, thus its results are reflective of the diagnostic yield of making ES available to an NGS-naïve cohort. Access to funds and research opportunities vary between programs, which would also impact availability of technologies beyond NGS such as long read sequencing and transcriptomics. On an individual scale, access to diagnostic genomic testing is more challenging for those with a lower socio-economic status, intellectual disability, cultural and linguistic diversity, who are Indigenous, and those living in regional/rural areas [61,62,63,64]. The UDNI has recognized this inequity on a global scale through its new ‘champions’ program aiming to support emerging UDP in low- and middle-income countries, but it is important individual UDPs consider contextual barriers to access.

Table 6 Comparison of the diagnostic yield of Undiagnosed Diseases Programs

Another factor contributing to the variability of diagnostic yield is what each UDP classifies as a ‘diagnosis’. Some studies only report individuals reaching a (likely) pathogenic diagnosis according to standards such as the American College of Medical Genetics diagnostic criteria [20]. Others, such as the Italian [30] and Australian (Victoria) [26] studies, also include those with ‘strong candidate’ variants that meet less stringent criteria; this allows for the inclusion of novel and unpublished diagnoses. This makes a meaningful comparison of diagnostic yield between UDPs challenging. Table 6 presents diagnostic yield limited to cases (individuals/families) with a definite molecular diagnosis, and Fig. 3 breaks down how these diagnoses were made. For example, the US UDP reached a molecular diagnosis in 231 of the 791 analyzed individuals (Table 6); 32% of these diagnoses were made by ES, 40% by WGS, 10% by reanalysis and 18% with other techniques (Fig. 3) [20]. In comparison, 120 of the 4,411 individuals in the European study reached a molecular diagnosis, and all were by reanalysis since this was the only approach reported [21].

Fig. 3
figure 3

Molecular diagnoses made by Undiagnosed Diseases Programs, stratified by testing method. Diagnoses made by each method are presented as percentage (to the nearest whole number) of the total molecular diagnoses made by each Undiagnosed Diseases Program. Other refers to the use of various or multiple techniques such as chromosomal microarray and RNA sequencing. Data in figure adapted from [20, 21, 24,25,26,27,28,29,30,31,32,33,34]. ES, exome sequencing; WGS, whole genome sequencing

Several UDPs also reported additional research-based outcomes, e.g., focusing on research impact beyond diagnostic yield or on patient-based outcomes relating to how the UDP impacted the individual. Additional research-based outcomes included novel gene discovery [20, 24,25,26, 29, 31,32,33,34,35], use of data sharing [25,26,27, 30, 31, 65, 66], and the utility of advanced technologies [20, 25,26,27, 31, 32].

Patient-based outcomes included time to diagnosis [20, 24, 26,27,28, 31, 32], clinical actionability such as changes to management [14, 17, 19, 24, 27, 31,32,33], geographical distribution/accessibility [20, 27, 28], and access to other resources resulting from a diagnosis [29]. There is a paucity of literature focused on an individual’s experience in a UDP, however the program in Australia (Victoria) has published a study exploring the experiences of receiving both diagnostic and non-diagnostic results for parents whose children were involved in the UDP [67]. The UK UDP published an interview-based study of young people’s “understanding, attitude and involvement” regarding WGS, but this focused on WGS in general terms, rather than feedback on involvement in the UDP [68]. The US program has published a series of vignettes, illustrative of the diagnostic progress [69]. Nonetheless, the lack of detailed exploration of the experience of enrolled patients and families is a limitation of many UDPs. The UDNI offers a viable model for active inclusion of the individual’s perspective through their Patient Engagement Working Group [11].


There is increasing recognition of the role of UDPs in providing a pathway for those undiagnosed, with this narrative review summarizing the outcomes of thirteen UDPs worldwide who have published their findings in peer-reviewed journals. More UDPs exist, based on involvement with the UDNI [11], but they have yet to publish findings in peer-reviewed literature or only commentaries [22, 37]. The evolving nature of genomic technologies means that UDPs are continuously updating their methods to maximize diagnostic potential, so that even recently published studies do not necessarily reflect current technologies and practices [43]. In addition, although this review has aimed to compare analogous features of each UDP, the details and outcomes reported in the literature are not necessarily comprehensive or uniform, limiting comparison.

