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The extent of single nucleotide polymorphism is reviewed, together with insights gained into the nature of allelic architecture in terms of haplotypes, linkage disequilibrium and recombination. The utility of SNPs in defining genetic determinants of common disease is discussed including the rationale, results and diverse applications of the International HapMap Project. The recent development and application of genome-wide association studies is reviewed including the Wellcome Trust Case Control Consortium study of seven common diseases. Issues relating to design, analysis and interpretation of such studies are described. A detailed review of age-related macular degeneration and inflammatory bowel disease is presented, two common multifactorial diseases where genome-wide association studies have recently enjoyed considerable success. Research in these diseases illustrates the timeline of different approaches used in defining genetic determinants of common disease and how such analyses can provide novel insights into disease pathogenesis.

Genetic diversity observed in human populations provides unique insights into the selective pressures which have shaped our recent evolutionary past. Evidence to support this view is described in this chapter including concepts of genetic hitchhiking and selective sweeps, together with signatures of selection found from analysis of genetic variation. Insights gained from sequencing the chimpanzee and rhesus macaque genomes into human genetic diversity and selection is described. Evidence for selection from genetic studies of lactase persistence is reviewed for European, African, and Middle Eastern populations. The complex genetics of skin pigmentation is discussed, including the role of model organisms in identification of a human gene (SLC24A5) within which nucleotide diversity plays a major role in determining the light skinned pigmentation phenotype observed among Europeans. The chapter concludes with discussion of genome wide analyses using high density SNP panels in which many new loci showing evidence of selection are being identified.

Chapter 3, “Quantifying genetic variation at the molecular level,” introduces quantitative methods for measuring variation directly in DNA sequences to help decipher fundamental properties of populations and what they can tell us about evolution. It provides an overview of the evolutionary factors that contribute to genetic variation, like mutational input, effective population size, genetic drift, migration rate, and models of migration. This chapter surveys the principal ways to measure and summarize polymorphisms within a single population and across multiple populations of a species, including heterozygosity, nucleotide polymorphism estimators of θ‎, the site frequency spectrum, and F_ST, and by providing illustrative natural examples. Populations are where evolution starts, after mutations arise as the spark of population genetic variation, and Chapter 3 describes how to quantify the variation to connect observations to predictions about how much polymorphism there ought to be under different circumstances.

High‐throughput single nucleotide polymorphism (SNP) arrays, typically used in genome‐wide association studies with a trait of interest, provide estimates of genotypes for up to several million loci. Most genotype estimates are very accurate, but genotyping errors do occur and can influence test statistics, p‐values and ranks. Some SNPs are harder to call than others due to probe properties and other technical/biological factors; uncertainties can be associated with features of interest. SNP‐ and case‐specific genotype posterior probabilities are available, but they are typically not used or used only informally, for example by setting aside the most uncertain calls. To improve on these approaches we take full advantage of Bayesian structuring and develop an analytic framework that accommodates genotype uncertainties. We show that the power of a score test (and statistical information more generally) is directly a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that compared to picking a single AA, AB or BB genotype or to setting aside difficult calls, Bayesian structuring can substantially increase statistical information for detecting a true association and for ranking SNPs, whether the ranking be frequentist or optimal Bayes. This improvement is primarily associated with genotypes that are difficult to call.

This chapter argues that neither race nor ethnicity account for the ethnoracial classificatory iterations found in genetic epidemiological research. It problematizes the simplistic race/no-race binary and highlights the political and social consequences of the summary dismissal of population-based medical genetics research. The admixture narrative presented demonstrates that the racial discourse of the Chicago lab draws upon population genetics, biological anthropology, evolution, statistics, human genetics, and physiology. Diabetes researchers found a model of susceptibility that consists of heterozygosity for two different patterns of genetic code. The positional cloning technique described deploys single nucleotide polymorphisms as candidate genetic material for disease susceptibility. An examination of the arguments in the no-race debate reveals the complexity of racial discourse in and out of the diabetes enterprise. The critiques of race in science on the grounds that it rebiologizes race imputes a power to “science” it does not have.

