genetic discrimination case study

Genetic Discrimination and Misuse of Genetic Information: Areas of Possible Discrimination, Current Legislation, and Potential Limitations

The emergence and the rapid development of various genetic technologies and their incorporation into precision medicine have greatly expanded medicine’s capabilities. At the same time, concerns about the availability and dissemination of vast amounts of personally relevant data have focused attention on potential problems of genetic discrimination. Over the past  three decades , concerns about genetic discrimination have drawn the attention of medical practitioners, patient advocacy groups, and academic institutions. In 2001, the American College of Medical Genetics and Genomics (ACMG) issued a position statement on ways to prevent genetic discrimination. They then released an updated 2021 statement  noting progress since 2001 in important federal legislation, recommending points to consider when dealing with such legislation. As the updated statement notes, despite considerable progress, substantial gaps and limitations remain. In this blog post we summarize aspects of genetic discrimination as considered more thoroughly in the ACMG statements and other resources. The opinions expressed in this blog post are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

What is Genetic Discrimination?

Collection, storage, and usage of genetic information can be extremely beneficial to biomedical research and preventive medicine. For example, the information can help produce more effective research results and more accurate diagnoses.  However, cases of possible genetic discrimination could involve issues of privacy, confidentiality, and informed consent regarding this information. Misuse of genetic information can adversely affect a person’s access to healthcare, employment, and other rights.  Given the broad range of potential issues, definitions of genetic discrimination can vary by context, institution and country. One useful, albeit broad and simple, definition of genetic discrimination is “ the unequal treatment of individuals based on an aspect of their genetic code or genome, such as the risk for genetic disorder[s] .”.

Current Applicable Legislations

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law that provides protection of certain health information from disclosure either without consent or as expressly allowed by the law. The Privacy Rule within HIPAA acts as a mechanism that seeks to balance the usage of health information for necessary and beneficial medical practices with adequate protection of privacy. HIPAA applies primarily to  certain groups and individuals called “covered entities.” Covered entities include healthcare providers, health insurance companies, and businesses that require access to certain health information. A potential drawback of HIPAA is the limitation on the enforcement of the Privacy Rule to covered entities. Groups and individuals that do not qualify as covered entities include life insurers, most employers, most schools and school districts, and state agencies.

The Privacy Rule also allows for the disclosure of de-identified or anonymous health information. However, advances in data mining science may increase  the potential for re-identification of anonymized data . Some information may also be so unique as to be easily identifiable. HIPAA also does not extend to protecting information used in all forms of research.

The Genetic Information and Nondiscrimination Act (GINA) is a federal law intended  to protect individuals from discrimination in employment, in the workplace , and in health insurance . Compared to HIPAA, GINA applies to more entities and focuses on the management and usage of genetic information. GINA prevents health insurers from using a person’s genetic information to determine eligibility or premiums. It also prevents insurance companies from requiring genetic testing for their clients as a condition for enrollment.

GINA also extends to the workplace. The law prohibits employers from using genetic information in hiring, firing, and job assignments. It also restricts employers from requesting, requiring, or somehow obtaining genetic information about a person or their family members.

Although GINA is one of the most effective laws in protecting against discrimination in employment and insurance, it is not without limitations. One of the most crucial limitations of GINA is that it does not cover all employers. Employers with fewer than 15 employees and the military need not adhere to GINA.

GINA does not apply to long-term insurances such as life and disability insurance. This limitation is due to an unresolved controversy over the fairness of not disclosing genetic information to an insurer.  Likewise, GINA also does not cover individuals who are already having symptoms of a genetic disorder, due to problems of fairness for insurance companies and employers. However, some states and countries have specific legislation that goes beyond the scope of GINA to include life and disability insurance, and those that are already manifesting symptoms are covered by the Patient Protection and Affordable Care Act (ACA) of 2010.

Discrimination in the Context of Genetic Testing

We have indicated that discrimination can occur during collection, usage, and storage of human genomic information, but discrimination can also occur before any of these activities have occurred. Issues involving distributive justice can arise in regard to what individuals or groups have or do not have access to the  benefits of genetic testing. Distributive justice is the fair allocation of benefits or resources to individuals or groups within a community or population. If burdens or disadvantages are also involved, distributive justice demands they fall proportionally on the groups involved. Issues of distributive injustice often occur with underserved populations that include racial minorities, who have limited access to genetic testing. Lack of access and or lack of adequate information about genetic testing can lead to the community’s mistrust of the medical institutions that provide genetic testing . Mistrust can impede the use of genetic medical technologies that could improve the health of such communities and reduce health disparities. For example, genetic testing has the potential to predict many hereditary conditions, and in some cases allow people to appropriately prepare for them. However, mistrust can lead to the under-enrollment of racial minorities in clinical genetic research thus resulting in the bias of genomic databases or of AI algorithms towards individuals of European descent .  Research that utilizes such limited data cannot produce meaningful findings to benefit diverse populations.

Where To Go From Here

In addition to providing a brief introduction to the topic and summarizing aspects of the latest ACMG statement on genetic discrimination , this blog post is intended to spark an interest among readers that may be kindled through reading the sources hyperlinked herein.

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Genetic Discrimination

Many Americans wonder if participating in genetics research or undergoing genetic testing will lead to being discriminated against based on their genetics. These questions may be the basis for why or why not patients decide to take genetics-based clinical tests or volunteer to participate in the research needed for the development of new tests, therapies, and cures. This page provides an overview of the Genetic Information Nondiscrimination Act (GINA) and describes what protections GINA does and does not offer.

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Genetic information nondiscrimination act of 2008, implications of gina, employee wellness programs.

The Genetic Information Nondiscrimination Act (GINA) of 2008 protects Americans from discrimination based on their genetic information in both health insurance (Title I) and employment (Title II). Title I amends the Employee Retirement Income Security Act of 1974 (ERISA), the Public Health Service Act (PHSA), and the Internal Revenue Code (IRC), through the Health Insurance Portability and Accountability Act of 1996 (HIPAA), as well as the Social Security Act, to prohibit health insurers from engaging in genetic discrimination. Title II of GINA is implemented by the Equal Employment Opportunity Commission (EEOC) and prevents employers from using genetic information in employment decisions and prevents employers from requesting and requiring genetic information from employees or those applying for jobs.

