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Methods for assessing gene content diversity of KIR with examples from a global set of populations.

ABSTRACT: A number of statistical methods are widely used to describe allelic variation at specific genetic loci and its implication on the evolutionary history of these loci. Although the methods were developed primarily to study allelic variation at loci that are virtually always present in the genome, they are often applied to data of gene content variation (i.e., presence/absence of multiple homologous genes) at the killer cell immunoglobulin-like receptor (KIR) gene cluster. In this paper, we discuss methodological issues involved in the analysis of gene content variation data in the KIR region and also its covariation with polymorphism at the human leukocyte antigen class I loci, which encode ligands for KIR. A comparison of several statistical methods and measures (gene frequency, haplotype frequency, and linkage disequilibrium estimation) using the Centre d'Etude du Polymorphisme Humain data will be provided using KIR haplotypes that have been determined by segregation analysis, noting the strengths and weaknesses of the methods when only the presence/absence data is considered. Finally, application of these methods to a set of globally distributed populations is described (see Single et al., Nat Genet 39:1114-1119, 2007) in order to illustrate the challenges faced when inferring the joint effects of natural selection and demographic history on these immune-related genes.

SUBMITTER: Single RM

PROVIDER: S-EPMC2663517 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Methods for assessing gene content diversity of KIR with examples from a global set of populations.

Single Richard M RM Martin Maureen P MP Meyer Diogo D Gao Xiaojiang X Carrington Mary M

Immunogenetics 20080917 12

A number of statistical methods are widely used to describe allelic variation at specific genetic loci and its implication on the evolutionary history of these loci. Although the methods were developed primarily to study allelic variation at loci that are virtually always present in the genome, they are often applied to data of gene content variation (i.e., presence/absence of multiple homologous genes) at the killer cell immunoglobulin-like receptor (KIR) gene cluster. In this paper, we discuss ...[more]

PMID: 18797862

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Project description:BackgroundMany gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the performances of Gene Set Enrichment Analysis (GSEA), Global Test (GT), Wald-type Test (WT) and Global Boost Test (GBST) methods in a simulation study and on two ovarian cancer data sets. We considered two versions of GSEA by allowing different weights: GSEA1 uses equal weights, yielding results similar to the Kolmogorov-Smirnov test; while GSEA2's weights are based on the correlation between genes and the phenotype.ResultsWe compared GSEA1, GSEA2, GT, WT and GBST in a simulation study with various settings for the correlation structure of the genes and the association parameter between the survival outcome and the genes. Simulation results indicated that GT, WT and GBST consistently have higher power than GSEA1 and GSEA2 across all scenarios. However, the power of the five tests depends on the combination of correlation structure and association parameter. For the ovarian cancer data set, using the FDR threshold of q < 0.1, the GT, WT and GBST detected 12, 6 and 8 significant pathways, respectively, whereas neither GSEA1 nor GSEA2 detected any significant pathways. In addition, among the pathways found significant by GT, WT, and GBST, three pathways--Purine metabolism, Leukocyte transendothelial migration and Jak-STAT signaling pathway--overlapped with those reported in previous ovarian cancer microarray studies.ConclusionSimulation studies and a real data example indicate that GT, WT and GBST tend to have high power, whereas GSEA1 and GSEA2 have lower power. We also found that the power of the five tests is much higher when genes are correlated than when genes are independent, when survival is positively associated with genes. It seems that there is a synergistic effect in detecting significant gene sets when significant genes have within-class correlation and the association between survival and genes is positive or negative (i.e., one-direction correlation).

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Methods for assessing gene content diversity of KIR with examples from a global set of populations.

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Methods for assessing gene content diversity of KIR with examples from a global set of populations.

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