Haplotype block structure and its applications to association studies: power and study designs

Association studies in candidate genes: strategies to select SNPs to be tested

OBJECTIVE

When numerous single nucleotide polymorphisms (SNPs) have been identified in a candidate gene, a relevant and still unanswered question is to determine how many and which of these SNPs should be optimally tested to detect an association with the disease. Testing them all is expensive and often unnecessary. Alleles at different SNPs may be associated in the population because of the existence of linkage disequilibrium, so that knowing the alleles carried at one SNP could provide exact or partial knowledge of alleles carried at a second SNP. We present here a method to select the most appropriate subset of SNPs in a candidate gene based on the pairwise linkage disequilibrium between the different SNPs.

METHOD

The best subset is identified through power computations performed under different genetic models, assuming that one of the SNPs identified is the disease susceptibility variant.

RESULTS

We applied the method on two data sets, an empirical study of the APOE gene region and a simulated study concerning one of the major genes (MG1) from the Genetic Analysis Workshop 12. For these two genes, the sets of SNPs selected were compared to the ones obtained using two other methods that need the reconstruction of multilocus haplotypes in order to identify haplotype-tag SNPs (htSNPs). We showed that with both data sets, our method performed better than the other selection methods.

Late life metabolic syndrome, early growth, and common polymorphism in the growth hormone and placental lactogen gene cluster

Low rates of fetal and infant growth are associated with the metabolic syndrome and cardiovascular disease in later life. We investigated common genetic variation in the GH-CSH gene cluster on chromosome 17q23 encoding GH, placental lactogens [chorionic somatomammotropins (CSH)], and placental GH variant in relation to fetal and infant growth and phenotypic features of the metabolic syndrome in subjects aged 59-72 yr from Hertfordshire, UK. Allele groups T, D1, and D2 of a locus herein designated CSH1.01 were examined in relation to GH-CSH single nucleotide polymorphisms and to specific phenotypes. Average birth weights were similar for all genotype groups. Men with T alleles were significantly lighter at 1 yr of age, shorter as adults, and had higher blood pressures, fasting insulin (T/T 66% higher than D2/D2) and triglyceride concentrations, and insulin and glucose concentrations during a glucose tolerance test. Birth weight and 1-yr weight associations with metabolic syndrome traits were independent of the CSH1.01 effects. Common diversity in GH-CSH correlates with low 1-yr weight and with features of the metabolic syndrome in later life. GH-CSH genotype adds substantially to, but does not account for, the associations between low body weight, at birth and in infancy, and the metabolic syndrome.

Association studies for finding cancer-susceptibility genetic variants

Cancer is the result of complex interactions between inherited and environmental factors. Known genes account for a small proportion of the heritability of cancer, and it is likely that many genes with modest effects are yet to be found. Genetic-association studies have been widely used in the search for such genes, but success has been limited so far. Increased knowledge of the function of genes and the architecture of human genetic variation combined with new genotyping technologies herald a new era of gene mapping by association.

The future of association studies: gene-based analysis and replication

Historically, association tests were limited to single variants, so that the allele was considered the basic unit for association testing. As marker density increases and indirect approaches are used to assess association through linkage disequilibrium, association is now frequently considered at the haplotypic level. We suggest that there are difficulties in replicating association findings at the single-nucleotide-polymorphism (SNP) or the haplotype level, and we propose a shift toward a gene-based approach in which all common variation within a candidate gene is considered jointly. Inconsistencies arising from population differences are more readily resolved by use of a gene-based approach rather than either a SNP-based or a haplotype-based approach. A gene-based approach captures all of the potential risk-conferring variations; thus, negative findings are subject only to the issue of power. In addition, chance findings due to multiple testing can be readily accounted for by use of a genewide-significance level. Meta-analysis procedures can be formalized for gene-based methods through the combination of P values. It is only a matter of time before all variation within genes is mapped, at which point the gene-based approach will become the natural end point for association analysis and will inform our search for functional variants relevant to disease etiology.

