| Title: | Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks |
| Journal: | Nature Communications |
| Published: | 16 Sep 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36114182/ |
| DOI: | https://doi.org/10.1038/s41467-022-32885-x |
| URL: | https://www.nature.com/articles/s41467-022-32885-x.pdf |
Publication 10468
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Abstract
With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.</p>
13 Authors
- Rounak Dey
- Wei Zhou
- Tuomo Kiiskinen
- Aki Havulinna
- Amanda Elliott
- Juha Karjalainen
- Mitja Kurki
- Ashley Qin
- Seunggeun Lee
- Aarno Palotie
- Benjamin Neale
- Mark Daly
- Xihong Lin
1 Application
| Application ID | Title |
|---|---|
| 52008 | Development of statistical methods to discover novel genetic associations, explain underlying biological mechanisms, and develop risk prediction models across varied complex diseases. |
Enabling scientific discoveries that improve human health