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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. This sample set of UK origin consists of clinically identified subjects with Autism Spectrum Disorders, mostly without intellectual disability (ie. Verbal IQs >70). The subjects represent children and adults with Autism, Asperger syndrome or Atypical Autism, identified according to standardized research criteria (ADI-algorithm, ADOS). A minority has identified comorbid neurodevelopmental disorders (e.g. ADHD). Family histories are available, with measures of broader phenotype in first-degree relatives.For further information on this cohort please contact David Skuse (

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
EGAD00001000231 Illumina HiSeq 2000 320
EGAD00001000313 Illumina HiSeq 2000 305
EGAD00001000614 Illumina HiSeq 2000 341
Publications Citations
The UK10K project identifies rare variants in health and disease.
Nature 526: 2015 82-90
scoreInvHap: Inversion genotyping for genome-wide association studies.
PLoS Genet 15: 2019 e1008203