2022
DOI: 10.1101/2022.08.11.22278618
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ADuLT: An efficient and robust time-to-event GWAS

Abstract: 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 ascertaine… Show more

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Cited by 7 publications
(6 citation statements)
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“…Second, although it is not the most ideal handling of data, our binary traits are treated as continuous ones in our analysis. In large samples, linear and logistic regression effect estimates correlate very strongly and hence, it is likely that this choice did not impact the clustering [36] . Third, although we have attempted to minimise the arbitrary choice of parameters in our analysis, the genetic correlation threshold that determines which traits are too similar to the exposure and outcome trait is arbitrarily set at 0.75 for BMI and EDU and could be modified, but the emerging clusters may change as a consequence.…”
Section: Colocalisation Analysismentioning
confidence: 99%
“…Second, although it is not the most ideal handling of data, our binary traits are treated as continuous ones in our analysis. In large samples, linear and logistic regression effect estimates correlate very strongly and hence, it is likely that this choice did not impact the clustering [36] . Third, although we have attempted to minimise the arbitrary choice of parameters in our analysis, the genetic correlation threshold that determines which traits are too similar to the exposure and outcome trait is arbitrarily set at 0.75 for BMI and EDU and could be modified, but the emerging clusters may change as a consequence.…”
Section: Colocalisation Analysismentioning
confidence: 99%
“…Eighth, we have analyzed British-ancestry samples from the UK Biobank, but an important future direction is to extend our analyses to cohorts of diverse genetic ancestry 52,53 , which may differ in their distributions of E variables, tagging of causal E variables by measured E variables, and/or causal GxE effects (analogous to differences in main G effects 45,54 ). Eighth, we do not analyze GxAge interaction (and we note the limited age variation in UK Biobank samples; age = 55 േ 8 years), but we highlight GxAge interaction and longitudinal data as important directions for future research 51,55,56 . Despite these limitations, our work quantifies and distinguishes three different types of GxE interaction across a broad set of diseases/traits and E variables.…”
Section: Discussionmentioning
confidence: 95%
“…In three examples of targets with approved therapies (PCSK9, ADRB1, ACE), this analytical framework identifies beneficial treatment effects seen in clinical trials, and a potential benefit in a clinical scenario for a novel target (BAG3) suggested by mouse models of ischemic cardiomyopathy. The approach described here is a natural extension of GWAS of clinical outcomes, Mendelian Randomization, and similar time-to-event GWAS [30][31][32] . Genetic survival analysis offers a critical advantage over standard applications of GWAS or Mendelian Randomization which typically only consider non-genetic covariates such as age and sex.…”
Section: Discussionmentioning
confidence: 99%