Search Results

This chapter discusses genetic modifiers of cancer risk. Topics covered include rationale for the study of low-penetrance genes, the role of low-penetrance genes in cancer susceptibility, methodological issues, gene selection in population studies, overview of candidate genes, overview of cancer-specific associations, and gene-environment interaction.

This chapter reviews the skin and hair pigmentation variation across Northern Island Melanesia. Advanced reflectance instruments now allow for detection of considerable regional variation in pigmentation both in the skin and hair. An island-by-island cline in skin pigmentation is revealed, with increasing M Index (heavily pigmented) values towards Bougainville Island. The skin pigmentation M values for Bougainville populations are as high as any surveyed population elsewhere, including West Africans. Papuan speakers in different islands have somewhat lighter hair pigmentation than their Austronesian-speaking neighbors. The distribution of six candidate genes for possible association/causation with pigmentation suggests that at least two (OCA2 and ASIP) could be associated with melanin phenotype variation in this region. While natural selection clearly must have an effect on pigmentation in this intensely irradiated region, it clearly does not dictate the pattern of melanin variation among these groups, which must be the result of ancient population associations.

The chapter suggests the need of a “second-generation” candidate gene approach to adapt to first-generation limitations and to strengthen efforts to study GE interactions. The sample sizes associated with many phenotypes and areas of study in the literature (e.g., clinical trials, neuroimaging studies) are unlikely to yield sample sizes in the area of GWA and NGS studies (i.e., 200,000-300,000 participants). However, by building upon prior limitations in the candidate gene literature, using findings from GWA and NGS studies and meta-analyses, using multiple methods of analyses (e.g., gene expression analysis; methylation analysis), and using theory and prior substantive research to guide hypothesis testing, progress can be made on G X E interactions for complex phenotypes. Several illustrative path models were provided in this chapter to provide a visual frame for how we have approached G X E interactions in the past, and how, going forward, we might proceed to investigate multiple polygenic by multiple environmental models. This level of complexity may be necessary to advance the field to address the many exciting research questions of interest, as well as the challenges that confront us as we attempt to move this knowledge from discovery to practice.

This chapter reviews research on genetic markers for antisocial behavior (ASB) and aggression. Following a brief description of the definition and scope of studies in ASB, the chapter summarizes the quantitative genetic literature showing heritability of ASB. The chapter also provides a detailed review of various candidate genes and their effects on ASB, as well as the extent to which these candidate genes may suggest mechanisms involved in the gene-behavior pathways.

This chapter discusses gene-environment interaction in coronary artery disease (CAD). Topics covered include risk factors for CAD, apolipoprotein E and effect on lipid levels and coronary heart disease risk, APOE genotype-smoking interaction, homeostasis and use of stressing the genotype to identify functional variants, and the role of inflammatory processes in atherogenesis.

This chapter could as well be titled “Population Genomics,” although many aspects of population genomics are integrated throughout the other chapters. It includes estimates of mutational variance and standing variance, phenotypic evolution under directional selection as measured by the linear selection gradient, and phenotypic evolution under stabilizing selection. It explores the strengths and limitations of genome-wide association studies of quantitative trait loci (QTLs) and expression (eQTLs) to detect genetic influencing complex traits in natural populations and genetic risk factors for complex diseases such as heart disease or diabetes. The number of genes affecting complex traits is considered, as well as evidence bearing on the issue of whether complex diseases are primarily affected by a very large number of genes, almost all of small effect, and how this bears on direct-to-consumer and over-the-counter genetic testing. The population genomics of adaptation is considered, including drug resistance, domestication, and local selection versus gene flow. The chapter concludes with the population genomics of speciation as illustrated by reinforcement of mating barriers, the reproducibility of phenotypic and genetic changes, and the accumulation of genetic incompatibilities.