Health Insurance (Title I)

GINA prohibits health insurers from discrimination based on the genetic information of enrollees. Specifically, health insurers may not use genetic information to determine if someone is eligible for insurance or to make  coverage ,  underwriting  or  premium -setting decisions. Furthermore, health insurers may not request or require individuals or their family members to undergo genetic testing or to provide genetic information. As defined in the law, genetic information includes family medical history, manifest disease in family members, and information regarding individuals' and family members' genetic tests. The  health insurance  protections of GINA extend to private health insurers, Medicare, Medicaid, Federal Employees Health Benefits, and the Veterans Health Administration. For the U.S. Military’s TRICARE insurance program, GINA offers more limited protection. TRICARE may not use genetic information for coverage, underwriting, or premium-setting, but eligibility for TRICARE insurance is contingent upon employment by the U.S. Military, and GINA’s employment protections do not apply to the U.S. Military. The U.S. military  is  permitted to use genetic and medical information to make employment decisions (see next section on “Employment (Title II)” for more information). 

GINA’s health insurance protections  do not  cover long-term care insurance, life insurance, or disability insurance, though some states have state laws that offer additional protections against genetic discrimination in these lines of insurance. Visit the  Genome Statute and Legislation Database  to search for relevant state laws.

The regulations governing the implementation of GINA in health insurance  took effect on December 7, 2009 and are implemented by the Internal Revenue Service, Department of Labor, and Department of Health and Human Services (HHS). GINA amends HIPAA to clarify that genetic information is health information and provides a  finalized  rule that went into effect March 26, 2013.

Employment (Title II)

Title II of GINA is implemented by the Equal Employment Opportunity Commission (EEOC) and prevents employers from using genetic information in employment decisions such as hiring, firing, promotions, pay, and job assignments. Furthermore, GINA prohibits employers or other covered entities (employment agencies, labor organizations, joint labor-management training programs, and apprenticeship programs) from requiring or requesting genetic information and/or genetic tests as a condition of employment. The  regulations  governing implementation of GINA in employment took effect on January 10, 2011.

An important exception to Title II of GINA involves the U.S. Military. The military  is  permitted to use genetic information to make employment decisions. Note that eligibility for TRICARE insurance is contingent upon employment by the military, and so genetic test results may affect one’s ability to access TRICARE insurance. 

Also, importantly, GINA does not apply to employers with fewer than 15 employees.

View the full text of  The Genetic Information Nondiscrimination Act of 2008, Public Law 110-223 .

GINA and Clinical Research

GINA has implications for individuals participating in research studies. The Office for Human Research Protections (OHRP) within HHS has issued  guidance on integrating GINA into clinical research , including information on GINA's research exemption, considerations for Institutional Review Boards, and integrating information on GINA into informed consent forms. To comply with GINA, informed consent forms should include information on any risks associated with participation in the research project and a statement describing how the confidentiality of records will be maintained. NHGRI has developed an  informed consent resource  for participants in genomics research.

Some workplaces implement wellness programs aiming to promote good health and disease prevention among employees. In exchange for participating in these wellness programs, employees may receive inducements in the form of discounts on employer-provided health insurance or extra paid leave, for example. An inducement may also be considered a penalty in certain circumstances; if employees participating in a wellness program do not provide certain types of information, they could see the cost of health insurance increase. Typically, wellness programs are run by third-party companies that collect employees' biometric data (e.g. weight, blood pressure, cholesterol levels) and use this information to design workplace interventions to improve health. Wellness programs might provide smokers with smoking cessation resources or recommend diet and exercise plans for individuals seeking to lose weight.

Since wellness programs involve the exchange of health information between employees and their employers, they are relevant to the enforcement of GINA in cases where wellness programs request genetic information from employees. Under GINA it is permissible for employers to request employees' genetic information for the purposes of voluntary wellness programs. However, employers cannot induce employees to provide their genetic information; this means that if an employee chooses to give genetic information to the wellness program, they cannot receive an additional reward for doing so. Conversely, if an employee chooses to withhold genetic information, they cannot be penalized.

Some interpreted GINA to be unclear about whether or not it is permissible to request the genetic information of employees' spouses and if employers may offer inducements in exchange for spouses' genetic information. According to GINA, the definition of "genetic information" includes health information of family members, which includes spouses.

On May 16, 2016, EEOC  amended  GINA regulations to provide clarification on the issue of spouses' genetic information. This amended rule states that it is permissible for wellness programs to offer limited inducements, in the form of a reward or penalty, in exchange for information about the manifestation of disease or disorders in spouses. Some argue that EEOC's ruling conflicts with GINA's definition of "genetic information" and that under GINA, should not be permissible for wellness programs to offer inducements for spouses' health information.

The maximum inducement that wellness programs may offer in exchange for employees' or their spouses' health information is 30 percent the cost of a self-only insurance plan (a plan that covers only one person). If both the employee and the spouse do not volunteer their health information, they could pay a combined 60 percent the cost of a self-only insurance plan on top of their current premium.

While EEOC's ruling amended GINA's treatment of wellness programs, it does not alter GINA's fundamental prohibition of employment discrimination based upon genetic information.

The Health Insurance Portability and Accountability Act

One part of Title I of GINA required HHS to amend the Health Insurance Portability and Accountability Act (HIPAA), which lays out privacy requirements for health information. The modification to HIPAA, made in 2013, states that genetic information is considered to be health information; therefore, it cannot be used by health insurers to make any decisions about health insurance benefits, eligibility for benefits, or the calculation of premiums under a health plan.

The Affordable Care Act

A major provision of  The Affordable Care Act of 2010 (ACA)  is to establish 'guaranteed issue'; issuers offering insurance in either the group or individual market must provide coverage for all individuals who request it. The law therefore prohibits issuers of health insurance from discriminating against patients with genetic diseases by refusing coverage because of 'pre-existing conditions'. ACA further provides additional protections for patients with genetic diseases by establishing that certain health insurers may only vary premiums based on a few specified factors such as age or geographic area, thereby prohibiting the adjustment of premiums because of medical conditions.

The Americans with Disabilities Act

The Americans with Disabilities Act (ADA)  prohibits discrimination in employment, public services, accommodations, and communications based on a disability. In 1995, EEOC issued an interpretation that discrimination based on genetic information relating to illness, disease, or other disorders is prohibited by the ADA. In a subsequent Senate hearing in 2000, EEOC Commissioner Paul Miller further affirmed that the ADA "can be interpreted to prohibit employment discrimination based on genetic information." However, these EEOC opinions are not legally binding, and whether the ADA protects against genetic discrimination in the workplace has never been tested in court.

On May 17, 2016, in conjunction with releasing amended regulations on GINA and wellness programs (see  "Employee Wellness Programs" ), EEOC amended ADA regulations to permit employers to offer inducements to employees who volunteer disability-related health information for the purposes of wellness programs. The amended ADA regulations also say that wellness programs may request medical examinations of participating employees. EEOC further stated that collecting disability-related information and requesting medical examinations for wellness programs would only be permissible provided that employers comply with existing nondiscrimination and nondisclosure protections dictated by ERISA and HIPAA. The permissibility of offering inducements in exchange for employees' health information is contingent upon the voluntary nature of wellness programs.

