Principal Investigator: Dr Verneri Anttila
Department: Medical and Population Genetics
Broad Institute, Medical and Population Genetics, 74 Ames Street, Cambridge MA 02142, United StatesTags: 18597, co-morbidity, genome-wide association study, heritability
Lead Collaborators: 1) Dr Alkes Price
2) Dr Cecilia Lindgren
Collaborating Institutions and Addresses:
1) Harvard School of Public Health, Epidemiology, 655 Huntington Avenue, Building 2, Room 211, Boston MA 02115, United States
2) University of Oxford, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, United Kingdom
Funding: Internally funded
1a: This proposal seeks to access UK Biobank data for an analysis of shared heritability. Recently, we developed a novel method which allows us to quantify the heritability of a phenotype, as well as the degree of shared heritability to any second phenotype, based on genome-wide summary statistics. The method takes advantage of known relational patterns between common markers (established from an outside reference, such as the 1000 Genomes Project reference panel) to estimate the overall heritability from the distribution of p-value statistics, and then use the same relationships to estimate the degree of genetic correlation between disparate phenotypes.
1b: The research we plan is in agreement with the stated aim of UK Biobank “research intended to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society”.
This proposal seeks to screen for shared heritability between phenotypes in an unbiased manner, to uncover new genetic connections as part of ongoing efforts to study disease co-morbidity and risk factors.
1c: The new method allows us to ask if, and to what degree, the genetic factors influencing each disease or measurement are also the genetic factors influencing the second disease or measurement. This provides us with a very efficient tool to identify previously unknown genetic connections, without the need to have conducted each measurement in each patient of each disease, leading to considerable boost in efficiency. For example, this would allow using genetic data from the roughly 37,000 schizophrenic patients from our recently published study (Schizophrenia Working Group of the Psychiatric Genomics Consortium, Nature 2014) against every measurement with sufficient numbers in the UK Biobank, to identify which share genetic factors with those influencing schizophrenia.
1d: We would wish to study the full UK Biobank cohort.