Clinical Strategies in Gene Screening Counseling for the Healthy General Population

Article information

Korean J Fam Med. 2024;45(2):61-68
Publication date (electronic) : 2024 March 20
doi :
1Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
2Department of Surgery, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
*Corresponding Author: Eun Kyung Choe Tel: +82-2-2112-5500, Fax: +82-2-2112-5794, E-mail:
Received 2023 November 17; Accepted 2023 November 26.


The burgeoning interest in precision medicine has propelled an increase in the use of genome tests for screening purposes within the healthy population. Gene screening tests aim to pre-emptively identify those individuals who may be genetically predisposed to certain diseases. However, as genetic screening becomes more commonplace, it is essential to acknowledge the unique challenges it poses. A prevalent issue in this regard is the occurrence of falsepositive results, which can lead to unnecessary additional tests or treatments, and psychological distress. Additionally, the interpretation of genomic variants is based on current research evidence, and can accordingly change as new research findings emerge, potentially altering the clinical significance of these variants. Conversely, a further prominent concern regards false assurances in genetic testing, as genetic tests can yield false-negative results, potentially posing a significant clinical risk. Moreover, the results obtained for the same disease can vary among different genetic testing services, due to differences in the types of variants assessed, the scope of tests, analytical methods, and the algorithms used for predicting diseases. Consequently, whereas genetic testing holds significant promise for the future of medicine, it poses unique challenges. If conducted without a full understanding of its implications, genetic testing may fail to achieve its purpose potentially hindering effective health management. Therefore, to ensure a comprehensive understanding of the implications of genetic testing within the general population, sufficient discussion and careful consideration should be given to counseling based on gene test results.


Regular health screening involving a comprehensive health check-up test may detect a disease or pre-morbid condition at an early stage, at which preventative measures or early intervention can be taken. These screening strategies typically consist of comprehensive laboratory, imaging, and endoscopic tests, and can distinguish populations at high risk with a current disease or with a potential risk of developing a disease [1]. In this context, there has recently been an increasing trend in the application of gene tests as a further screening tool to prevent or diagnose diseases more proactively, by identifying those individuals who may be at a genetically high risk before the onset of a disease [2,3].

Genetic testing is widely used in the clinical diagnosis of diseases, predicting prognoses, and determining treatment methods in cancer [4] or rare diseases [5], and with ongoing advances in whole-exome and whole-genome sequencing technologies, such testing is being more active and broadly adopted [6-8]. In tandem with this trend, there is an increasing interest in performing genome tests for screening in the healthy population, and the number and scope of tests conducted are steadily expanding [3]. However, many health check-up institutions are conducting genetic tests and associated counseling, there are currently insufficient protocols or guidelines in place for counseling healthy people, as opposed to those who have already developed diseases. In the medical field, there is still no clear consensus as to the necessity for gene screening and counseling among the general population. However, given that testing is increasingly being adopted in the healthcare industry, it is necessary to prepare measures, even if we postpone discussing the medical and academic validity and evidence-based justification for such testing.

In this review paper, rather than dealing with opportunistic gene screening that is secondarily performed in incidence-based testing, we will instead consider the clinical significance of population gene screening (commercial gene test services) conducted on healthy individuals for preventive medicine in the public health context of the general population, and highlight the necessary precautionary measures for clinical application from the perspective of gene counseling. The cases described in the paper are primarily taken from the South Korean healthcare system.


With the ongoing growth in the number of genetic tests performed in clinical practice, many secondary genetic findings, unrelated to the designated targets of such tests, have been revealed, leading to considerable discussion regarding the clinical interpretation of specific genes. To address this issue, in 2013, the American College of Genetics and Genomics (ACMG) provided guidelines and recommendations for action condition reports based on these secondarily discovered genes [9], a subsequent update of which was made in 2016 [10].

Currently, in the United States, most institutions that conduct genetic analysis report on secondary findings that correspond to actionable risk variants based on the ACMG’s guidelines when conducting genome sequencing [11]. These secondary findings serve as opportunistic screening for patients undergoing tests for other purposes, and it is believed that early detection of actionable risk variants can make an important contribution to disease prevention. Additionally, it has been proposed that obtaining such secondary findings would be potentially beneficial and applicable for healthy individuals, and consequently, there has been a gradual burgeoning in the concept of genetic health screening among healthy subjects. This should be considered indicative of a move toward broadening the application of genomics in the context of precision and personalized medicine for health promotion and disease prevention among the general population.