There is a need for ongoing evaluation to facilitate a better understanding of the utility of each component of a UDP, in order to inform best practice. Data on the impact and cost effectiveness of a UDP is needed to drive the policy change required to implement sustainable UDPs within health care systems and improve global equity of access to diagnostic technologies. Further research is also needed to understand how to best support undiagnosed families in the context of their diagnostic journey. For families remaining without a diagnosis, nonprofit organizations such as the international Wilhelm Foundation and national Syndrome Without A Name (SWAN) programs provide avenues for support, but comprehensive care navigation should be integrated into national health systems.

As UDPs continue to be developed internationally, future research should incorporate an understanding of the limitations and successes of existing UDPs. An important part of this is characterizing the goals and key components of each step within a UDP that maximize the likelihood of a successful diagnosis. We outline proposed goals and components in Table 7. Consistent reporting of key steps and comprehensive evaluation of relevant outcomes, incorporating both patient and clinician perspectives, will clarify the value and clinical utility of a UDP.

Table 7 Goals of each step of a UDP, and key aspects of each

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed.



Base pair


Chromosomal microarray analysis


Complementary deoxyribonucleic acid


Deoxyribonucleic acid


European Reference Network


Exome sequencing


Gene of uncertain significance


Likely pathogenic/pathogenic


Matchmaker Exchange


Magnetic resonance imaging


Nerve conduction studies


Next-generation sequencing


National Health Service (UK)


National Institutes of Health (US)


Rare disease


Ribonucleic acid


Single nucleotide variant


Undiagnosed Diseases Network


Undiagnosed Diseases Network International


Undiagnosed Diseases Program


Variant of uncertain significance


Western Australia


Whole genome sequencing


  1. Hartley T, Lemire G, Kernohan KD, Howley HE, Adams DR, Boycott KM. New diagnostic approaches for undiagnosed rare genetic diseases. Annu Rev Genomics Hum Genet. 2020;21(1):351–72.

    Article  CAS  PubMed  Google Scholar 

  2. Gahl WA, Markello TC, Toro C, Fajardo KF, Sincan M, Gill F, et al. The national institutes of health undiagnosed diseases program: insights into rare diseases. Genet Med. 2012;14(1):51–9.

    Article  CAS  PubMed  Google Scholar 

  3. Gahl WA, Tifft CJ. The NIH undiagnosed diseases program: lessons learned. JAMA. 2011;305(18):1904–5.

    Article  CAS  PubMed  Google Scholar 

  4. Montano C, Cassini T, Ziegler SG, Boehm M, Nicoli E-R, Mindell JA, et al. Diagnosis and discovery: insights from the NIH undiagnosed diseases program. J Inherit Metab Dis. 2022;45(5):907–18.

    Article  CAS  PubMed  Google Scholar 

  5. Gahl WA, Mulvihill JJ, Toro C, Markello TC, Wise AL, Ramoni RB, et al. The NIH undiagnosed diseases program and network: applications to modern medicine. Mol Genet Metab. 2016;117(4):393–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gahl WA, Wise AL, Ashley EA. The undiagnosed diseases network of the National Institutes of Health: a national extension. JAMA. 2015;314(17):1797–8.

    Article  CAS  PubMed  Google Scholar 

  7. Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA, Bernstein JA, et al. The undiagnosed diseases network: accelerating discovery about health and disease. Am J Hum Genet. 2017;100(2):185–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Undiagnosed Diseases Network. Undiagnosed Diseases Network Quarterly Report. National Institutes of Health. 2022. Accessed 13 Mar 2023.

  9. Gainotti S, Mascalzoni D, Bros-Facer V, Petrini C, Floridia G, Roos M, et al. Meeting patients’ right to the correct diagnosis: ongoing international initiatives on undiagnosed rare diseases and ethical and social issues. Int J Environ Res Public Health. 2018;15(10):2072.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Taruscio D, Floridia G, Salvatore M, Groft SC, Gahl WA. Undiagnosed diseases: Italy-US collaboration and international efforts to tackle rare and common diseases lacking a diagnosis. Adv Exp Med Biol. 2017;1031:25–38.