The ADA has been used to challenge genetic testing practices by an employer. In 2001, EEOC filed a suit against the Burlington Northern Santa Fe (BNSF) Railroad for secretly testing its employees for a rare genetic condition (hereditary neuropathy with liability to pressure palsies - HNPP) that causes carpal tunnel syndrome as one of its many symptoms. BNSF claimed that the testing was a way of determining whether the high incidence of repetitive-stress injuries among its employees was work-related. Besides testing for HNPP, company-paid doctors also were instructed to screen for several other medical conditions such as diabetes and alcoholism. EEOC and BNSF  announced a mediated settlement  in 2002.

A patchwork of state laws exists to protect Americans from genetic discrimination, although these laws vary widely in the scope, applicability, and amount of protection provided. GINA sets a floor of minimum protection against genetic discrimination and does not preempt state laws with stricter protections.

The earliest state laws focused on particular genetic conditions. For example, North Carolina was the first state to prohibit discrimination based on the presence of the sickle cell trait. In 1991, Wisconsin was the first state to prevent whole-sale discrimination based on genetic tests.

Some states have passed laws that go beyond the scope of GINA to prohibit genetic discrimination for "other insurances", including life insurance, disability insurance, and long-term care insurance. In 2011, California passed the "California Genetic Information Nondiscrimination Act" (CalGINA), which extended protections even further to prohibit genetic discrimination in emergency medical services, housing, mortgage lending, education, and other state-funded programs. You may visit the  Genome Statute and Legislation Database  to search for relevant state laws.

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Genetic Discrimination in the Workplace Factsheet

Myth: Genetic testing is an accurate way to predict disease.

Fact : While some genetic tests can accurately predict that an individual will develop a certain disease or condition (for example, Huntington’s Disease or sickle cell anemia), even those tests often do not indicate when the individual will develop symptoms or how severe the symptoms will be.

Many genetic tests now commercially available, including tests for such complex conditions as breast cancer, cannot predict whether a person will actually develop the disease, but only if they have a genetic predisposition and a greater likelihood of developing cancer. Many people who test positive for genetic mutations associated with certain conditions will never develop those conditions. Moreover, while individuals with genetic mutations associated with certain diseases like cancer may be at higher risk, there are many factors which determine whether someone will actually develop the disease. For example, most women who develop breast cancer (85 to 90%) do not have an inherited genetic susceptibility to the disease.

Most health care providers do not have training in genetics, and many who order genetic tests may not know how to interpret the results. There is no requirement for genetic counseling in commercial or many clinical settings, so individuals and their doctors often do not understand what the results of a genetic test really mean for the individual or family members.

Myth : Genetic tests are reliable and safe.

Fact : Currently the U.S. Food and Drug Administration (FDA) has declined to regulate genetic tests, despite the fact that many are commercially available and aggressively marketed by biotech firms. The absence of regulation means that there is no government oversight or quality control, and the accuracy and reliability of the tests are unproven.

While taking blood or tissue sample for the purpose of genetic testing may cause minimal risk of physical harm, the results of genetic tests often create enormous psychological problems for individuals tested and their families. Information gathered from genetic tests is not just about individuals – it has implications for all of one’s blood relatives as well. Sometimes genetic tests have implications for whole communities, since some genetic conditions are associated (sometimes inappropriately) with certain racial or ethnic groups. For example, sickle cell anemia is associated with African-Americans, and predisposition for breast cancer has been associated by some with Ashkenazi Jews, even though it is not clear that they as a group are at greater risk.

Myth : Genetic testing is not really a serious workplace issue.

Fact : Genetic testing in the workplace is on the rise. In 1982 a federal government survey found that 1.6% of companies who responded were using genetic testing for employment purposes. 1 In a similar survey conducted by the American Management Association in 1997, 6-10% of employers were found to be conducting genetic testing. 2

Moreover, there have been many documented cases of genetic discrimination. 3 In a survey of nearly 1,000 individuals who were at risk for genetic conditions, over 22% reported that they had experienced some form of discrimination based on their risk status. 4 The U.S. Department of Labor has found genetic information to be a very serious workplace issue. 5

Myth : Certain work environments may hasten the onset of genetically based diseases, and genetic testing can protect employees who are “at risk”.

Fact : Currently no one knows whether some work environments hasten the onset of genetic disease. According to the American Medical Association, “there is insufficient evidence to justify the use of any existing test for genetic susceptibility as a basis for employment decisions.” 6 There is no empirical data showing that “the genetic abnormality results in an unusually elevated susceptibility to occupational injury”. 7

Myth : Federal laws like the Americans with Disabilities Act (ADA) protect employees from genetic discrimination in the workplace.

Fact : The extent of federal protection against genetic discrimination is unclear, and limited at best. 8 The federal appeals courts are divided on the scope of protection of the ADA, particularly for employees who have not yet manifested symptoms of illness. 9 In any event, the ADA addresses workplace discrimination, but not privacy. There is currently no federal law which prohibits an employer from requesting genetic information or testing employees, and no law protecting the privacy of genetic information. 10

Myth : State employment discrimination laws protect workers against genetic discrimination.

Fact : Currently only 12 states have enacted laws that protect employees from genetic discrimination in the workplace (including California, Connecticut, Illinois, Iowa, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, Texas and Wisconsin); a handful of other states have legislation pending.

1 “The Role of Genetic Testing in the Prevention of Occupational Disease”. Office of Technology Assessment, April 1983.

2 American Management Association.

3 P. Billings, et al. “Discrimination as a Consequence of Genetic Testing,” American Journal of Human Genetics. 50 (1992): 476-482.

4 “Individual, Family, and Social Dimensions of Genetic Discrimination: A Case Study Analysis”. Lisa N. Geller et al., Science and Engineering Ethics, Volume 2, Issue 1, 1996.

5 Genetic Information and the Workplace. U.S. Department of Labor Report, January 20, 1997.

6 “Use of Genetic Testing By Employers”, Council on Ethical and Judicial Affairs, Journal of the American Medical Association. October 2, 1991, Vol. 266, No. 13.

8 42 U.S.C.A. Sec. 12111 et. seq.

9 There is a case, Bragdon v. Abbot , pending in the U.S. Supreme Court, which raises the issue of whether the ADA covers such conditions as asymptomatic HIV infection; the Court’s decision in that case may have broader implications regarding the applicability of the ADA to asymptomatic genetic conditions.