The healthcare industry can be driven and influenced by providers, and given the considerable size of the healthcare market, there is believed to be substantial scope for genomic sequence analyses, and many companies are gradually seeking to offer genome sequence services to healthy individuals [12]. In the United States, a service called “23AndMe” has been a pioneer in this area [13], and this has in turn had the effect of increasing supplier-induced demand, a phenomenon whereby customers seek more of a given product or service as a result of the healthcare industry increasing the production of a particular product or service [14]. Accordingly, gene screening services, previously offered only to patients, are now gradually being made available to the healthy population. This supplier-induced demand for gene screening has gained momentum in line with consumer interest and need, along with a BRCA testing-related incident highlighted in an editorial by Angelina Jolie published in the New York Times in 2013 [15], in which she sparked widespread interest in genetic testing by describing her own BRCA test results and the story of her preventive mastectomy [16]. A subsequent study revealed an increase in the frequency of BRCA gene testing as a consequence of this editorial [17]. Given the prominence of these social and industrial phenomena, it is widely anticipated that the demand for gene screening tests for the healthy population will progressively expand in the near future.

In recent years there has been a shift in emphasis in modern medical care from predominantly therapeutic medicine to a more preventive approach [18], which accordingly necessitates the identification of individuals who are vulnerable to a given disease or disorder, for which genetic tests are the most appropriate diagnostic tools. In the past, gene tests were recommended based on pedigree analysis of individuals with a high likelihood of vulnerability, those with a family history of a particular disease, or targeted individuals who were suspected to have an underlying genetic condition based on clinical symptoms. More recently, however, greater emphasis has been placed on identifying vulnerable individuals via genetic testing among healthy population groups, for whom there is no suspicion based on family history and no clinically significant findings. However, the use of genome sequencing as an approach for screening healthy individuals is still considered somewhat controversial [19,20]. This lack of consensus relates to the question of whether the clinical application of information on secondary actionable genes obtained by performing gene tests for a specific purpose (i.e., opportunistic screening) also has relevance for population screening in the context of preventive medicine in healthy individuals.


1. Dependence on Genome Type

With ongoing advances in gene analysis technology, there is an increasing attempt to exploit information inherent in different forms of biomolecules, including the genome [21], epigenome [22], transcriptome [23], proteome [24], metabolome [25], and microbiome [26], to predict diseases or health conditions.

In this regard, the most common types of biomolecule currently used in population gene screening are single-nucleotide polymorphisms (SNPs) associated with the traits of interest [27]. The genetic test services using SNPs vary for the types and numbers of SNPs selected by different genetic analysis companies, and there are differences regarding the algorithm technology used to predict traits based on the selected SNPs [28].

Recently, algorithms based on polygenic risk scores, which use multiple SNPs, as opposed to selecting only a few specific SNPs, have been attracting attention [29]. Additionally, there have increasing attempts to apply multi-omics technology that incorporates a comprehensive combination of diverse genomic information, including that relating to DNA, RNA, and methylation [30]. Moreover, different combinations of genomic and prediction algorithms are continually being developed, which will inevitably contribute to enhancing trait prediction performance. Accordingly, when selecting a specific genetic test, it is necessary to establish and evaluate the type of genome for which the test was designed and how the algorithm for predicting traits was developed [31].

2. Dependence on the Target Type

Genetic tests can be broadly classified with respect to the category of target. Genetic tests for diseases include those for chronic diseases (e.g., diabetes and hypertension), malignant diseases (e.g., colorectal and lung cancer), and a diverse range of other diseases, including retinal degeneration and endometriosis. Such traits are of the highest interest for genetic testing during health screenings [32-34].

A second class of traits comprises individual characteristics, including physical characteristics (e.g., obesity), appetite, satiety, nutrient deficiency, appropriate exercise (for identifying which strength and aerobic exercises might be genetically appropriate), and hair characteristics [33]. The genetic tests for these individual characteristics are being used as accessories for establishing health promotion plans through lifestyle interventions, and attempts are being made to introduce these in the food, cosmetics, and body management industries [35]. Moreover, they are increasingly being employed in healthcare institutions, including obesity and nutrition clinics [36].

Pharmaco-genomic genetic testing is intended to determine the type and dosage of drugs based on genetic characteristics, to ensure the effective and safe use of medications. This enables the tailoring of drugs to the individual, based on the evidence that the effect of a specific drug may not be sufficient depending on the genetic characteristics, or that there is a potential for prominent side effects [37].

A fourth category of genetic tests comprises those that can be used to trace ancestry. Although this type of testing is not actively conducted in South Korea, in countries, such as the United States, with populations of multiple races and mixed ethnicities, there is a heightened interest in determining ancestry and these tests are accordingly widely conducted [32,38].

3. Dependence on Whether Genetic Testing Takes Place under Prescription or the Guidance of a doctor in a Medical Institution

Genetic testing is conducted for the prevention, diagnosis, or treatment of diseases, under circumstances in which doctors in medical institutions determine the necessity of testing during the treatment process, and prescribe the necessary tests. Typically, tests are therefore conducted after the doctor has thoroughly explained the genetic test and obtained an appropriate written consent. Contrastingly, direct-toconsumer (DTC) genetic tests are those that consumers can take directly without visiting a medical institution [39]. A pioneer in this field has been the 23andMe company [40]. Currently, in Korea (as accessed on November 13th, 2023), the “Bioethics and Safety Act” stipulates that such genetic testing may be permitted for personal wellness issues, which include nutrition, exercise, skin, hair, eating habits, personal characteristics (e.g., alcohol metabolism, nicotine metabolism, sleep habits, and pain sensitivity), health management (e.g., osteoarthritis, motion sickness, uric acid level, and body fat percentage), and lineage (ancestry tracing), totaling 56 items [41,42]. Any genetic tests conducted for purposes other than these 56 specified items, along with those for the diagnosis or treatment of diseases, or other medical purposes, can only be performed under the guidance of a medical institution. However, the uses permitted for DTC are continuously being amended, and consequently, it is necessary to establish whether a specific type of genetic testing can be legitimately performed by genetic testing institutions other than medical institutions, according to the laws of individual countries [43].


To understand the nature of genetic testing, it is beneficial to gain at least a rudimentary knowledge of genes and genetics. Most of the currently developed genetic tests used for gene screening in the healthy population are based on the use of SNP markers. DNA comprises four types of bases, abbreviated as A, G, T, and C, each of which can undergo mutation, insertion, deletion, and other changes, and thereby give rise to different phenotypes and diseases [44].

1. Single-Nucleotide Polymorphisms

An SNP is essentially a type of mutation, in that it is a modification of a single base, although whereas a mutation can be considered an exceptional (or pathological) phenomenon, SNPs are generally a more common phenomenon, as the term “polymorphism” tends to imply. As a general rule of thumb, if the frequency of a rare allele (version) of a single base position exceeds 1% in the total population, it can be defined as an SNP, whereas if the frequency is less than 1%, it can be defined as a mutation. Among SNPs, those with an incidence frequency of at least 5% are considered common variants, and those with frequencies of between 1% and 5% are defined as rare variants [45-47].

2. Effect Size

The frequency of an allelic variant in the population and the degree of disease occurrence, that is, the effect size of the allele, can be divided into several categories. Low frequency variants can have a relatively large effect size, in that they can give rise to a meaningful phenotype. Contrastingly, the SNPs used in healthy population screening are typically commonly observed at a frequency of more than 5% among the general population, although the size of the effects associated with these variants tends to be small [48,49].

3. Penetrance

Even if individuals harbor the same variant, a few may show severe forms of a disease, whereas others might have negligible phenotypic manifestations, a phenomenon referred to as variant penetrance. Variants associated with disease and traits are typically discovered via genome-wide association studies (GWAS) and are generally characterized by low penetrance at common frequencies [50].


1. Subjects Should Be Informed of False-Positive Results

One of the most concerning issues regarding population gene screening is the occurrence of false-positive results. If such results are obtained, unnecessary additional tests or treatments may be performed, thereby heightening the likelihood of morbidity that would otherwise not have occurred. Moreover, such outcomes can represent a significant source of anxiety among the concerned individuals. This problem is exacerbated by the fact that the percentage of false positive results obtained during population gene screening is relatively high, particularly in the case of SNPs used by population gene screening services, as has been discovered by GWAS. GWAS is an association testing procedure for a phenotype of interest based on an analysis of hundreds of thousands of SNPs, and given that it involves multiple simultaneous comparisons, this inevitably increases the likelihood the possibility of type I errors [51]. Accordingly when performing GWAS analysis, issues arising from such multiple comparisons should be sufficiently supplemented through appropriate statistical measures [52]. Moreover, before conducting genetic testing, individuals should be made fully aware that a positive test result could be a false-positive outcome.