    Article  PubMed  Google Scholar 

  11. Taruscio D, Baynam G, Cederroth H, Groft SC, Klee EW, Kosaki K, et al. The undiagnosed diseases network international: five years and more! Mol Genet Metab. 2020;129(4):243–54.

    Article  CAS  PubMed  Google Scholar 

  12. Taruscio D, Groft SC, Cederroth H, Melegh B, Lasko P, Kosaki K, et al. Undiagnosed diseases network international (UDNI): white paper for global actions to meet patient needs. Mol Genet Metab. 2015;116(4):223–5.

    Article  CAS  PubMed  Google Scholar 

  13. Undiagnosed Diseases Network International. The Undiagnosed Diseases Network International. Accessed 09 Apr 2023.

  14. Macnamara EF, D’Souza P, Tifft CJ. The undiagnosed diseases program: approach to diagnosis. Transl Sci Rare Dis. 2020;4(3–4):179–88.

    PubMed  PubMed Central  Google Scholar 

  15. Azzariti DR, Hamosh A. Genomic data sharing for novel mendelian disease gene discovery: the Matchmaker Exchange. Annu Rev Genomics Hum Genet. 2020;21(1):305–26.

    Article  CAS  PubMed  Google Scholar 

  16. Palmer EE, Sachdev R, Macintosh R, Melo US, Mundlos S, Righetti S, et al. Diagnostic yield of whole genome sequencing after nondiagnostic exome sequencing or gene panel in developmental and epileptic encephalopathies. Neurology. 2021;96(13):e1770–82.

    Article  CAS  PubMed  Google Scholar 

  17. Splinter K, Adams DR, Bacino CA, Bellen HJ, Bernstein JA, Cheatle-Jarvela AM, et al. Effect of genetic diagnosis on patients with previously undiagnosed disease. N Engl J Med. 2018;379(22):2131–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Beaulieu CL, Majewski J, Schwartzentruber J, Samuels ME, Fernandez BA, Bernier FP, et al. FORGE Canada Consortium: outcomes of a 2-year national rare-disease gene-discovery project. Am J Hum Genet. 2014;94(6):809–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sawyer SL, Hartley T, Dyment DA, Beaulieu CL, Schwartzentruber J, Smith A, et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin Genet. 2016;89(3):275–84.

    Article  CAS  PubMed  Google Scholar 

  20. Schoch K, Esteves C, Bican A, Spillmann R, Cope H, McConkie-Rosell A, et al. Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science. Genet Med. 2021;23(2):259–71.

    Article  PubMed  Google Scholar 

  21. Matalonga L, Hernández-Ferrer C, Piscia D, Cohen E, Cuesta I, Danis D, et al. Solving patients with rare diseases through programmatic reanalysis of genome-phenome data. Eur J Hum Genet. 2021;29(9):1337–47.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Baynam G, Broley S, Bauskis A, Pachter N, McKenzie F, Townshend S, et al. Initiating an undiagnosed diseases program in the Western Australian public health system. Orphanet J Rare Dis. 2017;12(1):83.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Adachi T, Kawamura K, Furusawa Y, Nishizaki Y, Imanishi N, Umehara S, et al. Japan’s initiative on rare and undiagnosed diseases (IRUD): towards an end to the diagnostic odyssey. Eur J Hum Genet. 2017;25(9):1025–8.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bhatia NS, Lim JY, Bonnard C, Kuan J-L, Brett M, Wei H, et al. Singapore undiagnosed disease program: genomic analysis aids diagnosis and clinical management. Arch Dis Child. 2021;106(1):31.

    Article  PubMed  Google Scholar 

  25. Boycott KM, Hartley T, Kernohan KD, Dyment DA, Howley H, Innes AM, et al. Care4Rare Canada: outcomes from a decade of network science for rare disease gene discovery. Am J Hum Genet. 2022;109(11):1947–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Cloney T, Gallacher L, Pais LS, Tan NB, Yeung A, Stark Z, et al. Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria’s Undiagnosed Diseases Program. J Med Genet. 2022;59:748–58.

