UK10K NEURO FSZ
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. These Finnish schizophrenia samples have been collected from a population cohort using national registers. The entire sample collection consists of 2756 individuals from 458 families of whom 931 are diagnosed with schizophrenia spectrum disorder, each family having at least two affected siblings. 170 families originate from an internal isolate (Kuusamo) with a three-fold lifetime risk for the trait. The genealogy of the internal isolate is well documented and the individuals form a “megapedigree” reaching to the 17th Century. All diagnoses are based on DSM-IV and for a large fraction of cases there is cognitive data. For further details/descriptions with regard to this data set please contact Tiina Paunio (tiina.paunio@thl.fi)
- Type: Other
- Archiver: European Genome-Phenome Archive (EGA)
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 |
---|---|---|---|
EGAD00001000184 | Illumina HiSeq 2000 | 120 | |
EGAD00001000240 | Illumina HiSeq 2000 | 120 | |
EGAD00001000318 | Illumina HiSeq 2000 | 119 | |
EGAD00001000615 | Illumina HiSeq 2000 | 128 |
Publications | Citations |
---|---|
The UK10K project identifies rare variants in health and disease.
Nature 526: 2015 82-90 |
622 |