Comparison of 2 models for gene-environment interactions: an example of simulated gene-medication interactions on systolic blood pressure in family-based data

A Gene-Acculturation Study of Obesity Among US Hispanic/Latinos: The Hispanic Community Health Study/Study of Latinos

OBJECTIVE

In the United States, Hispanic/Latino adults face a high burden of obesity; yet, not all individuals are equally affected, partly due in part to this ethnic group's marked sociocultural diversity. We sought to analyze the modification of body mass index (BMI) genetic effects in Hispanic/Latino adults by their level of acculturation, a complex biosocial phenomenon that remains understudied.

METHODS

Among 11,747 Hispanic/Latinos adults in the Hispanic Community Health Study/Study of Latinos aged 18 to 76 years from four urban communities (2008-2011), we a) tested our hypothesis that the effect of a genetic risk score (GRS) for increased BMI may be exacerbated by higher levels of acculturation and b) examined if GRS acculturation interactions varied by gender or Hispanic/Latino background group. All genetic modeling controlled for relatedness, age, gender, principal components of ancestry, center, and complex study design within a generalized estimated equation framework.

RESULTS

We observed a GRS increase of 0.34 kg/m 2 per risk allele in weighted mean BMI. The estimated main effect of GRS on BMI varied both across acculturation level and across gender. The difference between high and low acculturation ranged from 0.03 to 0.23 kg/m 2 per risk allele, but varied across acculturation measure and gender.

CONCLUSIONS

These results suggest the presence of effect modification by acculturation, with stronger effects on BMI among highly acculturated individuals and female immigrants. Future studies of obesity in the Hispanic/Latino community should account for sociocultural environments and consider their intersection with gender to better target obesity interventions.

Studying Socioeconomic Status: Conceptual Problems and an Alternative Path Forward

Socioeconomic status (SES; or social class) is considered an important determinant of psychological and life outcomes. Despite this importance, how to appropriately conceive of and measure it remains unsettled. In this article, I argue that SES is, under conventional conceptions of the construct, an unmeasurable construct and present an alternative strategy for studying socioeconomic conditions. I make this argument using several lines of analysis. First, a literature review of 20 years of psychological research on SES reveals that psychologists rarely define SES theoretically (79.6% of articles did not) but call a great number of operationalizations measures of SES (147 in total). Second, current recommendations for studying SES permit contradictory predictions, rendering the recommendations unsatisfactory. Third, the appropriate measurement model for SES inhibits accumulation of results across studies, which makes studying the construct practically impossible. To rectify these issues, I reconceptualize SES as a set of socioeconomic conditions and develop a measurement strategy for studying these conditions. I conclude by considering implications for ongoing research on socioeconomic conditions and for interpreting past research on SES.

Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

This paper summarizes the contributions from the Population-Based Association group at the Genetic Analysis Workshop 19. It provides an overview of the new statistical approaches tried out by group members in order to take best advantage of population-based sequence data.Although contributions were highly heterogeneous regarding the applied quality control criteria and the number of investigated variants, several technical issues were identified, leading to practical recommendations. Preliminary analyses revealed that Hurdle-negative binomial regression is a promising approach to investigate the distribution of allele counts instead of called genotypes from sequence data. Convergence problems, however, limited the use of this approach, creating a technical challenge shared by environment-stratified models used to investigate rare variant-environment interactions, as well as by rare variant haplotype analyses using well-established public software. Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference. Another practical recommendation was that dissenting probability values from standard and small-sample tests of a particular hypothesis may reflect a lack of validity of large-sample approximations. Novel statistical approaches that integrate evolutionary information showed some advantage to detect weak genetic signals, and Bayesian adjustment for confounding was able to efficiently estimate causal genetic effects. Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data. This paper reports on the experience of members of the Population-Based Association group with several novel, promising approaches to preprocessing and analyzing sequence data, and to following up identified association signals.