Literature DB >> 19635737

Combining case-control and case-trio data from the same population in genetic association analyses: overview of approaches and illustration with a candidate gene study.

Claire Infante-Rivard1, Lucia Mirea, Shelley B Bull.   

Abstract

In genetic association studies, investigators compare allele or genotype frequencies in unrelated case and control subjects or examine preferential allele transmissions from parents to affected offspring. In many genetic case-control studies, the collection of DNA material extends to relatives such as parents of cases. Thus, case-control and case-parent trio association analyses are possible. Whereas the goal of collecting genetic information from family members in a study initially designed as a case-control study is to enrich the genetic analysis, increase power, or address concern about population structure bias, methods of combining genetic data from unrelated case and control subjects with genetic trio data from the same study population are not well known. A number of hybrid approaches have been developed that utilize such data together. In this paper, the authors describe key features of genetic case-control and case-parent trio studies and review commonly used methods of genetic analysis for case-parent trio designs. In addition, they provide a pragmatic review of statistical methods and available software for existing hybrid approaches that combine various components of case-control and genetic trio data. The application of all methods is illustrated using a candidate gene study of childhood leukemia that included case-control subjects and their parents.

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Year:  2009        PMID: 19635737     DOI: 10.1093/aje/kwp180

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  9 in total

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Authors:  M J Sillanpää
Journal:  Heredity (Edinb)       Date:  2010-07-14       Impact factor: 3.821

2.  Fisher's method of combining dependent statistics using generalizations of the gamma distribution with applications to genetic pleiotropic associations.

Authors:  Qizhai Li; Jiyuan Hu; Juan Ding; Gang Zheng
Journal:  Biostatistics       Date:  2013-10-29       Impact factor: 5.899

3.  Exploration and comparison of methods for combining population- and family-based genetic association using the Genetic Analysis Workshop 17 mini-exome.

Authors:  David W Fardo; Anthony R Druen; Jinze Liu; Lucia Mirea; Claire Infante-Rivard; Patrick Breheny
Journal:  BMC Proc       Date:  2011-11-29

4.  Association analysis of complex diseases using triads, parent-child dyads and singleton monads.

Authors:  Ruzong Fan; Annie Lee; Zhaohui Lu; Aiyi Liu; James F Troendle; James L Mills
Journal:  BMC Genet       Date:  2013-09-04       Impact factor: 2.797

5.  Increasing the power of association studies with affected families, unrelated cases and controls.

Authors:  William C L Stewart; Jane Cerise
Journal:  Front Genet       Date:  2013-10-24       Impact factor: 4.599

6.  Haplotype association analysis of combining unrelated case-control and triads with consideration of population stratification.

Authors:  Shu-Hui Wen; Miao-Yu Tsai
Journal:  Front Genet       Date:  2014-04-29       Impact factor: 4.599

7.  Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

Authors:  Klementy Shchetynsky; Lina-Marcella Diaz-Gallo; Lasse Folkersen; Aase Haj Hensvold; Anca Irinel Catrina; Louise Berg; Lars Klareskog; Leonid Padyukov
Journal:  Arthritis Res Ther       Date:  2017-02-02       Impact factor: 5.156

8.  SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS.

Authors:  Ivan Merelli; Andrea Calabria; Paolo Cozzi; Federica Viti; Ettore Mosca; Luciano Milanesi
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

9.  Combining genetic association study designs: a GWAS case study.

Authors:  Janice L Estus; David W Fardo
Journal:  Front Genet       Date:  2013-09-27       Impact factor: 4.772

  9 in total

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