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In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Ciliopathies samples will be part of the rare disease group, and will undergo exome sequencing. For further information with regard to this cohort please contact Phil Beales (

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001000168 Illumina Genome Analyzer II Illumina HiSeq 2000 50
EGAD00001000191 Illumina Genome Analyzer II Illumina HiSeq 2000 128
EGAD00001000217 Illumina Genome Analyzer II Illumina HiSeq 2000 150
EGAD00001000296 Illumina Genome Analyzer II Illumina HiSeq 2000 108
EGAD00001000414 Illumina Genome Analyzer II Illumina HiSeq 2000 122
EGAD00001000752 Illumina HiSeq 2000 4
EGAD00001000802 Illumina HiSeq 2000 2
Publications Citations
The UK10K project identifies rare variants in health and disease.
Nature 526: 2015 82-90
Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource.
Nucleic Acids Res 44: 2016 9611-9623