2. Genomic Variant Identity: Re-evaluation of Clinical Significance in Research

The identity of genomic variants is based on research evidence at the time reported, although clinical significance may need re-evaluation in light of subsequent research findings. Among the most important challenges when assessing genetic test results in actual clinical practice is that of variant interpretation [53]. As a consequence of continuing research on genomic variants, it is frequently necessary to update previous interpretations for particular variants. Moreover, even if a given variant is accurately interpreted, it may not necessarily manifest as a disease condition. Many variants, considered to be pathogenic based on initial research evidence, could, in the light of subsequent evidence, be re-evaluated as being benign.

3. Consideration of the Penetrance of Genomic Variants

When a certain variant is present in different individuals, it is said to have an incomplete penetrance rate if the associated clinical phenotype is expressed in some individuals but not others [53]. This differential outcome can be attributed to many different factors, including the influence of regulatory SNPs, epigenetics, environmental factors, and lifestyle [54]. Accordingly, it is important to explain the concept of penetrance during the counseling of individuals who receive positive test results.

4. Beware of False Assurances

In genetic testing, false-negative results are not infrequent and can pose a significant clinical risk. Even if there is a variant associated with a specific phenotype or disease, its relevance may not be fully established depending on the current research status. Consequently, a false-negative finding may occur in the case of variants for which the pathogenicity might not be established until a later date. Moreover, depending on the manufacturer of the provided service, genetic testing services can differ widely with respect to the types of variants used, scope, analytical methods, and algorithms for predicting diseases (disease risk estimation algorithms). Consequently, there is a possibility that test results obtained for the same disease may differ when using tests provided by different companies, as has been highlighted by a study that compared the test results obtained by the 23andMe service and two commercial genetic-testing services provided in Korea [28]. Among the three services, there were cases in which different interpretations of relative risks were obtained for the same disease. Moreover, in the case of lung cancer, there were cases for which opposite test results were obtained, with associated relative risks ranging from 0.9 to 2.09. These discrepant outcomes can be attributed to the types of SNP used and differences in the applied algorithms. Furthermore, most genetic tests involve models based on GWAS results, and in many GWAS, research is often conducted with a well-defined and sufficient number of case-control groups, whereas there are many cases in which multiple comparisons and ethnical genetic differences are not taken into consideration [55]. Consequently, this can adversely influence the selection of optimal SNPs for specific diseases. Thus, even if a negative test result is obtained for a specific disease, tested individuals should be made aware of the fact that a negative finding does not necessarily imply that the genetic test result is 100% negative and that fundamental screening tests and preventive healthy lifestyle necessary in the average population should be continued.


When introducing genetic testing at healthcare institutions, it is necessary to review and consider different circumstances when selecting from a range of genetic test products offered by genetic analysis companies.

Firstly, it is necessary to determine the characteristics of the study population, research which served as a basis for the development of a given genetic test product. Generally, the variants applied in test algorithms have a relatively low effect size, and accordingly, relevant studies should be conducted in a population group of a certain minimum size [48,49]. Moreover, given the differential effects of genes among different races, it is necessary to confirm whether there has been a study conducted targeting a particular ethnic group [56].

Secondly, it is worthwhile establishing whether replicate research has been conducted in a group that differs from the target group used in the initial product development, which will assist in assessing the reliability of the product [57].

Thirdly, preparations are necessary regarding the type and scope of interventional or health management strategies and action plans that should be available after a genetic test has been conducted and the results obtained. If evidence of a high risk is detected in a genetic test, educational materials should be prepared describing how the condition can be managed, and the types of tests that would facilitate followup observations.

Fourthly, in the context of genetic testing among healthy individuals, numerous social and ethical aspects should ideally be taken into consideration. Among these, one of the most important issues is that of genetic discrimination, which relates to possible prejudicial treatment in areas such as insurance [58], employment [59], healthcare [60], and marriage [61], based on the outcome of genetic testing [62]. In this regard, genetic testing of healthy individuals is not aimed at diagnosing diseases but instead seeks to identify genetic factors that may or may not occur in a given individual’s lifetime. If this leads to discrimination against an individual based on hypothetical characteristics, this information could be misused socially. Thus, it is necessary to ensure that the testee is fully informed of such consequences before undergoing a test. In this regard, thorough personal information management for gene test results is necessary to prevent the sharing of test results with anyone other than the individual concerned.

