The variable number of tandem repeats element in DAT1 regulates in vitro dopamine transporter density

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.     

Inference of missing SNPs and information quantity measurements for haplotype blocks

MOTIVATION: Missing data in genotyping single nucleotide polymorphism (SNP) spots are common. High-throughput genotyping methods usually have a high rate of missing data. For example, the published human chromosome 21 data by Patil et al. contains about 20% missing SNPs. Inferring missing SNPs using the haplotype block structure is promising but difficult because the haplotype block boundaries are not well defined. Here we propose a global algorithm to overcome this difficulty. RESULTS: First, we propose to use entropy as a measure of haplotype diversity. We show that the entropy measure combined with a dynamic programming algorithm produces better haplotype block partitions than other measures. Second, based on the entropy measure, we propose a two-step iterative partition-inference algorithm for the inference of missing SNPs. At the first step, we apply the dynamic programming algorithm to partition haplotypes into blocks. At the second step, we use an iterative process similar to the expectation-maximization algorithm to infer missing SNPs in each haplotype block so as to minimize the block entropy. The algorithm iterates these two steps until the total block entropy is minimized. We test our algorithm in several experimental data sets. The results show that the global approach significantly improves the accuracy of the inference. AVAILABILITY: Upon request.     

Model-based inference of haplotype block variation.

The haplotype block structure of SNP variation in human DNA has been demonstrated by several recent studies. The presence of haplotype blocks can be used to dramatically increase the statistical power of genetic mapping. Several criteria have already been proposed for identifying these blocks, all of which require haplotypes as input. We propose a comprehensive statistical model of haplotype block variation and show how the parameters of this model can be learned from haplotypes and/or unphased genotype data. Using real-world SNP data, we demonstrate that our approach can be used to resolve genotypes into their constituent haplotypes with greater accuracy than previously known methods.     

The effect of single-nucleotide polymorphism marker selection on patterns of haplotype blocks and haplotype frequency estimates

The definition of haplotype blocks of single-nucleotide polymorphisms (SNPs) has been proposed so that the haplotypes can be used as markers in association studies and to efficiently describe human genetic variation. The International Haplotype Map (HapMap) project to construct a comprehensive catalog of haplotypic variation in humans is underway. However, a number of factors have already been shown to influence the definition of blocks, including the population studied and the sample SNP density. Here, we examine the effect that marker selection has on the definition of blocks and the pattern of haplotypes by using comparable but complementary SNP sets and a number of block definition methods in various genomic regions and populations that were provided by the Encyclopedia of DNA Elements (ENCODE) project. We find that the chosen SNP set has a profound effect on the block-covered sequence and block borders, even at high marker densities. Our results question the very concept of discrete haplotype blocks and the possibility of generalizing block findings from the HapMap project. We comparatively apply the block-free tagging-SNP approach and discuss both the haplotype approach and the tagging-SNP approach as means to efficiently catalog genetic variation.     

Multipoint linkage-disequilibrium mapping with haplotype-block structure

The HapMap Project is providing a great deal of new information on high-resolution haplotype structure in various human populations. This information has the potential to greatly increase the power of association mapping for a fixed amount of genotyping. A number of methods have been proposed for the identification of haplotype blocks, common haplotypes, and tagging single-nucleotide polymorphisms. Here, we build on this work by developing novel methods for case-control multipoint linkage-disequilibrium (LD) mapping that gain power and speed by making explicit use of the inferred block structure. Specifically, we developed a virtual-variant approach that uses the haplotype-block information to greatly increase power for detection of untyped common variants associated with a trait. Because full multipoint LD mapping can be slow, we exploited the haplotype-block information to develop a fast single-block multipoint mapping method. Our methods are appropriate for genotype data and take into account the uncertainty in phase. We describe the methods in the context of case-parents trios, although they are also applicable to unrelated cases and controls. Our simulations indicate that the most important gains from taking into account the haplotype-block structure at the analysis stage of multipoint LD mapping come from (1) greatly increased power to detect association with untyped variants and (2) greatly improved localization of untyped variants associated with the trait. More-modest gains are obtained in improving power to detect association with a variant that is typed with a moderate amount of missing data. The methods are applied to a Crohn disease data set.     