This chapter explores the genetic epidemiology of cancer: the identification and quantification of inherited genetic factors, and their potential interaction with the environment, in the etiology of cancer in human populations. It also describes the techniques used to identify genetic variants that contribute to cancer susceptibility. It describes the older research methods for identifying the chromosomal localization of high-risk predisposing genes, such as linkage analysis within pedigrees and allele-sharing methods, as it is important to understand the foundations of the field. It also reviews the epidemiologic study designs that can be helpful in identifying low-risk alleles in candidate gene and genome-wide association studies, as well as gene–environment interactions. Finally, it describes some of the genotyping and sequencing platforms commonly employed for high-throughput genome analysis, and the concept of Mendelian randomization and how it may be useful in the study of biomarkers and environmental causes of cancer.

Ample evidence from model organisms has indicated that subtle variation in genes can dramatically influence lifespan. The key genes and molecular mechanisms that have been identified so far encode for metabolism, maintenance, and repair mechanisms. This chapter provides an overview of genes and mechanisms that have been shown to regulate lifespan in model organisms. It limits the discussion to those genes that have human homologues, and explores whether and how these influence human lifespan and other life history traits.

This chapter focuses on common empirical methods for studying the genetics of adaptation: quantitative genetics, quantitative trait locus (QTL) linkage mapping, association mapping, genome scans, gene expression, and candidate genes. It addresses various aspects of adaptation, speciation, and eco-evolutionary dynamics. The key questions include examining how much additive genetic variation exists in fitness-related traits, to what extent nonadditive genetic variation (dominance and epistasis) influences phenotypic variation, how many loci are involved in adaptation and how large their effects are, to what extent the adaptation of independent populations to similar environments involves parallel/convergent genetic changes, whether adaptation to changing environments is driven mainly by new mutations or by standing genetic variation, and to what extent the ecological effects of individuals transmitted among generations are.

Research in the last decades has extensively supported the widespread involvement of emotion regulation (i.e. the processes by which one attempts to modulate the experience and expression of affect) in emotion–cognition interactions, social functioning and behavior, and health. In particular, recent work has argued that emotion regulation is a transdiagnostic mechanism in psychopathology and could thus contribute to symptoms that characterize multiple mental disorders and explain some of the genetic overlap between these disorders. Therefore, an emerging literature has started to investigate the genetic underpinnings of emotion regulation and their commonality with psychopathology. After describing the process model, which has guided much of the recent research on emotion regulation, and its implications for psychopathology, the present chapter provides a systematic review of twin and candidate gene studies on the four emotion regulation strategies that have been examined to date: cognitive reappraisal, distraction, rumination, and expressive suppression. Several potential avenues for future research, suggested by recent advances in emotion regulation research and human genetics, are outlined in the final section of this chapter.

Chapter 6 discusses personality genetics, a revisited "novelty-seeking gene" (DRD4), as well as two important "trait theories" of personality: Cloninger's theory and the five-factor model. The chapter provides quotes from a number of prominent researchers interviewed in recent years. Important studies on depression and on the interaction between genes and environments by Caspi and Moffitt and critiques of their "candidate gene" approach are analyzed. These interaction approaches point behavioral and psychiatric genetics both toward environmental studies, in which genetics interacts significantly with the environment over humans' lifetimes, and toward more complex strategies that can still be characterized as reductionistic. Chapter 6 closes with some sobering reflections on the difficulty of obtaining replications in an area such as human personality genetics, which some investigators still consider to be in its infancy. Some prominent researchers believe that thousands of genes are likely to influence human personality.

This chapter introduces the reader to the concept of genetics and disease and includes review of more than 200 family, twin, and adoption studies for six important mental and behavioral disorders. The heritability estimates range from 30% for depressive disorder to as high as 80% for schizophrenia. Genome-wide association and sequencing studies are beginning to identify genetic risk variants associated with these disorders, but a large proportion of the genetic contribution remains unexplained. Major challenges in genetic studies include defining and agreeing on the phenotype that should be the subject of the study and the apparent complexity of the genetic etiology. In the near future, as larger samples with more detailed phenotype information are studied, a clear picture of the underlying genetic architecture of mental and behavioral disorders will emerge.

