| Application: | 12505, Dissecting the genetic basis of relationships between early-life and later-life events |
| Title: | Improved polygenic prediction by Bayesian multiple regression on summary statistics |
| Size: | 27.0 MB |
| Archived: | 26 Mar 2021 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
Return 3255
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Notes
In this study, we extended a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improved prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n 700, 000) on height and BMI, we showed that on average across traits and two independent data sets that SBayesR improved prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.Application 12505
Dissecting the genetic basis of relationships between early-life and later-life events
Differences among individuals in distinct changes in their physiology as they age lead to differences in their susceptibility to negative later-life outcomes, and ultimately to differences in lifespan. This proposal aims to test whether genetic differences among individuals influence changes in cognition and physiological function in later life, to identify the genomic regions and biochemical pathways associated with these changes, and to test for genetic associations between early-life reproduction and later-life outcomes. This is crucial to understanding and predicting transitions across different human life stages. Understanding the genetic basis of relationships between early-life phenotypes, reproductive events, and later-life outcomes is of considerable research and public health interest. This proposal will identify new genetic relationships among physiological and cognitive functions, identify genomic regions of age-specific effect, estimate genetic relationships among life stages, and test the effects of genetic homozygosity in the genome in humans across life. It will lead to a better understanding of the genetic factors and biochemical pathways underlying cognitive and physiological decline in people as they age. Establishing robust genetic links between early and later-life health outcomes is challenging as (i) it usually requires studying people across their lives, and, when using families, (ii) relationships are confounded by other factors, such as shared environment between relatives. A novel design for studying the genetics of ageing will be used, so that even when different traits are measured on different individuals, the genetic basis of changes across life can be studied unbiased of shared environment. This enables an assessment of the genomic basis of multiple later-life phenotypes across different ages, testing theories for the genetics of ageing. For sufficient power to accurately estimate genetic relationships between characters, we require access to the full cohort. Even though individuals in the UKBiobank sample are measured across different ages, we can utilize the estimated genetic relationships among them to ask whether the genetic basis of characters is consistent across later life. We can then assess whether variation in genetic effects across life alters genetic relationships among characters throughout life (i.e., do the impacts of early-life on cognitive or physiological function only become apparent in individuals over 65). This requires data on all individuals recorded across all ages.
| Lead investigator: | Professor Peter Visscher |
| Lead institution: | University of Queensland |
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1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3256 | Improved polygenic prediction by Bayesian multiple regression on summary statistics | Lloyd-Jones LR et al. | 2019 | Nat Commun |
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