10 Recently the Ninth Circuit ruled that Lawrence Berkeley Laboratory may have violated its employees’ constitution right to privacy by subjecting workers to genetic testing without their knowledge or consent, and remanded the case to the trial court. See Norman-Bloodsaw et al. v. Lawrence Berkeley Laboratory et al ., No. 96-16526, 9th Cir., Feb 3, 1998. This is the first U. S Appeals Court decision on genetic privacy in the workplace.

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• Am J Hum Genet
• v.50(3); 1992 Mar

Discrimination as a consequence of genetic testing.

Genetic discrimination refers to discrimination directed against an individual or family based solely on an apparent or perceived genetic variation from the "normal" human genotype. We describe here the results of a case history study designed to assess whether or not genetic discrimination exists. Using the above definition of genetic discrimination and applying stringent criteria for case selection, we find that genetic discrimination exists and is manifested in many social institutions, especially in the health and life insurance industries. Stigmatization, and denial of services or entitlements to individuals who have a genetic diagnosis but who are asymptomatic or who will never become significantly impaired, is noted. Follow-up comprehensive studies on the significance and varieties of genetic discrimination are needed. In order to avoid creating a new social underclass based on genetic discrimination (the "asymptomatic ill"), existing and future genetic testing or screening programs need review by medical, scientific, legal, and social policy experts, as well as the public, and may require modification.

Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (1.2M), or click on a page image below to browse page by page. Links to PubMed are also available for Selected References .

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

• Beckwith J. Foreword: the human genome initiative: genetics' lightning rod. Am J Law Med. 1991; 17 (1-2):1–13. [ PubMed ] [ Google Scholar ]
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• Research article
• Open access
• Published: 31 January 2013

Genetic discrimination and life insurance: a systematic review of the evidence

• Yann Joly 1 ,
• Ida Ngueng Feze 1 &
• Jacques Simard 2  

BMC Medicine volume  11 , Article number:  25 ( 2013 ) Cite this article

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Since the late 1980s, genetic discrimination has remained one of the major concerns associated with genetic research and clinical genetics. Europe has adopted a plethora of laws and policies, both at the regional and national levels, to prevent insurers from having access to genetic information for underwriting. Legislators from the United States and the United Kingdom have also felt compelled to adopt protective measures specifically addressing genetics and insurance. But does the available evidence really confirm the popular apprehension about genetic discrimination and the subsequent genetic exceptionalism?

This paper presents the results of a systematic, critical review of over 20 years of genetic discrimination studies in the context of life insurance.

The available data clearly document the existence of individual cases of genetic discrimination. The significance of this initial finding is, however, greatly diminished by four observations. First, the methodology used in most of the studies is not sufficiently robust to clearly establish either the prevalence or the impact of discriminatory practices. Second, the current body of evidence was mostly developed around a small number of 'classic' genetic conditions. Third, the heterogeneity and small scope of most of the studies prevents formal statistical analysis of the aggregate results. Fourth, the small number of reported genetic discrimination cases in some studies could indicate that these incidents took place due to occasional errors, rather than the voluntary or planned choice, of the insurers.

Important methodological limitations and inconsistencies among the studies considered make it extremely difficult, at the moment, to justify policy action taken on the basis of evidence alone. Nonetheless, other empirical and theoretical factors have emerged (for example, the prevalence and impact of the fear of genetic discrimination among patients and research participants, the (un)importance of genetic information for the commercial viability of the private life insurance industry, and the need to develop more equitable schemes of access to life insurance) that should be considered along with the available evidence of genetic discrimination for a more holistic view of the debate.

Peer Review reports

The prototypical issue used when discussing the ethical, legal and social issues associated with scientific progress in genetics has been genetic discrimination (GD). Lawyers and ethicists have been quick to point out the risk that uninhibited genetic progress would entice governments and institutions to treat people differently on the basis of their genetic constitution [ 1 ]. GD has been defined in many ways, a mark of the influence of divergent sociocultural and scholarly backgrounds. Insurers write of 'rational (actuarial)-irrational discrimination' [ 2 ], lawyers write of 'legal-illegal (illicit) discrimination' [ 3 ], whereas patients generally adopt a much broader definition encompassing all differential, negative treatments of an individual based on his or her genetic makeup [ 4 ]. However defined, widespread GD could potentially result in practices that exclude segments of the population from access to basic social necessities such as healthcare, insurance, housing, reproductive freedom and employment. Mass media has joined the debate, ensuring that the issue of GD is not confined to isolated academic discourse [ 5 ].

Among the fields of potential discrimination, one of the most commonly-debated topics has been the use of genetic information by the insurance industry to select applicants and determine insurance premiums. The dual nature of personal insurance, which is partly considered as both a public and private good in most jurisdictions, and the relatively limited amount of public trust in the practices of the private insurance sector might explain some of this attention. Policymakers themselves have entered the arena of debate following substantial pressure from their constituents. In continental Europe, the legislative response has been swift and strong. GD is prohibited by the Convention on Biomedicine (1997), the Charter of Fundamental Rights of the European Union (2000), and the national legislation of many individual countries [ 6 ]. In the United States, the much-discussed Genetic Information Nondiscrimination Act of 2008 ( GINA ) (2008) offers protection mainly in the domains of health insurance and employment [ 7 ]. In the United Kingdom, the Association of British Insurers and the British government have agreed on a Concordat and Moratorium on Genetics and Insurance that significantly restricts the capacity of British insurers to request genetic information from insurance applicants [ 8 ]. Australian (2008 amendment to the Disability Discrimination Act ), Canadian and East Asian policymakers have also been active in this area, although less so than their European counterparts [ 6 , 9 ].

This paper focuses on GD in the field of life insurance. Life insurance facilitates the economic security of the policy holder. It is often described as a quasi-essential social good, a gateway good necessary to have access to important social and economic activities that provide considerable peace of mind to the policyholder [ 10 ]. Access to life insurance is far from universal and it must generally be purchased through a contractual agreement with a private insurance company. The majority of life insurance applicants are accepted at a standard rate set by insurance companies. Nevertheless, for the small group of individuals excluded from the common pool, the consequences can be dire [ 11 ].

Is the substantial attention given to the question of GD in academic literature, popular media and policymaking circles justified by the empirical evidence currently available? In other words, are the observed concerns and responses based on documented cases of discrimination, anecdotes or other less visible factors? This question prompted us to undertake a study, which to our knowledge is the first attempt to systematically review all available empirical evidence of GD using the life insurance sector as a subject of analysis. This study analyzes actual cases of discrimination, the evidentiary limitations, and the possibility of drawing overarching conclusions from the available evidence.

To assess the state of the evidence, we looked for all studies published in the scientific literature documenting the occurrence of GD in the context of life insurance.

