We describe the R function LDheatmap() which produces a graphical display, as a heat map, of pairwise linkage disequilibrium measurements between single nucleotide polymorphisms within a genomic region. LDheatmap() uses the grid graphics system, an alternative to the traditional R graphics system. The features of the LDheatmap() function and the use of tools from the grid package to modify heat maps are illustrated by examples.

As in previous studies, complications in the current study occurred more frequently among patients who underwent lumbar spine fusion than among those who underwent laminectomy or discectomy alone. Reoperations were at least as frequent after fusion, but the authors could not assess treatment efficacy in terms of pain relief or improved function. Although the characteristics of patients undergoing fusion differed from those undergoing a laminectomy or discectomy alone, there appeared to be sufficient overlap in the clinical populations to warrant closer scrutiny of the safety, efficacy, and indications for spinal fusions, preferably in randomized trials.

Complex medical disorders, such as heart disease and diabetes, are thought to involve a number of genes which act in conjunction with lifestyle and environmental factors to increase disease susceptibility. Associations between complex traits and single nucleotide polymorphisms (SNPs) in candidate genomic regions can provide a useful tool for identifying genetic risk factors. However, analysis of trait associations with single SNPs ignores the potential for extra information from haplotypes, combinations of variants at multiple SNPs along a chromosome inherited from a parent. When haplotype-trait associations are of interest and haplotypes of individuals can be determined, generalized linear models (GLMs) may be used to investigate haplotype associations while adjusting for the effects of non-genetic cofactors or attributes. Unfortunately, haplotypes cannot always be determined cost-effectively when data is collected on unrelated subjects. Uncertain haplotypes may be inferred on the basis of data from single SNPs. However, subsequent analyses of risk factors must account for the resulting uncertainty in haplotype assignment in order to avoid potential errors in interpretation. To account for such uncertainty, we have developed hapassoc, software for R implementing a likelihood approach to inference of haplotype and non-genetic effects in GLMs of trait associations. We provide a description of the underlying statistical method and illustrate the use of hapassoc with examples that highlight the flexibility to specify dominant and recessive effects of genetic risk factors, a feature not shared by other software that restricts users to additive effects only. Additionally, hapassoc can accommodate missing SNP genotypes for limited numbers of subjects.

Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics. Exact inference for logistic regression can be problematic when data sets are large and the support of the conditional distribution cannot be represented in memory. Additionally, these methods are not widely implemented except in commercial software packages such as LogXact and SAS. Therefore, we have developed elrm, software for R implementing (approximate) exact inference for binomial regression models from large data sets. We provide a description of the underlying statistical methods and illustrate the use of elrm with examples. We also evaluate elrm by comparing results with those obtained using other methods.

In a large case-control study of Swedish incident type I diabetes patients and controls, 0-34 years of age, we tested the hypothesis that the GIMAP5 gene, a key genetic factor for lymphopenia in spontaneous BioBreeding rat diabetes, is associated with type I diabetes; with islet autoantibodies in incident type I diabetes patients or with age at clinical onset in incident type I diabetes patients. Initial scans of allelic association were followed by more detailed logistic regression modeling that adjusted for known type I diabetes risk factors and potential confounding variables. The single nucleotide polymorphism (SNP) rs6598, located in a polyadenylation signal of GIMAP5, was associated with the presence of significant levels of IA-2 autoantibodies in the type I diabetes patients. Patients with the minor allele A of rs6598 had an increased prevalence of IA-2 autoantibody levels compared to patients without the minor allele (OR=2.2; Bonferroni-corrected P=0.003), after adjusting for age at clinical onset (P=8.0 x 10(-13)) and the numbers of HLA-DQ A1*0501-B1*0201 haplotypes (P=2.4 x 10(-5)) and DQ A1*0301-B1*0302 haplotypes (P=0.002). GIMAP5 polymorphism was not associated with type I diabetes or with GAD65 or insulin autoantibodies, ICA, or age at clinical onset in patients. These data suggest that the GIMAP5 gene is associated with islet autoimmunity in type I diabetes and add to recent findings implicating the same SNP in another autoimmune disease.