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.

Age-dependent associations between type 1 diabetes risk genes HLA, INS VNTR, and CTLA-4 and autoantibodies to GAD65 (GADAs), ICA512/IA-2, insulin, and islet cells were determined by logistic regression analysis in 971 incident patients with type 1 diabetes and 702 control subjects aged 0 -34 years. GADAs were associated with HLA-DQ2 in young but not in older patients (P ‫؍‬ 0.009). Autoantibodies to insulin were negatively associated with age (P < 0.0001) but positively associated with DQ8 (P ‫؍‬ 0.03) and with INS VNTR (P ‫؍‬ 0.04), supporting possible immune tolerance induction. ICA512/IA-2 were negatively associated with age (P < 0.0001) and with DQ2 (P < 0.0001) but positively associated with DQ8 (P ‫؍‬ 0.04). Males were more likely than females to be negative for GADA (P < 0.0001), autoantibodies to islet cells (P ‫؍‬ 0.04), and all four autoantibody markers (P ‫؍‬ 0.004). The CTLA-4 3 end microsatellite marker was not associated with any of the autoantibodies. We conclude that age and genetic factors such as HLA-DQ and INS VNTR need to be combined with islet autoantibody markers when evaluating the risk for type 1 diabetes development. Diabetes 51: 1346 -1355, 2002

Supplementary data are available at Bioinformatics online.

Objective Individuals with deficiency of adenosine deaminase 2 (DADA2), a recently recognized autosomal recessive disease, present with various systemic vascular and inflammatory manifestations, often with young age at disease onset or with early onset of recurrent strokes. Their clinical features and histologic findings overlap with those of childhood‐onset polyarteritis nodosa (PAN), a primary “idiopathic” systemic vasculitis. Despite similar clinical presentation, individuals with DADA2 may respond better to biologic therapy than to traditional immunosuppression. The aim of this study was to screen an international registry of children with systemic primary vasculitis for variants in ADA2. Methods The coding exons of ADA2 were sequenced in 60 children and adolescents with a diagnosis of PAN, cutaneous PAN, or unclassifiable vasculitis (UCV), any chronic vasculitis with onset at age 5 years or younger, or history of stroke. The functional consequences of the identified variants were assessed by ADA2 enzyme assay and immunoblotting. Results Nine children with DADA2 (5 with PAN, 3 with UCV, and 1 with antineutrophil cytoplasmic antibody–associated vasculitis) were identified. Among them, 1 patient had no rare variants in the coding region of ADA2 and 8 had biallelic, rare variants (minor allele frequency <0.01) with a known association with DADA2 (p.Gly47Arg and p.Gly47Ala) or a novel association (p.Arg9Trp, p.Leu351Gln, and p.Ala357Thr). The clinical phenotype varied widely. Conclusion These findings support previous observations indicating that DADA2 has extensive genotypic and phenotypic variability. Thus, screening ADA2 among children with vasculitic rash, UCV, PAN, or unexplained, early‐onset central nervous system disease with systemic inflammation may enable an earlier diagnosis of DADA2.

HLA‐associated relative risks of type 1 (insulin‐dependent) diabetes mellitus were analysed in population‐based Swedish patients and controls aged 0–34 years. The age dependence of HLA‐associated relative risks was assessed by likelihood ratio tests of regression parameters in separate logistic regression models for each HLA category. The analyses demonstrated an attenuation with increasing age at onset in the relative risk for the positively associated DQB1*0201‐A1*0502/B1*0302‐A1*0301 (DQ2/8) genotype (P=0.02) and the negatively associated DQB1*0602‐A1*0102 (DQ6.2) haplotype (P=0.004). At birth, DQ6.2‐positive individuals had an estimated relative risk of 0.03, but this increased to 1.1 at age 35 years. Relative risks for individuals with DQ genotype 8/8 or 8/X or DQ genotype 2/2 or 2/X, where X is any DQ haplotype other than 2, 8 or 6.2, were not significantly age‐dependent. An exploratory analysis of DQ haplotypes other than 2, 8 and 6.2 suggested that the risk of type 1 diabetes increases with age for DQB1*0604‐A1*0102 (DQ6.4) and that the peak risk for the negatively associated DQB1*0301‐A1*0501 haplotype is at age 18 years. There was also weak evidence that the risk for DQB1*0303‐A1*0301 (DQ9), which has a positive association in the Japanese population, may decrease with age. We speculate that HLA‐DQ alleles have a significant effect on the rate of beta cell destruction, which is accelerated in DQ2/8‐positive individuals and inhibited, but not completely blocked, in DQ6.2‐positive individuals.

Genetic linkage studies based on pedigree data have limited resolution, because of the relatively small number of segregations. Disequilibrium mapping, which uses population associations to infer the location of a disease mutation, provides one possible strategy for narrowing the candidate region. The coalescent process provides a model for the ancestry of a sample of disease alleles, and recombination events between disease locus and marker may be placed on this ancestral phylogeny. These events define the recombinant classes, the sets of sampled disease copies descending from the meiosis at which a given recombination occurred. We show how Monte Carlo generation of the recombinant classes leads to a linkage likelihood for fine-scale mapping from disease haplotypes. We compare single-marker disequilibrium mapping with interval-disequilibrium mapping and discuss how the approach may be extended to multipoint-disequilibrium mapping. The method and its properties are illustrated with an example of simulated data, constructed to be typical of fine-scale mapping of a rare disease in the Japanese population. The method can take into account known features of population history, such as changing patterns of population growth.

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.