Selection of publications

We developed and applied the following inclusion criteria to identify and select eligible studies: published in English; focused on the collection or capture of information on the occurrence of GD in life insurance whether direct (through patients' or participants' self-report, such as interviews or surveys) or indirect (through professionals such as doctors, genetic counselors and insurers); focused on a primary or follow-up study (multiple publications on the same study were grouped together and treated as a single study). Narrative and systematic reviews were included only if they also presented additional empirical data on the occurrence of GD in the context of life insurance (that is, other than the data contained in the studies reviewed therein).

Eligible studies were excluded if: they did not focus in whole or part on the occurrence of GD in the context of life insurance; they offered insufficient evidence; or they contained serious methodological flaws. Editorial letters with no primary data, comments, opinions, abstracts and unpublished studies were excluded. Studies capturing the fear of GD rather than actual experiences were also excluded.

Literature search

Searches were conducted from March until May 2012 on PubMed, Google Scholar, Social Science Research Network and Hein Online, using the Boolean operators 'OR' and 'AND' with various permutations of the following keywords: 'genetic discrimination', 'study', 'life insurance', 'survey', 'data', 'empirical evidence' and 'genetic test'.

First, we conducted keyword searches that produced a list including 534 search results. Using the terms 'genetic discrimination' and 'life insurance' combined ('AND'), we obtained a list of 29 publications from the database PubMed. We executed a search on Google Scholar including all the following keywords combined ('AND'): 'genetic discrimination', 'study', 'life insurance', 'survey', 'data' and 'empirical evidence'. This produced 155 search results. Our search on Social Science Research Network using the terms 'genetic discrimination' yielded 100 results. Our last search was conducted on Hein Online using the following combination of terms '("genetic discrimination" AND "life insurance") AND "genetic test" OR "data" OR "empirical evidence" OR "survey"', and generated 250 results.

Second, applying our selection criteria, we reviewed the titles and available abstracts of each of the 534 search results identified through our keyword searches, and retained 29 publications.

Third, we performed a systematic hand search in the references list and bibliography of each of the 29 articles to identify additional relevant publications and assess cross-referencing. This led to 16 more eligible studies. Hence, we uncovered a total of 45 eligible studies relevant to GD in the context of life insurance.

Thereafter, eligible studies were each independently assessed for relevance by two researchers (YJ, INF). Applying the selection criteria above, we retained studies documenting experiences of discrimination through both direct and indirect evidence on the occurrence of GD. Based on these criteria, 12 studies were excluded. This final list was also compared with references and resources provided by a recent review dealing with GD in numerous contexts including life insurance [ 9 ]. The 33 studies retained represent a systematic overview of the empirical evidence of GD in the field of life insurance. For an overview of the search strategy, see Figure 1 .

Methodology for selecting eligible studies .

Data extraction

The vastly different nature of the available studies suggested that formal statistical analysis and comparison of discrimination cases would be inappropriate. Instead, we analyzed the data through a social science comparative approach that incorporates both quantitative descriptive analysis and qualitative content analysis. Key elements of the selected studies were coded independently according to their relevance to pre-selected themes. We extracted information on study scope (country and context), genetic conditions (whether the study focused on a single or multiple conditions), definition of GD (how GD was construed by the researchers), validation (whether a formal validation process was disclosed by the authors), conclusions (how results were qualified: whether evidence of GD was found, whether policies or laws were required), and number of citations (how often the publication was cited by peers). Two trained researchers (YJ, INF) independently evaluated the results and discrepancies were resolved by consensus. Results obtained on individual themes were qualitatively analyzed and, where appropriate, converted to statistical data (values were rounded according to convention; this process was based on the information presented in Table 1 [ 12 – 44 ]).

Results and discussion

Scope and context.

Together, the 33 studies represent two decades' worth of research published from 1991 to 2012 (see Table 1 ). A small peak in the number of studies (eight studies) can be identified between 2006 and 2007, which can be linked to the charged political climate in the US and renewed academic interest that prefaced the adoption of GINA . These studies generally gathered evidence through direct sources (that is, individuals commenting on their own experience with insurance companies), but some obtained their data through secondary sources such as insurers, insurance associations, health professionals or patient groups.

The majority of studies reviewed (73%) aimed at providing evidence of GD in a variety of fields (for example, employment, immigration, adoption and access to healthcare), including that of life insurance. A minority (27%) focused exclusively on the context of life insurance (see Table 1 ). The majority of the available evidence (58%) comes from studies involving North American population groups (see Figure 2 and Table 2 ).

Studies on genetic discrimination and life insurance by country .

Surprisingly, given the rather strong European policy response mentioned above, only six studies (18%) provided empirical data on the situation prevailing in continental Europe, and five of these studies were very specific in nature, addressing the context of familial hypercholesterolemia and hypertrophic cardiomyopathy in the Netherlands, hereditary breast and colorectal cancer in Norway, and Huntington's disease in Germany [ 24 , 38 , 40 , 44 ].

The substantial number of studies carried out in Canada (seven studies), where no specific laws have been adopted to limit the use of genetic information by life insurers, and the absence of studies in a highly legislated European context, could suggest that the number of GD studies carried out in a given country, as well as the number of GD cases reported, does not necessarily have a strong correlative impact on policymaking. A notable exception to this trend could be Australia, whose Disability Act was amended following the publication of major studies on GD and an extensive report from the Australian Law Reform Commission [ 45 ].

Genetic conditions investigated

The mitigated overall results are not easily interpretable and the task is exacerbated by the serious methodological challenges faced in some of these studies. One of the constraints concerns the range of genetic diseases investigated in the literature. Reviewing the scope of the 33 studies, it is apparent that they only uncovered evidence on a very limited number of highly penetrant, familial, adult-onset, relatively well-known genetic conditions.

The majority of the evidence is based on the following five conditions: Huntington's disease, hereditary breast and ovarian cancer, hemochromatosis, familial hypercholesterolemia and hereditary colorectal cancer (see Figure 3 ). Of the 33 studies reviewed, 19 (58%) specifically focus on one of these five conditions (see Figure 4 ). Moreover, evidence on genetic discrimination in the context of Huntington's disease is provided in over 14 of the 33 studies, and on hereditary breast and ovarian cancer in more than 10 (see Figure 3 ).

Most studied genetic conditions in genetic discrimination and life insurance studies .

Proportion of studies that focused on a single disease .

The high number of studies focusing on a single condition (see Figure 4 ) makes it particularly difficult to generalize from the results of a systematic comparison of the literature to reach a broad, robust conclusion on GD applicable to the whole research or clinical genetic context. Moreover, research on GD in the fields of personalized medicine and/or pharmacogenomics, infectious diseases and genome-wide association studies remains absent in the literature, thereby resulting in a complete lack of data on GD in the context of emerging 'omics' research. This is particularly concerning given that the amount of genomic information in the typical individual's medical record is likely to increase tremendously in the next few years as whole-genome sequencing costs are reduced and personalized medicine becomes more common in clinical settings [ 46 ].