Finding haplotype tagging SNPs by use of principal components analysis

The immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. One such challenge is to select an optimal subset of SNPs, commonly referred as "haplotype tagging SNPs" (htSNPs), to capture most of the haplotype diversity of each haplotype block or gene-specific region. This information-reduction process facilitates cost-effective genotyping and, subsequently, genotype-phenotype association studies. It also has implications for assessing the risk of identifying research subjects on the basis of SNP information deposited in public domain databases. We have investigated methods for selecting htSNPs by use of principal components analysis (PCA). These methods first identify eigenSNPs and then map them to actual SNPs. We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ability of the selected htSNPs to reconstruct genotypes of non-htSNPs. We also compared these methods with two other htSNP finders, one of which is PCA based. We applied these methods to three experimental data sets and found that the PCA-based methods tend to select the smallest set of htSNPs to achieve a 90% reconstruction precision.     

A statistical framework for haplotype block inference

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.     

Single Nucleotide Polymorphism (SNP)-Strings: An Alternative Method for Assessing Genetic Associations

Background

Genome-wide association studies (GWAS) identify disease-associations for single-nucleotide-polymorphisms (SNPs) from scattered genomic-locations. However, SNPs frequently reside on several different SNP-haplotypes, only some of which may be disease-associated. This circumstance lowers the observed odds-ratio for disease-association.

Methodology/Principal Findings

Here we develop a method to identify the two SNP-haplotypes, which combine to produce each person’s SNP-genotype over specified chromosomal segments. Two multiple sclerosis (MS)-associated genetic regions were modeled; DRB1 (a Class II molecule of the major histocompatibility complex) and MMEL1 (an endopeptidase that degrades both neuropeptides and β-amyloid). For each locus, we considered sets of eleven adjacent SNPs, surrounding the putative disease-associated gene and spanning ∼200 kb of DNA. The SNP-information was converted into an ordered-set of eleven-numbers (subject-vectors) based on whether a person had zero, one, or two copies of particular SNP-variant at each sequential SNP-location. SNP-strings were defined as those ordered-combinations of eleven-numbers (0 or 1), representing a haplotype, two of which combined to form the observed subject-vector. Subject-vectors were resolved using probabilistic methods. In both regions, only a small number of SNP-strings were present. We compared our method to the SHAPEIT-2 phasing-algorithm. When the SNP-information spanning 200 kb was used, SHAPEIT-2 was inaccurate. When the SHAPEIT-2 window was increased to 2,000 kb, the concordance between the two methods, in both of these eleven-SNP regions, was over 99%, suggesting that, in these regions, both methods were quite accurate. Nevertheless, correspondence was not uniformly high over the entire DNA-span but, rather, was characterized by alternating peaks and valleys of concordance. Moreover, in the valleys of poor-correspondence, SHAPEIT-2 was also inconsistent with itself, suggesting that the SNP-string method is more accurate across the entire region.

Conclusions/Significance

Accurate haplotype identification will enhance the detection of genetic-associations. The SNP-string method provides a simple means to accomplish this and can be extended to cover larger genomic regions, thereby improving a GWAS’s power, even for those published previously.     

A novel efficient dynamic programming algorithm for haplotype block partitioning

In this paper, a new efficient algorithm is presented for haplotype block partitioning based on haplotype diversity. In this algorithm, finding the largest meaningful block that satisfies the diversity condition is the main goal as an optimization problem. The algorithm can be performed in polynomial time complexity with regard to the number of haplotypes and SNPs. We apply our algorithm on three biological data sets from chromosome 21 in three different population data sets from HapMap data bulk; the obtained results show the efficiency and better performance of our algorithm in comparison with three other well known methods.     

