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1.  Single versus multiple imputation for genotypic data 
BMC Proceedings  2009;3(Suppl 7):S7.
Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In this paper, we assess the variation in imputation by completing both single and multiple imputations of genotypic data using MACH, a commonly used hidden Markov model imputation method. Using data from the North American Rheumatoid Arthritis Consortium genome-wide study, the use of single and multiple imputation was assessed in four regions of chromosome 1 with varying levels of linkage disequilibrium and association signals. Two scenarios for missing genotypic data were assessed: imputation of untyped markers and combination of genotypic data from two studies. This limited study involving four regions indicates that, contrary to expectations, multiple imputations may not be necessary.
PMCID: PMC2795971  PMID: 20018064
2.  Assessment of genotype imputation methods 
BMC Proceedings  2009;3(Suppl 7):S5.
Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE. We compared the methods' imputation error rates and performance of association tests using the imputed data, in the context of imputing completely untyped markers as well as imputing missing genotypes to combine two datasets genotyped at different sets of markers. As expected, all methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs. However, MACH and IMPUTE generated lower imputation error rates than fastPHASE and PLINK. Association tests based on allele "dosage" from MACH and tests based on the posterior probabilities from IMPUTE provided results closest to those based on complete data. However, in both situations, none of the imputation-based tests provide the same level of evidence of association as the complete data at SNPs strongly associated with disease.
PMCID: PMC2795949  PMID: 20018042
3.  Linkage analysis using principal components of gene expression data 
BMC Proceedings  2007;1(Suppl 1):S79.
The goal of this paper is to investigate the effect of using principal components as a data reduction method for expression data in linkage analysis. We used 45 probes normalized using the Affymetrix Global Scaling that had evidence of high heritability to estimate the first 10 principal components (PC). A genome-wide linkage scan was performed on the 45 expression values and the 10 PCs using 2272 single-nucleotide polymorphisms. Our conclusions were: 1) PC analyses under-performed the single-probe analysis for known signals; 2) the PC that best reproduced the single-probe analysis was primarily composed of that probe; 3) no new signals were detected in the PC analysis; 4) no new pleiotropic effects were detected in the PC analysis.
PMCID: PMC2367556  PMID: 18466581
4.  The genetics of gene expression: comparison of linkage scans using two phenotype normalization methods 
BMC Proceedings  2007;1(Suppl 1):S151.
The goal of this paper is to investigate the effects of normalization procedures for expression data on linkage results. We selected the two most commonly used expression data extraction and normalization methods, Affymetrix global scaling and dChip invariant. After applying these two methods in 3554 expression phenotypes, we identified 45 phenotypes that were more likely to be genetic for either normalization procedure. A genome-wide linkage scan was performed on these expression values (45 phenotypes × 2 normalizations) using 2272 SNPs. Our results showed that: 1) the dChip normalization might inflate the LOD scores because the dChip normalization yielded LOD scores > 3.0 30% more frequently than the Affy normalization, and 2) the difference in LODs between the normalizations were not correlated with their heritabilities. In summary, we conclude, as have other published reports, that normalization methods play an important role in the linkage results, and that some significant linkage signals might be due to a specific normalization method.
PMCID: PMC2367553  PMID: 18466496
5.  Analysis of variation in NF-κB genes and expression levels of NF-κB-regulated molecules 
BMC Proceedings  2007;1(Suppl 1):S126.
The nuclear factor-kappaB (NF-κB) family of transcription factors regulates the expression of a variety of genes involved in apoptosis and immune response. We examined relationships between genotypes at five NF-κB subunits (NFKB1, NFKB2, REL, RELA, and RELB) and variable expression levels of 15 NF-κB regulated proteins with heritability greater than 0.40: BCL2A1, BIRC2, CD40, CD44, CD80, CFLAR, CR2, FAS, ICAM1, IL15, IRF1, JUNB, MYC, SLC2A5, and VCAM1. SNP genotypes and expression phenotypes from pedigrees of Utah residents with ancestry from northern and western Europe were provided by Genetic Analysis Workshop 15 and supplemented with additional genotype data from the International HapMap Consortium. We conducted association, linkage, and family-based association analyses between each candidate gene and the 15 heritable expression phenotypes. We observed consistent results in association and linkage analyses of the NFKB1 region (encoding p50) and levels of FAS and IRF1 expression. FAS is a cell surface protein that also belongs to the TNF-receptor family; signals through FAS are able to induce apoptosis. IRF1 is a member of the interferon regulatory transcription factor family, which has been shown to regulate apoptosis and tumor-suppression. Analyses in the REL region (encoding c-Rel) revealed linkage and association with CD40 phenotype. CD40 proteins belong to the tumor necrosis factor (TNF)-receptor family, which mediates a broad variety of immune and inflammatory responses. We conclude that variation in the genes encoding p50 and c-Rel may play a role in NF-κB-related transcription of FAS, IRF1, and CD40.
PMCID: PMC2367504  PMID: 18466468
6.  Comparison of tagging single-nucleotide polymorphism methods in association analyses 
BMC Proceedings  2007;1(Suppl 1):S6.
Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe (CEPH-Utah, CEU), we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs (n = 210 SNPs across all regions), most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association (i.e., whether a SNP or haplotype is responsible); unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed.
PMCID: PMC2367496  PMID: 18466560
7.  Comparison of variable and model selection methods for genetic association studies using the GAW15 simulated data 
BMC Proceedings  2007;1(Suppl 1):S34.
We compared and evaluated several variable and model selection methods using Bayesian and non-Bayesian approaches for three replicates of the Genetic Analysis Workshop 15 (GAW15) simulated data. In doing so, two phenotypes were utilized: rheumatoid arthritis (RA) affection status as a binary trait and IgM as a continuous measure. The analyses were performed adjusting for sex, age, and smoking status. For both outcomes, all the methods were comparable in finding the single-nucleotide polymorphisms (SNPs) generated to have a genetic signal. We successfully identified the susceptibility SNPs for RA in the HLA region (chromosome 6), and chromosome 18, and the susceptibility SNP for IgM on chromosome 11; however, many of the methods produced false-positive results.
The answers to Problem 3 were requested and known to the authors.
PMCID: PMC2367491  PMID: 18466532

Results 1-7 (7)