Application of multi‐locus analytical methods to identify interacting loci in case‐control studies

To identify interacting loci in genetic epidemiological studies the application of multi‐locus methods of analysis is warranted. Several more advanced classification methods have been developed in the past years, including multiple logistic regression, sum statistics, logic regression, and the multifactor dimensionality reduction method. The objective of our study was to apply these four multi‐locus methods to simulated case‐control datasets that included a variety of underlying statistical two‐locus interaction models, in order to compare the methods and evaluate their strengths and weaknesses. The results showed that the ability to identify the interacting loci was generally good for the sum statistic method, the logic regression and MDR. The performance of the logistic regression was more dependent on the underlying model and multiple comparison adjustment procedure. However, identification of the interacting loci in a model with two two‐locus interactions of common disease alleles with relatively small effects was impaired in all methods. Several practical and methodological issues that can be considered in the application of these methods, and that may warrant further research, are identified and discussed.

[1]  Jason H. Moore,et al.  An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation , 2004, BMC Bioinformatics.

[2]  M. Reilly,et al.  MDR and PRP: A Comparison of Methods for High-Order Genotype-Phenotype Associations , 2005, Human Heredity.

[3]  D. D. de Quervain,et al.  Glucocorticoid-related genetic susceptibility for Alzheimer's disease. , 2003, Human molecular genetics.

[4]  A. G. Heidema,et al.  The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases , 2006, BMC Genetics.

[5]  J. Ott,et al.  Mathematical multi-locus approaches to localizing complex human trait genes , 2003, Nature Reviews Genetics.

[6]  D. Clayton,et al.  Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes. , 2001, Genetics.

[7]  Jason H. Moore,et al.  Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity , 2003, Genetic epidemiology.

[8]  J. Ott,et al.  Trimming, weighting, and grouping SNPs in human case-control association studies. , 2001, Genome research.

[9]  H. Cordell.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. , 2002, Human molecular genetics.

[10]  C Kooperberg,et al.  Sequence Analysis Using Logic Regression , 2001, Genetic epidemiology.

[11]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[12]  E R Martin,et al.  Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. , 2005, American journal of human genetics.

[13]  Ingo Ruczinski,et al.  Identifying interacting SNPs using Monte Carlo logic regression , 2005, Genetic epidemiology.

[14]  Eric Boerwinkle,et al.  A multilocus approach to the antihypertensive pharmacogenetics of hydrochlorothiazide , 2005, Pharmacogenetics and genomics.

[15]  T. Reich,et al.  A perspective on epistasis: limits of models displaying no main effect. , 2002, American journal of human genetics.

[16]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[17]  Marylyn D. Ritchie,et al.  Multilocus Analysis of Hypertension: A Hierarchical Approach , 2004, Human Heredity.

[18]  Wentian Li,et al.  A Complete Enumeration and Classification of Two-Locus Disease Models , 1999, Human Heredity.

[19]  P S Albert,et al.  Limitations of the case-only design for identifying gene-environment interactions. , 2001, American journal of epidemiology.

[20]  J. H. Moore,et al.  Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus , 2004, Diabetologia.

[21]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[22]  J L Haines,et al.  Multifactor dimensionality reduction reveals gene–gene interactions associated with multiple sclerosis susceptibility in African Americans , 2006, Genes and Immunity.

[23]  Jonathan L Haines,et al.  Genetics, statistics and human disease: analytical retooling for complexity. , 2004, Trends in genetics : TIG.

[24]  Ingo Ruczinski,et al.  Exploring interactions in high-dimensional genomic data: an overview of logic regression, with applications , 2004 .

[25]  Jurg Ott,et al.  Sum statistics for the joint detection of multiple disease loci in case‐control association studies with SNP markers , 2003, Genetic epidemiology.

[26]  David Curtis,et al.  Application of Logistic Regression to Case-Control Association Studies Involving Two Causative Loci , 2005, Human Heredity.

[27]  Kui Zhang,et al.  Detecting susceptibility genes in case-control studies using set association , 2003, BMC Genetics.