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Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.

Purpose – The goal of this chapter is to demonstrate the importance of incorporating gene by environment (G×E) interactions into behavioral science theory and research.

Design/methodology/approach – We critique behavioral genetics, discuss the emergence of epigenetics, review findings on G×E effects, and present the differential susceptibility model of gene–environment interplay.

Findings – The studies reviewed demonstrate that genetic variation often interacts with environmental context to influence the probability of various behaviors. Importantly, in many, and perhaps most, of the studies reviewed, the genetic variable, unlike the environmental variable, has little if any main effect on the outcome of interest. Rather, the influence of the genetic variable is limited to its moderation of the effect of the environmental construct.

Research limitations/implications – Molecular G×E research does not undermine the importance of environmental factors; rather it shows how social scientific explanations of human behavior might be made more precise by incorporating genetic information. This suggests expanded research opportunities for those interested in social causation.

Social implications – This model of molecular G×E research presented suggests that a substantial proportion of the population is genetically predisposed to be more susceptible than others to environmental influence. We argue that this model of G×E is particularly relevant to sociologists and psychologists and has the potential to enhance the development of theory in both areas.

Originality/value – This chapter will be of particular interest to sociologists and psychologists who have found the behavioral genetic paradigm off-putting because of its emphasis on genetic main effects and genetic determinism. The current chapter offers an alternative model that may better capture the available data and better integrate social processes with genetic and biological processes.

The 2005 APSR article by John Alford, Carolyn Funk, and John Hibbing presented data from the Virginia 30,000 Health & Lifestyle Questionnaire (VA30K), AARP twin studies, and an Australian twin study (ATR) to test their hypothesis that political attitudes are influenced by genetic as well as environmental factors. Political attitudes, they suggested, were expected to be highly heritable and particularly so on issues most correlated with personality. They employed survey responses from the Wilson–Patterson Attitude Inventory to measure political attitudes. To gauge heritability, they utilize the 2:1 genetic ratio between monozygotic (MZ) and dizygotic (DZ) twins. The authors argued that while previous studies in political attitudes had concentrated on measuring the influence of environmental variables, their test added explanatory power by considering heritability (Alford, Funk, & Hibbing, 2005).

The purpose of this paper was to study the genetic variability, heritability, heat tolerance indices and phenotypic and genotypic correlation studies for traits of 250 elite International Center for Agricultural Research in the Dry Areas (ICARDA) bread wheat genotypes under high temperature in Wad Medani, Center in Sudan.

Bread wheat is an important food on a global level and is used in the form of different products. High temperature associated with climate change is considered to be a detrimental stress in the future on world wheat production. A total of 10,250 bread wheat genotypes selected from different advanced yield trials introduction from ICARDA and three checks including were grown in two sowing dates (SODs) (1st and 2nd) 1st SOD heat stress and 2nd SOD non-stress at the Gezira Research Farm, of the Agricultural Research Corporation, Wad Medani, Sudan.

An alpha lattice design with two replications was used to assess the presence of phenotypic and genotypic variations of different traits, indices for heat stress and heat tolerance for 20 top genotypes and phenotypic and genotypic correlations. Analysis of variance revealed significant differences among genotypes for all the characters. A wide range, 944-4,016 kg/ha in the first SOD and 1,192-5,120 kg/ha in the second SOD, was found in grain yield. The average yield on the first SOD is less than that of the secondnd SOD by 717.7 kg/ha, as the maximum and minimum temperatures were reduced by 3ºC each in the second SOD when compared to the first SOD of the critical stage of crop growth shown.

Similar wide ranges were found in all morpho-physiological traits studied. High heritability in a broad sense was estimated for days to heading and maturity. Moderate heritability estimates found for grain yield ranged from 44 to 63.6 per cent, biomass ranged from 37.8 to 49.1 per cent and canopy temperature (CT) after heading ranged from 44.2 to 48 per cent for the first and secondnd SODs. The top 20 genotypes are better than the better check in the two sowing dates and seven genotypes (248, 139, 143, 27, 67, 192 and 152) were produced high grain yield under both 1st SOD and 2nd SOD.

The same genotypes in addition to Imam (check) showed smaller tolerance (TOL) values, indicating that these genotypes had a smaller yield reduction under heat-stressed conditions and that they showed a higher heat stress susceptibility index (SSI). A smaller TOL and a higher SSI are favored. Both phenotypic and genotypic correlations of grain yield were positively and significantly correlated with biomass, harvest index, number of spikes/m², number of seeds/spike and days to heading and maturity in both SODs and negatively and significantly correlated with canopy temperature before and after heading in both SODs.

