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Primary ciliary dyskinesia: a big data genomics approach

Mitchison, HM; Smedley, D; (2022) Primary ciliary dyskinesia: a big data genomics approach. The Lancet Respiratory Medicine 10.1016/S2213-2600(22)00009-1. (In press). Green open access

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Abstract

How can the real-life frequency of a complicated condition that occurs irregularly, with a clinically variable presentation that is often difficult to diagnose, be calculated? This is a challenge for understanding the societal and medical impacts of an estimated 7000 rare disorders (those affecting more than one in 2000 people each) that can be poorly understood and often missed, especially if lacking clear-cut indicatory tests. The collective rare disease burden affects over one in 20 people, involving a disparate range of health-care services and creating unmet medical needs with potential for incorrect management. Better diagnostics and disease prevalence estimates are key to improving inequalities for millions of affected families worldwide.

Type: Article
Title: Primary ciliary dyskinesia: a big data genomics approach
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/S2213-2600(22)00009-1
Publisher version: https://doi.org/10.1016/S2213-2600(22)00009-1
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Genetics and Genomic Medicine Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10142581
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