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

• Published in: Nature communications Vol. 13; no. 1; pp. 5437 - 13
• Main Authors: Dey, Rounak, Zhou, Wei, Kiiskinen, Tuomo, Havulinna, Aki, Elliott, Amanda, Karjalainen, Juha, Kurki, Mitja, Qin, Ashley, Lee, Seunggeun, Palotie, Aarno, Neale, Benjamin, Daly, Mark, Lin, Xihong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.09.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 45/43; 631/114/2415; 631/208/205/2138; 692/499; Association analysis; Biobanks; Biological Specimen Banks; Computer applications; Cost analysis; Electronic health records; Electronic medical records; Frailty; Frailty - genetics; Genetic analysis; Genetic diversity; Genetic variance; Genetics; Genome-Wide Association Study - methods; Genomes; Humanities and Social Sciences; Humans; Life span; multidisciplinary; Natural history; Optimization; Phenomics; Phenotype; Phenotypes; Population structure; Science; Science (multidisciplinary); Statistical methods; Survival; United Kingdom > UK
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32885-x

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. The proliferation of large biobanks necessitates statistical methods designed for genetic analysis on biobank data. Here, the authors have developed a frailty model-based method for GWAS analysis of time-to-event phenotypes in large biobanks that accounts for relatedness in samples and censoring of phenotypes.