Tabea Schoeler
@tabeasch.bsky.social
Researcher at University of Lausanne | interested in genetic epidemiology, mental health & behaviour
7/ However, effect size estimates showed less agreement between the two designs (r = 0.74). Similar to the phenotypic findings, differences were primarily due to gene-by-cohort effects, where genetic associations vary across birth years, introducing bias into cross-sectional estimates.
July 8, 2025 at 12:47 PM
7/ However, effect size estimates showed less agreement between the two designs (r = 0.74). Similar to the phenotypic findings, differences were primarily due to gene-by-cohort effects, where genetic associations vary across birth years, introducing bias into cross-sectional estimates.
6/ Among the identified SNPs, 86% showed consistent interpretation across designs regarding the direction of age-varying genetic effects. These included both attenuation with age (e.g., for obesogenic traits) and intensification over time (e.g., for disease burden and medication use).
July 8, 2025 at 12:47 PM
6/ Among the identified SNPs, 86% showed consistent interpretation across designs regarding the direction of age-varying genetic effects. These included both attenuation with age (e.g., for obesogenic traits) and intensification over time (e.g., for disease burden and medication use).
5/ At the genetic level, we identified 57 SNPs with significant age-varying effects. Most were detected in the cross-sectional design, likely reflecting greater statistical power due to larger sample sizes and broader age ranges.
July 8, 2025 at 12:47 PM
5/ At the genetic level, we identified 57 SNPs with significant age-varying effects. Most were detected in the cross-sectional design, likely reflecting greater statistical power due to larger sample sizes and broader age ranges.
4/ We observed that this likely reflects confounding by year-of-birth effects (e.g., younger cohorts tend to smoke less), which can bias age estimates in cross-sectional analyses.
July 8, 2025 at 12:47 PM
4/ We observed that this likely reflects confounding by year-of-birth effects (e.g., younger cohorts tend to smoke less), which can bias age estimates in cross-sectional analyses.
3/ RESULTS:
At the phenotypic level, cross-sectional and longitudinal age effects showed only moderate agreement. For several traits, especially lifestyle behaviours, effects differed in their direction: e.g., smoking appeared to increase with age cross-sectionally but declined longitudinally.
At the phenotypic level, cross-sectional and longitudinal age effects showed only moderate agreement. For several traits, especially lifestyle behaviours, effects differed in their direction: e.g., smoking appeared to increase with age cross-sectionally but declined longitudinally.
July 8, 2025 at 12:47 PM
3/ RESULTS:
At the phenotypic level, cross-sectional and longitudinal age effects showed only moderate agreement. For several traits, especially lifestyle behaviours, effects differed in their direction: e.g., smoking appeared to increase with age cross-sectionally but declined longitudinally.
At the phenotypic level, cross-sectional and longitudinal age effects showed only moderate agreement. For several traits, especially lifestyle behaviours, effects differed in their direction: e.g., smoking appeared to increase with age cross-sectionally but declined longitudinally.
2/ Using data on 31 health-related traits from the UK Biobank, we focused on two questions:
🔹 Do the two designs lead to the same conclusions?
🔹 If not, what are the sources of bias that account for the observed discrepancies?
🔹 Do the two designs lead to the same conclusions?
🔹 If not, what are the sources of bias that account for the observed discrepancies?
July 8, 2025 at 12:47 PM
2/ Using data on 31 health-related traits from the UK Biobank, we focused on two questions:
🔹 Do the two designs lead to the same conclusions?
🔹 If not, what are the sources of bias that account for the observed discrepancies?
🔹 Do the two designs lead to the same conclusions?
🔹 If not, what are the sources of bias that account for the observed discrepancies?
Further, we found little evidence of common risks shared by (cross-sectional) level of functioning and (longitudinal) decline in cognitive and physical outcomes (11/11)
May 19, 2025 at 12:29 PM
Further, we found little evidence of common risks shared by (cross-sectional) level of functioning and (longitudinal) decline in cognitive and physical outcomes (11/11)
In total, 7 loci associated with longitudinal decline, implicating APOE and DUSP6 as the top genes associated with cognitive and physical decline, respectively. Overall, there was little overlap between the genetics of decline and physical/cognitive function (8/)
May 19, 2025 at 12:29 PM
In total, 7 loci associated with longitudinal decline, implicating APOE and DUSP6 as the top genes associated with cognitive and physical decline, respectively. Overall, there was little overlap between the genetics of decline and physical/cognitive function (8/)
We find that residual change scores introduce bias by falsely associating cross-sectional (time-invariant) genetic effects with change. In contrast, models capturing absolute and relative change are more robust in identifying longitudinal genetic effects (4/)
May 19, 2025 at 12:29 PM
We find that residual change scores introduce bias by falsely associating cross-sectional (time-invariant) genetic effects with change. In contrast, models capturing absolute and relative change are more robust in identifying longitudinal genetic effects (4/)