bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative Trait Loci in Inbred Lines
Authors: Jansen R.C. 
Published: July 2004

AbstractQuantitative traits result from the influence of multiple genes (quantitative trait loci) and environmental factors. Detecting and mapping the individual genes underlying such ‘complex’ traits is a difficult task. Fortunately, populations obtained from crosses between inbred lines are relatively ideal for this—at least far more ideal than livestock and human populations—and true multigenic models are now available and have been applied successfully. In this chapter we will introduce the reader to statistical tools for segregation analysis and genetic mapping with the aid of molecular markers.
Mapping Quantitative Trait Loci in Outbred Pedigrees
Authors: Hoeschele I. 
Published: July 2004

AbstractIn this chapter, we present statistical methods for mapping quantitative trait loci (QTLs) in outbred or complex pedigrees. Such pedigrees exist primarily in livestock populations, also in human populations, and occasionally in experimental animal or plant populations. The main focus of this chapter is on linkage mapping, but methods for linkage disequilibrium (LD) and combined linkage/LD mapping are also outlined. The latter are very recent proposals and are at the time of writing less developed than linkage methods. We describe least‐squares and maximum likelihood (ML) methods for estimating QTL effects, and variance components analysis by approximate (residual) ML for estimating QTL variance contributions. We describe Bayesian QTL mapping, its prior distributions and other distributional assumptions, its implementation via Markov chain Monte Carlo (MCMC) algorithms, its inferences, and contrast it with frequentist methodology. Genotype sampling algorithms using genotypic peeling, allelic peeling, or descent graphs are described. Genotype samplers are a critical component of MCMC algorithms implementing ML and Bayesian analyses for complex pedigrees. Lastly, fine‐mapping methods including chromosome dissection and linkage disequilibrium mapping using current and historical recombinations, respectively, are outlined, and initial method developments combining linkage disequilibrium and linkage are presented.
Inferences about Breeding Values
Authors: Gianola D. 
Published: July 2004

AbstractStatistical methods that have been applied for inferring breeding values in animal breeding are reviewed. Landmarks include statistical genetic models; best linear unbiased prediction; Henderson's mixed model equations and associated computing techniques; variance and covariance component estimation, with emphasis on likelihood‐based methods; Bayesian procedures; methods for categorical responses, longitudinal data and survival analysis. The problem caused by the effects of selection on inferences is discussed. An inventory of some available computing software is presented. Finally, some areas for future development are discussed.
Mathematical Models in Population Genetics
Authors: Neuhauser C. 
Published: July 2004

AbstractThroughout the history of population genetics, mathematical models have played an important role in elucidating the effects of mutation and selection on the genetic diversity of organisms. Mathematical models provided the theoretical foundation of neo‐Darwinism; sophisticated mathematical tools aided Kimura in establishing the neutral molecular theory. Mathematical models in population genetics today are crucial in the development of statistical tools for analyzing molecular data. This chapter emphasizes models for selection, but also includes the discussion of neutral models. After a brief history of the role of selection in evolution, basic mathematical models are introduced together with the diffusion approximation. A discussion of coalescent theory follows, with primary focus on selection. A short discussion on how to detect selection concludes the chapter.
Inference Under the Coalescent
Authors: Stephens M. 
Published: July 2004

AbstractThis chapter introduces some modern statistical methods for extracting information from molecular population genetic data. The methods are based on use of the coalescent to model the genealogy relating a random sample of chromosomes from a population, and help us to say something about the demographic and genetic factors that shaped the evolution of populations from which the samples were taken. Unlike methods which rely on summaries of the observed data, such as pairwise differences, or sample heterozygosity, these coalescent‐based methods of inference make full use of the high resolution of modern genetic data. However, their complexity can be rather off‐putting. The aim of this chapter is to provide practical guidelines for those who wish to apply the methods using existing software, and sufficient theoretical background to understand how the methods work, at least in outline. The chapter also provides an introduction to importance sampling and Markov chain Monte Carlo—statistical methods which are likely to play a major role in analyses of future genetic data.
Linkage Disequilibrium and Recombination
Authors: Hudson R.R. 
Published: July 2004

AbstractLinkage disequilibrium is a measure of the association of alleles on gametes or chromosomes. A population is said to be at linkage equilibrium at a set of loci if the alleles are independently distributed on chromosomes. This chapter describes statistical tests of linkage equilibrium. In addition, the levels of linkage disequilibrium expected under simple neutral models are described.
Analysis of Population Subdivision
Authors: Excoffier L. 
Published: July 2004