Definition of genetic discrimination

The authors of the 33 studies all struggled with the meaning of GD (examples of GD definitions are provided in Table 3 ). Several studies refrained from using the term 'genetic discrimination' in their questionnaire so as to avoid biasing responses. However, the paradoxical consequence of this methodological approach was over-reporting due to the tendency of participants to declare any negative outcome they faced while applying for life insurance as an instance of discrimination [ 47 ]. When specifically included in survey questionnaires or data analysis strategies, the definition of GD varied widely (see Table 3 ), greatly reducing the possibility of meaningful comparison. Indeed, the challenge of defining GD led a study author to conclude that '[t]he notion of finding wholly objective and overt evidence, as opposed to subjective and implicit accounts of discrimination may [n]ot be entirely realistic' [ 42 ]. Studies choosing to adopt a broad definition, or no definition at all, tended to report the highest incidence of GD cases [ 36 ]. Studies using a legal definition of GD obtained lower results [ 3 ]. But, because laws on this topic vary significantly across jurisdictions, these studies are difficult to compare or integrate with one another outside of their national context.

To obtain more robust and comparable results, some studies have used the criteria of 'irrational discrimination' - discrimination that is not based on scientifically validated and actuarially relevant genetic information - as a selection criterion to assess the practice of insurers. However, because negative decisions by life insurers against some of the most genetically at-risk individuals who might have pressing need to obtain life insurance (for example, an asymptomatic patient having tested positive for a monogenic dominant serious condition) would not necessarily constitute irrational GD, use of this criterion could arguably be perceived as unethical. The partly subjective nature of the underwriting process (illustrated by the high variability between the guidelines and questionnaires used by different insurance companies) and our limited knowledge of the genomics of complex diseases further limit the use of the rationality criterion to determine objectively the prevalence of GD in insurance.

The context of Huntington's disease can be used to illustrate the impact of definitional choices on the results of GD studies. Because this disease is a relatively well-known autosomal dominant genetic condition, obtaining a positive test result has serious implications for the future health of an asymptomatic individual. This explains the life insurers' interest in being able to use test results or family history information, regarding this particular disease, for underwriting. This is in turn reflected in the results of GD studies. Studies investigating GD in the context of Huntington's disease and using a broad definition of GD or no definition at all would likely identify a significant number of GD cases (exclusion, higher premiums or conditional acceptance). However, studies using a rationality or legality criteria would generally report a much lower number of discrimination incidents.

Evidence of genetic discrimination

Around half of the studies reviewed (48%) found that, although GD had some empirical basis, its incidence was rare and it was not a significant source of insurance denials [ 4 , 14 , 16 , 17 , 19 – 22 , 27 , 31 , 39 , 40 , 42 , 44 ]. A second category, comprising a considerable number of studies (42%), concluded that the existence of GD in life insurance was documented by the evidence they provided and that the situation gave grounds for serious concern. Within this category, Huntington's disease came up often [ 36 – 38 , 43 ]. Early US studies in this category often advocated the adoption of laws and policies to prohibit access to genetic information by life insurers [ 17 ] or the development of a more generous public insurance system that would provide a minimum amount of life insurance to all applicants [ 16 ]. Finally, a minority of studies (9%) found no empirical evidence to support the existence of GD in the life insurance context.

It should be mentioned that some, but not all, of the countries covered by these studies, have already adopted laws prohibiting insurers' access to genetic information (for example, the Netherlands, Norway, and Germany) (see Table 1 ). Authors of the 9% of studies finding no empirical evidence of GD were often of the opinion that the GD problem was more linked to media hype and fear of discrimination than to GD itself. These studies consequently pointed out the importance of educating the public and reassuring the concerns of patients and research participants about GD [ 22 , 40 ].

Among the 19 studies dealing with a single genetic condition (see Table 2 ), a significant number of studies (47%) concluded that there was sufficient evidence to raise serious concerns about GD. Half of these studies concerned Huntington's disease [ 36 – 38 , 43 ]. A second important category (42%) found that, while GD existed, it was of rare occurrence [ 19 , 31 , 39 , 40 , 44 ]. Finally, a minority of studies (11%) concluded there was no evidence of GD [ 25 , 28 ].

To highlight the broad trends, it is possible to further group the 33 reviewed studies into two categories: a majority of studies (58%) that believes that GD in the context of life insurance is a negligible issue that does not warrant the substantial societal debate and policy concern generated to date, while a substantial minority (42%) concludes that GD exists and has impacted access to life insurance negatively.

Validation and methodological limitations

Validating the study results (that is, avoiding biases and concealments as well as ensuring that the data reflect cases of 'real' rather than 'perceived' discrimination) was another significant hurdle. For the purpose of our research, validation was considered to be any additional independent step(s) or method(s) taken by the researcher to confirm the accuracy of reported discrimination events. A majority of the reviewed studies (76%) could not be considered as validated (see Table 1 and 2 ); in this case it seems more accurate to talk about studies assessing the 'perceived' level of GD rather than objective manifestations of it. Testifying to the importance of the validation process, Wertz found that, '[W]hen asked to give details of their refusals, almost all [participants] described situations that are characteristic of general insurance practice. They were apparently objecting to what they perceived as unfair insurance practices in general, rather than practices specific to genetics.' [ 20 ].

Studies of patients were not the only ones in which investigators noted the importance of verifying findings. Otlowski et al. observed that insurers surveyed on the topic of GD were likely to under-report unfavorable underwriting decisions [ 3 ].

To attempt to reduce the biases associated with non-validated results, several verification techniques have been used over the years. They include follow-up phone calls or in-person interviews to elicit additional information about reported cases of discrimination; review of the participant's medical file (to verify if an unfavorable decision could be due to a pre-existing condition); audit of the documentation or correspondence relating to any discrimination complaint; corroboration of discrimination reports by independent sources (ombudsman or similar administrative instances); case law; and so on. These validation techniques were used, alone or in combination, with various degrees of rigor by researchers and these choices significantly impacted the results of the reviewed studies.

Other study limitations that impacted the results and their compatibility with one another included the sample size and type of people surveyed. Some studies would include individuals already affected by a genetic disease but asymptomatic [ 16 ], whereas others would include healthy carriers [ 30 ]. Some would include asymptomatic untested individuals with a family history of disease [ 37 ], and some would include information on patients that was obtained from indirect sources (family members, genetic counselors or members of a disease support group) [ 21 ]. The lack of large-scale studies of well-characterized individuals also made it difficult to extrapolate from the results to objectively estimate the prevalence of GD in the life insurance sector.

