Characterisation of the genomic architecture of human chromosome 17q and evaluation of different methods for haplotype block definition

BACKGROUND: The selection of markers in association studies can be informed through the use of haplotype blocks. Recent reports have determined the genomic architecture of chromosomal segments through different haplotype block definitions based on linkage disequilibrium (LD) measures or haplotype diversity criteria. The relative applicability of distinct block definitions to association studies, however, remains unclear. We compared different block definitions in 6.1 Mb of chromosome 17q in 189 unrelated healthy individuals. Using 137 single nucleotide polymorphisms (SNPs), at a median spacing of 15.5 kb, we constructed haplotype block maps using published methods and additional methods we have developed. Haplotype tagging SNPs (htSNPs) were identified for each map. RESULTS: Blocks were found to be shorter and coverage of the region limited with methods based on LD measures, compared to the method based on haplotype diversity. Although the distribution of blocks was highly variable, the number of SNPs that needed to be typed in order to capture the maximum number of haplotypes was consistent. CONCLUSION: For the marker spacing used in this study, choice of block definition is not important when used as an initial screen of the region to identify htSNPs. However, choice of block definition has consequences for the downstream interpretation of association study results

High-density SNP association study of the 17q21 chromosomal region linked to autism identifies CACNA1G as a novel candidate gene.

Chromosome 17q11-q21 is a region of the genome likely to harbor susceptibility to autism (MIM(209850)) based on earlier evidence of linkage to the disorder. This linkage is specific to multiplex pedigrees containing only male probands (MO) within the Autism Genetic Resource Exchange (AGRE). Earlier, Stone et al.(1) completed a high-density single nucleotide polymorphism association study of 13.7 Mb within this interval, but common variant association was not sufficient to account for the linkage signal. Here, we extend this single nucleotide polymorphism-based association study to complete the coverage of the two-LOD support interval around the chromosome 17q linkage peak by testing the majority of common alleles in 284 MO trios. Markers within an interval containing the gene, CACNA1G, were found to be associated with Autism Spectrum Disorder at a locally significant level (P=1.9 × 10(-5)). While establishing CACNA1G as a novel candidate gene for autism, these alleles do not contribute a sufficient genetic effect to explain the observed linkage, indicating that there is substantial genetic heterogeneity despite the clear linkage signal. The region thus likely harbors a combination of multiple common and rare alleles contributing to the genetic risk. These data, along with earlier studies of chromosomes 5 and 7q3, suggest few if any major common risk alleles account for Autism Spectrum Disorder risk under major linkage peaks in the AGRE sample. This provides important evidence for strategies to identify Autism Spectrum Disorder genes, suggesting that they should focus on identifying rare variants and common variants of small effect

Assessing the Performance of the Haplotype Block Model of Linkage Disequilibrium

Several recent studies have suggested that linkage disequilibrium (LD) in the human genome has a fundamentally “blocklike” structure. However, thus far there has been little formal assessment of how well the haplotype block model captures the underlying structure of LD. Here we propose quantitative criteria for assessing how blocklike LD is and apply these criteria to both real and simulated data. Analyses of several large data sets indicate that real data show a partial fit to the haplotype block model; some regions conform quite well, whereas others do not. Some improvement could be obtained by genotyping higher marker densities but not by increasing the number of samples. Nonetheless, although the real data are only moderately blocklike, our simulations indicate that, under a model of uniform recombination, the structure of LD would actually fit the block model much less well. Simulations of a model in which much of the recombination occurs in narrow hotspots provide a much better fit to the observed patterns of LD, suggesting that there is extensive fine-scale variation in recombination rates across the human genome

Haplotype-based quantitative trait mapping using a clustering algorithm

BACKGROUND: With the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers, substantial effort has been made in identifying disease-causing genes using linkage disequilibrium (LD) mapping by haplotype analysis of unrelated individuals. In addition to complex diseases, many continuously distributed quantitative traits are of primary clinical and health significance. However the development of association mapping methods using unrelated individuals for quantitative traits has received relatively less attention. RESULTS: We recently developed an association mapping method for complex diseases by mining the sharing of haplotype segments (i.e., phased genotype pairs) in affected individuals that are rarely present in normal individuals. In this paper, we extend our previous work to address the problem of quantitative trait mapping from unrelated individuals. The method is non-parametric in nature, and statistical significance can be obtained by a permutation test. It can also be incorporated into the one-way ANCOVA (analysis of covariance) framework so that other factors and covariates can be easily incorporated. The effectiveness of the approach is demonstrated by extensive experimental studies using both simulated and real data sets. The results show that our haplotype-based approach is more robust than two statistical methods based on single markers: a single SNP association test (SSA) and the Mann-Whitney U-test (MWU). The algorithm has been incorporated into our existing software package called HapMiner, which is available from our website at . CONCLUSION: For QTL (quantitative trait loci) fine mapping, to identify QTNs (quantitative trait nucleotides) with realistic effects (the contribution of each QTN less than 10% of total variance of the trait), large samples sizes (≥ 500) are needed for all the methods. The overall performance of HapMiner is better than that of the other two methods. Its effectiveness further depends on other factors such as recombination rates and the density of typed SNPs. Haplotype-based methods might provide higher power than methods based on a single SNP when using tag SNPs selected from a small number of samples or some other sources (such as HapMap data). Rank-based statistics usually have much lower power, as shown in our study