AbstractThis chapter reviews the basic measures of intraspecific population subdivision, without assuming any particular spatial model. The structure of the chapter follows from the historical development of the field, starting with the presentation of Wright's fixation indexes as correlations of gene frequencies, then Nei's extension ofF‐statistics to multiple alleles as function of heterozygosities and his introduction ofG‐statistics as function of gene diversities, and ending with Cockerham's intraclass correlations obtained under an analysis of variance framework. We also present an extension of the analysis of variance approach to handle molecular data (DNA sequences, microsatellites) as well as dominant data. The relationships between different estimators, different approaches and their potential limitations are discussed.
Forensics
Authors: Weir B.S. 
Published: July 2004

AbstractThe use of DNA profiles for human identification often requires statistical genetic calculations. The probabilities for a matching DNA profile can be evaluated under alternative hypotheses about the contributor(s) to the profile, and presented as likelihood ratios. It is conditional probabilities that are needed, the probabilities of profiles given that they have already been seen, and these depend on the relationships between known and unknown people. The algebraic treatment is greatly simplified when it can be assumed that allelic frequencies have Dirichlet distributions over populations.
Epidemiology and Genetic Epidemiology
Authors: Burton P.R. , Tobin M.D. 
Published: July 2004

AbstractThis chapter focuses on descriptive analysis in genetic epidemiology and some of its links with mainstream epidemiology. It starts with incidence and prevalence, briefly reviewing some of the relevant statistical methods, and then moves on to consider the analysis of correlated responses in mainstream epidemiology. The core of the chapter describes methods used in descriptive analysis in genetic epidemiology. These methods are primarily used to describe and draw inferences about the structure of phenotypic dependencies within families. The appropriate focus of traditional descriptive epidemiology on representative sampling frames and high response rates is paralleled by the equal importance of sampling schemes in descriptive genetic epidemiology, and a full section is therefore devoted to non‐random ascertainment.
Linkage Analysis
Authors: Thompson E.A. 
Published: July 2004

AbstractLinkage analysis is the analysis of the dependence in inheritance of genes at different genetic loci, on the basis of phenotypic observations on individuals. The methods of linkage analysis that evolved over the years 1920 to 1970 closely followed more general developments in approaches to statistical inference. In recent years, the wealth of DNA markers and the near completion of the human genome project have led to changes in approaches to linkage analysis. New computational methods have been developed to make optimal use of available multilocus marker data. With the mapping of most simple Mendelian traits and the consequent emphasis on mapping the genes contributing to more complex traits, methods involving analysis of genome identity by descent are increasingly used. In view of the limited numbers of meioses often available for inference, fine‐scale mapping and estimation of accurate meiotic maps remain open challenges. As data on genetic variation become available at more detailed levels, sources of variation in the processes underlying meiosis can be investigated and more accurate genetic maps obtained. Genomic data at high resolution can allow models for meiosis outcomes to be tested and more accurate models developed. These may then be used in the mapping of trait genes.
Population Association
Authors: Clayton D. 
Published: July 2004

AbstractThis chapter discusses the analysis of population‐based case–control studies of association between disease and genotype. Much of the methodology differs little from that in widespread use in general epidemiology, but one difference arises in that association may be demonstrated by analyses at either the level of the chromosome (haplotype) or at the level of the person (genotype). The chapter reviews established methods, categorizing them in terms of the assumptions made, notably in respect to Hardy–Weinberg equilibrium, and in terms of the model of association assumed or implied. Population stratification, whether observed or unobserved, is discussed. Finally the problem of association involving extended marker haplotypes of several adjacent loci is briefly discussed.
The Transmission/Disequilibrium Test
Authors: Ewens W.J. , Spielman R.S. 
Published: July 2004

Abstract
The transmission‐disequilibrium test (TDT) is an association‐based test for linkage. It is a family‐based test and thus avoids the problems potentially arising in case–control tests from population stratification. However it does this at some loss of power. Various other positive and negative features of the test are examined from a quantitative viewpoint.
Pharmacogenetics
Authors: Schork N.J. , Fallin D. , Tiwari H.K. , Schork M.A. 
Published: July 2004

AbstractPharmacogenetics research is concerned with the identification and characterization of genes that influence individual responses to drug treatments and other exogenous stimuli. Although there are many study designs one could employ to identify and characterize genetic influences on drug responses (e.g., model organism and homology studies, gene expression studies, and controlled pharmacokinetic studies), many modern pharmacogenetic initiatives involve the evaluation of associations between genetic polymorphisms and outcomes in large‐scale clinical trials typically undertaken to evaluate the efficacy of a particular drug in the population at large. In this chapter we describe novel statistical methods for evaluating and testing pharmacogenetic outcomes in the context of population‐based clinical trials. We consider intuitions behind, and derivations of, statistical methods for a number of relevant issues. These issues include the assessment of clinical trial outcomes for evidence of a potential pharmacogenetic effect; optimally choosing individuals from a clinical trial for genetic studies; assessing the genetic homogeneity of a trial population; making efficient use of multilocus data; and conducting sequential pharmacogenetic trials. We also offer brief descriptions of additional useful extensions of the proposed analytic methods. Given that population‐based pharmacogenetics, as a discipline, is in its infancy, it is hoped that our proposals will spark further interest in the subject.
Ethics in the Use of Statistics in Genetics
Authors: Beyleveld D. 
Published: July 2004

