ABSTRACT
[Introduction] To explore the gene-based logistic kemel-machine regression model and its application in genome-wide association study (GWAS).Using the simulated genome-wide singlenucleotide polymorphism (SNPs) genotypes data,we proposed a practical statistical analysis strategynamed ‘ the logistic kernel-machine regression model',based on the gene levels to assess the association between genetic variations and complex diseases.The results from simulation showed that the P value of genes in related diseases was the smallest among all the genes.The results of simulation indicated that not only it could borrow information from different SNPs that were grouped in genes and reducing the degree of freedom through hypothesis testing,but could also incorporate the covariate effects and the complex SNPs interactions.The gene-based logistic kernel-machine regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in GWAS.
ABSTRACT
To explore the gene-based principal component logistic regression model and its application in genome-wide association study.Using the simulated genome-wide single nucleotide polymorphism (SNPs) genotypes data,we proposed a practical statistical analysis strategy-'the principal component logistic regression model',based on the gene levels to assess the association between genetic variations and complex diseases.The simulation results showed that the P value of genes in related diseases was the smallest among the results from all the genes.The results of simulation indicated that not only it could reduce the degree of freedom through hypothesis testing but could also better understand the correlations between SNPs.The gene-based principal component logistic regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in the genome-wide association studies.
ABSTRACT
Genome-wide association study is an important approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping on hundreds of thousands of markers on thousands of subjects, a more cost-effective two-stage case-control design is applied by most genome-wide association studies. To describe the design and statistical methods of the two-stage case-control study, this paper introduces the principles of two-stage case-control design, its implementing steps in genome-wide association study and the features of its application.The method is illustrated with an example.
ABSTRACT
Objective The purpose of this study was to approach the relation of SNP43,SNP44 locus, main haplotypes and haplotype combinations with type 2 diabetes mellitus(T2DM).Methods According to the theory and principles of systematic review,data from case-control studies regarding the association between calpain-10(CAPN10) gene and T2DM were derived through electronic search of PubMed and Chinese journals databases.To gain a more precise estimation of the relationship,a stratified Meta-analysis with four subgroups was pertbrmed according to the races.Publication bias Was also assessed.Results The association with T2DM in different races was evaluated.In Mongoloid race,SNP43-G allele,G/G genotype and 111/221 haplotype combination showed notable association with T2DM with Ors (95%CI) as 1.368(1.155-1.620),1.437(1.186-1.741) and 2.762 (1.287-5.927) respectively.In Caucasoid race,SNP44-C allele,111/111 hapotype combination showed strong relationship with T2DM with Ors(95%CI) as 1.144(1.023-1.278),1.291(1.050-1.586) respectively.In Hybrid race,only one positive finding Was obtained which Was SNP44-C allele with OR(95%CI)as 1.653(1.025-2.665).Conclusion SNP43-G allele,G/G genotype,111/221 were risk factors to Mongoloid race.And SNP-C allele,111/111 haplotype combination were risk factors to Caucasoid race,and SNP44-C allele to Hybrid race.
ABSTRACT
<p><b>OBJECTIVE</b>To introduce the design and statistical methods of case-sibling control design and to analyze the published data.</p><p><b>METHODS</b>Data from an association study between the coronary heart disease and methylenetetrahydrofolate reductase gene C677T polymorphism was analyzed by the sib transmission/disequibrium test (s-TDT) and the sibship disequilibrium (SDT) methods.</p><p><b>RESULTS</b>Using s-TDT method, Z value was 0.27 with P > 0.05. The result of SDT method showed that chi-square was 0.31 with 1 df, P > 0.05. All results suggested that neither s-TDT nor SDT showed significant difference between the transmitted and untransmitted methylenetetrahydrofolate reductase gene C677T allele distributions.</p><p><b>CONCLUSION</b>Case-sibling control design might avoid population stratification by using siblings as controls thus might be used to test association and linkage between genes and disease.</p>
Subject(s)
Humans , Case-Control Studies , Chi-Square Distribution , Coronary Disease , Genetics , Epidemiologic Methods , Gene Frequency , Genetic Association Studies , Genetic Linkage , Methylenetetrahydrofolate Reductase (NADPH2) , Genetics , Polymorphism, Genetic , Research Design , SiblingsABSTRACT
<p><b>OBJECTIVE</b>To introduce the methodology on the design and statistical method as well as analysis of published data related to case-parental control study.</p><p><b>METHODS</b>Data from a research on association between the human neural tube defects and the T allelic variant TIVS7-2 was analyzed by haplotype relative risk (HRR), genotype relative risk (GRR) and transmission/disequilibrium test (TDT) methods.</p><p><b>RESULTS</b>Using the HRR method, HRR value was 1.33 (chi-square statistic is 1.4618 with P = 0.2266). The results of the GRR method showed that psi(1) value was 1.26 and psi(2) value was 0.98 (chi-square statistic is 3.1809 with 2 df, P = 0.2038). The transmission/disequilibrium test showed that TDT was 1.4516 with P = 0.2283. All results suggested that when a weak association of TIVS7-2 allele with neural tube defects was found, there was no evidence showing the statistical difference.</p><p><b>CONCLUSION</b>The case-parental control design method avoids population stratification when parents used as controls. This method might be used to test the association between genes and disease, as well as to evaluate the gene environmental interaction.</p>