| Title: | Brain age prediction using deep learning uncovers associated sequence variants |
| Journal: | Nature Communications |
| Published: | 27 Nov 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31776335/ |
| DOI: | https://doi.org/10.1038/s41467-019-13163-9 |
| URL: | https://www.nature.com/articles/s41467-019-13163-9.pdf |
| Citations: | 298 (126 in last 2 years) as of 8 Aug 2024 |
Publication 2283
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Abstract
Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual's predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N=12378$$N=12378$$, replication set: N=4456$$N=4456$$) yielded two sequence variants, rs1452628-T (β=−0.08$$\beta =-0.08$$, P=1.15×10−9$$P=1.15\times{10}^{-9}$$) and rs2435204-G (β=0.102$$\beta =0.102$$, P=9.73×10−12$$P=9.73\times 1{0}^{-12}$$). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).</p>
9 Authors
- B. A. Jonsson
- G. Bjornsdottir
- T. E. Thorgeirsson
- L. M. Ellingsen
- G. Bragi Walters
- D. F. Gudbjartsson
- H. Stefansson
- K. Stefansson
- M. O. Ulfarsson
1 Application
| Application ID | Title |
|---|---|
| 24898 | Genetics of lumbar disc disease and associated sciatica |
Enabling scientific discoveries that improve human health