Investigation of serotonin-related genes in antidepressant response

In this study, we sought out to test the hypothesis that genetic factors may influence antidepressant response to fluoxetine. The investigation focused on seven candidate genes in the serotonergic pathway involved in the synthesis, transport, recognition, and degradation of serotonin. Our clinical sample consisted of 96 subjects with unipolar major depression treated with fluoxetine with response variables assessed after a 12-week trial. Patient data were also collected to investigate the pattern of drug response. Using a high-throughput single-nucleotide polymorphism (SNP) genotyping platform and capillary electrophoresis, we genotyped patients at 110 SNPs and four repeat polymorphisms located in seven candidate genes (HTR1A, HTR2A, HTR2C, MAOA, SLC6A4, TPH1, and TPH2). Statistical tests performed included single-locus and haplotype association tests, and linkage disequilibrium (LD) estimation. Little evidence of population stratification was observed in the sample with 20 random SNPs using a genomic control procedure. Our most intriguing result involved three SNPs in the TPH1 gene and one SNP in the SLC6A4 gene, which show significant single-locus association when response to fluoxetine is compared to nonresponse (P=0.02-0.04). All odds ratios indicated an increased risk of not responding to fluoxetine. In the specific response vs nonspecific and nonresponse comparison, three SNPs in the TPH2 gene (P=0.02-0.04) were positively associated and one SNP in the HTR2A gene (P=0.02) was negatively associated. When comparing specific response to nonspecific response, we found significant negative associations in three SNPs in the HTR2A gene (P=0.001-0.03) and two SNPs in the MAOA gene (P=0.03-0.05). We observed variable, although strong LD, in each gene and unexpectedly low numbers of estimated haplotypes, formed from tagged SNPs. Significant haplotype associations were found in all but the HTR1A and HTR2C genes. Although these data should be interpreted cautiously due to the small sample size, these results implicate TPH1 and SLC6A4 in general response, and HTR2A, TPH2, and MAOA in the specificity of response to fluoxetine. Intriguingly, we observe that a number of the less frequent alleles of many of the SNP markers were associated with the nonresponse and nonspecific phenotypes.

Haplotype sharing correlation analysis using family data: a comparison with family-based association test in the presence of allelic heterogeneity

The haplotype-sharing correlation (HSC) method for association analysis using family data is revisited by introducing a permutation procedure for estimating region-wise significance at each marker on a study segment. In simulation studies, the HSC method has a correct type 1 error rate in both unstructured and structured populations. The HSC signals on disease segments occur in the vicinity of a true disease locus on a restricted region without recombination hotspots. However, the peak signal may not pinpoint the true disease location in a small region with dense markers. The HSC method is shown to have higher power than single- and multilocus family-based association test (FBAT) methods when the true disease locus is unobserved among the study markers, and especially under conditions of weak linkage disequilibrium and multiple ancestral disease alleles. These simulation results suggest that the HSC method has the capacity to identify true disease-associated segments under allelic heterogeneity that go undetected by the FBAT method that compares allelic or haplotypic frequencies.

Bayesian spatial modeling of haplotype associations

We review methods for relating the risk of disease to a collection of single nucleotide polymorphisms (SNPs) within a small region. Association studies using case-control designs with unrelated individuals could be used either to test for a direct effect of a candidate gene and characterize the responsible variant(s), or to fine map an unknown gene by exploiting the pattern of linkage disequilibrium (LD). We consider a flexible class of logistic penetrance models based on haplotypes and compare them with an alternative formulation based on unphased multilocus genotypes. The likelihood for haplotype-based models requires summation over all possible haplotype assignments consistent with the observed genotype data, and can be fitted using either Expectation-Maximization (E-M) or Markov chain Monte Carlo (MCMC) methods. Subtleties involving ascertainment correction for case-control studies are discussed. There has been great interest in methods for LD mapping based on the coalescent or ancestral recombination graphs as well as methods based on haplotype sharing, both of which we review briefly. Because of their computational complexity, we propose some alternative empirical modeling approaches using techniques borrowed from the Bayesian spatial statistics literature. Here, space is interpreted in terms of a distance metric describing the similarity of any pair of haplotypes to each other, and hence their presumed common ancestry. Specifically, we discuss the conditional autoregressive model and two spatial clustering models: Potts and Voronoi. We conclude with a discussion of the implications of these methods for modeling cryptic relatedness, haplotype blocks, and haplotype tagging SNPs, and suggest a Bayesian framework for the HapMap project.

Global genetic analysis

The introduction of molecular markers in genetic analysis has revolutionized medicine. These molecular markers are genetic variations associated with a predisposition to common diseases and individual variations in drug responses. Identification and genotyping a vast number of genetic polymorphisms in large populations are increasingly important for disease gene identification, pharmacogenetics and population-based studies. Among variations being analyzed, single nucleotide polymorphisms seem to be most useful in large-scale genetic analysis. This review discusses approaches for genetic analysis, use of different markers, and emerging technologies for large-scale genetic analysis where millions of genotyping need to be performed.