Most phenotypic traits are affected by a multitude of genes, which may interact in complex ways. This means that the single locus model explored in chapters 3 and 4 is not always able to capture the full complexity of genetic evolution. In many cases, multiple genes are involved and so this chapter formalizes the analysis of multilocus evolution. Concepts such as linkage disequilibrium and epistasis are introduced, both of which are necessary to properly understand multilocus evolution. The currently highly active field emerging as a result of a crossover between quantitative genetics and genomics is further explored, including methods such as quantitative trait locus (QTL) analysis and genome wide association study (GWAS) that allow phenotypic variation to be associated with likely causative genes and that have made important advances in our understanding of the genetic underpinnings of disease.

The February 16th, 2001 issue of Science magazine announced the completion of the human genome project—making the entire nucleotide sequence of the genome available (Venter, Adams et al. 2001). For the first time a comprehensive data set was available with nucleotide sequences for every gene. This marked the beginning of a new era, the ‘‘genomics’’ era, where molecular biological science began a shift from the investigation of single genes towards the investigation of all genes in an organism simultaneously. Alongside the completion of the genome project came the introduction of new high throughput experimental approaches such as gene expression microarrays, rapid single nucleotide polymorphism detection, and proteomics methods such as yeast two hybrid screens (Brown and Botstein 1999; Kwok and Chen 2003; Sharff and Jhoti 2003; Zhu, Bilgin et al. 2003). These methods permitted the investigation of hundreds if not thousands of genes simultaneously. With these high throughput methods, the limiting step in the study of biology began shifting from data collection to data interpretation. To interpret traditional experimental results that addressed the function of only a single or handful of genes, investigators needed to understand only those few genes addressed in the study in detail and perhaps a handful of other related genes. These investigators needed to be familiar with a comparatively small collection of peer-reviewed publications and prior results. Today, new genomics experimental assays, such as gene expression microarrays, are generating data for thousands of genes simultaneously. The increasing complexity and sophistication of these methods makes them extremely unwieldy for manual analysis since the number and diversity of genes involved exceed the expertise of any single investigator. The only practical solution to analyzing these types of data sets is using computational methods that are unhindered by the volume of modern data. Bioinformatics is a new field that emphasizes computational methods to analyze such data sets (Lesk 2002). Bioinformatics combines the algorithms and approaches employed in computer science and statistics to analyze, understand, and hypothesize about the large repositories of collected biological data and knowledge.

Optimism, spirituality, and a sense of meaning in life are all considered to be critical components of an individual’s character. This chapter examines the degree to which these character traits are heritable, and begins by assessing how these three important aspects of a person’s character have been defined within psychological science and cognitive neuroscience. It then provides a comprehensive review of the empirical work aimed at investigating heritability and molecular genetics in optimism, spirituality, and meaning in life. It considers quantitative behavioral genetics studies, GWAS, as well as candidate gene studies, and discovers that there are very few studies examining the constructs of optimism, spirituality, and meaning in life from a genetic perspective. However, conclusions are drawn from this small literature and some suggestions made as to what key studies and directions of research would be optimally informative for the future.

This chapter reviews the evidence for maternal effects in wild ungulate populations, discussing their prevalence and the mechanisms by which they are mediated. The analysis reveals that maternal effects contribute not only to observed phenotypic diversity in wild ungulates but also in evolutionary dynamics. This chapter stresses the need to determine what specific behavioral, physiological, and genetic mechanisms mediate maternal effects and suggests that candidate gene approaches could offer a useful alternative for scrutinizing loci underlying genetic maternal effects.

