File Name: genetics and analysis of quantitative traits .zip
The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus QTL mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Compairing quantitative trait analysis to qualitative trait analysis for complex traits disease: A genome wide association study for hyperlipidemia Abstract: Current standard genome-wide association studies GWAS have relied on the simple analysis by focusing on the association between single genetic factor and one single common complex trait. However, since most common complex traits are associated with multiple genetic factors and their epistasis, this simple analysis is not powerful enough to detect multiple genetic factors.
Metrics details. Quantitative, or complex, traits are traits for which phenotypic variation is continuously distributed in natural populations, with population variation often approximating a statistical normal distribution on an appropriate scale. Quantitative traits include aspects of morphology height, weight ; physiology blood pressure ; behavior aggression ; as well as molecular phenotypes gene expression levels, high- and low-density cholesterol levels. The continuous variation for complex traits is due to genetic complexity and environmental sensitivity. Genetic complexity arises from segregating alleles at multiple loci.
QTL mapping is an increasingly useful approach to the study and manipulation of complex traits important in agriculture, evolution, and medicine. The molecular dissection of quantitative phenotypes, supplementing the principles of classical quantitative genetics, is accelerating progress in the manipulation of plant and animal genomes. A growing appreciation of the similarities among different organisms and the usefulness of comparative genetic information is making genome analysis more efficient, and providing new opportunities for using model systems to overcome the limitations of less-favorable systems. The expanding repertoire of techniques and information available for studying heredity is removing obstacles to the cloning of QTLs. Although QTL mapping alone is limited to a resolution of 0.
The genetic dissection of quantitative traits in crops. Keywords: association mapping, linkage disequilibrium, markers assisted selection, molecular breeding, molecular markers, quantitative trait, QTL mapping, QTL analysis. Most traits of interest in plant breeding show quantitative inheritance, which complicate the breeding process since phenotypic performances only partially reflects the genetic values of individuals. The genetic variation of a quantitative trait is assumed to be controlled by the collective effects of quantitative trait loci QTLs , epistasis interaction between QTLs , the environment, and interaction between QTL and environment. Exploiting molecular markers in breeding involve finding a subset of markers associated with one or more QTLs that regulate the expression of complex traits.
The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus QTL mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species. The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used.
We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems. Disease states are often associated with multiple, correlated traits that may result from shared genetic and nongenetic factors.
Generation mean analysis for quantitative traits in sesame Sesamum indicum L. Send correspondence to. To study the nature and magnitude of gene effects for yield and its components in sesame Sesamum indicum L. The analysis showed the presence of additive, dominance and epistatic gene interactions.
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