The clinical utility of genome sequencing is multi-dimensional: experience from the Hong Kong Genome Project

Abstract

Background

Assessing clinical utility of genome sequencing (GS) is essential for healthcare decisions. This study quantified the multidimensional utility of GS using the validated Clinician-reported Genetic testing Utility InDEx (C-GUIDE) within a diverse rare disease cohort at the Hong Kong Genome Project.

Methods

Adult and paediatric patients suspected of genetic disorders were recruited from the Hong Kong Children’s Hospital. Clinical geneticists evaluated GS utility based on 17 items. Total C-GUIDE utility scores and global item scores were calculated, with individual item scores ranging from −2 to 2.

Results

Between March and July 2024, three clinical geneticists completed 247 C-GUIDE ratings for 245 probands, with 25% receiving positive, 7% inconclusive, and 69% negative GS results. Total C-GUIDE scores ranges from −1 to 30, with a mean of 6.1 (SD = 10.0). Multivariate regression analysis indicates that positive GS findings are associated with a 16.9-point increase in C-GUIDE scores compared to inconclusive or negative results (p < 0.001). Notably, the highest mean scores are observed in psychosocial benefits for patients and families, regardless of GS results. The mean global item score, representing overall assessment of clinical utility, is 0.53 (SD = 0.06). Baseline patient characteristics are not independently associated with C-GUIDE scores.

Conclusions:

This study represents the first and largest of its kind in the Asia Pacific region, highlighting the multidimensional benefits of GS and the importance of nationwide Genome Projects. By highlighting that clinical utility is primarily influenced by test results rather than patient characteristics, this study underscores the importance of equitable GS implementation across populations.

Plain language summary

Genome sequencing (GS) is a next-generation sequencing technology that reads a person’s complete DNA, enabling the comprehensive detection of genetic variants related to diseases. Its value is often viewed in terms of its diagnostic benefits for patients, mainly through changes in their medical care. This study examined the broader impact of GS on patients involved in the Hong Kong Genome Project, the first large-scale GS project in Hong Kong which aims to enhance the clinical application of GS to benefits patients and their families. It reveals that the most significant benefits are related to emotional and social well-being, also known as psychosocial well-being, regardless of whether the GS results were positive, inconclusive, or negative. We found that the perceived value of GS is influenced more by the test results rather than by patients’ characteristics like age or gender. This highlights the potential for equitable access to GS for all individuals across diverse populations. By recognising these multi-dimensional benefits, we can enhance the implementation of GS in healthcare, ultimately improving patient support and well-being.

Chu, Chung et al. quantify the clinical utility of genome sequencing (GS) using the Clinician-reported Genetic testing Utility InDEx (C-GUIDE) in a diverse cohort from the Hong Kong Genome Project. Findings reveal the highest utility in psychosocial benefits, highlighting that the value of GS is driven more by test results than by patient characteristics.

Introduction

Advancements in genomic sequencing technologies have rapidly transformed the diagnostic trajectory and the broader landscape of precision medicine for rare disease patients. With the proven diagnostic capabilities of genome sequencing (GS), national genome projects have been launched worldwide, aiming to provide earlier genetic diagnoses for those affected by rare disorders^1–3. Emerging research has increasingly recognised the translational value of GS in enhancing clinical management and improving outcomes for rare disease patients and families. The test results, including positive, negative or variants of unknown significance (VUS), can significantly impact a clinician’s approach to differential diagnosis, either by confirming an existing hypothesis, providing additional strength to it, or offering reassurance by confirming or ruling out a suspected diagnosis. More importantly, this growing evidence base is increasingly recognised to be fundamental in informing funding and resource allocation, implementation strategies and policy development to better support patients and families within the rare disease community.

Evaluating the impact of GS on patients’ clinical management, commonly referred to as clinical utility, presents significant challenges. The definition and research methodologies surrounding clinical utility are highly heterogenous in the literature⁴. The American College of Medical Genetics and Genomics (ACMG) proposes a comprehensive definition of clinical utility for genetic tests encompassing a broad impact across multiple domains, including diagnosis, therapeutic management and prognosis for patients; the health and psychological well-being of patients and their family members; as well as the economic implications for healthcare systems⁵. While various frameworks have been developed to assist researchers in operationalising this multifaceted construct, most of the existing evidence measures clinical utility based solely on the percentage of individuals who experience any alternations to their clinical care as a result of receiving a genetic diagnosis, without providing further details into the strength and types of impact. In a systematic review by Shickh et al.⁴, genetic diagnoses triggered clinical management changes in 4–100% of the patients across 25 studies. Similar findings were reported in a meta-analysis by Chung et al.⁶, which illustrated changes in diagnosis-predicated clinical management in 2–100% of patients across 62 studies using ES/GS. The extremely broad range of strategies for measuring clinical utility across studies highlighted the heterogeneity in methodology and reporting, as well as the inconsistent definitions used in the literature, which precluded meaningful comparisons and conclusions. These findings underscore the need for a consistent approach to capturing clinical utility of GS beyond just quantifying the percentage of individuals with any changes to their clinical care.