Genome association studies of complex diseases by case-control designs

One way to perform linkage-disequilibrium (LD) mapping of genetic traits is to use single markers. Since dense marker maps-such as single-nucleotide polymorphism and high-resolution microsatellite maps-are available, it is natural and practical to generalize single-marker LD mapping to high-resolution haplotype or multiple-marker LD mapping. This article investigates high-resolution LD-mapping methods, for complex diseases, based on haplotype maps or microsatellite marker maps. The objective is to explore test statistics that combine information from haplotype blocks or multiple markers. Based on two coding methods, genotype coding and haplotype coding, Hotelling's T2 statistics TG and TH are proposed to test the association between a disease locus and two haplotype blocks or two markers. The validity of the two T2 statistics is proved by theoretical calculations. A statistic TC, an extension of the traditional chi2 method of comparing haplotype frequencies, is introduced by simply adding the chi2 test statistics of the two haplotype blocks together. The merit of the three methods is explored by calculation and comparison of power and of type I errors. In the presence of LD between the two blocks, the type I error of TC is higher than that of TH and TG, since TC ignores the correlation between the two blocks. For each of the three statistics, the power of using two haplotype blocks is higher than that of using only one haplotype block. By power comparison, we notice that TC has higher power than that of TH, and TH has higher power than that of TG. In the absence of LD between the two blocks, the power of TC is similar to that of TH and higher than that of TG. Hence, we advocate use of TH in the data analysis. In the presence of LD between the two blocks, TH takes into account the correlation between the two haplotype blocks and has a lower type I error and higher power than TG. Besides, the feasibility of the methods is shown by sample-size calculation.     

Novel strategies to mine alcoholism-related haplotypes and genes by combining existing knowledge framework

High-throughout single nucleotide polymorphism detection technology and the existing knowledge provide strong support for mining the disease-related haplotypes and genes. In this study, first, we apply four kinds of haplotype identification methods (Confidence Intervals, Four Gamete Tests, Solid Spine of LD and fusing method of haplotype block) into high-throughout SNP genotype data to identify blocks, then use cluster analysis to verify the effectiveness of the four methods, and select the alcoholism-related SNP haplotypes through risk analysis. Second, we establish a mapping from haplotypes to alcoholism-related genes. Third, we inquire NCBI SNP and gene databases to locate the blocks and identify the candidate genes. In the end, we make gene function annotation by KEGG, Biocarta, and GO database. We find 159 haplotype blocks, which relate to the alcoholism most possibly on chromosome 1∼22, including 227 haplotypes, of which 102 SNP haplotypes may increase the risk of alcoholism. We get 121 alcoholism-related genes and verify their reliability by the functional annotation of biology. In a word, we not only can handle the SNP data easily, but also can locate the disease-related genes precisely by combining our novel strategies of mining alcoholism-related haplotypes and genes with existing knowledge framework. Supported by the National Natural Science Foundation of China (Grant Nos. 30570424, 60601010 and 30600367), the National High-Tech Research and Development Program of China, (Grant No.2007AA02Z329), the Key Science and Technology Program of Heilongjiang Province(Grant No.GB03C602-4), Natural Science Foundation of Heilongjiang Province (Grant No. F2008-02), Youth Science Foundation of Harbin Medical University (Grant No. 060045) and Science Foundation of Heilongjiang Province Education Department (Grant Nos. 11531113 and 1152hq28).     

Large-scale single-nucleotide polymorphism (SNP) and haplotype analyses, using dense SNP Maps, of 199 drug-related genes in 752 subjects: the analysis of the association between uncommon SNPs within haplotype blocks and the haplotypes constructed with haplotype-tagging SNPs