Mapping complex disease loci in whole-genome association studies

Identification of the genetic polymorphisms that contribute to susceptibility for common diseases such as type 2 diabetes and schizophrenia will aid in the development of diagnostics and therapeutics. Previous studies have focused on the technique of genetic linkage, but new technologies and experimental resources make whole-genome association studies more feasible. Association studies of this type have good prospects for dissecting the genetics of common disease, but they currently face a number of challenges, including problems with multiple testing and study design, definition of intermediate phenotypes and interaction between polymorphisms.

Does haplotype diversity predict power for association mapping of disease susceptibility?

Many recent studies have established that haplotype diversity in a small region may not be greatly diminished when the number of markers is reduced to a smaller set of "haplotype-tagging" single-nucleotide polymorphisms (SNPs) that identify the most common haplotypes. These studies are motivated by the assumption that retention of haplotype diversity assures retention of power for mapping disease susceptibility by allelic association. Using two bodies of real data, three proposed measures of diversity, and regression-based methods for association mapping, we found no scenario for which this assumption was tenable. We compared the chi-square for composite likelihood and the maximum chi-square for single SNPs in diplotypes, excluding the marker designated as causal. All haplotype-tagging methods conserve haplotype diversity by selecting common SNPs. When the causal marker has a range of allele frequencies as in real data, chi-square decreases faster than under random selection as the haplotype-tagging set diminishes. Selecting SNPs by maximizing haplotype diversity is inefficient when their frequency is much different from the unknown frequency of the causal variant. Loss of power is minimized when the difference between minor allele frequencies of the causal SNP and a closely associated marker SNP is small, which is unlikely in ignorance of the frequency of the causal SNP unless dense markers are used. Therefore retention of haplotype diversity in simulations that do not mirror genomic allele frequencies has no relevance to power for association mapping. TagSNPs that are assigned to bins instead of haplotype blocks also lose power compared with random SNPs. This evidence favours a multi-stage design in which both models and density change adaptively.

Challenges to environmental toxicology and epidemiology: where do we stand and which way do we go?

Modern toxicology investigates a wide array of both old and new health hazards. Priority setting is needed to select agents for research from the plethora of exposure circumstances. The changing societies and a growing fraction of the aged have to be taken into consideration. A precise exposure assessment is of importance for risk estimation and regulation. Toxicology contributes to the exploration of pathomechanisms to specify the exposure metrics for risk estimation. Combined effects of co-existing agents are not yet sufficiently understood. Animal experiments allow a separate administration of agents which can not be disentangled by epidemiological means, but their value is limited for low exposure levels in many of today's settings. As an experimental science, toxicology has to keep pace with the rapidly growing knowledge about the language of the genome and the changing paradigms in cancer development. During the pioneer era of assembling a working draft of the human genome, toxicogenomics has been developed. Gene and pathway complexity have to be considered when investigating gene-environment interactions. For a best conduct of studies, modern toxicology needs a close liaison with many other disciplines like epidemiology and bioinformatics.

Haplotype block partitioning and tag SNP selection using genotype data and their applications to association studies

Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed by regions of low LD. A small fraction of SNPs (tag SNPs) is sufficient to capture most of the haplotype structure of the human genome. In this paper, we develop a method to partition haplotypes into blocks and to identify tag SNPs based on genotype data by combining a dynamic programming algorithm for haplotype block partitioning and tag SNP selection based on haplotype data with a variation of the expectation maximization (EM) algorithm for haplotype inference. We assess the effects of using either haplotype or genotype data in haplotype block identification and tag SNP selection as a function of several factors, including sample size, density or number of SNPs studied, allele frequencies, fraction of missing data, and genotyping error rate, using extensive simulations. We find that a modest number of haplotype or genotype samples will result in consistent block partitions and tag SNP selection. The power of association studies based on tag SNPs using genotype data is similar to that using haplotype data.