Currently, many countries manage genetic testing at the national level, regardless of its medical utility or validity [41,61,63]. In the case of South Korea, according to the “Bioethics and Safety Act” (accessed on November 13th, 2023), there are several genes for which gene screening in the healthy population is currently prohibited, among which are the Mt5178A gene associated with longevity and the SLC6A4 gene linked to violent behavior [41,42,61,63]. Similarly, tests for the BRCA1 or BRCA2 genes associated with breast cancer are currently prohibited, although are permitted if a doctor in charge of treatment determines that individuals belong to a high-risk group for the disease, or if there is a definite diagnosis of the disease. Testing for the dementia-associated apolipoprotein E gene is likewise currently prohibited, although exceptions may be made if an adult is suspected of having the disease, or if a doctor in charge of treatment determines that they belong to a high-risk group. In this context, however, it is necessary to keep updated as to the changes in legal amendments to establish current national restrictions regarding genetic testing.


The outcome of genetic testing is a source of considerable anxiety among the affected individuals, whereas conversely, those receiving favorable outcomes may be overly reassured and lose sight of the perceived need for regular health screenings. For individuals who undergo genetic testing, the following three points must be thoroughly explained beforehand to fully accomplish the purpose of the genetic test and to prevent issues arising from any misunderstandings regarding tests.

1. If the Genetic Risk Level for a Certain Disease Is Deemed High Based on Current Test Results, Does That Mean This Disease Will Occur in the Future?

No. The genetic tests conducted during screening are not designed for diagnostic purposes but are instead a means of evaluating genetic factors for susceptibility to certain diseases. Even if test results indicate a high genetic risk, this does not imply that the disease will occur with 100% certainty. Multiple factors contribute to the development of a disease, including modifiable environmental factors. Consequently, given this scenario, we would recommend improvements in lifestyle habits based on management guidelines for the disease, in conjunction with regular examination-based check-ups.

2. Has This Test Comprehensively Assessed the Genes Associated with a Certain Disease?

No. The genetic variants underlying a given disease are often quite diverse, and among these variants, tests typically target only a small selection, testing for certain genes (or SNPs) that are known to be meaningful based on current research. The assessed variants may differ depending on the current level of scientific advancement and the gene test service company. Consequently, as science advances, the types of genetic tests offered may change, as may the interpretation of genetic test results.

3. Does a Test Result Indicating a Low Genetic Risk Imply That There Is No Likelihood of Developing the Disease in the Future?

No. Genetic tests do not provide comprehensive coverage of all potential genetic variants. Moreover, apart from genetic factors, additional factors, such as environmental factors, lifestyle habits, age, gender, and physical characteristics, all contribute to varying extents in disease development. Accordingly, even if test results indicate a low genetic risk, the occurrence of a disease may be influenced by many non-genetic factors that are not covered by specific tests, including interactions with other pathological conditions and environmental factors. Consequently, regardless of the test results, individuals should continue to make fundamental healthcare efforts, involving lifestyle modifications and ensuring they undergo regular health examinations.


Regardless of issues pertaining to the accuracy and utility of genetic tests per se, it is necessary for healthcare institutions to thoroughly consider and prepare for the introduction of genetic testing among individuals in the healthy population, given the increasingly high demand among consumers. Genetic testing is suitable for personalized treatment and preventive intervention, based on prediction before the onset of a given disease using genetic information. However, if a test is conducted without a comprehensive understanding of its uniqueness, given that such tests are targeted at healthy individuals, they may not fully achieve the intended purpose, and may even have little or no benefit from the perspective of health management. Consequently, we highlight the need for sufficient discussion and consideration within medical institutions before the introduction of general genetic testing.



No potential conflict of interest relevant to this article was reported.


