Dataset Information

The UK10K project identifies rare variants in health and disease.

ABSTRACT: The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

SUBMITTER: UK10K Consortium

PROVIDER: S-EPMC4773891 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The UK10K project identifies rare variants in health and disease.

Walter Klaudia K Min Josine L JL Huang Jie J Crooks Lucy L Memari Yasin Y McCarthy Shane S Perry John R B JR Xu ChangJiang C Futema Marta M Lawson Daniel D Iotchkova Valentina V Schiffels Stephan S Hendricks Audrey E AE Danecek Petr P Li Rui R Floyd James J Wain Louise V LV Barroso Inês I Humphries Steve E SE Hurles Matthew E ME Zeggini Eleftheria E Barrett Jeffrey C JC Plagnol Vincent V Richards J Brent JB Greenwood Celia M T CM Timpson Nicholas J NJ Durbin Richard R Soranzo Nicole N

Nature 20150914 7571

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adi ...[more]

PMID: 26367797

Similar Datasets

Project description:BackgroundIn European Union countries, any disease affecting less than 5 people in 10,000 is considered rare. As expertise is scarce and rare diseases (RD) are complex, RD patients can remain undiagnosed for many years. The period of searching for a diagnosis, called diagnostic delay, sometimes leads to a diagnostic dead end when the patient's disease is impossible to diagnose after undergoing all available investigations. In recent years, extensive efforts have been made to support the implementation of ORPHA nomenclature in health information systems (HIS) so as to allow RD coding. Until recently, the nomenclature only encompassed codes for specific RD. Persons suffering from a suspected RD who could not be diagnosed even after full investigation, could not be coded with ORPHAcodes. The recognition of the RD status is necessary for patients, even if they do not have a precise diagnosis. It can facilitate reimbursement of care, be socially and psychologically empowering, and grant them access to scientific advances.ResultsThe RD-CODE project aimed at making those patients identifiable in HIS in order to produce crucial epidemiological data. Undiagnosed patients were defined as patients for whom no clinically-known disorder could be confirmed by an expert center after all reasonable efforts to obtain a diagnosis according to the state-of-the-art and diagnostic capabilities available. Three recommendations for the coding of undiagnosed RD patients were produced by a multi-stakeholder panel of experts: 1/ Capture the diagnostic ascertainment for all rare disease cases; 2/ Use the newly created ORPHAcode (ORPHA:616874 "Rare disorder without a determined diagnosis after full investigation"), available in the Orphanet nomenclature: as the code is new, guidelines are essential to ensure its correct and homogeneous use for undiagnosed patients' identification in Europe and beyond; 3/ Use additional descriptors in registries.ConclusionsThe recommendations can now be implemented in HIS (electronic health records and/or registries) and could be a game-changer for patients, clinicians and researchers in the field, enabling assessment of the RD population, including undiagnosed patients, adaptation of policy measures including financing for care and research programs, and to improved access of undiagnosed patients to research programs.

Dataset Information

The UK10K project identifies rare variants in health and disease.

Publications

The UK10K project identifies rare variants in health and disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets