A measure of phase ambiguity in pairs of SNPs in the presence of linkage disequilibrium

The extent of haplotype ambiguity in a string of single-nucleotide polymorphisms (SNPs) was quantified by Hodge et al. [Nat Genet 1999;21:360]. In their measure, the level of ambiguity increases with increasing numbers of loci and as loci become more polymorphic. That work assumed linkage equilibrium (LE). However, linkage disequilibrium (LD) provides additional information about the haplotypes at a site, thereby diluting the level of ambiguity. The ambiguity vanishes altogether when LD reaches its maximum value. Here, we introduce the ambiguity measure, Phi, to allow for LD (between pairs of SNPs). We derive the formula Phi = 4x(2)x(3) for ambiguity in individuals, where x(1), x(2), x(3) and x(4) are the probabilities of the A(1)A(2), A(1)B(2), B(1)A(2) and B(1)B(2) haplotypes, respectively, and w.l.o.g. x(1)x(4) > or = x(2)x(3). Alternatively, Phi can be expressed in terms of the allele frequencies and the LD parameter delta. We also extend the formula to triads of two parents plus one child. We estimate our measure Phi for relevant SNPs in the published lipoprotein lipase (LPL) gene dataset [Clark et al., Am J Hum Genet 1998;63:595; Nickerson et al., Nat Genet 1998;19:233], obtaining values ranging from a low of 0 to a high of 0.11 among adjacent pairs of sites. In genome-wide LD studies to map common disease genes, a dense map of SNPs may be utilized to detect association between a marker and disease. Therefore, the measurement of ambiguity can potentially help investigators to determine a more efficient map, designed to minimize ambiguity and subsequent information loss.     

Genome-wide association filtering using a highly locus-specific transmission/disequilibrium test

Multimarker transmission/disequilibrium tests (TDTs) are powerful association and linkage tests used to perform genome-wide filtering in the search for disease susceptibility loci. In contrast to case/control studies, they have a low rate of false positives for population stratification and admixture. However, the length of a region found in association with a disease is usually very large because of linkage disequilibrium (LD). Here, we define a multimarker proportional TDT (mTDT _P ) designed to improve locus specificity in complex diseases that has good power compared to the most powerful multimarker TDTs. The test is a simple generalization of a multimarker TDT in which haplotype frequencies are used to weight the effect that each haplotype has on the whole measure. Two concepts underlie the features of the metric: the ‘common disease, common variant’ hypothesis and the decrease in LD with chromosomal distance. Because of this decrease, the frequency of haplotypes in strong LD with common disease variants decreases with increasing distance from the disease susceptibility locus. Thus, our haplotype proportional test has higher locus specificity than common multimarker TDTs that assume a uniform distribution of haplotype probabilities. Because of the common variant hypothesis, risk haplotypes at a given locus are relatively frequent and a metric that weights partial results for each haplotype by its frequency will be as powerful as the most powerful multimarker TDTs. Simulations and real data sets demonstrate that the test has good power compared with the best tests but has remarkably higher locus specificity, so that the association rate decreases at a higher rate with distance from a disease susceptibility or disease protective locus.     

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.     

<Emphasis Type="Italic">KIR</Emphasis> haplotypes defined by segregation analysis in 59 Centre d’Etude Polymorphisme Humain (CEPH) families

The killer cell immunoglobulin-like receptor (KIR) gene cluster exhibits extensive allelic and haplotypic diversity. Variation at the locus is associated with an increasing number of human diseases, reminiscent of the HLA loci. Characterization of diversity at the KIR locus has progressed over the past several years, particularly since the sequence of entire KIR haplotypes have become available. To determine the extent of KIR haplotypic variability among individuals of northern European descent, we genotyped 59 CEPH families for presence/absence of all KIR genes and performed limited allelic subtyping at several KIR loci. A total of 20 unique haplotypes differing in gene content were identified, the most common of which was the previously defined A haplotype (f = 0.52). Several unusual haplotypes that probably arose as a consequence of unequal crossing over events were also identified. Linkage disequilibrium (LD) analysis indicated strong negative and positive LD between several pairs of genes, values that may be useful in determining haplotypic structure when family data are not available. These data provide a resource to aid in the interpretation of disease association data involving individuals of European descent. An erratum to this article can be found at     

A multilocus linkage disequilibrium measure based on mutual information theory and its applications

