Family History Assessment Significantly Enhances Delivery of Precision Medicine in the Genomics Era Article Swipe
Yasmin Bylstra
,
Weng Khong Lim
,
Sylvia Kam
,
Koei Wan Tham
,
R. Ryanne Wu
,
Jing Xian Teo
,
Sonia Dávila
,
Jyn Ling Kuan
,
Sock Hoai Chan
,
Nicolas Bertin
,
Chengxi Yang
,
Steve Rozen
,
Bin Tean Teh
,
Khung Keong Yeo
,
Stuart A. Cook
,
Lori A. Orlando
,
Saumya Shekhar Jamuar
,
Geoffrey S. Ginsburg
,
Patrick Tan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.01.29.926139
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.01.29.926139
Background Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs, with less emphasis on family history assessment. We evaluated the utility of family history for genomic sequencing selection. Methods We analysed whole genome sequences of 1750 healthy research participants, with and without preselection based on standardised family history collection, screening 95 cancer genes. Results The frequency of likely pathogenic/ pathogenic (LP/P) variants in 884 participants with no family history available (FH not available group) (2%) versus 866 participants with family history available (FH available group) (3.1%) was not significant ( p =0.158). However, within the FH available group, amongst 73 participants with an increased family history cancer risk (increased FH risk), 1 in 7 participants carried a LP/P variant inferring a six-fold increase compared with 1 in 47 participants assessed at average family history cancer risk (average FH risk) and a seven-fold increase compared to the FH not available group. The enrichment was further pronounced (up to 18-fold) when assessing the 25 cancer genes in the ACMG 59-gene panel. Furthermore, 63 participants had an increased family history cancer risk in absence of an apparent LP/P variant. Conclusion Our findings show that systematic family history collection remains critical for health risk assessment, providing important actionable data and augmenting the yield from genomic data. Family history also highlights the potential impact of additional hereditary, environmental and behavioural influences not reflected by genomic sequencing.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.01.29.926139
- https://www.biorxiv.org/content/biorxiv/early/2020/01/30/2020.01.29.926139.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3003918639
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3003918639Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.01.29.926139Digital Object Identifier
- Title
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Family History Assessment Significantly Enhances Delivery of Precision Medicine in the Genomics EraWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-30Full publication date if available
- Authors
-
Yasmin Bylstra, Weng Khong Lim, Sylvia Kam, Koei Wan Tham, R. Ryanne Wu, Jing Xian Teo, Sonia Dávila, Jyn Ling Kuan, Sock Hoai Chan, Nicolas Bertin, Chengxi Yang, Steve Rozen, Bin Tean Teh, Khung Keong Yeo, Stuart A. Cook, Lori A. Orlando, Saumya Shekhar Jamuar, Geoffrey S. Ginsburg, Patrick TanList of authors in order
- Landing page
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https://doi.org/10.1101/2020.01.29.926139Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2020/01/30/2020.01.29.926139.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2020/01/30/2020.01.29.926139.full.pdfDirect OA link when available
- Concepts
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Family history, Medicine, Genomics, Population, Genetics, Biology, Genome, Internal medicine, Gene, Environmental healthTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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