1. Lee C, Choe EK, Choi JM, Hwang Y, Lee Y, Park B, et al. Health and Prevention Enhancement (H-PEACE): a retrospective, populationbased cohort study conducted at the Seoul National University Hospital Gangnam Center, Korea. BMJ Open 2018;8e019327.
2. Murray MF, Giovanni MA, Doyle DL, Harrison SM, Lyon E, Manickam K, et al. DNA-based screening and population health: a points to consider statement for programs and sponsoring organizations from the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021;23:989–95.
3. Foss KS, O’Daniel JM, Berg JS, Powell SN, Cadigan RJ, Kuczynski KJ, et al. The rise of population genomic screening: characteristics of current programs and the need for evidence regarding optimal implementation. J Pers Med 2022;12:692.
4. Naito Y, Aburatani H, Amano T, Baba E, Furukawa T, Hayashida T, et al. Clinical practice guidance for next-generation sequencing in cancer diagnosis and treatment (edition 2.1). Int J Clin Oncol 2021;26:233–83.
5. Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: beyond the exome. Genome Med 2022;14:23.
6. Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM, Ong HS. Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Front Genet 2019;10:49.
7. Offit K. Decade in review: genomics: a decade of discovery in cancer genomics. Nat Rev Clin Oncol 2014;11:632–4.
8. Weng L, Zhang L, Peng Y, Huang RS. Pharmacogenetics and pharmacogenomics: a bridge to individualized cancer therapy. Pharmacogenomics 2013;14:315–24.
9. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 2013;15:565–74.
10. Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med 2017;19:249–55.
11. Brothers KB, Vassy JL, Green RC. Reconciling opportunistic and population screening in clinical genomics. Mayo Clin Proc 2019;94:103–9.
12. Strom CM. Changing trends in laboratory testing in the United States: a personal, historical perspective. Clin Lab Med 2012;32:651–64.
13. Annas GJ, Elias S. 23andMe and the FDA. N Engl J Med 2014;370:985–8.
14. Seyedin H, Afshari M, Isfahani P, Hasanzadeh E, Radinmanesh M, Bahador RC. The main factors of supplier-induced demand in health care: A qualitative study. J Educ Health Promot 2021;10:49.
15. Jolie A. My medical choice. New York Times [Internet]. 2013 May 14 [cited 2024 Jan 10]. Available from:
16. Juthe RH, Zaharchuk A, Wang C. Celebrity disclosures and information seeking: the case of Angelina Jolie. Genet Med 2015;17:545–53.
17. Desai S, Jena AB. Do celebrity endorsements matter?: observational study of BRCA gene testing and mastectomy rates after Angelina Jolie’s New York Times editorial. BMJ 2016;355:i6357.
18. Russo A, Incorvaia L, Capoluongo E, Tagliaferri P, Gori S, Cortesi L, et al. Implementation of preventive and predictive BRCA testing in patients with breast, ovarian, pancreatic, and prostate cancer: a position paper of Italian Scientific Societies. ESMO Open 2022;7:100459.
19. Adams MC, Evans JP, Henderson GE, Berg JS. The promise and peril of genomic screening in the general population. Genet Med 2016;18:593–9.
20. Vassy JL, Christensen KD, Schonman EF, Blout CL, Robinson JO, Krier JB, et al. The impact of whole-genome sequencing on the primary care and outcomes of healthy adult patients: a pilot randomized trial. Ann Intern Med 2017;167:159–69.
21. Uffelmann E, Huang QQ, Munung NS, De Vries J, Okada Y, Martin AR, et al. Genome-wide association studies. Nat Rev Methods Primers 2021;1:59.
22. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet 2011;12:529–41.
23. Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023;6:899.
24. Brandes N, Linial N, Linial M. PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins. Genome Biol 2020;21:173.
25. Chadeau-Hyam M, Ebbels TM, Brown IJ, Chan Q, Stamler J, Huang CC, et al. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J Proteome Res 2010;9:4620–7.
26. Gilbert JA, Quinn RA, Debelius J, Xu ZZ, Morton J, Garg N, et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 2016;535:94–103.
27. Johnson AD, Bhimavarapu A, Benjamin EJ, Fox C, Levy D, Jarvik GP, et al. CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies. Genet Med 2010;12:355–63.
28. Kim S, Eom KW, Cho CR, Um TH. Comparison of commercial genetictesting services in Korea with 23andMe service. Biomed Res Int 2014;2014:539151.
29. Lewis AC, Green RC. Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues. Genome Med 2021;13:14.
30. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017;18:83.
