Novel discoveries and enhanced genomic prediction from modelling genetic risk of cancer age-at-onset

Abstract

Genome-wide association studies seek to attribute disease risk to DNA regions and facilitate subject-specific prediction and patient stratification. For later-life disease, inference from case-control studies is hampered by the uncertainty that control group subjects might later be diagnosed. Time-to-event analysis treats controls as right-censored, making no additional assumptions about future disease occurrence and representing a more sound conceptual alternative for more accurate inference. Here, using data on 11 common cancers from the UK and Estonian Biobank studies, we provide empirical evidence that discovery and genomic prediction are greatly improved by analysing age-at-diagnosis, compared to a case-control model of association. We replicate previous findings from large-scale case-control studies and find an additional 59 previously unreported independent genomic regions, out of which 16 replicated in independent data (an increase of 18% and 6% over current findings). Our novel discoveries provide new insights into underlying cancer pathways, and our model yields a better understanding of the polygenicity and genetic architecture of the 11 tumours. We find that heritable germline genetic variation plays a vital role in cancer occurrence, with risk attributable to many thousands of underlying genomic regions. Finally, we show that Bayesian modelling strategies utilising time-to-event data increase prediction accuracy. As sample size increases, incorporating time-to-event data should be commonplace, improving case-control studies by using richer information about the disease process.

Polygenic risk scores for cervical HPV infection, neoplasia and cancer show potential for personalised screening: Comparison of two methods

“…Hence, these models simultaneously estimate the effects of all variants, potentially giving an optimal predictor. The models were estimated for cervical cancer using only UK Biobank data of N = 248,798 European ancestry women (discovery sample), including 8,680 cervical cancer cases and 2,174,071 SNPs [21]. All PRSs were standardized, and effect sizes corresponded to an increase by one standard deviation.…”

ADuLT: An efficient and robust time-to-event GWAS

“…snpnet-Cox[28]. As other possible future directions, the ADuLT model may also provide an alternative framework for examining interactions between age and genetic variants[35], and provide insight into the genetics underlying disease trajectory. Like LT-FH[21] and LT-FH++[1], ADuLT also has the advantage that it produces quantitative posterior liabilities which can be treated as quantitative phenotypes and analysed with advanced GWAS method, such as BOLT-LMM[30], fastGWA[22], or REGENIE[32].…”