Phenotypic and genetic factors associated with differential consent to record linkage for prescription history in the Australian Genetics of Depression Study
Santiago Diaz Torres,
Luis Garcia Marin,
Enda M. Byrne,
Penelope A. Lind,
Naomi R. Wray,
Sarah E. Medland,
Nicholas G Martin,
Posted 26 Oct 2021
medRxiv DOI: 10.1101/2021.10.26.21265507
Posted 26 Oct 2021
Samples can be prone to ascertainment and attrition biases.The Australian Genetics of Depression Study is a large publicly recruited cohort (n=20,689) established to increase the understanding of depression and antidepressant treatment response. As part of the recruitment, participants donated a saliva sample and were given the option to consent to linkage of prescription records for research purposes. This study investigates differences between participants who donated a saliva sample or agreed to linkage of their records compared to those who did not. We observed that older, male participants with a higher education were more likely to donate a saliva sample. Self-reported bipolar disorder, ADHD, panic disorder, PTSD, substance use disorder and social anxiety disorder were associated with lower odds of donating a saliva sample whereas anorexia was associated with higher odds of donation. Male and younger participants showed higher odds of agreeing to record linkage. Participants with higher neuroticism scores and those with a history of bipolar disorder were also more likely to agree to record linkage whereas participants with a diagnosis of anorexia were less likely to agree. Increased likelihood of consent was also associated with increased genetic susceptibility to anorexia and reduced genetic risk for depression, and schizophrenia whereas there was no significant genetic effect for neuroticism. Overall, our results show moderate differences among these subsamples. Most current epidemiological studies do not adjust, nor search, for attrition biases at the genetic level. The possibility to do so is a strength of samples such as the AGDS. Our results suggest that analyses can be made more robust by identifying attrition biases both on the phenotypic and genetic level, and either contextualising them as a potential limitation or performing sensitivity analyses adjusting for them.
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