31. Mihaescu R, Moonesinghe R, Khoury MJ, Janssens AC. Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability. Genome Med 2011;3:51.
32. 23andMe [Internet]. South San Francisco (CA): 23andMe; c2024 [cited 2024 Jan 10]. Available from:
33. Macrogen [Internet]. Seoul: Macrogen; c2024 [cited 2024 Jan 10]. Available from:
34. DNALINK [Internet]. Seoul: DNALINK; c2024 [cited 2024 Jan 10]. Available from:
35. Lee J, Kwon KH. DTC genetic test for customized cosmetics in COVID-19 pandemic: focused on women in their 40s and 60s in Seoul, Republic of Korea. J Cosmet Dermatol 2021;20:3085–92.
36. Ng MC, Bowden DW. Is genetic testing of value in predicting and treating obesity? N C Med J 2013;74:530–3.
37. Lu M, Lewis CM, Traylor M. Pharmacogenetic testing through the direct-to-consumer genetic testing company 23andMe. BMC Med Genomics 2017;10:47.
38. FamilyTreeDNA [Internet]. Houston (TX): Gene by Gene; c2024 [cited 2024 Jan 10]. Available from:
39. Roberts JS, Ostergren J. Direct-to-consumer genetic testing and personal genomics services: a review of recent empirical studies. Curr Genet Med Rep 2013;1:182–200.
40. Haga SB, Kim E, Myers RA, Ginsburg GS. Primary care physicians’ knowledge, attitudes, and experience with personal genetic testing. J Pers Med 2019;9:29.
41. Kim NK. Legislation on genetic diagnosis: comparison of South Korea and Germany: with focus on the application and communication structure. Dev Reprod 2015;19:111–8.
42. Bioethics and Safety Act, Law No. 16372 (Apr 23, 2019) [Internet]. Sejong: Korea Legislation Research Institute; c2019 [cited 2024 Jan 10]. Available from:
43. Park S, Kim CJ. Incorporating the ethical concerns of direct-to-consumer genetic testing in employee education. Korean J Med Ethics 2022;25:21–41.
44. 1000 Genomes Project Consortium; Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, et al. A map of human genome variation from population-scale sequencing. Nature 2010;467:1061–73.
45. Bush WS, Moore JH. Chapter 11: Genome-wide association studies. PLoS Comput Biol 2012;8e1002822.
46. Sun X, Namkung J, Zhu X, Elston RC. Capability of common SNPs to tag rare variants. BMC Proc 2011;5(Suppl 9):S88.
47. Kent JW Jr. Rare variants, common markers: synthetic association and beyond. Genet Epidemiol 2011;35(Suppl 1):S80–4.
48. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008;9:356–69.
49. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747–53.
50. Frayling TM. Genome-wide association studies: the good, the bad and the ugly. Clin Med (Lond) 2014;14:428–31.
51. Zeng P, Zhao Y, Qian C, Zhang L, Zhang R, Gou J, et al. Statistical analysis for genome-wide association study. J Biomed Res 2015;29:285–97.
52. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 2006;7:781–91.
53. Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, et al. Assessing the pathogenicity, penetrance, and expressivity of putative diseasecausing variants in a population setting. Am J Hum Genet 2019;104:275–86.
54. Kingdom R, Wright CF. Incomplete penetrance and variable expressivity: from clinical studies to population cohorts. Front Genet 2022;13:920390.
55. Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA 2008;299:1335–44.
56. Fitipaldi H, Franks PW. Ethnic, gender and other sociodemographic biases in genome-wide association studies for the most burdensome non-communicable diseases: 2005-2022. Hum Mol Genet 2023;32:520–32.
57. Kraft P, Zeggini E, Ioannidis JP. Replication in genome-wide association studies. Stat Sci 2009;24:561–73.
58. Joly Y, Feze IN, Song L, Knoppers BM. Comparative approaches to genetic discrimination: chasing shadows? Trends Genet 2017;33:299–302.
59. Natowicz MR, Alper JK, Alper JS. Genetic discrimination and the law. Am J Hum Genet 1992;50:465–75.
60. Rosen E. Genetic information and genetic discrimination how medical records vitiate legal protection: a comparative analysis of international legislation and policies. Scand J Public Health 1999;27:166–72.
61. Kim H, Ho CW, Ho CH, Athira PS, Kato K, De Castro L, et al. Genetic discrimination: introducing the Asian perspective to the debate. NPJ Genom Med 2021;6:54.
62. Chapman CR, Mehta KS, Parent B, Caplan AL. Genetic discrimination: emerging ethical challenges in the context of advancing technology. J Law Biosci 2019;7:lsz016.
63. de Paor A. Genetic discrimination: a case for a European legislative response? Eur J Health Law 2017;24:135–59.

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