Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of diseaserelated 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-FHþþ, an extension of the liability threshold model conditioned on family history (LT-FH), which 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 statistically significant power gains over LT-FH and large power gains over 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 and found 20 genome-wide significant associations with LT-FHþþ, compared to ten for LT-FH and eight 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-FHþþ, to become even more beneficial.Currently, most case-control GWASs are conducted with a regression model where the outcome is the case-control status or occasionally the age of onset of disease. 20 In this paper, we have opted for using the phrase age of onset over age at first diagnosis because they commonly refer to the same underlying thing, i.e., when a diagnosis is given. Recently, researchers have proposed several methods that leverage additional information to improve the power to detect genetic associations without having to increase the number of genotyped individuals. These include multivariate methods that leverage shared environmental or genetic correlations between traits and diseases [21][22][23][24][25] as well as methods that account for age of onset. [26][27][28][29] Perhaps the most fruitful development has come from methods that leverage family information to increase statistical power to identify associations, such as genome-wide association study by proxy (GWAX) 30,31 and liability-thresholdmodel-based approach. 32 The liability threshold model conditioned on family history (LT-FH) 32 estimates the

Objective: Stimulant-drugs are effective for treating attention-deficit/hyperactivity disorder (ADHD), yet discontinuation and switch to non-stimulant ADHD-drugs is common. This study aimed to identify genetic, clinical and socio-demographic factors influencing stimulant-treatment initiation, discontinuation and switch to non-stimulants in individuals ADHD. Methods:We obtained genetic and national-register data for 9,133 individuals with ADHD from the Danish iPSYCH2012 sample, and defined stimulant-treatment initiation, discontinuation and switch from prescriptions. For each stimulant-treatment outcome, we examined associations with polygenic risk scores (PRSs) for psychiatric disorders, clinical, and socio-demographic factors using survival analyses, and conducted genome-wide association studies (GWASs) and estimated SNP-heritabilities (h 2 SNP).Results: 81% initiated stimulant-treatment. Within two years, 45% discontinued stimulants and 15% switched to non-stimulants. Bipolar-PRS (hazard ratio[HR]=1.05, 95%confidence interval[CI]=1.02-1.09) and schizophrenia-PRS (HR=1.07,95%CI=1.03-1.11) were associated with discontinuation. Depression, bipolar and schizophrenia PRSs were marginally associated with switch (HRrange=1.05-1.07) with CIs including one. No associations were observed for ADHD-PRS and autism-PRS. Individuals diagnosed with ADHD ≥13 years-of-age had higher rates of stimulant initiation, discontinuation, and switch (HRrange=1.27-2.01). Psychiatric comorbidities generally reduced rates of initiation (HRrange=0.84-0.88), and increased rates of discontinuation (HRrange=1.19-1.45) and switch (HRrange=1.40-2.08). Estimated h 2 SNP were not significantly different from zero. No GWAS-hits were identified for stimulant initiation or discontinuation. A locus on chromosome 16q23.3 reached genome-wide significance for switch (p=4.7×10 −8 ). Conclusion:Our findings suggest that individuals with ADHD with higher polygenic liability for mood/psychotic disorders, delayed ADHD diagnosis, and psychiatric comorbidities have higher risk for 4 stimulant-treatment discontinuation and switch to non-stimulants. Despite limited sample size, one possible GWAS-hit for switch was identified, illustrating the potential of utilizing prescription databases in pharmacogenomics.

Proportional hazards models have previously been proposed as a method to analyse time-to-event phenotypes in genome-wide association studies. While proportional hazards models have many useful applications, their ability to identify genetic associations under different generative models where ascertainment is present in the analysed data is poorly understood. This includes commonly used study designs such as case-control and case-cohort designs (e.g. the iPSYCH study design) where cases are commonly ascertained. Here we examine how recently proposed and computationally efficient Cox regression for GWAS perform under different generative models with and without ascertainment. We also propose the age-dependent liability threshold model (ADuLT), first introduced as the underlying model for the LT-FH++ method, as an alternative approach for time-to-event GWAS. We then benchmark ADuLT with SPACox and standard case-control GWAS using simulated data with varying degrees of ascertainment. We find Cox regression GWAS to underperform when cases are strongly ascertained (cases are oversampled by a factor larger than 5), regardless of the generative model used. In contrast, we found ADuLT to be robust to case-control ascertainment, and be orders of magnitude more efficient computationally. We then used the methods to conduct GWAS for four psychiatric disorders, ADHD, Autism, Depression, and Schizophrenia in the iPSYCH case-cohort sample, which has a strong case-ascertainment. Summarising across all four mental disorders, ADuLT found 20 independent genome-wide significant associations, while case-control GWAS found 17 and SPACox found 8, consistent with our simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information have the opportunity to increase power in analyses. We find that ADuLT to be a robust time-to-event GWAS method that performs on par with or better than Cox-regression GWAS, both in simulations and real data analyses of four psychiatric disorders. ADuLT has been implemented in an R package called LTFHPlus, and is available on GitHub.