    Article  PubMed  Google Scholar 

  27. Kim SY, Lee S, Woo H, Han J, Ko YJ, Shim Y, et al. The Korean undiagnosed diseases program phase I: expansion of the nationwide network and the development of long-term infrastructure. Orphanet J Rare Dis. 2022;17(1):372.

    Article  PubMed  PubMed Central  Google Scholar 

  28. López-Martín E, Martínez-Delgado B, Bermejo-Sánchez E, Alonso J, Posada M. SpainUDP: the Spanish undiagnosed rare diseases program. Int J Environ Res Public Health. 2018;15(8):1746.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Moosa S, Coetzer KC, Lee E, Seo GH. Undiagnosed disease program in South Africa: results from first 100 exomes. Am J Med Genet A. 2022;188(9):2684–92.

    Article  PubMed  Google Scholar 

  30. Salvatore M, Polizzi A, De Stefano MC, Floridia G, Baldovino S, Roccatello D, et al. Improving diagnosis for rare diseases: the experience of the Italian undiagnosed rare diseases network. Ital J Pediatr. 2020;46(1):130.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Schuermans N, Hemelsoet D, Terryn W, Steyaert S, Van Coster R, Coucke PJ, et al. Shortcutting the diagnostic odyssey: the multidisciplinary program for undiagnosed rare diseases in adults (UD-PrOZA). Orphanet J Rare Dis. 2022;17(1):210.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Smedley D, Smith KR, Martin A, Thomas EA, McDonagh EM, Cipriani V, et al. 100,000 genomes pilot on rare-disease diagnosis in health care—preliminary report. N Engl J Med. 2021;385(20):1868–80.

    Article  CAS  PubMed  Google Scholar 

  33. Stranneheim H, Lagerstedt-Robinson K, Magnusson M, Kvarnung M, Nilsson D, Lesko N, et al. Integration of whole genome sequencing into a healthcare setting: high diagnostic rates across multiple clinical entities in 3219 rare disease patients. Genome Med. 2021;13(1):40.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Takahashi Y, Date H, Oi H, Adachi T, Imanishi N, Kimura E, et al. Six years’ accomplishment of the initiative on rare and undiagnosed diseases: nationwide project in Japan to discover causes, mechanisms, and cures. J Hum Genet. 2022;67(9):505–13.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kim SY, Lim BC, Lee JS, Kim WJ, Kim H, Ko JM, et al. The Korean undiagnosed diseases program: lessons from a one-year pilot project. Orphanet J Rare Dis. 2019;14(1):68.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zurek B, Ellwanger K, Vissers LELM, Schüle R, Synofzik M, Töpf A, et al. Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases. Eur J Hum Genet. 2021;29(9):1325–31.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Puri RD, Dalal A, Moirangthem A. Indian undiagnosed diseases program (I-UDP)-the Unmet need. Indian Pediatr. 2022;59(3):198–200.

    Article  PubMed  Google Scholar 

  38. Undiagnosed Disease Network. Frequently asked questions about the undiagnosed diseases network. Accessed 09 Apr 2023.

  39. Walley NM, Pena LDM, Hooper SR, Cope H, Jiang Y-H, McConkie-Rosell A, et al. Characteristics of undiagnosed diseases network applicants: implications for referring providers. BMC Health Serv Res. 2018;18(1):652.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wise AL, Manolio TA, Mensah GA, Peterson JF, Roden DM, Tamburro C, et al. Genomic medicine for undiagnosed diseases. Lancet. 2019;394(10197):533–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bick D, Jones M, Taylor SL, Taft RJ, Belmont J. Case for genome sequencing in infants and children with rare, undiagnosed or genetic diseases. J Med Genet. 2019;56(12):783–91.

    Article  PubMed  Google Scholar 

  42. Shickh S, Mighton C, Uleryk E, Pechlivanoglou P, Bombard Y. The clinical utility of exome and genome sequencing across clinical indications: a systematic review. Hum Genet. 2021;140(10):1403–16.

    Article  PubMed  Google Scholar 

  43. Boycott KM, Hartley T, Biesecker LG, Gibbs RA, Innes AM, Riess O, et al. A diagnosis for all rare genetic diseases: the horizon and the next frontiers. Cell. 2019;177(1):32–7.

