Biology subjects

Schowe, A. M.

Publications and source records attributed to Schowe, A. M..

3 recordsLinked to original sources

bioRxiv2026

Developmental Correlates of Epigenetic and Polygenic Indices of Cognition and Educational Attainment from Birth to Young Adulthood

Large-scale genomic studies have identified biomarkers of adult cognitive functioning and educational attainment, yet the developmental pathways connecting these biomarkers to adult outcomes remain unclear. Drawing on four cohorts, we examined the developmental correlates of an epigenetic index of adult cognitive function ( Epigenetic-g) alongside polygenic indices of cognition and education. Epigenetic-g and polygenic indices were uncorrelated and captured distinct variation in childrens cognitive and academic performance. Longitudinal analyses revealed that Epigenetic-g is plastic in early childhood, reaching moderate stability by adolescence, and, unlike polygenic indices, is not related to longitudinal cognitive growth. Twin models indicated that Epigenetic-g captures genetic and unique environmental variation relevant to cognitive and academic achievement that is not identified by current polygenic indices. Epigenetic indices relevant to psychological development can be generated from DNA methylation studies of adults, with most variation in these indices emerging early in life.

genomics↗

bioRxiv2026

(Epi-)Genomic Data in the German TwinLife Study: TwinSNPs and TECS Cohort Profiles

The German Twin Family Panel TwinLife is a nationwide longitudinal study of twins and their family members. Primarily focusing on the development of social inequalities over the life course, TwinLife has been collecting data since October 2014 starting with 4,096 twin families (Ntotal = 16,951 individuals). As Germanys largest twin study to date, TwinLife has been surveying four birth cohorts of monozygotic and dizygotic same-sex twin pairs (initially [~]5, 11, 17, and 23 years old) and their families for 11 years. Survey data have been collected through five biennial face-to-face interviews with four computer-assisted telephone interviews in the years between. In addition, saliva samples were collected before the COVID-19 pandemic (2018-2020), during the pandemic (2021), and after (2022-2024). In this Cohort Profile, we describe the curation and initial analyses of molecular genetic and epigenetic data from the two TwinLife satellite projects TwinSNPs and TECS. Together, these projects currently comprise 12,108 processed DNA samples from 6,450 participants, extracted from the first two saliva collections before and during the COVID-19 pandemic. We compared the subsamples with the overall TwinLife sample and provide an overview of derived polygenic scores (PGS), epigenetic clocks and other methylation profile scores (MPS). We found that PGS predicted sample attrition in TwinLife, with small but significant associations between higher PGS for educational attainment and continued participation. Epigenetic clocks derived from saliva were highly correlated with chronological age (r = .71 to r = .94) and were generally more stable over time than other MPS. PGS for epigenetic clocks were associated with the respective clock only during but not before the start of the pandemic. We discuss opportunities of combining prospectively assessed molecular (epi)genetic data in within-family designs such as TwinLife and its implications and avenues for future research.

genetics↗

bioRxiv2024

Age-Associated Genetic and Environmental Contributions to Epigenetic Aging Across Adolescence and Emerging Adulthood

BackgroundEpigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion). ResultsHalf to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually-unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47% to 49%; PedBE Acceleration: 33% to 68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18% to 39%; GrimAge Acceleration: 24% to 43%; DunedinPACE: 42% to 57%). ConclusionsTransition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.

genomics↗