More recently, Hayeems et al.⁷ developed the clinician-reported genetic testing utility InDex (C-GUIDE) as an index of items to operationalise the different components of value for diagnostic genetic testing from the clinician’s perspective. It is validated in multiple studies and generates a score that reflects the strength of post-test clinical utility across several aspects, including impacts on medication management, surveillance, recurrence risk, access to community or educational services and psychosocial benefits etc. Positive scores indicate benefits generated from the genetic test, while negative scores represent disutility. This allows comparison across populations and clinical indications using a standardised method^8–10. The application of C-GUIDE in Genome Projects will allow quantification and comparison of clinical utility of GS and may provide valuable insights across regions to inform healthcare resource planning and clinical adoption.

In the 7.5 million population in Hong Kong, the Hong Kong Genome Institute (HKGI) was established in May 2020 within the Health Bureau to spearhead the important work of integrating genomic medicine into mainstream healthcare by implementing the Hong Kong Genome Project (HKGP)¹¹. The HKGP is the first large-scale GS project in Hong Kong, which aims to identify disease-causing variant(s) in patients with undiagnosed disorders and hereditary cancers and enhance the clinical application of genomic medicine to benefit patients and their families with more precise diagnosis and personalised medicine. The current study leveraged this large-scale genome project to evaluate the clinical utility of GS on individuals recruited in the pilot phase of the HKGP using C-GUIDE, revealing the broad impact of GS across 17 utility items in a cohort of 245 paediatric and adult rare disease patients, and highlighting its multidimensional benefits irrespective of age, gender and GS results. Findings from the pilot phase serve to provide a foundation for clinical adoption of GS for better clinical care and outcomes for rare disease patients and families.

Methods

Ethics approvals were granted by the Central Institutional Review Board (IRB) (HKGP-2021-001, HKGP-2022-001 and HKGP-2023-002).

Settings

Clinical utility of GS was prospectively assessed through the HKGP based at a Partnering Centre of the HKGI, the Hong Kong Children’s Hospital (HKCH). In Hong Kong the Department of Clinical Genetics is exclusively based at the HKCH, providing care to both paediatric and adult patients.

Three meetings were held to ensure that all clinicians raters were informed of the study objectives and details. They were provided an information sheet that indicated survey completion constituted consent.

Sample and recruitment

Patients suspected with a genetic disease were recruited to the HKGP at the HKCH after receiving pre-test genetic counselling and providing informed written consent in accordance with the unique three-tier consent and assent model designed by the HKGI. The recruitment criteria for the HKGP were deliberately inclusive, ranging from monogenic cause to more complex aetiology.

Clinical geneticists who were involved in the management of probands recruited to the HKGP at the HKCH were eligible to complete the C-GUIDE ratings after disclosing GS results to their patients or their patients’ family members. Eligible cases included probands for whom (i) GS was performed as a diagnostic genetic test; (ii) GS findings were discussed and confirmed with the collaborative insight of referring clinicians and other genetic specialist; and (iii) GS results were reported directly to the patient or family members prior to completing the C-GUIDE rating. Cases that were considered ineligible for C-GUIDE ratings included family members who were recruited as part of cascade family testing, those with a known genetic diagnosis prior GS, and prenatal cases.

For all participants recruited to the HKGP, detailed phenotype information, including family history and symptom onset, was collected and recorded using human phenotype ontology terms at the pre-test counselling session to standardise clinical data input and facilitate genomic data interpretation. All sequencing was performed at the in-house laboratory based at the HKGI. GS was performed with the Illumina NovaSeq 6000 sequencing platform, and all generated data had undergone sequencing quality control checks followed by secondary and tertiary analysis using an in-house bioinformatics pipeline built upon standard open-source tools. Details of the sequencing workflow and bioinformatics pipeline have been described previously¹². An automated analytic pipeline was implemented to filter and prioritise rare and likely disease-causing variants that can potentially explain the proband’s primary clinical indication. Following variant prioritisation, candidate variants in genes with well-established gene-disease associations were investigated by HKGI genome curators, who are analysts trained with knowledge of genetic diseases and specialised in tertiary analysis, using an in-house pipeline and a commercial decision support system, Congenica. Pathogenicity of the variants were determined by the ACMG guidelines and up-to-date recommendations from ClinGen sequence variant interpretation (SVI) working group^13,14. Multi-disciplinary team (MDT) meetings (with a panel of members consisting of clinicians, clinical geneticists, laboratory scientists, genetic counsellors, genome curators and bioinformaticians) were conducted for variants that required further assessment of the phenotypic fit for the proband or for VUS findings that have a posterior probability of pathogenicity (Post_P) ≥ 0.675 according to the Bayesian classification framework¹⁵. With the consensus of referring clinicians, VUS were returned to participants if the variant was deemed to be likely attributable to the proband’s phenotype. Reportable positive (pathogenic/likely pathogenic) and VUS results were confirmed using orthogonal methods.