    Article  CAS  PubMed  Google Scholar 

  44. Clark MM, Stark Z, Farnaes L, Tan TY, White SM, Dimmock D, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genomic Med. 2018;3:16.

    Article  Google Scholar 

  45. Ewans LJ, Minoche AE, Schofield D, Shrestha R, Puttick C, Zhu Y, et al. Whole exome and genome sequencing in mendelian disorders: a diagnostic and health economic analysis. Eur J Hum Genet. 2022;30(10):1121–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sachdev R, Field M, Baynam GS, Beilby J, Berarducci M, Berman Y, et al. Paediatric genomic testing: navigating medicare rebatable genomic testing. J Paediatr Child Health. 2021;57(4):477–83.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Robertson AJ, Tan NB, Spurdle AB, Metke-Jimenez A, Sullivan C, Waddell N. Reanalysis of genomic data: an overview of the mechanisms and complexities of clinical adoption. Genet Med. 2022;24(4):798–810.

    Article  CAS  PubMed  Google Scholar 

  48. Baker SW, Murrell JR, Nesbitt AI, Pechter KB, Balciuniene J, Zhao X, et al. Automated clinical exome reanalysis reveals novel diagnoses. J Mol Diagn. 2019;21(1):38–48.

    Article  CAS  PubMed  Google Scholar 

  49. Ewans LJ, Schofield D, Shrestha R, Zhu Y, Gayevskiy V, Ying K, et al. Whole-exome sequencing reanalysis at 12 months boosts diagnosis and is cost-effective when applied early in Mendelian disorders. Genet Med. 2018;20(12):1564–74.

    Article  PubMed  Google Scholar 

  50. Fung JLF, Yu MHC, Huang S, Chung CCY, Chan MCY, Pajusalu S, et al. A three-year follow-up study evaluating clinical utility of exome sequencing and diagnostic potential of reanalysis. NPJ Genom Med. 2020;5(1):37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. James KN, Clark MM, Camp B, Kint C, Schols P, Batalov S, et al. Partially automated whole-genome sequencing reanalysis of previously undiagnosed pediatric patients can efficiently yield new diagnoses. NPJ Genom Med. 2020;5(1):33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schmitz-Abe K, Li Q, Rosen SM, Nori N, Madden JA, Genetti CA, et al. Unique bioinformatic approach and comprehensive reanalysis improve diagnostic yield of clinical exomes. Eur J Hum Genet. 2019;27(9):1398–405.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wenger AM, Guturu H, Bernstein JA, Bejerano G. Systematic reanalysis of clinical exome data yields additional diagnoses: implications for providers. Genet Med. 2017;19(2):209–14.

    Article  PubMed  Google Scholar 

  54. Tan NB, Stapleton R, Stark Z, Delatycki MB, Yeung A, Hunter MF, et al. Evaluating systematic reanalysis of clinical genomic data in rare disease from single center experience and literature review. Mol Genet Genomic Med. 2020;8(11):e1508.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Setty ST, Scott-Boyer MP, Cuppens T, Droit A. New developments and possibilities in reanalysis and reinterpretation of whole exome sequencing datasets for unsolved rare diseases using machine learning approaches. Int J Mol Sci. 2022;23(12):6792.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. O’Brien TD, Campbell NE, Potter AB, Letaw JH, Kulkarni A, Richards CS. Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory. Genet Med. 2022;24(1):192–200.

    Article  CAS  PubMed  Google Scholar 

  57. Boycott KM, Campeau PM, Howley HE, Pavlidis P, Rogic S, Oriel C, et al. The Canadian rare diseases models and mechanisms (RDMM) network: connecting understudied genes to model organisms. Am J Hum Genet. 2020;106(2):143–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3:160018.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Harrison SM, Dolinsky JS, Knight Johnson AE, Pesaran T, Azzariti DR, Bale S, et al. Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genet Med. 2017;19(10):1096–104.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Byrd JB, Greene AC, Prasad DV, Jiang X, Greene CS. Responsible, practical genomic data sharing that accelerates research. Nat Rev Genet. 2020;21(10):615–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Best S, Vidic N, An K, Collins F, White SM. A systematic review of geographical inequities for accessing clinical genomic and genetic services for non-cancer related rare disease. Eur J Hum Genet. 2022;30(6):645–52.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Luke J, Dalach P, Tuer L, Savarirayan R, Ferdinand A, McGaughran J, et al. Investigating disparity in access to Australian clinical genetic health services for Aboriginal and Torres Strait Islander people. Nat Commun. 2022;13(1):4966.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Fraiman YS, Wojcik MH. The influence of social determinants of health on the genetic diagnostic odyssey: who remains undiagnosed, why, and to what effect? Pediatr Res. 2021;89(2):295–300.

