Life insurance: genomic stratification and risk classification View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-05

AUTHORS

Yann Joly, Hilary Burton, Bartha Maria Knoppers, Ida Ngueng Feze, Tom Dent, Nora Pashayan, Susmita Chowdhury, William Foulkes, Alison Hall, Pavel Hamet, Nick Kirwan, Angus Macdonald, Jacques Simard, Ine Van Hoyweghen

ABSTRACT

With the development and increasing accessibility of new genomic tools such as next-generation sequencing, genome-wide association studies, and genomic stratification models, the debate on genetic discrimination in the context of life insurance became even more complex, requiring a review of current practices and the exploration of new scenarios. In this perspective, a multidisciplinary group of international experts representing different interests revisited the genetics and life insurance debate during a 2-day symposium 'Life insurance: breast cancer research and genetic risk prediction seminar' held in Quebec City, Canada on 24 and 25 September 2012. Having reviewed the current legal, social, and ethical issues on the use of genomic information in the context of life insurance, the Expert Group identified four main questions: (1) Have recent developments in genomics and related sciences changed the contours of the genetics and life insurance debate? (2) Are genomic results obtained in a research context relevant for life insurance underwriting? (3) Should predictive risk assessment and risk stratification models based on genomic data also be used for life insurance underwriting? (4) What positive actions could stakeholders in the debate take to alleviate concerns over the use of genomic information by life insurance underwriters? This paper presents a summary of the discussions and the specific action items recommended by the Expert Group. More... »

PAGES

575

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ejhg.2013.228

DOI

http://dx.doi.org/10.1038/ejhg.2013.228

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025629662

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24129434


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/ejhg.2013.228'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ejhg.2013.228'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ejhg.2013.228'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ejhg.2013.228'


 

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