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Linkage Analysis of Quantitative Traits

  • Christopher I. AmosEmail author
  • Bo Peng
  • Yaji Xu
  • Jianzhong Ma
Chapter
  • 1.7k Downloads

Abstract

Nearly three quarters of a century of statistical innovations have resulted from the development of methods to perform genetic linkage analysis in humans and other outbred organisms. Lionel Penrose was among the first investigators to develop methods that could be used to identify genetic linkages for quantitative traits. His methods predated the development of modern likelihood methods or wide acceptance of analysis of variance techniques. He initially sought to partition variance among sibs according to marker similarity [73], assuming particular modes of inheritance. His later publications provided approaches that could be applied for a range of potential inheritance patterns [74]. Oscar Kempthorne [53, 52] developed analysis of variance methods that form a basis for some linkage analytical approaches, building on the earlier work of Sir Ronald Fisher [37]. Fisher developed u-scores which form a basis for efficient score statistics for linkage analysis [38]. Many of the methods developed by these pioneers remain in use, with some modifications to allow their application in a modern era in which thousands of markers are available for analysis in extended families. This chapter reviews the statistical approaches that are now in use for linkage analysis of quantitative data. We first describe the data that we used to demonstrate methods of analysis. Then, we provide a statement of the genetic model and typical likelihood formulation that are applicable for pedigrees. Next, we discuss a variety of linkage methods that have been developed for model-free linkage analysis. Finally, we describe models for multivariate analysis.

Keywords

Quantitative Trait Linkage Analysis Genetic Epidemiology Extended Pedigree Human Heredity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christopher I. Amos
    • 1
    Email author
  • Bo Peng
    • 1
  • Yaji Xu
    • 1
  • Jianzhong Ma
    • 1
  1. 1.Department of EpidemiologyThe University of Texas M. D. Anderson Cancer CenterHoustonUSA

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