    Article  PubMed  Google Scholar 

  64. Strnadova I, Loblinzk J, Scully JL, Danker J, Tso M, Jackaman KM, et al. “I am not a number!” Opinions and preferences of people with intellectual disability about genetic healthcare. Eur J Hum Genet. 2023;31:1057–65.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kobren SN, Baldridge D, Velinder M, Krier JB, Leblanc K, Esteves C, et al. Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases. Genet Med. 2021;23(6):1075–85.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Osmond M, Hartley T, Dyment DA, Kernohan KD, Brudno M, Buske OJ, et al. Outcome of over 1500 matches through the Matchmaker Exchange for rare disease gene discovery: the 2-year experience of Care4Rare Canada. Genet Med. 2022;24(1):100–8.

    Article  CAS  PubMed  Google Scholar 

  67. Martinussen J, Chalk M, Elliott J, Gallacher L. receiving genomic sequencing results through the victorian undiagnosed disease program: exploring parental experiences. J Pers Med. 2022;12(8):1250.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Lewis C, Hammond J, Hill M, Searle B, Hunter A, Patch C, et al. Young people’s understanding, attitudes and involvement in decision-making about genome sequencing for rare diseases: a qualitative study with participants in the UK 100, 000 Genomes Project. Eur J Med Genet. 2020;63(11):104043.

    Article  PubMed  Google Scholar 

  69. Macnamara EF, Schoch K, Kelley EG, Fieg E, Brokamp E, Undiagnosed Diseases N, et al. Cases from the Undiagnosed Diseases Network: the continued value of counseling skills in a new genomic era. J Genet Couns. 2019;28(2):194–201.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med. 2022;14(1):23.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Clinical and genetic evaluation of individuals with undiagnosed disorders through the Undiagnosed Diseases Network (NCT02450851). [] Accessed 13 Mar 2023.

  72. Lalonde E, Rentas S, Lin F, Dulik MC, Skraban CM, Spinner NB. Genomic diagnosis for pediatric disorders: revolution and evolution. Front Pediatr. 2020;8:373.

    Article  PubMed  PubMed Central  Google Scholar 

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This work was supported in part by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health, USA. Natalie Roberts assisted with editing and submitting the manuscript.

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WG leads the Undiagnosed Disease Network International. The UDNI’s specific goals are to (1) Improve the level of diagnosis and care for patients with undiagnosed diseases through the development of common protocols designed by a large community of investigators. (2) Facilitate research into the etiology of undiagnosed diseases, by collecting and sharing standardized, high-quality clinical and laboratory data, including genotyping, phenotyping, and documentation of environmental exposures. (3) Create an integrated and collaborative community across multiple clinical sites and among laboratory and clinical investigators prepared to investigate the pathophysiology of these newly recognized and rare diseases. AN is a Professor and Senior Consultant in Clinical Genetics. AN, LB and EP are co-leads of the Undiagnosed Disease Network International Diagnostic Working Group which aims to improve the equity of access to undiagnosed disease programs and share knowledge about successful approaches. LE and EP co-lead a newly established UDP at Sydney Children’s Hospital Network, and EC was the first medical student to participate in this new UDP.


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EC conducted the literature review and wrote the manuscript. EP and LE conceptualized the idea for the literature review. LE, RP, FT, LB, AN, WG and EP contributed personal insights from their experiences as part of Undiagnosed Disease Programs. All authors reviewed and provided input into the manuscript, and read and approved the final version.

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Correspondence to Elizabeth Emma Palmer.

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Curic, E., Ewans, L., Pysar, R. et al. International Undiagnosed Diseases Programs (UDPs): components and outcomes. Orphanet J Rare Dis 18, 348 (2023).

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