Treloar and colleagues have written that, 'conceptualizing, investigating and verifying individual's experience of genetic discrimination constitute a challenging endeavour' [ 47 ]. Discrimination surely can take many subtle forms. For example, rather than charging a higher premium or excluding an applicant, an insurance company could decide to process an application more slowly or ignore phone calls and emails in the hope of discouraging pursuit of the application process. In this case, the applicant might not even be aware that she or he has been discriminated against.

Given these serious challenges, it should come as no surprise that the five most influential studies on GD within academia, as measured by Web of Science and Google Scholar citation rates (see Table 4 ), all suffer from important limitations. For example, the most cited article on GD in life insurance, a precursor 1992 pilot study by Billings et al. , used a broad definition of GD and reported 29 responses describing 41 separate incidents of possible discrimination (32 in the field of insurance) [ 13 ]. The study undertook an extensive advertising campaign (1,119 letters mailed to genetic professionals, an advertisement in the American Journal of Human Genetics, and similar advertisements published in several patient organization newsletters) to elicit this relatively small number of potential discrimination cases from an under-defined population that included symptomatic respondents. The authors of the study acknowledged the limitations of their work stating that it is not meant to demonstrate the prevalence or the full range of discriminatory practices. Nevertheless, their conclusion that unfair and discriminatory use of genetic data existed and that new laws and sanctions should be considered does not seem to accord with the limited data and exploratory methodology provided in the study. Learning from early experiences and challenges in this field, more recent studies tend to draw more cautious or qualified conclusions and to recognize their own substantial methodological limitations [ 32 ].

Conclusions

This systematic review offers evidence that the literature recognizes the existence of incidents of GD in North America, Australia and the UK. We note four key observations. First, the methodology used in most of the studies is not sufficiently robust to clearly establish either the prevalence or impact of discriminatory practices in these regions. Second, the current body of evidence was mostly developed around a very small number of 'classic' genetic conditions. Third, the heterogeneity and small scope of most of the studies prevent formal statistical analysis of the aggregate results. Fourth, the small number of reported cases of GD in some studies could indicate that these incidents of GD took place due to error(s), rather than voluntary or planned choice, of the insurers.

These observations should not be interpreted as dismissing the importance of the significant work that has been accomplished by researchers in this field over the past 20 years. Our review has allowed us to confirm the existence of GD, to identify large areas of evidentiary gaps (for example, discrimination in the context of 'omics' studies) and methodological challenges (defining GD, verification of reported incidents of GD), and to identify promising methodologies to build upon for future studies such as the one used for the Australian Genetic Discrimination Project. In this project, a rich body of evidence was gathered from a variety of sources with special attention given to validation and methodological concerns [ 3 , 32 , 33 , 47 ]. This information can be used by the international research community to continue monitoring and documenting experiences of GD and its psychosocial and economic impact on individuals with improved, more streamlined research strategies.

To return to our original question, can the intense debate around GD in the life insurance context that has taken place this past quarter-century be justified on the basis of the available evidence? We must answer in the negative. With the notable exception of studies on Huntington's disease, none of the studies reviewed here (or their combination) brings irrefutable evidence of a systemic problem of GD that would yield a highly negative societal impact. From an ethical and policy standpoint, looking at the evidence alone suggests that targeted policies (in the case of Huntington's disease) and careful monitoring of the situation as it evolves is likely the most adequate course of action.

Nonetheless, other empirical and theoretical factors have emerged that should be considered along with this empirical data. They include the prevalence and impact of the fear of GD in patients and research participants, the importance (or not) of genetic information for the commercial viability of the private life insurance industry, and the need to develop more equitable schemes of access to life insurance. These factors, along with sociocultural and historical elements linked to particular societies (such as early experience with eugenics), would likely offer a better explanation as to why the GD debate became so polarized in popular and academic media. Finally, we wish to highlight that it remains to be determined whether the current GD dilemma is a sign of a broader discomfort with actuarial practices, public policies and access to life insurance at a time when many increasingly view this type of contractual protection as a good of important psychosocial value that is necessary to obtain other important social and commercial goods in post-industrial countries.

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The authors would like to acknowledge the financial support of the Ministère du Développement économique, de l'Innovation et de l'Exportation (Quebec) and the Canadian Institutes of Health Research.

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Joly, Y., Ngueng Feze, I. & Simard, J. Genetic discrimination and life insurance: a systematic review of the evidence. BMC Med 11 , 25 (2013). https://doi.org/10.1186/1741-7015-11-25

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Genetic discrimination in the workplace.

Published online by Cambridge University Press:  01 January 2021

The surge in genetic research and technology, fuelled in large part by the Human Genome Project, has resulted in the continuing expansion of the range of genetic tests and other genetic information available to physicians, insurance companies, employers, and the general public.’ Genetic tests can provide presymptomatic medical information about an individual, including information about an individual's increased risk of future disease, disability, or early death. These tests can reveal information about an individual's carrier status, that is, the likelihood of parents passing on to their children a genetic condition, and about the health of the individual's family members. Although genetic information provides the promise of early detection and treatment of certain illnesses and disorders, it also poses risks. As a result of the increase in genetic testing and information, legal issues regarding employment discrimination on the basis of genetic information are emerging.

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• Volume 26, Issue 3
• Paul Steven Miller
• DOI: https://doi.org/10.1111/j.1748-720X.1998.tb01419.x

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Genetic discrimination and life insurance: a systematic review of the evidence

Affiliation.

• 1 Department of Human Genetics, Faculty of Medicine, McGill University, 740 Dr Penfield Avenue, Suite 5200, Montreal, H3A 1A5 Canada. [email protected]
• PMID: 23369270
• PMCID: PMC3606414
• DOI: 10.1186/1741-7015-11-25

Background: Since the late 1980s, genetic discrimination has remained one of the major concerns associated with genetic research and clinical genetics. Europe has adopted a plethora of laws and policies, both at the regional and national levels, to prevent insurers from having access to genetic information for underwriting. Legislators from the United States and the United Kingdom have also felt compelled to adopt protective measures specifically addressing genetics and insurance. But does the available evidence really confirm the popular apprehension about genetic discrimination and the subsequent genetic exceptionalism?

Methods: This paper presents the results of a systematic, critical review of over 20 years of genetic discrimination studies in the context of life insurance.

