| Application: | 12505, Dissecting the genetic basis of relationships between early-life and later-life events |
| Title: | A unified framework for association and prediction from vertex-wise grey-matter structure |
| Size: | 2.0 MB |
| Archived: | 22 Jun 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3583
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Notes
The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey-matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex-wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex-wise complexity; (b) comparison of the information retained by different MRI processings.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 |
|---|---|---|---|---|
| 3584 | A unified framework for association and prediction from vertex-wise grey-matter structure | Couvy-Duchesne B et al. | 2020 | Hum Brain Mapp |
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