To optimize the strategies for population-based pharmacogenetic studies, we extensively analyzed single-nucleotide polymorphisms (SNPs) and haplotypes in 199 drug-related genes, through use of 4,190 SNPs in 752 control subjects. Drug-related genes, like other genes, have a haplotype-block structure, and a few haplotype-tagging SNPs (htSNPs) could represent most of the major haplotypes constructed with common SNPs in a block. Because our data included 860 uncommon (frequency <0.1) SNPs with frequencies that were accurately estimated, we analyzed the relationship between haplotypes and uncommon SNPs within the blocks (549 SNPs). We inferred haplotype frequencies through use of the data from all htSNPs and one of the uncommon SNPs within a block and calculated four joint probabilities for the haplotypes. We show that, irrespective of the minor-allele frequency of an uncommon SNP, the majority (mean +/- SD frequency 0.943+/-0.117) of the minor alleles were assigned to a single haplotype tagged by htSNPs if the uncommon SNP was within the block. These results support the hypothesis that recombinations occur only infrequently within blocks. The proportion of a single haplotype tagged by htSNPs to which the minor alleles of an uncommon SNP were assigned was positively correlated with the minor-allele frequency when the frequency was <0.03 (P<.000001; n=233 [Spearman's rank correlation coefficient]). The results of simulation studies suggested that haplotype analysis using htSNPs may be useful in the detection of uncommon SNPs associated with phenotypes if the frequencies of the SNPs are higher in affected than in control populations, the SNPs are within the blocks, and the frequencies of the SNPs are >0.03.     

Haplotype reconstruction from genotype data using Imperfect Phylogeny

Critical to the understanding of the genetic basis for complex diseases is the modeling of human variation. Most of this variation can be characterized by single nucleotide polymorphisms (SNPs) which are mutations at a single nucleotide position. To characterize the genetic variation between different people, we must determine an individual's haplotype or which nucleotide base occurs at each position of these common SNPs for each chromosome. In this paper, we present results for a highly accurate method for haplotype resolution from genotype data. Our method leverages a new insight into the underlying structure of haplotypes that shows that SNPs are organized in highly correlated 'blocks'. In a few recent studies, considerable parts of the human genome were partitioned into blocks, such that the majority of the sequenced genotypes have one of about four common haplotypes in each block. Our method partitions the SNPs into blocks, and for each block, we predict the common haplotypes and each individual's haplotype. We evaluate our method over biological data. Our method predicts the common haplotypes perfectly and has a very low error rate (<2% over the data) when taking into account the predictions for the uncommon haplotypes. Our method is extremely efficient compared with previous methods such as PHASE and HAPLOTYPER. Its efficiency allows us to find the block partition of the haplotypes, to cope with missing data and to work with large datasets. AVAILABILITY: The algorithm is available via a Web server at http://www.calit2.net/compbio/hap/     

Effectiveness of computational methods in haplotype prediction

Haplotype analysis has been used for narrowing down the location of disease-susceptibility genes and for investigating many population processes. Computational algorithms have been developed to estimate haplotype frequencies and to predict haplotype phases from genotype data for unrelated individuals. However, the accuracy of such computational methods needs to be evaluated before their applications can be advocated. We have experimentally determined the haplotypes at two loci, the N-acetyltransferase 2 gene ( NAT2, 850 bp, n=81) and a 140-kb region on chromosome X ( n=77), each consisting of five single nucleotide polymorphisms (SNPs). We empirically evaluated and compared the accuracy of the subtraction method, the expectation-maximization (EM) method, and the PHASE method in haplotype frequency estimation and in haplotype phase prediction. Where there was near complete linkage disequilibrium (LD) between SNPs (the NAT2 gene), all three methods provided effective and accurate estimates for haplotype frequencies and individual haplotype phases. For a genomic region in which marked LD was not maintained (the chromosome X locus), the computational methods were adequate in estimating overall haplotype frequencies. However, none of the methods was accurate in predicting individual haplotype phases. The EM and the PHASE methods provided better estimates for overall haplotype frequencies than the subtraction method for both genomic regions.     