Results: The available data clearly document the existence of individual cases of genetic discrimination. The significance of this initial finding is, however, greatly diminished by four observations. First, the methodology used in most of the studies is not sufficiently robust to clearly establish either the prevalence or the impact of discriminatory practices. Second, the current body of evidence was mostly developed around a small number of 'classic' genetic conditions. Third, the heterogeneity and small scope of most of the studies prevents formal statistical analysis of the aggregate results. Fourth, the small number of reported genetic discrimination cases in some studies could indicate that these incidents took place due to occasional errors, rather than the voluntary or planned choice, of the insurers.

Conclusion: Important methodological limitations and inconsistencies among the studies considered make it extremely difficult, at the moment, to justify policy action taken on the basis of evidence alone. Nonetheless, other empirical and theoretical factors have emerged (for example, the prevalence and impact of the fear of genetic discrimination among patients and research participants, the (un)importance of genetic information for the commercial viability of the private life insurance industry, and the need to develop more equitable schemes of access to life insurance) that should be considered along with the available evidence of genetic discrimination for a more holistic view of the debate.

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Case Study: Genetic Discrimination

Jim Sanders is a small business owner; he owns a bakery in the corner of town. The bakery currently has ten employees. Jim is interested in hiring one more employee, a web content editor, who can help launch the bakery’s transition to an online medium. Two candidates apply for the position and both are highly qualified with similar work experience and skills. Even after Jim has interviewed both candidates, he isn’t sure which one he will hire. However, he learns soon after, through a Facebook post, that Candidate #1 has a genetic risk for multiple sclerosis (MS). The candidate is healthy as of now, but it is  possible  that the disease will manifest in the future. Jim and his wife Sandra have three children who are in high school and will be attending college soon. Jim knows that he will not be able to afford his children’s education AND the potential increase in insurance premiums and/or increased sick days, should Candidate #1 develop the disease. Jim decides that he is not willing to take the chance, and he ends up hiring Candidate #2, who is not genetically predisposed to any disease. Because his business is small with only 10 employees, the federal Genetic Information Nondiscrimination Act does not apply to him, and he is not breaking any laws.

If you were Jim, would you have hired Candidate #2 also?

• Yes, Jim’s primary responsibility is to his family. His financial concerns are too important in this case to hire the candidate who may be diagnosed with MS in the future.
• No, in this case genetics are not a guarantee and the disease may never manifest. Deciding against hiring someone with a genetic predisposition is discriminatory, even if it’s not illegal.

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Title: adjusting for participation bias in case-control genetic association studies for rare diseases.

Abstract: Collection of genotype data in case-control genetic association studies may often be incomplete for reasons related to genes themselves. This non-ignorable missingness structure, if not appropriately accounted for, can result in participation bias in association analyses. To deal with this issue, Chen et al. (2016) proposed to collect additional genetic information from family members of individuals whose genotype data were not available, and developed a maximum likelihood method for bias correction. In this study, we develop an estimating equation approach to analyzing data collected from this design that allows adjustment of covariates. It jointly estimates odds ratio parameters for genetic association and missingness, where a logistic regression model is used to relate missingness to genotype and other covariates. Our method allows correlation between genotype and covariates while using genetic information from family members to provide information on the missing genotype data. In the estimating equation for genetic association parameters, we weight the contribution of each genotyped subject to the empirical likelihood score function by the inverse probability that the genotype data are available. We evaluate large and finite sample performance of our method via simulation studies and apply it to a family-based case-control study of breast cancer.

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The optimization of urban traffic routes using an enhanced genetic algorithm: a case study of beijing south railway station.

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Ding, B.; Rao, Z.; Yin, W.; Liu, Y.; Fang, J.; Wang, Y.; Jin, P. The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station. Appl. Sci. 2024 , 14 , 6130. https://doi.org/10.3390/app14146130

Ding B, Rao Z, Yin W, Liu Y, Fang J, Wang Y, Jin P. The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station. Applied Sciences . 2024; 14(14):6130. https://doi.org/10.3390/app14146130

Ding, Biao, Zhiqiang Rao, Weichuan Yin, Yanxia Liu, Jianjun Fang, Yuanming Wang, and Pengpeng Jin. 2024. "The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station" Applied Sciences 14, no. 14: 6130. https://doi.org/10.3390/app14146130

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By Nazin Sedehi and Laura Rivard

Introduction :

Predictive testing for genetic disorders and diseases became a reality beginning in the 1990s. At the time, many expressed concern over genetic privacy and potential discrimination by health insurance companies and employers. The past two decades saw a dramatic increase in rates of genetic testing, but reports of subsequent discrimination were isolated. In 2008, Congress passed the Genetic Information Nondiscrimination Act ( GINA ). Briefly, the law prohibits health insurers from denying coverage or raising premiums based on genetic information. The law also stipulates that employers cannot use genetic information for hiring, firing, or promotion decisions. However, GINA applies only to individuals on group health insurance plans; persons with an individual health insurance plan, or those employed by a small business of 15 or fewer people, are exempt. A few other laws also cover genetic discrimination. For example, some believe the Americans with Disabilities Act of 1990 (ADA), which prohibits discrimination in employment, public services, accommodations, and communications based on a disability, should be interpreted to cover genetic discrimination. However, the law does not explicitly cover genetic discrimination, and a broad interpretation has not been tested in court.

Case Study:

Jim Sanders is a small business owner; he inherited a bakery from his father and is now the third generation in his family to run the business. The bakery currently has ten employees. Jim is interested in hiring one more employee, a web content editor, who can help launch the bakery's transition to online sales. Two candidates apply for the position and both are highly qualified with similar work experience and skills. After interviewing both candidates, Jim favors Candidate #1 by a small margin based on her creative vision for the bakery's website. However, soon after the interviews, he learns through a Facebook post that Candidate #1 has a genetic risk for adult-onset muscular dystrophy (a disease characterized by muscle wasting). The candidate is healthy as of now, but it is probable that the disease will manifest in the future. The genetic test cannot reveal how severe the muscular dystrophy will be, how quickly muscle degeneration will occur, or at what age it will onset. Jim and his wife Sandra have three children who are in high school and will be attending college soon. Jim knows that his business will not be able to withstand the financial burden associated with an increasingly disabled employee. Jim decides that he is not willing to take the chance, and he ends up hiring Candidate #2. Because his business is small with only 10 employees, the federal Genetic Information Nondiscrimination Act does not apply to him.

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I wish I could say my answer was "no," but, in that situation, I would view the potential of Candidate 1 to get sick as too risky for my family. Of course, it is also possible that Candidate 2 already has a very severe disease that they just haven't put on FB.

I encourage you to be honest and place yourself in Jim's situation. Would you do what might be considered the "honorable" thing if it meant financial harm to YOUR family? Do small business owners deserve the GINA exemption since their resources are typically more limited?

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