Data collection

Prior to data collection, the study team had multiple meetings with Prof Hayeems’ team (original developer of C-GUIDE) from the Hospital for Sick Children (SickKids), University of Toronto, to translate C-GUIDE to Traditional Chinese and Simplified Chinese and to contextualise the tool to the Hong Kong Chinese setting¹⁶. In particular, a formal process of forward and backward translation was conducted by two bilingual researchers from the study team as well as a translation agency. The aim of this process was not merely a word-for-word translation but rather one that accurately reflects the research context. Multiple meetings were conducted among the study team, translation agency, and Prof Hayeem’s team to identify, discuss and resolve the discrepancies, and modifications were made following the backward translation. Certain English terminologies (e.g. `differential diagnosis') were retained in brackets alongside the Chinese wording in the Chinese versions to ensure that these terms are clearly understood by clinician raters. Subsequently, the tool was reviewed by an expert panel of local paediatricians, clinical geneticists and genetic counsellors to ensure that its content and language were comprehensible to healthcare professionals serving as raters. Through rounds of discussion and revision, the C-GUIDE tool has achieved face validity and semantic equivalence. The reliability and validity of the Chinese versions will be reported separately in future studies.

After finalising the C-GUIDE translation, the study team had multiple meetings with the clinical team at HKCH and provided detailed explanation on the study objectives and training sessions for the clinician raters. Clinical geneticists who were directly involved in the management of HKGP probands completed a one-time baseline questionnaire as well as the C-GUIDE and characteristics for each case.

The baseline questionnaire collected clinician raters’ characteristics such as their clinical role and years of clinical experience. C-GUIDE captures utility of test results related to the primary indication for testing (17 items), and if applicable, secondary (9 items) and pharmacogenomic (4 items) variants as well⁷. Individual item scores in each section range from 0 to 2 while the question on psychosocial concern is scored from −2 to 0. Specifically item scores >0 indicate positive utility, <0 indicate negative utility (disutility) and item scores of 0 indicate no utility. Item scores are in ordinal scale and are not weighted, and they are summed to yield a total C-GUIDE score, which illustrates the overall strength of clinical utility. The C-GUIDE also captures a global item of utility, which is a single question on whether the GS results `did', `may', or `did not' change care provided to the patient or the family members. Disclosure details included two items on the disclosure of results (disclosure modality and time elapsed between reporting test results to the patient/family and completing C-GUIDE). Details of the case (e.g. age, gender, ethnicity, consanguinity, primary indication for testing, any prior genetic tests) were collected during pre-test counselling session by the genetic counsellor and stored securely in HKGI’s clinical information system database.

Data analysis

The characteristics of the rated cases were summarised using descriptive statistics. Disease categories were based on the most prominent phenotypes referred as the primary indication for testing. For each case, total C-GUIDE utility scores were calculated by summing the item scores. Scoring was based only on the core set of 17 C-GUIDE items on primary indication for testing and excludes secondary and pharmacogenomics findings, as secondary variants were not identified in any of the rated cases at the time of the study and pharmacogenomics analysis was conducted as a large population screening to enhance clinical management rather than on a patient-specific basis. To account for the correlation of ratings among individual clinician raters, a generalised estimating equation (GEE) regression model was used to determine the association between C-GUIDE utility scores and patient characteristics. Sub-group analyses for children (≤18 years old) and adults (>18 years old) were performed. The significance level was set at p < 0.05 for two tails. All statistical analyses were performed using Statistical software for data science (StataSE, version 18).

Results

Rater and patient characteristics

Between March and July 2024, three clinical geneticists completed 247 C-GUIDE ratings for a total of 245 probands. Two patients received dual diagnoses with C-GUIDE completed separately for each variant.

Among the three clinician raters, two are male and one is female. Two of the raters have over 20 years of clinical experience, while the third has between 6 and 10 years of experience.

The characteristics of the cases are summarised in Table 1. Among the 245 prospectively recruited probands, a total of 172 (70%) paediatric and 73 (30%) adult patients were rated, with the mean age being 17.9 years (SD = 16.7). The proportion of male (51%; n = 125) and female (49%; n = 120) probands is similar. The majority of rated cases are Chinese (95%). Clinical presentations are highly diverse, with the most common indication for GS being neurodevelopmental disorders (30%; n = 73), cancers (15%; n = 36), neurological disorders (11%; n = 26) and multiple congenital anomalies (MCA) (11%; n = 26). Two probands were deceased before results disclosure. Consanguinity in this cohort is uncommon (1%; n = 3).

Table 1.

Characteristics of rated probands (n = 245)

Characteristic		All rated patients (n = 245)
Gender	Male	125 (51%)
	Female	120 (49%)
Age at recruitment	≤18 y	172 (70%)
	>18 y	73 (30%)
	Mean (range)	18 y (12d–73 y)
Self-reported ethnicity and race	Chinese	233 (95%)
	Chinese with mixed ancestry	3 (1%)
	Othersa	9 (4%)
Known parental consanguinity		3 (1%)
Primary indication for testing	Cancer	36 (15%)
	Cardiovascular	8 (3%)
	Dermatological	5 (2%)
	Endocrine	4 (2%)
	Gastrointestinal	4 (2%)
	Growth disorder	5 (2%)
	Hearing or ear disorder	6 (2%)
	Immune	3 (1%)
	Multiple congenital abnormalities	26 (11%)
	Metabolic	9 (4%)
	Musculoskeletal	15 (6%)
	Neurodevelopmental	73 (30%)
	Neurological	26 (11%)
	Ophthalmological	12 (5%)
	Renal	9 (4%)
	Rheumatological	2 (1%)
	Urogenital	2 (1%)
Prior genetic tests	No	43 (17%)
	Yes	204 (83%)
Type of analysis	Singleton	27 (11%)
	Duo	20 (8%)
	Trio	176 (72%)
	Larger family	15 (6%)
	Others	7 (3%)
Results disclosure modality	In person	123 (50%)
	By phone	122 (50%)
GS results	Positive	61 (25%)
	Inconclusive	16 (7%)
	Negative	168 (69%)

GS genome sequencing, y years.

