Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks

Rounak Dey, Wei Zhou, Tuomo Kiiskinen, Aki Havulinna, Amanda Elliott, Juha Karjalainen, Mitja Kurki, Ashley Qin, Seunggeun Lee, Aarno Palotie, Benjamin Neale, Mark Daly and Xihong Lin ()
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Rounak Dey: Harvard T.H. Chan School of Public Health
Wei Zhou: Massachusetts General Hospital
Tuomo Kiiskinen: University of Helsinki
Aki Havulinna: University of Helsinki
Amanda Elliott: Harvard T.H. Chan School of Public Health
Juha Karjalainen: Massachusetts General Hospital
Mitja Kurki: Massachusetts General Hospital
Ashley Qin: Harvard T.H. Chan School of Public Health
Seunggeun Lee: Seoul National University
Aarno Palotie: Massachusetts General Hospital
Benjamin Neale: Massachusetts General Hospital
Mark Daly: Massachusetts General Hospital
Xihong Lin: Harvard T.H. Chan School of Public Health

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-32885-x

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