Genetic variations, heritability, heat tolerance indices and correlation studies for traits of bread wheat genotypes under high temperature

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

In the process of evolution, nature uses genetic algorithms to help with genetic selection. These algorithms can also bring immense benefit to industry. It is briefly explained what such computer‐based genetic algorithms are, how they can be applied in an industrial setting and their benefits.

The products of transgenic technology have captured the attention of enthusiasts and detractors, but transgenics are just one tool of agricultural biotechnology. Other applications enable scientists to understand biodiversity, to track genes through generations in breeding programs, and to move genes among closely related as well as unrelated organisms. These applications all have the potential to lead to substantial productivity gains.

In this chapter we provide an introduction to basic plant genetic concepts, defining molecular markers, transgenic and cisgenic techniques. We briefly summarize the status of commercialized biotechnology applications to agriculture. We consider the likely future commercialization of products like drought tolerant crops, crops designed to improve human nutrition, pharmaceuticals from transgenic plants, biofuels, and crops for environmental remediation. We identify genomic selection as a potentially powerful new technique and conclude with our reflections on the state of agricultural biotechnology.

Research at universities and other public-sector institutions, largely focused on advancing knowledge, has aroused enormous optimism about the promise of these DNA-based technologies. This in turn has led to large private-sector investments on maize, soybean, canola, and cotton, with wide adoption of the research products in about eight countries. Much has been made of the potential of biotechnology to address food needs in the low-income countries, and China, India, and Brazil have large public DNA-based crop variety development efforts. But other lower income developing countries have little capability to use these tools, even the most straightforward marker applications. Ensuring that these and other applications of biotechnology lead to products that are well adapted to local agriculture requires adaptive research capacity that is lacking in the lowest income, most food-insecure nations. We are less optimistic than many others that private research will fund these needs.

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in the “4Ps” of health care (predictive, preventive, personalized, and participatory). Big Data offers striking development opportunities in health care and life sciences. Healthcare research is already using Big Data to analyze the spatial distribution of diseases such as diabetes mellitus at detailed geographic levels. Big Data is also being used to assess location-specific risk factors based on data of health insurance claims. Other studies in systems medicine utilize bioinformatics approaches to human biology which necessitate Big Data statistical analysis and medical informatics tools. Big Data is also being used to develop electronic algorithms to forecast clinical events in real time, with the intent to improve patient outcomes and thus reduce costs.

Yet, this Big Data era also poses critically difficult ethical challenges, since it is breaking down the traditional divisions between what belongs to public and private domains in health care and health research. Big Data in health care raises complex ethical concerns due to use of huge datasets obtained from different sources for varying reasons. The clinical translation of this Big Data is thus resulting in key ethical and epistemological challenges for those who use these data to generate new knowledge and the clinicians who eventually apply it to improve patient care.

Underlying this challenge is the fact that patient consent often cannot be collected for the use of individuals’ personal data which then forms part of this Big Data. There is also the added dichotomy of healthcare providers which use such Big Data in attempts to reduce healthcare costs, and the negative impact this may have on the individual with respect to privacy issues and potential discrimination.

Big Data thus challenges societal norms of privacy and consent. Many questions are being raised on how these huge masses of data can be managed into valuable information and meaningful knowledge, while still maintaining ethical norms. Maintaining ethical integrity may lack behind in such a fast-changing sphere of knowledge. There is also an urgent need for international cooperation and standards when considering the ethical implications of the use of Big Data-intensive information.

This chapter will consider some of the main ethical aspects of this fast-developing field in the provision of health care, health research, and public health. It will use examples to concretize the discussion, such as the ethical aspects of the applications of Big Data obtained from clinical trials, and the use of Big Data obtained from the increasing popularity of health mobile apps and social media sites.

This study analyzes the rise of genome instability in the life sciences and traces the problematic of instability as it relates to the sociology of health. Genome instability is the study of how genomes change and become variable between generations and within organisms over the life span. Genome instability reflects a significant departure from the Platonic genome imagined during the Human Genome Project. The aim of this chapter is to explain and analyze research on copy number variation and somatic mosaicism to consider the implications of these sciences for sociologists interested in genomics.

This chapter draws on two multi-sited ethnographies of contemporary biomedical science and literature in the sociology of health, science, and biomedicine to document a shift in thinking about the genome from fixed and universal to highly variable and influenced by time and context.

Genomic instability has become a framework for addressing how genomes change and become variable between generations and within organisms over the life span. Instability is a useful framework for analyzing changes in the life sciences in the post-genomic era.

Genome instability requires life scientists to address how differences both within and between individuals articulate with shifting disease categories and classifications. For sociologists, these findings have implications for studies of identity, sociality, and clinical experience.

This is the first sociological analysis of genomic instability. It identifies practical and conceptual implications of genomic instability for life scientists and helps sociologists delineate new approaches to the study of genomics in the post-genomic era.