^aOthers include 3 White, 2 Pakistani, 1 Filipino, 1 Indian, 1 Nepalese, 1 Thai.

GS was performed for 176 trios (72%) 27 singletons (11%), 20 duos (8%), 15 larger family structures (6%) and 7 others (3%). Of the primary test results that were rated for the full sample 25% received positive GS results, 7% were inconclusive and 69% were negative. Results were reported to the family in person (50%) and by phone (50%).

C-GUIDE scores

C-GUIDE assessments were completed more than 4 weeks after results were disclosed to the patients or their families. The C-GUIDE scores ranges from −1 to 30 with a mean score 6.1 (SD = 10.0). Table 2 presents the mean C-GUIDE scores based on patient characteristics and GS results. The highest mean scores are observed in cases with positive GS findings (22.8; SD = 0.4), showing consistency across various factors such as gender, age group, ethnicity, indication for testing, prior genetic testing and type of analysis. Patients with positive GS results have significantly higher mean scores compared to those with inconclusive (22.8 vs. 4.1; p < 0.001) and negative (22.8 vs. 0.1; p < 0.001) findings. Moreover, mean scores are higher for patients with inconclusive results compared to those with negative findings (4.1 vs. 0.1; p < 0.001).

Table 2.

C-GUIDE scores by characteristics (n = 247)

Characteristic		Mean C-GUIDE score (n; SD) (n = 247)*
		Overall	Positive results (n = 63)*	Inconclusive results (=16)	Negative results (n = 168)
Gender	Male	5.7 (0.8)	22.1 (0.5)**	3.9 (0.8)	0.2 (0.0)
	Female	6.6 (1.0)	23.5 (0.5)**	4.5 (1.6)	0.0 (0.0)
Age at recruitment	≤18 y	5.8 (0.7)	22.6 (0.5)	4.5 (2.7)	0.1 (0.0)
	>18 y	7.0 (1.3)	23.2 (0.7)	2.0 (1.5)	0.0 (0.0)
Self-reported ethnicity and race	Chinese	6.2 (0.7)	22.8 (0.4)	4.1 (2.8)	0.1 (0.0)
	Others	4.3 (2.9)	25.5 (0.5)	N/A	0.0 (0.0)
Primary indication for testing	Cancer	7.5 (1.9)	24.6 (0.9)	N/A	0.0 (0.1)
	Cardiovascular	2.8 (2.8)	22.0 (N/A)	N/A	0.0 (N/A)
	Dermatological	9.2 (5.6)	23.0 (1.0)	N/A	0.0 (N/A)
	Endocrine	12.5 (5.8)	22.5 (1.5)	4.0 (N/A)	1.0 (N/A)
	Gastrointestinal	−0.3 (0.3)	N/A	N/A	−0.3 (0.3)
	Growth disorder	0.2 (0.2)	N/A	N/A	0.2 (0.2)
	Hearing or ear disorder	6.8 (3.4)	17.0 (1.0)	6.0 (N/A)	0.3 (0.3)
	Immune	0.0 (N/A)	N/A	N/A	0.0 (N/A)
	Multiple congenital abnormalities	2.2 (1.1)	21.5 (0.5)	5.0 (1.0)	0.1 (0.1)
	Metabolic	8.6 (3.8)	23.7 (1.5)	5.0 (N/A)	0.2 (0.2)
	Musculoskeletal	6.1 (2.6)	22.0 (1.0)	4.0 (N/A)	−0.1 (0.1)
	Neurodevelopmental	7.0 (1.2)	22.7 (0.6)	3.6 (1.1)	0.1 (0.1)
	Neurological	5.9 (1.9)	22.3 (1.7)	5.7 (2.9)	0.1 (0.1)
	Ophthalmological	12.0 (3.6)	23.8 (1.2)	0.0 (N/A)	0.2 (0.2)
	Renal	2.1 (2.0)	18.0 (N/A)	1.0 (N/A)	0.0 (N/A)
	Rheumatological	0.0 (N/A)	N/A	N/A	0.0 (N/A)
	Urogenital	12.5 (12.5)	25.0 (N/A)	N/A	0.0 (N/A)
Prior genetic tests	No	6.2 (1.5)	21.8 (0.9)	2.5 (1.5)	0.0 (0.1)
	Yes	6.1 (0.7)	23.1 (0.4)	4.3 (0.8)	0.1 (0.0)
Type of analysis	Singleton	3.2 (1.4)	20.5 (0.9)	3.0 (2.0)	0.0 (N/A)
	Duo	7.2 (2.5)	23.7 (1.5)	N/A	0.1 (0.1)
	Trio	6.8 (0.8)	22.8 (0.4)	4.5 (0.7)	0.1 (0.0)
	Larger family	5.1 (2.7)	24.7 (2.7)	0.0 (N/A)	0.3 (0.2)
	Others	0.1 (0.1)	N/A	N/A	0.1 (0.1)
Results disclosure modality	In person	12.1 (1.0)***	22.8 (2.9)	4.1 (2.8)	0.2 (0.1)
	By phone	0.5 (0.0)***	N/A	N/A	0.0 (0.0)

A two-sample t-test was used to compare the mean C-GUIDE score between two groups across the overall sample positive cases, inconclusive results and negative results: gender, age at recruitment, self-reported ethnicity and rare, prior genetic test and results disclosure modality.