This chapter analyses the neural mechanisms that generate the aesthetic emotion of musical enjoyment. First, it focuses on the bottom-up succession of neural events from the periphery to the central nervous system that leads to musical enjoyment (the sensory hypothesis of musical pleasure). Then it examines the cognitive, top-down route from prefrontal and associative cortices to peripheral reactions (the conceptual hypothesis of musical pleasure). These two distinct neural mechanisms explain paradoxical cases when the cognitive enjoyment of music does not match sensory pleasure. Finally, internal contextual factors such as expertise, mood, personality, and genes, and external contextual factors such as the presence of peers and the environment, are pointed out as the plausible origins of idiosyncrasies in musical enjoyment.

The distinction between genetic variation that is present in more than 5% of the population (defined as common) and genetic variation that does not meet this threshold (defined as rare) is often lost in the discussion of psychiatric genetics. As a general proposition, the field has come to equate the hunt for common variants (or alleles) with the search for genes causing or contributing to psychiatric illness. Indeed, the majority of studies on mood disorders, autism, schizophrenia, obsessive–compulsive disorder, attention-deficit/hyperactivity disorder, and Tourette syndrome have restricted their analyses to the potential contribution of common alleles. Studies focusing on rare genetic mutations have, until quite recently, been viewed as outside the mainstream of efforts aimed at elucidating the biological substrates of serious psychopathology. Both the implicit assumption that common alleles underlie the lion’s share of risk for most common neuropsychiatric conditions and the notion that the most expeditious way to elucidate their biological bases will be to concentrate efforts on common alleles deserve careful scrutiny. Indeed, key findings across all of human genetics, including those within psychiatry, support the following alternative conclusions: (1) for disorders such as autism and schizophrenia, the study of rare variants already holds the most immediate promise for defining the molecular and cellular mechanisms of disease (McClellan, Susser, & King, 2007; O’Roak & State, 2008); (2) common variation will be found to carry much more modest risks than previously anticipated (Altshuler & Daly, 2007; Saxena et al., 2007); and (3) rare variation will account for substantial risk for common complex disorders, particularly for neuropsychiatric conditions with relatively early onset and chronic course. This chapter addresses the rare variant genetic approach specifically with respect to mental illness. It first introduces the distinction between the key characteristics of common and rare genetic variation. It then briefly addresses the methodologies employed to demonstrate a causal or contributory role for genes in complex disease, focusing on how these approaches differ in terms of the ability to detect and confirm the role of rare variation.

Doctors and researchers have been able to identify the causes of a variety of medical conditions, such as the common cold, a heart attack, and gout, to name a few. For example, there are different types of viruses that cause the symptoms of a common cold. By knowing what causes a medical problem, doctors are able to treat the condition in the most focused way possible. In the previous chapter, three general categories of causes of psychosis were presented: medical causes, substances, including certain drugs of abuse and several medicines, and a number of psychiatric illnesses. This chapter presents what is currently known about the causes of the third of these, psychiatric illnesses, especially primary psychotic disorders like schizophrenia. Some health conditions have a single, straight-forward cause. As mentioned earlier, a common cold is caused by a virus. However, many illnesses do not have a single identifiable cause. Rather, they are caused by a combination of risk factors. A risk factor is any event, exposure, or entity that occurs before the illness and that research has shown plays a role in causing the illness. For example, cigarette smoking is a well-known risk factor for lung cancer. Smoking occurs before the lung cancer develops, and researchers have proven that smoking cigarettes plays a part in causing many cases of lung cancer. Because schizophrenia and related psychotic disorders are such complex illnesses, it is sometimes unclear if some of the risk factors truly occur before the illness. Some risk factors may make some people more psychosis-prone. In other words, some risk factors are best thought of as increasing one’s tendency towards psychosis rather than actually causing psychosis. Over the past several decades, researchers have identified some of the likely causes of complex medical conditions like diabetes, high blood pressure, and psychosis. For each of these, as is true of most medical conditions, there is no single cause. Rather, a number of risk factors, both internal (like certain genes) and external (like exposures that stress the body, such as stressful life events or drugs) combine in complex ways to bring about the illness.