A stochastic epigenetic Mendelian oligogenic disease model for type 1 diabetes.

The incidence of type 1 diabetes (T1D) and some other complex diseases is increasing. The cause has been attributed to an undefined changing environment. We examine the role of the environment (or any changing non-genetic mechanism) in causing the rising incidence, and find much evidence against it: 1) Dizygotic twin T1D concordance is the same as siblings of patients in general; 2) If the environment is responsible for both the discordance among identical twins of patients with T1D and its rising incidence, the twin concordance rate should be rising, but it is not; 3) Migrants from high-to low-incidence countries continue to have high-incidence children; 4) TID incidence among the offspring of two T1D parents is identical to the monozygotic twin rate. On the other hand, genetic association studies of T1D have revealed strong susceptibility in the major histocompatibility complex and many optional additive genes of small effect throughout the human genome increasing T1D risk. We have, from an analysis of previously published family studies, developed a stochastic epigenetic Mendelian oligogenic (SEMO) model consistent with published observations. The model posits a few required recessive causal genes with incomplete penetrance explaining virtually all of the puzzling features of T1D, including its rising incidence and the specific low T1D incidence rates among first-degree relatives of patients. Since historic selection against any causal gene could prevent T1D, we postulate that the rising incidence is because of increasing population mixing of parents from some previously isolated populations that had selected against different causal genes.

Dominant sequences of human major histocompatibility complex conserved extended haplotypes from HLA-DQA2 to DAXX.

We resequenced and phased 27 kb of DNA within 580 kb of the MHC class II region in 158 population chromosomes, most of which were conserved extended haplotypes (CEHs) of European descent or contained their centromeric fragments. We determined the single nucleotide polymorphism and deletion-insertion polymorphism alleles of the dominant sequences from HLA-DQA2 to DAXX for these CEHs. Nine of 13 CEHs remained sufficiently intact to possess a dominant sequence extending at least to DAXX, 230 kb centromeric to HLA-DPB1. We identified the regions centromeric to HLA-DQB1 within which single instances of eight "common" European MHC haplotypes previously sequenced by the MHC Haplotype Project (MHP) were representative of those dominant CEH sequences. Only two MHP haplotypes had a dominant CEH sequence throughout the centromeric and extended class II region and one MHP haplotype did not represent a known European CEH anywhere in the region. We identified the centromeric recombination transition points of other MHP sequences from CEH representation to non-representation. Several CEH pairs or groups shared sequence identity in small blocks but had significantly different (although still conserved for each separate CEH) sequences in surrounding regions. These patterns partly explain strong calculated linkage disequilibrium over only short (tens to hundreds of kilobases) distances in the context of a finite number of observed megabase-length CEHs comprising half a population's haplotypes. Our results provide a clearer picture of European CEH class II allelic structure and population haplotype architecture, improved regional CEH markers, and raise questions concerning regional recombination hotspots.

A New Pedigree-Based SNP Haplotype Method for Genomic Polymorphism and Genetic Studies.

Single nucleotide polymorphisms (SNPs) are usually the most frequent genomic variants. Directly pedigree-phased multi-SNP haplotypes provide a more accurate view of polymorphic population genomic structure than individual SNPs. The former are, therefore, more useful in genetic correlation with subject phenotype. We describe a new pedigree-based methodology for generating non-ambiguous SNP haplotypes for genetic study. SNP data for haplotype analysis were extracted from a larger Type 1 Diabetes Genetics Consortium SNP dataset based on minor allele frequency variation and redundancy, coverage rate (the frequency of phased haplotypes in which each SNP is defined) and genomic location. Redundant SNPs were eliminated, overall haplotype polymorphism was optimized and the number of undefined haplotypes was minimized. These edited SNP haplotypes from a region containing HLA-DRB1 (DR) and HLA-DQB1 (DQ) both correlated well with HLA-typed DR,DQ haplotypes and differentiated HLA-DR,DQ fragments shared by three pairs of previously identified megabase-length conserved extended haplotypes. In a pedigree-based genetic association assay for type 1 diabetes, edited SNP haplotypes and HLA-typed HLA-DR,DQ haplotypes from the same families generated essentially identical qualitative and quantitative results. Therefore, this edited SNP haplotype method is useful for both genomic polymorphic architecture and genetic association evaluation using SNP markers with diverse minor allele frequencies.

Genetic fixity in the human major histocompatibility complex and block size diversity in the class I region including HLA-E.

BACKGROUND: The definition of human MHC class I haplotypes through association of HLA-A, HLA-Cw and HLA-B has been used to analyze ethnicity, population migrations and disease association. RESULTS: Here, we present HLA-E allele haplotype association and population linkage disequilibrium (LD) analysis within the ~1.3 Mb bounded by HLA-B/Cw and HLA-A to increase the resolution of identified class I haplotypes. Through local breakdown of LD, we inferred ancestral recombination points both upstream and downstream of HLA-E contributing to alternative block structures within previously identified haplotypes. Through single nucleotide polymorphism (SNP) analysis of the MHC region, we also confirmed the essential genetic fixity, previously inferred by MHC allele analysis, of three conserved extended haplotypes (CEHs), and we demonstrated that commercially-available SNP analysis can be used in the MHC to help define CEHs and CEH fragments. CONCLUSION: We conclude that to generate high-resolution maps for relating MHC haplotypes to disease susceptibility, both SNP and MHC allele analysis must be conducted as complementary techniques.