Abstract

What is meant by ‘ethics’ is discussed and linked to ‘morality’, after which there is a brief overview of normative moral theories and comments are made about whether it is possible to choose rationally between moral theories. Regardless of this, there is considerable consensus in Western societies on principles (which is codified in various human rights conventions and the
World Medical Federation Declaration of Helsinki
). However, at the level of substance (which concerns which activities are right, wrong, or permissible), this consensus may be more apparent than real, and it is suggested that effective consensus only exists at a procedural level concerning principles governing how to resolve conflicts about issues of substance. With this as background, the use of statistics when citing DNA analysis as forensic evidence; and the use of heritability studies to make claims about the extent to which genetic factors are implicated in various conditions are discussed. In relation to both uses, it is argued that lack of understanding of statistics in general and of statistics in human genetics in particular has been exploited in ways that are unethical in terms of the existing procedural consensus, and that this exploitation also provides good reason for the public to mistrust scientists.
Overview of Applications of Plant Biotechnology
Authors: Murphy Denis J. 
Published: July 2004

AbstractThe purpose of this chapter is to provide a brief overview of the spectrum of applications of plant biotechnology that are in current use or are under development in research labs around the world. Plant biotechnology, in the sense of the application of recombinant DNA techniques to crop improvement, or the production of valuable molecules in plants, is still a relatively new endeavour. Plants are already used as sources of an immense array of useful molecules. These, especially the starches, proteins and oils in seeds, are raw materials for most of our food and feedstuffs. Plants are also the major sources of fibre for building materials, clothing and paper. Many of our leading drugs were originally or still are derived from phytochemicals, e.g. aspirin or taxol. Therefore, the possibilities for improving current products and making new products by means of plant biotechnology are, in principle, almost limitless. It is considered by many scientists that the tools of plant biotechnology potentially offer humankind one of its most significant opportunities to manage the ever growing and ever changing demands for food, feed and fibre production, while also contributing to the sustainability of agriculture.
Introduction to Classical Genetics and Plant Breeding
Authors: Powell Wayne , Waugh Robbie , Bradshaw John , Russell Joanne , Ramsay Luke , Forster Brian P. 
Published: July 2004

AbstractDuring the last century genetics has played a pivotal role in improving our understanding of many aspects of biology. The principles underlying heredity were first established in plants and the main practical beneficiary of these discoveries was agriculture, notably plant breeding. The purpose of this chapter is to highlight some of the key concepts that lay the genetical foundations for plant breeding. The significance and power of Mendelian genetics is presented as a fundamental foundation for plant breeding. In addition, the relationship between Mendelian genetics and the biometrical approach to continuous variation is explored from the perspective of providing a genetical foundation for manipulating quantitative traits in breeding programmes. The genetical principles of plant breeding are described in relation to the species breeding system. The application of genetic marker technology to enhance the efficiency of plant breeding is considered. The advent of DNA technology to improve the speed, precision and scale of plant breeding is outlined.
Tools for Gene Tagging and Mutagenesis
Authors: Jeon Jong‐Seong , Kang Hong‐Gyu , An Gynheung 
Published: July 2004

AbstractThe ultimate goal of current genome research in plants is to identify all the genes and discover their functions. Researchers are much closer to achieving the first of these goals now that theArabidopsisand rice genome‐sequencing projects have been completed. As a result of elucidating the structural genomics in these species, large‐scale functional studies are being launched. Recent progress on insertional mutagenesis should provide an excellent opportunity for using functional genomics. Here, we summarise the tools available for gene tagging and mutagenesis in plant species, especially inArabidopsisand rice.
Methods and Utility of EST and Whole Genome Sequencing
Authors: Rabinowicz Pablo D. , Martienssen Robert A. 
Published: July 2004

AbstractSince the Human Genome Project started in 1990 numerous large‐scale genomic sequencing projects have been conducted by different consortia of laboratories. As a result, the complete genetic information of an ever growing number of higher eukaryotes is now available in the public databases, which constitute an invaluable resource for biological research. Although eukaryote genomes are very large, most of their DNA is repetitive and do not code for genes. This is particularly true for plants, where repetitive DNA can account for more than 90 % of the genome, posing a major challenge to pursue full genomic sequencing. Large‐scale sequencing of cDNA (EST sequencing) is an efficient way to specifically sequence expressed genes, although no more than half of the genes can be recovered with this strategy. Gene targeted genomic sequencing approaches, like methylation filtration, overcome some of the limitations of EST sequencing and constitute an affordable alternative for plant genomic sequencing.
Gene Expression Profiling
Authors: Payton Paxton , Alba Rob , Moore Shanna 
Published: July 2004