Analysis of variance (ANOVA) was used to compare the mean C-GUIDE score among three or more groups to determine if at least one group mean is significantly different from the others: primary indication for testing, type of analysis.

C-GUIDE clinician-reported genetic testing utility InDEx, n number. SD standard deviation.

*Two patients received dual diagnoses, with C-GUIDE completed separately for each variant.

**Significantly different at p < 0.05 for two tails; p = 0.046.

***Significantly different at p < 0.05 for two tails; p < 0.0001.

For primary indication-related results, mean C-GUIDE scores are significantly higher among female probands as compared to male probands for those with positive GS results (23.5 vs. 22.1; p = 0.046). Regarding the primary indication for testing, the highest C-GUIDE scores are observed for endocrine disorders (12.5; SD = 5.8) and urogenital disorders (12.5; SD = 12.5). Probands where their primary indication for testing was MCA have significantly lower mean C-GUIDE scores as compared to those with non-MCA problems (2.2 vs. 6.6; p = 0.031). Higher C-GUIDE scores are also significantly higher among those who received GS results in-person compared to those who received results by phone (12.1 vs 0.5; p < 0.001). Mean scores do not differ significantly by gender, ethnicity, prior genetic tests and type of analysis.

C-GUIDE scores by item is reported in Supplementary Table 1. The highest mean C-GUIDE scores are seen in the item related to generating psychosocial benefits (item 16) for the patient and/or his/her family (0.98; SD = 0.04), with 78% of the patients or their families experiencing moderate to significant psychosocial benefits. The mean C-GUIDE scores for psychosocial benefits are 1.77 (SD = 0.06), 0.94 (SD = 0.06) and 0.68 (SD = 0.04) among probands with positive, inconclusive and negative GS results, respectively.

Among probands with positive GS results, the highest mean C-GUIDE scores are found in the item of facilitating access to or continuation of community or educational service (item 6; score: 2.0; SD = 0.0), followed by identifying support groups (item 8; score: 1.97; SD = 0.03) and providing recurrence risk information for the patient’s family (item 14; score: 1.97; SD = 0.03). Among probands with inconclusive GS results, the highest mean C-GUIDE scores are found in the item of generating psychosocial benefits for the patient and/or the family (item 16; score: 0.94; SD = 0.06), followed by providing information about the natural history of or medical issues associated with the patient’s condition (item 3; score: 0.69; SD = 0.12). Among probands with negative GS results, the highest mean C-GUIDE scores are found in the item of generating psychosocial benefits (item 16; score: 0.68; SD = 0.04), followed by avoiding further testing to identify a genetic diagnosis (item 4; score: 0.01; SD = 0.01). Psychosocial concern is experienced by 65% (n = 161) of probands and/or their families with the mean C-GUIDE scores being −1.35 (SD = 0.11), −0.75 (SD = 0.14) and −0.61 (SD = 0.04) among those with positive, inclusive and negative GS results.

The C-GUIDE global item score reflects the overall clinical utility rated per patient. The overall mean score is 0.53 (SD = 0.06). Among probands with positive GS results, the global item score is 1.94 (SD = 0.04). Among those with VUS and negative results, the global item score is 0.50 (SD = 0.16) and 0.01 (SD = 0.01), respectively. Among probands where global item ratings indicated that GS prompted better care (n = 62), the mean C-GUIDE score is 22.4 (SD = 4.6; range: 0–30). Where global item ratings indicated GS results may prompt better care (n = 8), the mean C-GUIDE score is 8.1 (SD = 5.5; range: 4–18); and where global item ratings indicated that test results did not change care (n = 177), the mean C-GUIDE score is 0.4 (SD = 1.7; range: −1 to 18).

Association between independent variables and C-GUIDE score

The associations between clinician raters and both the C-GUIDE scores (p = 0.214) and the global item score (p = 0.545) are not statistically significant. Similarly, years of clinical experience of the raters do not show a significant association with either the C-GUIDE score (p = 0.084) or the global item score (p = 0.270).

Using a GEE regression model to account for the correlation among clinician raters reveals that the global item score, positive GS results and probands with non-MCA phenotypes are significantly associated with higher C-GUIDE scores (p < 0.05), although primary indication for testing is deemed insignificant in multivariate regression analysis (Table 3).

Table 3.

Associations between C-GUIDE score, global item score, GS result type and baseline characteristics using GEE regression model

Factors	Crude coefficient	95% CI	P-value	Adjusted coefficient*	Adjusted 95% CI*	Adjusted P-value*
Global item score	10.96	10.53–11.38	<0.001	2.88	2.10 to 3.66	<0.001
GS results
Inconclusive/negative findings	–	–	–	–	–	–
Positive findings	22.39	21.84–22.94	<0.001	16.93	15.38–18.48	<0.001
Older age	−0.01	−0.09 to 0.06	0.754	−0.003	−0.02 to 0.02	0.773
Gender
Male	–	–	–	–	–	–
Female	0.89	−1.59 to 3.37	0.481	0.06	−0.38 to 0.50	0.794
Underwent prior genetic tests	−0.04	−3.31 to 3.24	0.982	0.32	−0.28 to 0.91	0.299
Type of analysis
Singleton	–	–	–	–	–	–
Duo/trio/larger family/others	3.21	−0.75 to 7.17	0.112	0.22	−0.78 to 1.22	0.667
Primary indication for testing
Non-MCA	–	–	–	–	–	–
MCA	−4.50	−8.50 to −0.49	0.028	−0.15	−0.88 to 0.58	0.687

C-GUIDE clinician-reported genetic testing utility InDEx, CI confidence interval, GEE generalised estimating equation, GS genome sequencing, MCA multiple congenital abnormalities.