Identity by descent mapping of founder mutations in cancer using high-resolution tumor SNP data

Dense genotype data can be used to detect chromosome fragments inherited from a common ancestor in apparently unrelated individuals. A disease-causing mutation inherited from a common founder may thus be detected by searching for a common haplotype signature in a sample population of patients. We present here FounderTracker, a computational method for the genome-wide detection of founder mutations in cancer using dense tumor SNP profiles. Our method is based on two assumptions. First, the wild-type allele frequently undergoes loss of heterozygosity (LOH) in the tumors of germline mutation carriers. Second, the overlap between the ancestral chromosome fragments inherited from a common founder will define a minimal haplotype conserved in each patient carrying the founder mutation. Our approach thus relies on the detection of haplotypes with significant identity by descent (IBD) sharing within recurrent regions of LOH to highlight genomic loci likely to harbor a founder mutation. We validated this approach by analyzing two real cancer data sets in which we successfully identified founder mutations of well-characterized tumor suppressor genes. We then used simulated data to evaluate the ability of our method to detect IBD tracts as a function of their size and frequency. We show that FounderTracker can detect haplotypes of low prevalence with high power and specificity, significantly outperforming existing methods. FounderTracker is thus a powerful tool for discovering unknown founder mutations that may explain part of the "missing" heritability in cancer. This method is freely available and can be used online at the FounderTracker website.     

Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history,recombination, and mutation

Recent studies suggest that haplotypes are arranged into discrete blocklike structures throughout the human genome. Here, we present an alternative haplotype block definition that assumes no recombination within each block but allows for recombination between blocks, and we use it to study the combined effects of demographic history and various population genetic parameters on haplotype block characteristics. Through extensive coalescent simulations and analysis of published haplotype data on chromosome 21, we find that (1) the combined effects of population demographic history, recombination, and mutation dictate haplotype block characteristics and (2) haplotype blocks can arise in the absence of recombination hot spots. Finally, we provide practical guidelines for designing and interpreting studies investigating haplotype block structure.     

A novel splicing variant of CADM2 as a protective transcript of psoriasis

CADM2, a candidate gene for psoriasis, was identified by a genome-wide association study using microsatellites in the Japanese population (561 cases and 561 controls). Moreover, haplotype analysis included an additional 68 SNPs and indicated that a 110-kb haplotype block was detected for the protective risk haplotype of psoriasis. We also identified an initial exon of novel splicing variants in this haplotype block. A functional analysis by qRT-PCR using RNAs from the blood of 56 cases and 64 controls significantly demonstrated an inverse correlation between expression frequencies in a novel splicing variant and the number of alleles associated with psoriasis. To confirm these results, we must perform replication studies using other ethnic groups and more functional analysis particularly for skin tissues.     

Inference on haplotype effects in case-control studies using unphased genotype data

A variety of statistical methods exist for detecting haplotype-disease association through use of genetic data from a case-control study. Since such data often consist of unphased genotypes (resulting in haplotype ambiguity), such statistical methods typically apply the expectation-maximization (EM) algorithm for inference. However, the majority of these methods fail to perform inference on the effect of particular haplotypes or haplotype features on disease risk. Since such inference is valuable, we develop a retrospective likelihood for estimating and testing the effects of specific features of single-nucleotide polymorphism (SNP)-based haplotypes on disease risk using unphased genotype data from a case-control study. Our proposed method has a flexible structure that allows, among other choices, modeling of multiplicative, dominant, and recessive effects of specific haplotype features on disease risk. In addition, our method relaxes the requirement of Hardy-Weinberg equilibrium of haplotype frequencies in case subjects, which is typically required of EM-based haplotype methods. Also, our method easily accommodates missing SNP information. Finally, our method allows for asymptotic, permutation-based, or bootstrap inference. We apply our method to case-control SNP genotype data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) Genetics study and identify two haplotypes that appear to be significantly associated with type 2 diabetes. Using the FUSION data, we assess the accuracy of asymptotic P values by comparing them with P values obtained from a permutation procedure. We also assess the accuracy of asymptotic confidence intervals for relative-risk parameters for haplotype effects, by a simulation study based on the FUSION data.