Is the Gene-Environment Interaction Paradigm Relevant to Genome-Wide Studies? The Case of Education and Body Mass Index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.     

COVID‐19 as a Transformative Opportunity for Families and Therapists: Harnessing the Possibilities That Constraints Offer Us

This article demonstrates several ways in which the constraints that the COVID‐19 pandemic has posed for families can provide transformative opportunities to strengthen family relationships, become more resilient, and resolve conflicts and longstanding difficulties. Since most families worldwide have been greatly impacted on many fronts by the pandemic, they are looking to us for hope, guidance, and solutions for coping with high levels of anxiety about living in a world where life as we knew it has been turned on its head and we are struggling to cope with uncertainty about what the future will bring. However, in spite of all the limitations and health consequences COVID‐19 has posed for our client families, the majority have been quite resourceful and creative in coming up with their own novel self‐generated coping, problem‐solving, and health‐enhancing strategies well before we see them for the first time. Finally, the article discusses how the constraints of COVID‐19 afford family therapists with many opportunities to further hone their therapeutic alliance‐building skills, gain an intimate insider’s view of their clients’ daily lives that are not accessible in our offices, and co‐design with them therapeutic experiments that can produce high‐quality solutions.     

Efficient Bayesian estimation of the multivariate Double Chain Markov Model

The Double Chain Markov Model (DCMM) is used to model an observable process $Y = \{Y_{t}\}_{t=1}^{T}$ as a Markov chain with transition matrix, $P_{x_{t}}$ , dependent on the value of an unobservable (hidden) Markov chain $\{X_{t}\}_{t=1}^{T}$ . We present and justify an efficient algorithm for sampling from the posterior distribution associated with the DCMM, when the observable process Y consists of independent vectors of (possibly) different lengths. Convergence of the Gibbs sampler, used to simulate the posterior density, is improved by adding a random permutation step. Simulation studies are included to illustrate the method. The problem that motivated our model is presented at the end. It is an application to real data, consisting of the credit rating dynamics of a portfolio of financial companies where the (unobserved) hidden process is the state of the broader economy.     

Work Accident Insurance and Prevention in Southern Africa: The Case of Zimbabwe

The paper uses Zimbabwe as a case study to depict the situation in southern Africa with respect to work accident insurance and prevention, provided by workers' compensation schemes which are among the earliest forms of social security to be introduced in the region. There has been an unfortunate tendency to concentrate on compensation issues at the expense of prevention initiatives. Zimbabwe, however, has attempted to integrate the two, notwithstanding the fact that the coverage of the scheme is narrow and the benefits inadequate.     

Segmented Assimilation,Local Context and Determinants of Drug Violence in Miami and San Diego: Does Ethnicity and Immigration Matter?1

Does the ethnic and immigrant composition of a community and existence of immigrant enclaves or barrios influence community level drug violence? This study explores the relationship between these and other factors in Miami and San Diego census tracts. We employ data about the distribution of Cubans, Central Americans, Haitians, Mexicans and Southeast Asians, controlling for social and economic influences of drug versus non‐drug violence. We also analyze the impact of various waves of immigration and immigrant communities to understand the circumstances under which drug violence occurs or is limited at the census tract level. The findings lend some support to the positive and negative aspects of Portes and Rumbaut's (2001) segmented assimilation hypothesis in Miami and San Diego neighborhoods. The strength of this conclusion varies and is contingent upon ethnic composition, new versus old immigration, and the all‐encompassing effects of economic deprivation     

A DIAGNOSIS FOR OUR TIMES:

This paper explores two submerged networks in the Pittsburgh area alternative health movement through the use of thirty two intensive interviews and ethnographic research. Utilizing Jürgen Habermas's colonization theory as well as Alberto Melucci's emphasis on the process of collective identity formation, the paper describes a "conservative Christian' and a culturally progressive network demonstrating how these networks differ and what they have in common. The paper examines how new oppositional identities and meanings emerge from face to face interactions between alternative practitioners, patients and activists in a variety of settings. Overall, the paper argues that resistance to the "medical industrial complex,' one leading instrument of "colonization,' is coming from the Christian right as well as elements of the cultural left.     

Deserts and oases: the continuing concentration of population in the American Mountain West

The Mountain West is a region that seems to be simultaneously rural and urban. With its wide-open spaces, many national parks, monuments, and forests, and high degree of federal land ownership the West appears as the quintessential rural area. However, over 70 percent of the West's population live in metropolitan areas. This simultaneous rural and urban nature of the West is important in understanding the changing population geography of the region. We examine this by focusing on changing patterns of population concentration among metro and nonmetro counties. Unlike other regions in the US, the Mountain West has never experienced a period of counterurbanization or population deconcentration. Not only is current in-migration to the region increasingly concentrated in old and new metro areas, it is also concentrated in a select number of nonmetro areas as well—particularly nonmetro counties adjacent to metro areas, in retirement destinations, and in recreation centers.     

CONCENTRATED POVERTY, RACE, AND HOMICIDE

Research on the link between levels of poverty and homicide in urban areas has persistently reported the existence of a relationship for whites but not for blacks. This is despite the fact that most analysts expect that the higher levels of urban black homicide are due in part to the higher levels of urban black poverty. The present research introduces a more meaningful, spatially based measure of concentrated poverty and argues that the effect of concentrated poverty on homicide rates should be the same for both racial groups. The hypotheses are tested with race-disaggregated data for a sample of central cities circa 1990. The results suggest that, when poverty is measured as a linear spatially based phenomenon, in is a more important determinat of race-specific homicide rates than overall city levels of disadvantage and that the concentration of poverty increases both black and white homicide rather equally.     

USING LINEAR PREDICTORS TO IMPUTE ALLELE FREQUENCIES FROM SUMMARY OR POOLED GENOTYPE DATA

Recently-developed genotype imputation methods are a powerful tool for detecting untyped genetic variants that affect disease susceptibility in genetic association studies. However, existing imputation methods require individual-level genotype data, whereas in practice it is often the case that only summary data are available. For example this may occur because, for reasons of privacy or politics, only summary data are made available to the research community at large; or because only summary data are collected, as in DNA pooling experiments. In this article, we introduce a new statistical method that can accurately infer the frequencies of untyped genetic variants in these settings, and indeed substantially improve frequency estimates at typed variants in pooling experiments where observations are noisy. Our approach, which predicts each allele frequency using a linear combination of observed frequencies, is statistically straight-forward, and related to a long history of the use of linear methods for estimating missing values (e.g. Kriging). The main statistical novelty is our approach to regularizing the covariance matrix estimates, and the resulting linear predictors, which is based on methods from population genetics. We find that, besides being both fast and flexible - allowing new problems to be tackled that cannot be handled by existing imputation approaches purpose-built for the genetic context - these linear methods are also very accurate. Indeed, imputation accuracy using this approach is similar to that obtained by state-of-the art imputation methods that use individual-level data, but at a fraction of the computational cost.