iHAP – integrated haplotype analysis pipeline for characterizing the haplotype structure of genes

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at , provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies

Strong Evidence of a Combination Polymorphism of the Tyrosine Kinase 2 Gene and the Signal Transducer and Activator of Transcription 3 Gene as a DNA-Based Biomarker for Susceptibility to Crohn’s Disease in the Japanese Population

OBJECTIVE: An association between susceptibility to inflammatory bowel disease (IBD) and polymorphisms of both the tyrosine kinase 2 gene (TYK2) and the signal transducer and activator of transcription 3 gene (STAT3) was examined in a Japanese population in order to identify the genetic determinants of IBD. METHODS: The study subjects comprised 112 patients with ulcerative colitis, 83 patients with Crohn's disease (CD), and 200 healthy control subjects. Seven tag single-nucleotide polymorphisms (SNPs) in TYK2 and STAT3 were detected by PCR-restriction fragment length polymorphism. RESULTS: The frequencies of a C allele and its homozygous C/C genotype at rs2293152 SNP in STAT3 in CD patients were significantly higher than those in control subjects (P = 0.007 and P = 0.001, respectively). Furthermore, out of four haplotypes composed of the two tag SNPs (rs280519 and rs2304256) in TYK2, the frequencies of a Hap 1 haplotype and its homozygous Hap 1/Hap1 diplotype were significantly higher in CD patients in comparison to those in control subjects (P = 0.023 and P = 0.024, respectively). In addition, the presence of both the C/C genotype at rs2293152 SNP in STAT3 and the Hap 1/Hap 1 diplotype of TYK2 independently contributes to the pathogenesis of CD and significantly increases the odds ratio to 7.486 for CD (P = 0.0008). CONCLUSION: TYK2 and STAT3 are genetic determinants of CD in the Japanese population. This combination polymorphism may be useful as a new genetic biomarker for the identification of high-risk individuals susceptible to CD

Haplotype block partitioning as a tool for dimensionality reduction in SNP association studies

<p>Abstract</p> <p>Background</p> <p>Identification of disease-related genes in association studies is challenged by the large number of SNPs typed. To address the dilution of power caused by high dimensionality, and to generate results that are biologically interpretable, it is critical to take into consideration spatial correlation of SNPs along the genome. With the goal of identifying true genetic associations, partitioning the genome according to spatial correlation can be a powerful and meaningful way to address this dimensionality problem.</p> <p>Results</p> <p>We developed and validated an MCMC Algorithm To Identify blocks of Linkage DisEquilibrium (MATILDE) for clustering contiguous SNPs, and a statistical testing framework to detect association using partitions as units of analysis. We compared its ability to detect true SNP associations to that of the most commonly used algorithm for block partitioning, as implemented in the Haploview and HapBlock software. Simulations were based on artificially assigning phenotypes to individuals with SNPs corresponding to region 14q11 of the HapMap database. When block partitioning is performed using MATILDE, the ability to correctly identify a disease SNP is higher, especially for small effects, than it is with the alternatives considered.</p> <p>Advantages can be both in terms of true positive findings and limiting the number of false discoveries. Finer partitions provided by LD-based methods or by marker-by-marker analysis are efficient only for detecting big effects, or in presence of large sample sizes. The probabilistic approach we propose offers several additional advantages, including: a) adapting the estimation of blocks to the population, technology, and sample size of the study; b) probabilistic assessment of uncertainty about block boundaries and about whether any two SNPs are in the same block; c) user selection of the probability threshold for assigning SNPs to the same block.</p> <p>Conclusion</p> <p>We demonstrate that, in realistic scenarios, our adaptive, study-specific block partitioning approach is as or more efficient than currently available LD-based approaches in guiding the search for disease loci.</p

iHAP – integrated haplotype analysis pipeline for characterizing the haplotype structure of genes

Abstract Background The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. Results To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. Conclusion The iHAP resource, available at http://ihap.bii.a-star.edu.sg, provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies.</p