| Title: | Shared heritability analysis of phenotypic data from the UK Biobank cohort. |
| Lead Institution: | Broad Institute |
| Principal investigator: | Dr Verneri Anttila |
Application 18597
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About
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. 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. 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. We would wish to study the full UK Biobank cohort.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7329 | Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies | Zhaozhong Zhu (+3) | 2018 | PLOS ONE |
| 8886 | The impact of GDF-15, a biomarker for metformin, on the risk of coronary artery disease, breast and colorectal cancer, and type 2 diabetes and metabolic traits: a Mendelian randomisation study | Shiu Lun Au Yeung (+2) | 2019 | Diabetologia |
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