^*Adjusted for each and all of the variables in the table.

Multivariate regression analysis reveals that a one unit increase in the global item score is significantly associated with a 2.9 increase in the C-GUIDE score (p < 0.001) (Table 3). Probands with positive GS results are associated with an increase of 16.9 in the C-GUIDE score as compared to those with inconclusive or negative results (p < 0.001).

Subgroup multivariate regression analyses for children and adults are consistent with the overall sample analysis.

Age, gender, clinical indication, sequencing strategy and prior genetic tests are not independently associated with unit changes in C-GUIDE scores in multivariate regression analysis.

Discussion

This study presents a comprehensive analysis of the multifaceted impact of GS on 17 clinical utility items in a cohort of 245 paediatric and adult rare disease patients, offering insights from the clinician’s perspective using the C-GUIDE tool. Notably, this study represents the largest cohort to date and the first in the Asia Pacific region that evaluates the clinical utility of GS through a standardised and validated measure, encompassing patients with positive, inconclusive and negative GS results.

Existing research has highlighted the challenges in drawing definitive conclusions regarding the clinical utility of GS, often relying on simplistic metrics such as the percentage of individuals whose healthcare management has been altered due to genetic diagnoses. However this approach may fall short in providing nuanced insights into the true nature and magnitude of these impacts^4,6,17. The absence of consistent definitions for clinical utility in research has further hindered the accuracy of assessing the impact of GS, ultimately diminishing the quality of evidence supporting the clinical benefits of this technology. By employing C-GUIDE as a singular validated metric to evaluate clinical utility, this study exemplifies the strength and efficacy of GS across 17 diverse clinical utility dimensions, revealing a mean score of 6.1 (SD = 10.0) with a range of −1 to 30 within a heterogenous patient population in Hong Kong, showcasing the multi-dimensional impact of GS. This aligns with the findings from other national programs and research studies such as the Genomics England 100,000 Genomes Project and the Australian Genomics initiative, which have illustrated the value of GS for patients and families through patient-reported outcome surveys^1,2. Notably, the mean and range of C-GUIDE scores align closely with findings from locations such as Huntsville and Birmingham in the United States, as well as in Toronto, Canada^8,9. In particular, Hayeems et al. reported a mean total score of 6.2 (range: −1 to 26; SD: 7.1) for primary variants across 103 rated patients in two US-based tertiary-care genetics settings, which is very similar to the results reported in the current study, indicating a consistent pattern of clinical utility across different geographical regions and healthcare systems⁸.

Understanding the factors that shape patient interpretation and counselling expectations is crucial for optimising the utility of genetic testing. In this study, a high rate of prior genetic testing (83%) among probands likely influenced their approach to the current GS testing. Patients may have heightened expectations for the new results, anticipating that they could yield different or more definitive insights compared to previous experiences. Such expectations can lead them to interpret new findings with a mix of hope and scepticism, prompting more focused discussions during counselling sessions with specific questions or concerns rooted in their past experiences. Additionally, the very low prevalence of consanguinity indicates that patients are less likely to encounter complex genetic inheritance issues, thereby simplifying the counselling process and impacting emotional responses. Recognising these dynamics is essential for enhancing the effectiveness of genetic counselling and improving patient care.

Unsurprisingly, probands with positive GS results exhibit significantly higher clinical utility compared to those with inconclusive and negative results, as demonstrated by the global item score and the multivariate regression analysis. This distinction is potentially explained by the much greater impact on traditionally evaluated clinical utility metrics, such as referral for surveillance and medication changes, which were only assessed among patients with positive genetic findings.

Remarkably, the highest C-GUIDE scores are observed in generating psychosocial benefits for the patients and their families (0.98; SD = 0.04) irrespective to their GS results, with 78% of patients or their families experiencing moderate to significant psychosocial benefits. This expansion of focus beyond the conventional assessment of clinical utility in previous studies underscores the importance of considering the broader impacts of GS.