Fine mapping a gene for pediatric gastroesophageal reflux on human chromosome 13q14

We previously mapped a gene for severe pediatric gastroesophageal reflux disease ( GERD1) to a 9-cM interval on chromosome 13q14. In this report, we present the results of DNA sequencing and allelic association analyses that were done in an attempt to clone the GERD1 gene. Using a candidate transcript approach, we screened affected individuals for mutations in all transcribed regions of all genes, putative genes, and ESTs identified within the 6.2-Mb GERD1 locus based on alignments with the GenBank cDNA databases. From a total of 50 identifiable genes and 99 EST clusters in the GERD1 locus, we identified 163 polymorphisms (143 SNPs and 20 INDELs) in 21 genes and 37 ESTs. The patterns of inheritance and/or the high population frequencies of all polymorphic alleles identified in this study argued against causative relationships between any of the alleles and the GERD phenotype. Using a subset of 51 SNPs distributed throughout the GERD1 locus, we performed case-control and family (TDT) allelic association analyses on two sets of samples. The case-control study was performed with 73 GERD cases and 93 controls, and the family study was performed using 22 small families. SNP 160 (position 38,925,329 Mb, UCSChg15 map) gave a significant P value prior to multiple test correction in both the case control and family studies, while SNP168 (at 40,442,903 Mb) showed significant association after multiple test correction in the case-control sample, but was uninformative in the family sample. The results suggest that the GERD1 gene might be located near SNP160 or SNP168.

Multicenter initiative seeking critical genes in respiratory papillomatosis

OBJECTIVES

To determine the host genes that govern susceptibility to recurrent respiratory papillomatosis (RRP). RRP is caused by human papillomavirus (HPV) 6 and 11. Millions of babies are exposed during the birthing process, but relatively few develop the disease and the aggressiveness of the course is highly variable. Genetically encoded host susceptibility is postulated. Determining the host genes that govern susceptibility will enhance our understanding not only of RRP but also of host-viral interaction in general.

STUDY DESIGN

A genome-wide association study on familial triads consisting of an RRP-affected child and his or her parents. Using the HapMap data from the human genome project, we will identify those alleles that are over-transmitted by the parents to their affected offspring as compared to those alleles that are under-transmitted.

METHODS

Approximately 400 patients and their parents will be recruited through a collaboration between the Center for Genomic Sciences and the RRP Task Force. DNA will be extracted from blood specimens and viral typing will be performed on biopsy specimens. Patients will be genotyped using single nucleotide polymorphism (SNP) markers and compared to their respective parents' genotype using the transmission disequilibrium test. Both a genome scan and a candidate gene approach will be utilized. RESULTS Institutional Review Board authorization has been obtained at three hospitals and the process is underway at 18 more. Patient and parent recruitment has begun. Specimens have been forwarded to Pittsburgh, Pennsylvania, where the DNA has been extracted and is being stored.

CONCLUSIONS

A novel approach combining a nationwide patient resource and the mapping power of the sub-centimorgan human haplotype map has been developed to elucidate the biological mechanisms of RRP by determining the genetically encoded susceptibilities of host-virus interaction.

Will haplotype maps be useful for finding genes?

From its introduction into the literature, the idea of haplotype map-based linkage disequilibrium (LD) studies has been the subject of disputes. These queries involve the extent to which the haplotype blocks exist, the validity of fundamental concepts such as the recombination hotspot, and the application of this idea in the form of the HapMap project. In this article, we review the relevant literature to evaluate the potential importance of haplotype maps for psychiatric genetics. We first take a closer look at the nature of haplotype blocks and then address the impact of block definitions and methodological factors, such as single-nucleotide polymorphism density and sample size, on findings from haplotype block studies. After distinguishing between two types of haplotype map-based LD studies, we discuss the importance of the recombination hotspot and the nature of the disease mutations affecting complex traits. In the final section, we summarize our main conclusions and comment on the usefulness of haplotype maps for finding genes.

Quantitative Analysis of Nucleic Acids - the Last Few Years of Progress

DNA and RNA quantifications are widely used in biological and biomedical research. In the last ten years, many technologies have been developed to enable automated and high-throughput analyses. In this review, we first give a brief overview of how DNA and RNA quantifications are carried out. Then, five technologies (microarrays, SAGE, differential display, real time PCR and real competitive PCR) are introduced, with an emphasis on how these technologies can be applied and what their limitations are. The technologies are also evaluated in terms of a few key aspects of nucleic acids quantification such as accuracy, sensitivity, specificity, cost and throughput.

