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Analytical Methods for Disease Association Studies with Immunogenetic Data

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Immunogenetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 882))

Abstract

Disease association studies involving highly polymorphic immunogenetic data may involve analyses at one or many units of analysis, including amino acid, allele, genotype and haplotype levels, as well as consideration of gene–gene or gene–environment interactions. The selection of the appropriate statistical tests is critical and will be dependent on the nature of the dataset (e.g., case-control vs. family data) as well as the specific research hypotheses being tested. This paper describes the various study and analysis categories used for such analyses, including the advantages and limitations of such techniques.

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Acknowledgments

This work was supported by National Institutes of Health (NIH) grants U01AI067068 (JAH, SJM) and U19 AI067152 (PAG) awarded by the National Institute of Allergy and Infectious Diseases (NIAID) and 1R01DK061722 (JAH) awarded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Center for Genetics, Children’s Hospital and Research Center Oakland, Oakland, CA, USA

    Jill A. Hollenbach & Steven J. Mack

  2. Department of Integrative Biology, University of California, Berkeley, CA, USA

    Glenys Thomson

  3. Department of Neurology, University of California, San Francisco, CA, USA

    Pierre-Antoine Gourraud

Authors
  1. Jill A. Hollenbach
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  2. Steven J. Mack
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  3. Glenys Thomson
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  4. Pierre-Antoine Gourraud
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Corresponding author

Correspondence to Jill A. Hollenbach .

Editor information

Editors and Affiliations

  1. PathWest Laboratory Medicine, Department of Clinical Immunology, Royal Perth Hospital, Wellington Street, Perth, 6000, Australia

    Frank T. Christiansen

  2. Australian Red Cross Blood Service, National Transplant Services, The Royal Melbourne Hospital, 150-154 Batman Street, Melbourne VIC, 3000, Australia

    Brian D. Tait

Glossary

Bonferroni correction

The most commonly used method of correcting for multiple comparisons. Generally, the test significance level is divided by the number of comparisons made in the study, thereby increasing the overall stringency of the significance testing.

Confidence interval

A likely range of values for an estimate of a particular parameter within a particular level of significance.

Contingency table

Also known as a cross-tabulation. A 2  ×  n table used to analyze heterogeneity between two sets of observations of two or more categorical variables.

Genetic association

The occurrence within a population, greater than that expected by chance, of a genetic trait with a particular phenotype.

Logistic regression

A statistical method to determine which in a set of independent variables has a predictive relationship to a binary-dependent outcome variable.

Multiple comparisons

Also know as multiple testing. Performing a statistical test multiple times in the same analysis, thereby increasing the number of chances that the null hypothesis will be incorrectly rejected, leading to false positive associations.

Odds ratio

The ratio of odds of an outcome occurring in one group to the odds of it occurring in another group.

Population stratification

Also referred to as population substructure. Allele frequency differences between subpopulations within a study population due to ancestry differences or selection biases.

Relative risk

A measure that describes the risk of having the outcome of interest relative to exposure.

Yates correction for continuity

An adjustment to the χ 2 test statistic performed by subtracting 0.5 from the (O-E) value for each cell in a contingency table. The purpose of this correction is to account for sparse cells in the table which may introduce discontinuity with regard to the χ 2 distribution.

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Hollenbach, J.A., Mack, S.J., Thomson, G., Gourraud, PA. (2012). Analytical Methods for Disease Association Studies with Immunogenetic Data. In: Christiansen, F., Tait, B. (eds) Immunogenetics. Methods in Molecular Biology, vol 882. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-842-9_14

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  • DOI: https://doi.org/10.1007/978-1-61779-842-9_14

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-841-2

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