AbstractDNA microarray technology, developed in the late 1990s, is fast becoming a standard tool for gene expression analysis. Many of the initial plant genome sequencing projects are providing large sets of expressed sequence tag (EST) or full length complementary DNA (cDNA) clones and printed microarrays to the public for use in gene expression studies. With the completion of sequencing theArabidopsisgenome, biologists are studying gene expression patterns on a genome‐wide scale. Also taking advantage of the rapid availability of sequence data, oligonucleotide arrays or sets of oligos suitable for in‐house arraying, are now available from commercial entities. Here we provide a basic outline of gene expression profiling in plants using DNA microarrays, discuss ESTs and oligonucleotides, probe synthesis and data analysis, as well as public resources for array information. In addition, we provide a brief summary of alternatives to array‐based expression profiling, the advantages and uses of ESTs databases and large EST data sets.
Plant Proteomics
Authors: Saravanan R. S. , Bashir Sajid , Rose Jocelyn K. C. 
Published: July 2004

AbstractProteomics, or the study of the protein complement of the genome, promises to help bridge the divide between genomic DNA sequence and biological state and represents one of the most rapidly developing and innovative fields in genome‐scale research. The term is now commonly and broadly used to encompass almost any aspect of protein structure, expression and function. However, as used here it refers to characterising quantitative and qualitative characteristics of global protein expression, with an emphasis on scale, including polypeptide synthesis, degradation, post‐translational modification, the structure of protein complexes and interactions with other proteins and cellular components. The systematic characterisation of complex protein populations can be pursued using many approaches and numerous reviews have recently been published describing developments in diverse areas of proteomics, including protein structure, function and protein–protein, or protein–ligand interactions A number of reviews have also been specifically devoted to plant proteomics. Consequently, the multiplicity of proteomics‐related experimentation and developing technologies are not reiterated here in detail. Instead, this chapter intends to provide an overview of the diversity of proteomics‐based studies that have been reported in plants to date and emerging technologies, which in turn should underscore the tremendous potential utility of proteomics for plant biologists and give an indication of future applications.
Computational Tools and Resources in Plant Genome Informatics
Authors: Vision Todd J. , McLysaght Aoife 
Published: July 2004

AbstractThough all biologists deal with information, only recently have the computational challenges of systematically collecting, storing, organizing, manipulating, visualizing and analysing large amounts of biological information come to be widely appreciated. The cause of this is the explosive growth of genomics. The termbioinformaticswas originally coined for the application of information technology to large volumes of biological, and particularly genomic, data. The field of bioinformatics has come to be intermingled with traditional computational biology and biostatistics, which are strictly concerned not with how to handle the information itself, but rather how to extract biological meaning from it. Thus bioinformatics, in its broad sense, can be seen as providing both the infrastructure and the scientific framework in which biologists take information and use computers to help convert it into knowledge.Despite the relative youth of the field as a recognized discipline, there is an impressive diversity of bioinformatics resources currently available. By necessity, we only focus on a small slice of this diversity here. We pay particular attention to sequence analysis because of its centrality to genomics. Though a wide array of commercial resources exist, some of which are ideally suited to specific tasks, many of the most fundamental and long‐lived bioinformatics tools are freely available. For this reason, we primarily describe non‐commercial software in this chapter. Many of the databases and analysis tools we describe are hosted by government or academic research centres and can be accessed via user‐friendly web interfaces.
Plant Transformation Technology: Particle Bombardment
Authors: Twyman Richard M. , Christou Paul 
Published: July 2004

AbstractStable gene transfer to plants can be achieved by two general strategies—Agrobacterium‐mediated transformation and the direct introduction of DNA into the plant cell.Agrobacterium‐mediated transformation is dependent on complex biological interactions between the bacterium and host plant, while direct DNA transfer is based on chemical or physical principles. Techniques included in this category, however, require particular cell types as transformation targets, and it is not always possible to regenerate plants from such cells. Only particle bombardment, the introduction of DNA into plants using high‐velocity microprojectiles, has the potential to transform any cell type from any plant, providing a truly universal transformation strategy. In this chapter, we discuss the principles of particle bombardment and variables that must be optimised for efficient transient and stable transformation. We also discuss the fate of exogenous DNA once it has entered the plant cell, and how this influences transgene expression. A literature survey is presented, listing all the plant species that have been transformed using this method, up to the end of the year 2001.