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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.

Political scientists have taken up behavior genetics (BG) at a momentous time in the science of genetics. Momentous, because the science of genetics is undergoing a paradigm shift [Petronis, A. (2010). Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature, 465(7299), 721–727]. This shifting paradigm poses a significant challenge to both the prevailing methodologies of behavior genetics – twin, family, adoption studies – and one of the most noteworthy findings to emerge from such studies, that is, which we can call the principle of minimal parental effects. This is the supposition that the effect of the shared parental rearing environment on the behavioral phenotypes of offspring is statistically equivalent to zero (Plomin & Daniels, 1987). It is not uncommon nowadays to find twin, adoption, and family studies utilized in the study of political behavior (e.g., Alford, J., Funk, C. L., & Hibbing, J. R. (2005). Are political orientations genetically transmitted? American Political Science Review, 99(2), 153–167.); likewise, the principle of minimal parental effects is frequently invoked in such studies (e.g., Mondak, J. J., Hibbing, M. V., Canache, D., Seligson, M. A., & Anderson, M. A. (2010). Personality and civic engagement: An integrative framework for the study of trait effects on political behavior. American Political Science Review, 104(1), 85–110.). As we shall see, the challenge comes from recent discoveries in genetics that are radically transforming our understanding of the genome and its relationship to environment.

Copy Number Variations (CNVs) are not infrequently observed in aberrant neurodevelopment. CNVs can alter gene expression and have been linked to a wide range of neuropsychiatric disorders. The purpose of this case study is to report the association of CNVs with a mixed neurodevelopmental disorder.

Array-Comparative Genomic Hybridisation analysis was carried out in a case of an eight-year-old boy presenting with a mixed neurodevelopmental disorder including autism spectrum disorder, intellectual disability, tic disorder, anxiety and severe aggression. The child's parents also underwent the same investigation.

A 6q27 deletion and multiple copies within 20q11.23 were identified. The boy's father shared the 6q27 deletion and his mother also had multiple copies within 20q11.23.

This is the first report linking the combination of 6p27 and 20q11 CNVs with a mixed neurodevelopmental presentation. Identifying CNVs that may underlie aberrant neurodevelopment is likely to assist in unravelling the aetiology of neurodevelopmental and psychiatric disorders and lead to more effective strategies for their characterisation and management.

Attention deficit hyperactivity disorder (ADHD) is a common disorder that is highly prevalent in children and frequently persists into adulthood. The purpose of this paper is to consider the need for practitioners to be aware of the disorder.

This paper reviews quantitative genetic findings in ADHD, primarily focussing on twin studies that describe the role of genetic influences throughout the lifespan and the associated overlap between ADHD and other syndromes, disorders and traits.

This paper concludes that ADHD is a lifespan condition that shares genetic risk factors with other psychiatric, neurodevelopmental disorders and intellectual disabilities.

This paper makes the case that clinicians working in the area of intellectual disability should be fully aware of the potential impact of ADHD and its associated impairments.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.