Testing Models for the Contributions of Genes and Environment to Developmental Change in Adolescent Depression

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance-covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available-genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development.

Notes on Three Decades of Methodology Workshops

Since 1987, a group of behavior geneticists have been teaching an annual methodology workshop on how to use state-of-the-art methods to analyze genetically informative data. In the early years, the focus was on analyzing twin and family data, using information of their known genetic relatedness to infer the role of genetic and environmental factors on phenotypic variation. With the rapid evolution of genotyping and sequencing technology and availability of measured genetic data, new methods to detect genetic variants associated with human traits were developed and became the focus of workshop teaching in alternate years. Over the years, many of the methodological advances in the field of statistical genetics have been direct outgrowths of the workshop, as evidence by the software and methodological publications authored by workshop faculty. We provide data and demographics of workshop attendees and evaluate the impact of the methodology workshops on scientific output in the field by evaluating the number of papers applying specific statistical genetic methodologies authored by individuals who have attended workshops.

The gene-environmental architecture of the development of adolescent substance use

BACKGROUND

Using a longitudinal twin design and a latent growth curve/autoregressive approach, this study examined the genetic-environmental architecture of substance use across adolescence.

METHODS

Self-reports of substance use (i.e. alcohol, marijuana) were collected at ages 13, 14, 15, and 17 years from 476 twin pairs (475 boys, 477 girls) living in the Province of Quebec, Canada. Substance use increased linearly across the adolescent years.

RESULTS

ACE modeling revealed that genetic, as well as shared and non-shared environmental factors explained the overall level of substance use and that these same factors also partly accounted for growth in substance use from age 13 to 17. Additional genetic factors predicted the growth in substance use. Finally, autoregressive effects revealed age-specific non-shared environmental influences and, to a lesser degree, age-specific genetic influences, which together accounted for the stability of substance use across adolescence.

CONCLUSIONS

The results support and expand the notion that genetic and environmental influences on substance use during adolescence are both developmentally stable and developmentally dynamic.

Contributions of Genes and Environment to Developmental Change in Alcohol Use

The precise nature of how genetic and environmental risk factors influence changes in alcohol use (AU) over time has not yet been investigated. Therefore, the aim of the present study is to examine the nature of longitudinal changes in these risk factors to AU from mid-adolescence through young adulthood. Using a large sample of male twins, we compared five developmental models that each makes different predictions regarding the longitudinal changes in genetic and environmental risks for AU. The best-fitting model indicated that genetic influences were consistent with a gradual growth in the liability to AU, whereas unique environmental risk factors were consistent with an accumulation of risks across time. These results imply that two distinct processes influence adolescent AU between the ages of 15-25. Genetic effects influence baseline levels of AU and rates of change across time, while unique environmental effects are more cumulative.

Depressive vulnerability, stressful life events and episode onset of major depression: a longitudinal model

BACKGROUND

The nature of the relationship between depressive vulnerability (DV) and acute adversity in the etiology of major depression (MD) remains poorly understood.

METHOD

Stressful life events (SLEs) and MD onsets in the last year were assessed at four waves in cohort 1 (females) and at two waves in cohort 2 (males and females) from the Virginia Adult Twin Study. Structural equation modeling was conducted in Mplus.

RESULTS

In cohort 1, DV was strongly indexed by depressive episodes over the four waves (paths from +0.72 to 0.79) and predicted by SLEs in the month of their occurrence (+0.31 to 0.36). Wave-specific DV was associated both with stable DV (+0.29 to 0.33) and by forward transmission of DV from the preceding wave (+0.33 to 0.36). SLEs were predicted by stable DV (+0.29) and from SLEs in the preceding month (+0.06). As the cohort aged, MD onsets were better indexed by DV and more poorly predicted by SLEs. Parameter estimates were similar in males and females from cohort 2. In individuals with prior depressive episodes, the association between MD onset and SLEs was weakened while the prediction of SLEs from DV was substantially strengthened. We found no evidence for 'reverse causation' from MD episodes to SLEs.

CONCLUSION

The interrelationship between DV and acute adversity in the etiology of MD is complex and temporally dynamic. DV impacts on MD risk both directly and indirectly through selection into high stress environments. Over time, depressive episodes become more autonomous. Both DV and SLEs transmit forward over time and therefore form clear targets for intervention.