The human genome varies by about 0.5% between individuals as a result of single nucleotide polymorphisms and insertions, deletions, inversions, and duplications of longer sequences. Patterns of sequence variation between populations provide information about human origins and the selective pressures that humans have experienced. Only a small proportion of sequence variation causes detectable phenotypic change. Although monogenic diseases such as cystic fibrosis are individually rare, selection has not altogether eliminated the deleterious alleles from the population because of recurrent mutation, delayed onset after reproductive age, and heterozygote advantage. Genetic susceptibility to common non-communicable diseases such as type 2 diabetes probably arises from the combined effects of numerous risk alleles, each having small effects, coupled with environmental and developmental factors. Not all inheritance is genetic. Disease risk can be transmitted by cultural or behavioral factors, and there is increasing evidence for transgenerational persistence of epigenetic modifications of DNA and gene expression.

Recent development of high-throughput genomics tools has made it possible and affordable to examine the molecular basis of variation in quantitative traits in studies of non-model species in the wild. High-density single nucleotide polymorphism data and genome sequences provide promising methodological advances complementing and strengthening traditional quantitative genetic analyses from long-term pedigrees. This chapter, discusses how high-density genomic data can be used to determine the actual or realised genetic relationship between relatives, which then can be accounted for in further analyses to improve estimates of quantitative genetic parameters, perhaps even without the need to construct a pedigree. Furthermore, this chapter suggests how combining long-term field data with high-density genomic data, to carry out genome-wide association studies or genomic predictions of phenotypes, can provide important insight into the genetic architecture and evolutionary dynamics of fitness-related traits. Empirical results thus far provide good support for the notion that most quantitative genetic traits studied in wild populations have a highly polygenic basis; a key assumption of quantitative genetic analyses. This chapter also discusses how high-density genomic data can be used to identify past signatures of selection in genetic data that can be further compared to loci currently responsible for variation in individual fitness. Finally, this chapter presents some important issues to consider when sampling, storing and preparing DNA for high-throughput genomics analyses. The application of high-throughput genomics tools in quantitative genetic studies of non-model species in the wild shows great promise to increase understanding of ecological and evolutionary processes in natural populations.

This chapter discusses the importance of genetics in questions of ecology and conservation, as genetic analyses have provided tremendous insights into the behaviour of reptiles, their evolution, and the history and dynamics of populations. To that end, the chapter discusses the functions and limitations of certain genetic markers: allozymes and restriction fragment length polymorphisms, mitochondrial DNA (mtDNA) sequencing, nuclear gene sequencing (NGS), nuclear microsatellites, single nucleotide polymorphisms (SNP), and whole genome research. Additionally, this chapter offers some pointers on sampling and labwork, such as the design and considerations for sampling; the process of collection, storage, and preservation; curating; and the subsequent data analysis and management.

This chapter tries to demonstrate how genetic epidemiologists are required to overtly position their knowledge within the sociohistorical context of its production, and advances the concept of bioethnic conscription as the process by which ethnicity comes to be constructed as meaningful for scientific research. It explores how concepts of race and ethnicity appear in genetics research and in the process of scientific publication and the marketing of pharmaceutical products, and how the ideological fault-lines between genes/environment, biology/society, and race/ethnicity are revealed. The chapter starts by summarizing the origins of the notion of genetic susceptibility to diabetes in ethnic groups and compares them with the environmental causation narratives for health disparities between ethnic groups. Diabetes is a profoundly social condition for Sun County residents. The deployment of racial labels and symbols within the diabetes research-and-development apparatus shapes the political context of their production. Single nucleotide polymorphism-based research does not create biological race.

Military personnel train and conduct operations in environments that result in exposure to multiple stressors such as caloric deprivation, physical and psychological strain, and increased energy expenditure, which have profound effects on cognitive and physical performance. The objective of this chapter is to draw on the peer-reviewed literature, including laboratory studies, applied field studies, and controlled trials conducted in the military environment to detail the contribution of nutrition and genetics to human performance and protection from injury. In summary, relevant studies indicate that nutrition status and genetic factors hold promise as risk biomarkers and intervention targets for the development of tailored solutions to optimize human performance and prevent injury during occupationally demanding tasks.

