Accounting for age-of-onset and family history improves power in genome-wide association studies

Abstract

Genome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

A comprehensive analysis of age of onset and cumulative incidence of mental disorders: A Danish register study

“…In conclusion, our study provides important insights into the impact of mental disorders across the life course. Our new estimates of incidence rates and cumulative incidence of disorders can guide future mental health policy and service development, and inform studies related to genetic epidemiology 38 and the burden of mental disorders 39 …”

Genetics of age-at-onset in major depression

“…Recent advances in statistical methods for case-control GWAS demonstrate that GWAS power can be increased by incorporating age at onset and family history information into case-control GWAS [45]. These methods rely on age at onset being heritable as well as a genetic correlation between age at onset and disease liability to increase statistical power.…”

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

“…Here, we provide empirical evidence using data on 11 common tumours from the UK and Estonian Biobank studies that GWAS discovery and genomic prediction are greatly improved by analysing age-at-diagnosis, compared to a case-control model of association. We extend our recently presented BayesW approach [20], a Bayesian modelling framework that enables joint effect size estimation for time-to-event data, to provide marginal leave-one-chromosome-out mixed-linear age-at-onset adjusted association estimates, in contrast to using Cox mixed model [22] or age-at-onset informed genomic reconstruction of the phenotype [21]. We focus on a re-analysis in the UK Biobank data alone, and we replicate previous findings from large-scale case-control GWAS and find an additional 59 previously unreported independent genomic regions, out of which 16 replicated in independent data (a respective increase of 18% and 6% over current findings).…”

“…Although most association studies use methods that account for the impact of other genetic regions (fastGWA [14], GMRM [15], BoltLMM [16], REGENIE [17]), it is sometimes still preferred to resort to the basic association testing. In addition, most genome-wide analyses have been performed using a case-control phenotype rather than utilising the cancer diagnosis age as a phenotype, and there is some evidence that analysing data using time-to-event informed methods can have more power for detecting associations [18][19][20][21].…”