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Central nervous system: Adult-onset and psychiatric disorders
Published in Angus Clarke, Alex Murray, Julian Sampson, Harper's Practical Genetic Counselling, 2019
Two disorders, schizophrenia and affective disorder (which includes both manic-depressive illness or bipolar affective disorder and unipolar disorder with episodes of depression only), account for a considerable proportion of all serious mental illness. They are not only extremely disabling and distressing conditions, often occurring in young people, but also result in a major burden for the community in terms of long-term care. A considerable body of genetic research has been developed over several decades, utilising carefully planned twin and adoption studies as well as family data. Diagnostic criteria have been standardised to overcome inevitable difficulties of interpretation, while quantitative genetic analysis has been developed to circumvent the lack of any clear Mendelian inheritance pattern.
Patterns of Inheritance: Mendelian and Non-Mendelian
Published in Merlin G. Butler, F. John Meaney, Genetics of Developmental Disabilities, 2019
Merlin G. Butler, Michael Begleiter, Shannon Lillis, Molly Lund, F. John Meaney
During the late 19th century, Sir Francis Galton studied quantitative traits such as intellectual functioning and height in humans. He developed quantitative methods for dealing with such traits, but never an adequate theory of the inheritance of these traits. Galton’s methods were further elaborated and refined by the scientists who became known as biometricians for their early applications of quantitative methods to the study of biological variation. It took the rediscovery of Mendel’s research and the brilliant work of Sir Ronald Fisher in the early part of the 20th century to produce an adequate theoretical basis for the inheritance of quantitative traits. What Fisher accomplished was the reconciliation of the statistical approaches to quantitative traits of the biometricians with Mendelian genetics by considering the multiple effects of single genetic loci. In doing so, he founded the subfield of quantitative genetics that through the years has focused on the genetics of quantitative traits.
Genetic influences on antisocial behaviour, problem substance use and schizophrenia: evidence from quantitative genetic and molecular genetic studies
Published in John C. Gunn, Pamela J. Taylor, Forensic Psychiatry, 2014
Pamela J Taylor, Marianne BM van den Bree, Nigel Williams, Terrie E Moffitt
The concept of development into a criminal career carries with it some inevitable connotation of a mingling of factors which are variously intrinsic and extrinsic to the person. The previous chapter, while mainly concentrating on psychological and social environmental factors which may predispose to a criminal career, or protect against it, introduced some preliminary consideration of relevant individual differences which may in part be founded in genetics – such as impulsivity and intelligence. This chapter considers the genetic contribution in more detail. The first part provides a basic introduction to concepts and an overview of research methods in the fields of most relevance to forensic psychiatry. These include quantitative genetic studies, which can help disentangle the extent to which personal traits are influenced by genes rather than environmental factors, and molecular genetic studies aimed at finding the particular genes involved in those disorders which have been most consistently linked with offending – antisocial personality traits, problem substance use and schizophrenia. The second part of the chapter provides a summary of quantitative genetic and molecular genetic findings in these areas. Genetic factors relevant among people with intellectual disabilities who may get caught up with the criminal justice system, and in particular some specific relevant genetic syndromes are covered in the chapter on offenders with intellectual disabilities (chapter 10).
Learning about quantitative genetics from Marla Sokolowski
Published in Journal of Neurogenetics, 2021
Marla Sokolowski is a true pioneer, breaking important new ground in the behavioral genetics of Drosophila. Her work displays a rare degree of insight, innovation and fearlessness. Marla blazed a trail as the first person successfully to combine a classical behavioral-quantitative-genetic approach with the other major tradition in Drosophila genetics of isolating and studying single-gene mutations. These have sometimes been depicted as the Benzerian approach (single-gene analysis, after Seymour Benzer) and the Hirschian (quantitative genetics, after Jerry Hirsch) tradition. These traditions were sometimes in ideological conflict over the years and have achieved a synthesis in the current era as the growth of molecular QTL mapping has finally made it possible to understand each approach in terms of the other (Greenspan, 2004).
Birth weight and anthropometric measurements of twins
Published in Annals of Human Biology, 2018
Mx software was used for genetic analysis. It is a matrix algebra interpreter and numerical optimiser for structural equation modelling and other types of statistical modelling of data. Mx software is developed by Mike Neale and is freely available for several platforms from http://www.vcu.edu/mx. Genetic analysis was conducted using maximum likelihood quantitative methods. In a quantitative genetic study, the phenotype variation is assumed to result from four factors: an additive genetic component (A), the genetic effects caused by dominance (D), the effects of the shared environment (C) and the effects of the unshared environment (E) (Tan et al. 2015). Components of these factors are calculated separately for MZ and DZ twin pairs and four models are estimated inclusive of AE, CE, ACE and ADE. Chi-square statistics were used to evaluate which model best fit the data. The relative fit of nested models can be estimated by first evaluating the heritability (h2) for a general model and comparing with a more constrained model. For instance, if shared environmental effects were set to zero and compared with the general model, a statistically significant h2 would mean shared environmental effects were a significant component of the variance for the variable under consideration.
Learning to collaborate: bringing together behavior and quantitative genomics
Published in Journal of Neurogenetics, 2020
Patricka A. Williams-Simon, Mathangi Ganesan, Elizabeth G. King
Mapping the genetic variants contributing to complex traits in general has presented a major challenge due to the difficulty of characterizing the effect of a single variant when there are many other variants also affecting a phenotype and the effects at individual loci are subtle (Boyle, Li, & Pritchard, 2017; Rockman, 2012). If trait categories are viewed as a hierarchy, Garland and Kelly (2006) have argued that behavior is expected to be one of the most complex, because it will be influenced by physiology, morphology, etc. at the lower hierarchical levels, leading to the expectation that the genetic basis of most behaviors will be highly complex. In addition, the processes of learning and memory are themselves the products of many other processes, such as sensory and motor functions, which further argues for their expected complexity (Schultzhaus, Saleem, Iftikhar, & Carney, 2017; Dolan et al., 2019). Early quantitative genetic approaches to map genetic variants used two-way quantitative trait loci (QTL) mapping, in which two parental strains are crossed to create an F1, then the F1s are either crossed to themselves or backcrossed to one of the parents to create an F2 generation. This creates a population with recombination breakpoints at different positions throughout the genome, allowing one to identify the association between the genotype at a given position and the phenotype of interest. However, because the individuals are only crossed for just a few generations, resulting in large haplotype blocks, the resolution for identifying individual genes, rather than regions of the genome is low (Mackay, 2001; Slate, 2004), with mapping regions typically wider than 10 cM (centiMorgans) and encompassing hundreds of genes. This has made it difficult to hone in on candidate genes that are influencing a particular phenotype.