This chapter reviews approaches to how populations of sexually reproducing individuals are defined statistically via genetic criteria. It addresses the question of whether there is a meaningful and practical concept of a population in biology, in the context of recent genomic methods. Current approaches are simpler than a full modelling of relations between individuals, as differences between whole populations need not be described. Rather, when individuals reproduce according to standard genealogical processes, wider populations can then be correctly characterized. This statistical approach is, further, robust to incorrect modelling of differences between populations, and can be extended to whole-genome datasets by means of haplotype modelling. Simple examples and applications to data are used to illustrate these points. Despite limitations that arise when true data contain no discrete populations, the population concept remains a helpful tool in describing biodiversity.

Over the twentieth century, population genetics has enabled progressively finer subpopulation specification. This chapter traces the development of concepts and methods, beginning with the use of blood types to differentiate human populations into Mendelian groups. As variation in blood grouping began to be studied in terms of constituent proteins, and other genetic marker systems became available, further subpopulation differentiation was possible, so that spatial distribution could identify broadly defined continental groups across the world. Following the discovery of DNA structure, molecular genetics has steadily expanded the range of identifiable subpopulations, opening up further sources of population variation. Markers of genetic inheritance visible in the genetic make-up of regional populations enable major historical population movements to be traced, an approach which is shown to illuminate the historical peopling of Britain.

Cities are home to a continuum of species that range from those specially adapted to exploit urban habitats, to others passing through as transient dispersers. Urbanization often has a profound impact on the movement and gene flow of these species. Compared to natural landscapes, urban environments are complex matrices of roads, buildings, bare soil, slopes, green space, and subterranean infrastructure. Urban neighbourhoods also vary greatly in their socioeconomic and cultural characteristics. This heterogeneity can lead to complex movement patterns in wildlife that are difficult or impossible to characterize using direct tracking methods. Population genetic analyses provide powerful approaches to evaluate spatial patterns of genetic variation and even signatures of adaptive evolution across the genome. When analysed with landscape, environmental, and socioeconomic data, genetic approaches may also identify which features of urban habitats impede or facilitate gene flow. These landscape genetic approaches, when paired with high-resolution sampling and replicated studies across multiple cities, identify dynamic processes that underpin wildlife movement in cities. This chapter reviews the use of spatially explicit genetic approaches in understanding urban wildlife movement, and highlights the many insights gained from rodents in particular as models for urban landscape genetics.

Genome-wide association studies (GWAS) have evolved over the past ten years into a very successful tool for investigating the genetic architecture of multifactorial human traits and disorders. One major advantage of GWAS is that they do not require any a priori knowledge about the biological mechanisms underlying the traits and disorders under study. This chapter describes the scientific and technological developments that made GWAS possible and the underlying basic concept of these studies. The chapter considers what has been learned from GWAS in psychiatric research so far, what are the limitations, and looks forward to the future of GWAS.

The author is concerned with evolutionary origins of the human propensity for ethnic conflict, a kind of conflict not present between breeds of dogs or between subspecies in other mammals and birds. Using data from chips that genotype hundreds of thousands of loci in individuals, the author looks at the distribution of kinship within populations, asking whether and under what conditions there would occur a fitness advantage to the ability to detect kinship. In large, apparently homogeneous groups, there is almost no variation in kinship between people, whereas in diverse societies and in small, isolated communities there is a lot. If ethnic conflict and even chronic clan rivalries have some genetic basis, it likely evolved either in ancient urban environments with very diverse populations or else in a context of small, endogamous populations that are rare in the world today.

The analytical framework of human population genetics has completed a cycle: the lineage-based description of mitochondrial DNA and Y chromosome diversity, where often populations vanished, to be replaced by marauding hordes of disembodied haplogroups, is giving way to the multivariant analysis of SNP allele frequencies, rehashing the toolkit pioneered by Cavalli-Sforza in the late 1980s. Now, however, population definition is left to Bayesian algorithms, creating a tension between the individual, the population as socially defined, and reified Bayesian population constructs. This chapter reviews recent literature on African population genetics. East Africa can be imagined as a source or sink of population movements. Some argue that Southern Africa is a serious candidate to be humankind’s cradle, though contrary evidence suggests that the Bantu expansion homogenized a vast swathe of Africa. The multifaceted nature of Pygmy populations is explored in relation to this, and the place of populations north of the Sahara.