Genetic epidemiology, genetic maps and positional cloning

Genetic epidemiology developed in the middle of the last century, focused on inherited causes of disease but with methods and results applicable to other traits and even forensics. Early success with linkage led to the localization of genes contributing to disease, and ultimately to the Human Genome Project. The discovery of millions of DNA markers has encouraged more efficient positional cloning by linkage disequilibrium (LD), using LD maps and haplotypes in ways that are rapidly evolving. This has led to large international programmes, some promising and others alarming, with laws about DNA patenting and ethical guidelines for responsible research still struggling to be born.

Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium

Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.

Haplotype parsing: methods for extracting information from human genetic variations

While the shared consensus genetic sequence of our species contains a great deal of information about our common biology, there is also much to be learned from the subtle genetic variations across our species. These variations are believed to be generally of little or no direct functional significance and predominantly reflect the chance accumulation of small genetic changes since our emergence as a species. Therefore, they carry little useful information when observed in a single individual. When tallied across a whole population though, these chance mutations can teach us a great deal about our evolutionary history and the patterns of inheritance in particular individuals. In particular, frequently observed patterns of single nucleotide polymorphisms (SNPs) in a population can identify segments of chromosome that have been passed down largely intact through long stretches of our evolution. Finding these frequently conserved chromosomal segments, or haplotypes, and developing methods to identify haplotype patterns in particular individuals, will in turn help us to identify those particular segments that carry genetic factors influencing risk for many common human diseases. To make the best use of this data, we will need to develop new models for the encoding of information in genome variations--the "language of genetic variation"--and new algorithms for fitting datasets to those models. This article surveys past work by the author and colleagues on this problem, utilising computational methods for locating frequent patterns in haploid sequence data, and "parsing" sequences so as to optimally explain them given the knowledge of the general population structure. The author's recent work in this area has been compiled into a set of computational tools available at http://www-2.cs.cmu.edu/~russells/software/hapmotif.html.

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.

Defining haplotype blocks and tag single-nucleotide polymorphisms in the human genome

Recent studies suggest that the genome is organized into blocks of haplotypes, and efforts to create a genome-wide haplotype map of single-nucleotide polymorphisms (SNPs) are already underway. Haplotype blocks are defined algorithmically and to date several algorithms have been proposed. However, little is known about their relative performance in real data or about the impact of allele frequencies and parameter choices on the detection of haplotype blocks and the markers that tag them. Here we present a formal comparison of two major algorithms, a linkage disequilibrium (LD)-based method and a dynamic programming algorithm (DPA), in three chromosomal regions differing in gene content and recombination rate. The two methods produced strikingly different results. DPA identified fewer and larger haplotype blocks as well as a smaller set of tag SNPs than the LD method. For both methods, the results were strongly dependent on the allele frequency. Decreasing the minor allele frequency led to an up to 3.7-fold increase in the number of haplotype blocks and tag SNPs. Definition of haploytpe blocks and tag SNPs was also sensitive to parameter changes, but the results could not be reconciled simply by parameter adjustment. These results show that two major methods for detecting haplotype blocks and tag SNPs can produce different results in the same data and that these results are sensitive to marker allele frequencies and parameter choices. More information is needed to guide the choice of method, marker allele frequencies, and parameters in the development of a haplotype map.

Haplotype Tagging Single Nucleotide Polymorphisms and Association Studies

OBJECTIVES

Discrete blocks of low haplotype diversity exist within the human genome. The non-redundant subset of 'haplotype tagging' single nucleotide polymorphisms (htSNPs) in such blocks can distinguish a majority of the haplotypes. Several approaches have been proposed to determine htSNPs, ranging from visual inspection to formal analytic procedures. Optimal htSNPs can be estimated using a small subgroup of an association study population that have been genotyped for a dense SNP map, and it is just these htSNPs that are genotyped in the remainder of the samples. We investigated by simulation how the size of the subsample affects the power of association studies, and what type of subjects it should include.

METHODS

We used the program tagSNPs [Stram et al., Hum Hered 2003;55:27-36], which selects htSNPs to minimize the uncertainty in predicting common haplotypes for individuals with unphased genotype data.

RESULTS

On average, 27% of the SNPs were designated as htSNPs. Genotyping as few as 25 unphased individuals to select the htSNPs did not appear to reduce the power of an association study, as compared with using all SNPs. For the disease models considered, selecting htSNPs based on cases, controls, or a mixture of both gave similar results.

CONCLUSIONS

These results suggest that the genotyping effort in an association study can be substantially reduced with little loss of power by identifying htSNPs in a small subsample of individuals.

Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping

Association studies are widely seen as the most promising approach for finding polymorphisms that influence genetically complex traits, such as common diseases and responses to their treatment. Considerable interest has therefore recently focused on the development of methods that efficiently screen genomic regions or whole genomes for gene variants associated with complex phenotypes. One key element in this search is the use of linkage disequilibrium to gain maximal information from typing a selected subset of highly informative single-nucleotide polymorphism (SNP) markers, now often called "tagging SNPs" (tSNPs). Probably the most common approach to linkage-disequilibrium gene mapping involves a three-step program: (1) characterization of the haplotype structure in candidate genes or genomic regions of interest, (2) identification of tSNPs sufficient to represent the most common haplotypes, and (3) typing of tSNPs in clinical material. Early definitions of tSNPs focused on the amount of haplotype diversity that they explained. To select tSNPs that would have maximal power in a genetic association study, however, we have developed optimization criteria based on the r2 measure of association and have compared these with other criteria based on the haplotype diversity. To evaluate the full program and to assess how well the selected tags are likely to perform, we have determined the haplotype structure and have assessed tSNPs in the SCN1A gene, an important candidate gene for sporadic epilepsy. We find that as few as four tSNPs are predicted to maintain a consistently high r2 value with all other common SNPs in the gene, indicating that the tags could be used in an association study with only a modest reduction in power relative to direct assays of all common SNPs. This implies that very large case-control studies can be screened for variation in hundreds of candidate genes with manageable experimental effort, once tSNPs are identified. However, our results also show that tSNPs identified in one population may not necessarily perform well in another, indicating that the preliminary study to identify tSNPs and the later case-control study should be performed in the same population. Our results also indicate that tSNPs will not easily identify discrepant SNPs, which lie on importantly discriminating but apparently short genealogical branches. This could significantly complicate tagging approaches for phenotypes influenced by variants that have experienced positive selection.

Finding haplotype block boundaries by using the minimum-description-length principle

We present a method for detecting haplotype blocks that simultaneously uses information about linkage-disequilibrium decay between the blocks and the diversity of haplotypes within the blocks. By use of phased single-nucleotide polymorphism data, our method partitions a chromosome into a series of adjacent, nonoverlapping blocks. The partition is made by choosing among a family of Markov models for block structure in a chromosomal region. Specifically, in the model, the occurrence of haplotypes within blocks follows a time-inhomogeneous Markov process along the chromosome, and we choose among possible partitions by using the two-stage minimum-description-length criterion. When applied to data simulated from the coalescent with recombination hotspots, our method reliably situates block boundaries at the hotspots and infrequently places block boundaries at sites with background levels of recombination. We apply three previously published block-finding methods to the same data, showing that they either are relatively insensitive to recombination hotspots or fail to discriminate between background sites of recombination and hotspots. When applied to the 5q31 data of Daly et al., our method identifies more block boundaries in agreement with those found by Daly et al. than do other methods. These results suggest that our method may be useful for designing association-based mapping studies that exploit haplotype blocks.

Haplotype blocks and linkage disequilibrium in the human genome

There is great interest in the patterns and extent of linkage disequilibrium (LD) in humans and other species. Characterizing LD is of central importance for gene-mapping studies and can provide insights into the biology of recombination and human demographic history. Here, we review recent developments in this field, including the recently proposed 'haplotype-block' model of LD. We describe some of the recent data in detail and compare the observed patterns to those seen in simulations.

Minimum description length block finder, a method to identify haplotype blocks and to compare the strength of block boundaries

We describe a new probabilistic method for finding haplotype blocks that is based on the use of the minimum description length (MDL) principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly and colleagues. The results expose some problems that exist in the current methods for the evaluation of the significance of predicted block boundaries. Our method, MDL block finder, can be used to compare block borders in different sample sets, and we demonstrate this by applying the MDL-based method to define the block structure in chromosomes from population isolates.