For patients receiving positive genetic diagnoses, GS can profoundly affect their psychological well-being by offering clarity, empowerment and hope, reducing uncertainty and anxiety that may have persisted for years. While dual diagnoses can present complex clinical challenges requiring concerted multidisciplinary management strategies, they also offer significant benefits when navigated effectively, influencing not only clinical decisions but also psychosocial well-being for patients and their families. For instance, a 17-year-old male patient with complex multisystem phenotypes, including global developmental delay, autism spectrum disorder and neurological manifestations such as epilepsy and bilateral hand tremors that began at age 16, underwent extensive diagnostic tests over the years, yet his diagnosis remained elusive. He was referred to the HKGP, and subsequent GS identified a homozygous pathogenic variant in MBOAT7 and a heterozygous likely pathogenic variant in MFN2, confirming a dual diagnosis of autosomal recessive intellectual developmental disorder 57 (OMIM: 617188) and autosomal dominant Charcot-Marie-Tooth disease type 2A2 (OMIM: 609260). The dual genetic diagnosis resolved the patient’s diagnostic odyssey by explaining his complex phenotypes as the result of two conditions. This finding made it clear that a single disease could not account for his overlapping neurodevelopmental and neurological issues, which now required a coordinated multidisciplinary effort. Management required careful coordination among clinical geneticists, neurologists and genetic counsellors to formulate a safe, effective and patient-specific care strategy. This included ensuring pharmacological safety by avoiding medications such as vincristine, isoniazid and nitrofurantoin, known to cause nerve damage. The findings also indicated the need for ongoing surveillance to monitor disease progression and developmental outcomes, while highlighting the need to mitigate exacerbating factors such as obesity, alcohol use and malnutrition. Additionally the findings provided valuable recurrence risk information for the family, prompting further genetic testing for relatives and offering psychosocial support to alleviate anxiety related to the dual diagnoses while providing treatment plans for the patient’s family. This approach was essential to translate the complex genomic findings into a holistic and effective management plan for the patient and his family.

Importantly, the psychological benefits of GS perceived by the clinical geneticists raters are not limited to patients with positive diagnoses but also extend to those with inconclusive or negative results. Notably, this study reveals that patients with inconclusive and negative GS results also experienced psychosocial benefits (mean C-GUIDE score of 0.94 and 0.68). For individuals with VUS results, GS can provide direction and hope, potentially alleviating anxiety and uncertainty. Similarly, in cases where GS yields negative results, the comfort derived from excluding certain genetic conditions can be profoundly reassuring, alleviating ongoing health concerns and fostering peace of mind. Patients often report a sense of closure upon receiving a negative result which can reduce the perceived need for additional testing. In particular, patients and parents in a previous study found solace and hope in negative results, ceasing their active pursuit of a genetic diagnosis¹⁸. Importantly, these psychological benefits highlight the holistic impact of GS, extending beyond diagnostic certainty to encompass emotional support and enhance well-being across a spectrum of genetic testing outcomes. This is especially relevant in the context of translational omics where the emotional impact of test results plays a crucial role in patient management and influences clinical decisions regarding the pursuit or avoidance of further diagnostic testing.

Additionally beyond the psychological impact on patients and their families, high mean C-GUIDE scores among patients with positive GS results are observed in facilitating access to or continuation of community or educational services (score: 2.0; SD = 0.0), identifying support groups (score: 1.97; SD = 0.03) and providing recurrence risk information for the patient’s family (score: 1.97; SD = 0.03). These positive outcomes which extend beyond the traditional clinical management changes, underscore the broader societal implications of GS and emphasise the need for a comprehensive evaluation that considers its effects across clinical, psychological, familial and societal domains¹⁹. In addition to the valuable insights gained from clinician assessments to further enrich the understanding of the impact of GS on patients and families, the HKGP has also collected data from patients and caregivers through validated patient-reported measures, such as the hospital anxiety and depression scale (HADS), the perceived stress scale (PSS-4), the EQ-5D and the client service receipt inventory for the rare disease population (CSRI-Ra)²⁰. Results from the outcome evaluation of HKGP will be reported separately in another publication.

The significant difference in clinical utility between in-person and phone-based disclosures can be attributed to the nature of GS findings associated with each approach in this study. In the Hong Kong clinical setting, phone-based disclosure of GS results is becoming increasingly common, particularly for negative results and for positive results that align closely with clinical presentations and diagnoses. This method allows healthcare providers to facilitate timely discussions, addressing any questions or concerns participants may have, thereby improving patient satisfaction and managing anxiety related to prolonged uncertainty. However, the majority of patients opting for phone-based disclosure in this study were those receiving negative results. While this approach aims to enhance communication efficiency, especially given the lengthy waiting times often experienced in clinical settings, patients with positive GS results often prefer in-person consultations and counselling to discuss further clinical follow-up actions. This preference may stem from several factors, including the anticipated clinical interventions associated with a positive diagnosis and the psychological support afforded by face-to-face counselling sessions. Such interactions can provide reassurance and allow for a more nuanced discussion of the implications of the results. Looking ahead, the implementation of phone-based disclosure is expected to evolve gradually within the Hong Kong clinical setting. It may take additional time for patients with positive findings to become comfortable with this approach. Factors such as cultural attitudes toward communication, the perceived importance of personal interaction in healthcare, and the need for emotional support will play crucial roles in shaping the acceptance of phone-based disclosure among this patient population. Future result disclosures may integrate aspects of telemedicine to enhance the quality of interactions and facilitate more comprehensive discussions about results, particularly when additional context is necessary. As understanding of patient preferences evolves, there will be a concerted effort to enhance the clarity and sensitivity of communications to meet the diverse needs of the patient population.

