Methods for Combining Multiple Genome-Wide Linkage Studies

  • Trecia A. Kippola
  • Stephanie A. Santorico
Part of the Methods in Molecular Biology book series (MIMB, volume 620)


Cardiovascular disease, metabolic syndrome, schizophrenia, diabetes, bipolar disorder, and autism are a few of the numerous complex diseases for which researchers are trying to decipher the genetic composition. One interest of geneticists is to determine the quantitative trait loci (QTLs) that underlie the genetic portion of these diseases and their risk factors. The difficulty for researchers is that the QTLs underlying these diseases are likely to have small to medium effects which will necessitate having large studies in order to have adequate power. Combining information across multiple studies provides a way for researchers to potentially increase power while making the most of existing studies.

Here, we will explore some of the methods that are currently being used by geneticists to combine information across multiple genome-wide linkage studies. There are two main types of meta-analyses: (1) those that yield a measure of significance, such as Fisher’s p-value method along with its extensions/modifications and the genome search meta-analysis (GSMA) method, and (2) those that yield a measure of a common effect size and the corresponding standard error, such as model-based methods and Bayesian methods. Some of these methods allow for the assessment of heterogeneity. This chapter will conclude with a recommendation for usage.

Key words

Meta-analysis genome search meta-analysis combining multiple studies heterogeneity Fisher’s p-value method 


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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Trecia A. Kippola
    • 1
  • Stephanie A. Santorico
    • 2
  1. 1.Department of StatisticsOklahoma State UniversityStillwaterUSA
  2. 2.Department of Mathematical and Statistical SciencesUniversity of ColoradoDenverUSA

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