Haplotype block partition with limited resources and applications to human chromosome 21 haplotype data

Recent studies have shown that the human genome has a haplotype block structure such that it can be decomposed into large blocks with high linkage disequilibrium (LD) and relatively limited haplotype diversity, separated by short regions of low LD. One of the practical implications of this observation is that only a small fraction of all the single-nucleotide polymorphisms (SNPs) (referred as "tag SNPs") can be chosen for mapping genes responsible for human complex diseases, which can significantly reduce genotyping effort, without much loss of power. Algorithms have been developed to partition haplotypes into blocks with the minimum number of tag SNPs for an entire chromosome. In practice, investigators may have limited resources, and only a certain number of SNPs can be genotyped. In the present article, we first formulate this problem as finding a block partition with a fixed number of tag SNPs that can cover the maximal percentage of the whole genome, and we then develop two dynamic programming algorithms to solve this problem. The algorithms are sufficiently flexible to permit knowledge of functional polymorphisms to be considered. We apply the algorithms to a data set of SNPs on human chromosome 21, combining the information of coding and noncoding regions. We study the density of SNPs in intergenic regions, introns, and exons, and we find that the SNP density in intergenic regions is similar to that in introns and is higher than that in exons, results that are consistent with previous studies. We also calculate the distribution of block break points in intergenic regions, genes, exons, and coding regions and do not find any significant differences.

Haplotype analysis of the prostacyclin synthase gene and essential hypertension

Previously, we discovered 3 polymorphisms in the prostacyclin synthase (PGIS) gene: 1) T-192G, in the 5-flanking region, a novel single-nucleotide polymorphism (SNP) that is not associated with essential hypertension (EH); 2) a variable number of tandem repeat (VNTR) polymorphism, 6 nucleotides upstream from the ATG start codon, that is associated with risk of cerebral infarction; and 3) C1117A, in exon 8, an SNP that does not cause an amino acid change in codon 373, and that is associated with risk of myocardial infarction (MI). The purpose of the present study was to establish haplotypes of the PGIS gene consisting of these 3 polymorphisms, and to assess the association between these haplotypes and EH. We detected 19 haplotypes. There was no significant difference in the overall distribution of haplotypes between EH and normotensive subjects. To summarize, we successfully identified haplotypes of the PGIS gene, and these haplotypes were not associated with EH.

Nonparametric disequilibrium mapping of functional sites using haplotypes of multiple tightly linked single-nucleotide polymorphism markers

As the speed and efficiency of genotyping single-nucleotide polymorphisms (SNPs) increase, using the SNP map, it becomes possible to evaluate the extent to which a common haplotype contributes to the risk of disease. In this study we propose a new procedure for mapping functional sites or regions of a candidate gene of interest using multiple linked SNPs. Based on a case-parent trio family design, we use expectation-maximization (EM) algorithm-derived haplotype frequency estimates of multiple tightly linked SNPs from both unambiguous and ambiguous families to construct a contingency statistic S for linkage disequilibrium (LD) analysis. In the procedure, a moving-window scan for functional SNP sites or regions can cover an unlimited number of loci except for the limitation of computer storage. Within a window, all possible widths of haplotypes are utilized to find the maximum statistic S* for each site (or locus). Furthermore, this method can be applied to regional or genome-wide scanning for determining linkage disequilibrium using SNPs. The sensitivity of the proposed procedure was examined on the simulated data set from the Genetic Analysis Workshop (GAW) 12. Compared with the conventional and generalized TDT methods, our procedure is more flexible and powerful.

SNP haplotype tagging from DNA pools of two individuals

BACKGROUND

DNA pooling is a technique to reduce genotyping effort while incurring only minor losses in accuracy of allele frequency estimates for single nucleotide polymorphism (SNP) markers.

RESULTS

We present an algorithm for reconstructing haplotypes (alleles for multiple SNPs on same chromosome) from pools of two individual DNAs, in which Hardy-Weinberg equilibrium conditions or other assumptions are not required. The program outputs, in addition to inferred haplotypes, a minimal number of haplotype-tagging SNPs that are identified after an exhaustive search procedure.

CONCLUSION

Our method and algorithms lead to a significant reduction in genotyping effort, for example, in case-control disease association studies while maintaining the possibility of reconstructing haplotypes under very general conditions.

From plant genomics to breeding practice

New alleles are constantly accumulated during intentional crop selection. The molecular understanding of these alleles has stimulated new genomic approaches to mapping quantitative trait loci (QTL) and haplotype multiplicity of the genes concerned. A limited number of quantitative trait nucleotides responsible for QTL variation have been described, but an acceleration in their rate of discovery is expected with the adoption of linkage disequilibrium and candidate gene strategies for QTL fine mapping and cloning. Additional layers of regulatory variation have been studied that could also contribute to the molecular basis of quantitative genetics of crop traits. Despite this progress, the role of marker-assisted selection in plant breeding will ultimately depend on the genetic model underlying quantitative variation.