The absence of independent associations between age, gender, clinical indication, sequencing strategy and prior genetic tests with unit changes in C-GUIDE scores, as revealed by the multivariate regression analysis and subgroup analyses, suggests that baseline characteristics do not significantly impact the strength of clinical utility measured by the C-GUIDE scores. While previous studies have demonstrated that age, clinical indications and type of analysis, particularly in paediatric patients, those with neurological indications and trios, tend to have higher diagnostic yields^6,17, their influence on clinical utility appears to be limited. In our current study, considerable variability in clinical utility is observed across different clinical indications, with notably higher scores in probands with endocrine (12.5) and urogenital (12.5) conditions, likely due to their well-defined phenotypes, clearer diagnostic pathways and established treatment options. However, these findings should be interpreted cautiously due to the small sample sizes (n = 4 and n = 2, respectively). In contrast, lower clinical utility among MCA cases (2.2) may be attributed to diverse and overlapping symptoms that complicate definitive diagnosis, reflecting greater diagnostic ambiguity and counselling challenges inherent in these complex cases. Enhanced support and tailored counselling strategies will be essential to address these challenges and improve the utility of genetic testing in these situations. Nevertheless, the multivariate regression analysis indicates that clinical indications do not significantly influence clinical utility. Additionally, variations among raters and years of clinical experience have minimal impact on clinical utility within this cohort. Instead, the type of GS results emerges as the pivotal factor shaping clinical utility. This finding underscores the potential for GS to be implemented equitably across diverse populations irrespective of demographic variables or the specific clinical contexts. By emphasising that the clinical utility of GS is predominantly shaped by the test outcomes rather than inherent patient traits or rater characteristics, this study highlights the potential for genetic testing to offer valuable insights and benefits across a wide range of individuals and clinical scenarios.

Several limitations are acknowledged. First, the small sample sizes for certain baseline characteristics hindered the testing of hypotheses regarding their effects on C-GUIDE scores. For instance, the limited representation of some primary indications for testing constrained the assessment of how these variations may be associated with strength of clinical utility. Second, the study did not evaluate the impact of secondary variants and pharmacogenomic findings on C-GUIDE scores. Further evaluation of secondary and pharmacogenomic findings in the local setting will facilitate a better understanding of the clinical utility of these additional GS capabilities. Lastly, the C-GUIDE ratings were based on clinical notes recorded over four weeks after results disclosure, despite previous studies indicating the need for a more extended evaluation period to capture delayed management changes. In particular, a longer follow-up period of 6–12 months or more may reveal additional clinical utility, especially in areas such as surveillance, care planning and accessing community or educational services that may not have been immediately available. Future research is warranted to explore the differences between real-time and retrospective assessments of clinical utility, offering insights into both short-term and long-term impacts of GS.

Conclusion

This study demonstrates that GS achieves clinical utility, defined as a multi-dimensional construct, within a diverse cohort of rare disease patients. By elucidating the broader implications and holistic benefits of GS beyond conventional metrics, this study sheds light for a comprehensive and nuanced approach to the evaluation of genetic testing outcomes in clinical practice. The implementation of a standardised tool for assessing clinical utility will facilitate comparisons across populations, offering valuable insights for healthcare planning and the enhancement of patient outcomes within the rare disease community.

Author contributions

A.T.W.C., C.C.Y.C. and B.H.Y.C. contributed to the conception and design of the study. A.T.W.C., C.C.Y.C., R.H., S.L. and B.H.Y.C. led the translation and contextualisation of the C-GUIDE tool for the HKGP. C.C.Y.C., H.M.L., S.S.W.C. and B.H.Y.C. carried out acquisition, analysis, or interpretation of data. C.C.Y.C. conducted the statistical analyses and was involved in data organisation and presentation. C.C.Y.C. drafted the manuscript. A.T.W.C., H.M.L., S.S.W.C., R.H., S.L. and B.H.Y.C. critically reviewed the manuscript with suggestions for improvement and revision. A.T.W.C. and B.H.Y.C. oversaw and supervised the project. All authors contributed to the overall data interpretation, reviewed and approved the final draft for submission.

Peer review

Peer review information

Communications Medicine thanks Zornitza Stark, Matheus Wilke and the other anonymous reviewer for their contribution to the peer review of this work.

Data availability

Variants identified in the diagnostic cohort were uploaded to ClinVar in batches. (https://www.ncbi.nlm.nih.gov/clinvar/submiTers/510250/). Deidentified individual-level genotype of variants associated with the diagnostic cohort presented in this manuscript and additional aggregate-level data not included in the manuscript is currently available to researchers upon reasonable request by following these steps: 1. Researchers should submit a Data Access Request to HKGI (hkgi_gc_team@genomics.org.hk) outlining the proposed research, including its purpose, scope of data to be accessed and researcher information. 2. The HKGI Data Access Review Panel will review the application in a quarterly meeting to assess the scientific, clinical, technical, resource and regulatory feasibility of the proposal. All feasible proposals will be approved. 3. The HKGI team will collaborate with applicants to prepare the formal proposal and related Institutional Review Board (IRB) documentation. 4. Anonymous, aggregate data will then be provided to applicants either directly or within designated HKGI facilities (for 3–12 months), depending on the assessment of the proposal. The same application process also applies to other individual-level genomic data beyond this manuscript. As the HKGP is actively recruiting new participants at the time of writing, access to such data will be granted to external researchers after the completion of the main phase of this project in 2030. The datasets related to C-GUIDE scoring generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains Supplementary material available at 10.1038/s43856-026-01441-9.