| Application: | 27412, Boosting the power of GWAS using novel statistical tools |
| Title: | Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes |
| Size: | 1.2 MB |
| Archived: | 15 Jan 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3110
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Notes
The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields genetic architecture. T1-weighted brain scans (n?=?21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p?<?1? ?10 16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.Application 27412
Boosting the power of GWAS using novel statistical tools
This proposal seeks to apply UK Biobank data to study the genetic architecture of human traits using novel statistical tools. We aim to investigate the relationship between mental disorders and co-morbid diseases such as cardiovascular disease, cancer and metabolic disease (as well as protective phenotypes). Genome-wide association studies (GWAS) have successfully identified many genetic variants influencing complex human traits. However, the identified genetic variants only explain a small portion of the heritability of these traits. To improve discovery of genetic variants in complex human traits, we have developed statistical tools building on a Bayesian statistical framework. This proposal seeks to increase discovery of genetic loci influencing a range of human traits and disorders. Identifying genetic factors that confer risk or protect against health-related traits is critical for understanding the causal mechanisms underlying disease, and the causal relationship shared between clinical conditions. Improved gene discovery might inform the development of genetic prediction tools and ultimately improve treatment strategies for large patient groups. Hence, the proposed research is entirely congruent with the stated aim of UK Biobank ?to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society?. We will analyze the GWAS data on complex traits in the UK Biobank cohort using novel statistical methodology. Using software and computational tools we are able to enhance gene discovery by integrating GWAS data with additional knowledge about genetic variants, including their association in related traits or their genomic position. To assess the replicability (i.e. the robustness of the results) of the identified variants, we will evaluate their association in independent GWAS cohorts. Finally, the results may inform the development of novel genetic prediction tools. We would wish to study the full UK Biobank cohort.
| Lead investigator: | Professor Ole Andreassen |
| Lead institution: | University of Oslo |
2 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 3112 | 27412 | Common brain disorders are associated with heritable patterns of apparent aging of the brain | 18 Jan 2021 |
| 4183 | 27412 | Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology | 2 Nov 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3111 | Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes | van der Meer D et al. | 2020 | Mol Psychiatry |
Enabling scientific discoveries that improve human health