Evaluating the patterns of linkage disequilibrium (LD) is important for association mapping study as well as for studying the genomic architecture of human genome (e.g., haplotype block structures). Commonly used bi-allelic pairwise measures for assessing LD between two loci, such as r ² and D′, may not make full and efficient use of modern multilocus data. Though extended to multilocus scenarios, their performance is still questionable. Meanwhile, most existing measures for an entire multilocus region, such as normalized entropy difference, do not consider existence of LD heterogeneity across the region under investigation. Additionally, these existing multilocus measures cannot handle distant regions where long-range LD patterns may exist. In this study, we proposed a novel multilocus LD measure developed based on mutual information theory. Our proposed measure described LD pattern between two chromosome regions each of which may consist of multiple loci (including multi-allele loci). As such, the proposed measure can better characterize LD patterns between two arbitrary regions. As potential applications, we developed algorithms on the proposed measure for partitioning haplotype blocks and for selecting haplotype tagging SNPs (htSNPs), which were helpful for follow-up association tests. The results on both simulated and empirical data showed that our LD measure had distinct advantages over pairwise and other multilocus measures. First, our measure was more robust, and can capture comprehensively the LD information between neighboring as well as disjointed regions. Second, haplotype blocks were better described via our proposed measure. Furthermore, association tests with htSNPs from the proposed algorithm had improved power over tests on single markers and on haplotypes.     

Inheritance mode of multiple sclerosis: the effect of <Emphasis Type="Italic">HLA</Emphasis> class II alleles is stronger than additive

We previously identified on chromosome 6 an interval of 51 kb as the most likely interval in the HLA region for a disease-susceptibility locus for multiple sclerosis (MS). The interval was located between markers G511525 and D6S1666 and identified by the haplotype sharing statistic (HSS). The study comprised 124 patients with ancestry within the northeastern part of the Netherlands. Haplotype clustering indicated that two different ancestral haplotypes likely include a polymorphism involved in susceptibility to MS. To investigate the dominance characteristics of the MS susceptibility locus in the HLA class II region, we reanalyzed our data, performing genotype association analyses for both marker loci separately and for the two-locus haplotype. The two-locus genotype association analysis showed that in individuals who carry only one of the risk haplotypes the risk for MS is moderately increased (odds ratio (OR) 2.82; 95% confidence interval (CI) 1.50–5.31). However, in individuals carrying two risk haplotypes the risk for MS is highly increased compared with individuals who carry no risk haplotypes (OR 37.00; 95% CI 8.31–164.74). This susceptibility locus for MS seems to follow an intermediate mode of inheritance. Fitting additive, multiplicative and third power risk models to the data, the effect appears to be significantly stronger than additive.     

The structure of linkage disequilibrium at the DBH locus strongly influences the magnitude of association between diallelic markers and plasma dopamine beta-hydroxylase activity

There is currently a great deal of interest in using linkage disequilibrium (LD) mapping to locate both disease and quantitative-trait loci on a genomewide scale. Recent findings suggest that much of the human genome is organized in discrete "blocks" of low haplotype diversity, but the utility of such blocks in identifying genes influencing complex traits is not yet known and must ultimately be tested empirically through use of real data. We recently identified a putative functional polymorphism (-1021C-->T) in the 5' upstream region of the DBH gene that accounted for 35%-52% of the total phenotypic variance in plasma dopamine beta-hydroxylase (DBH) activity in samples from three distinct populations. In the present study, we genotyped 11 diallelic markers at the DBH locus surrounding -1021C-->T in 386 unrelated individuals of European origin. We identified a single 10-kb block containing -1021C-->T, in which four haplotypes comprised 93% of the observed chromosomes. Only markers within the block were highly associated with phenotype (P< or =2.2 x 10(-10)), with one exception. In general, association with phenotype was strongly correlated with the degree of LD between each marker and -1021C-->T. Of four LD measures assessed, d(2) was the best predictor of this relationship. Had one attempted to map quantitative-trait loci for plasma DBH activity on a genomewide basis without prior knowledge of candidate regions and not included (by chance) markers within this haplotype block, the DBH locus might have been missed entirely. These results provide a direct example of the potential value of constructing a haplotype map of the human genome prior to embarking on large-scale association studies.     

Comparison of haplotype inference methods using genotypic data from unrelated individuals

OBJECTIVE: Haplotypes are gaining popularity in studies of human genetics because they contain more information than does a single gene locus. However, current high-throughput genotyping techniques cannot produce haplotype information. Several statistical methods have recently been proposed to infer haplotypes based on unphased genotypes at several loci. The accuracy, efficiency, and computational time of these methods have been under intense scrutiny. In this report, our aim was to evaluate haplotype inference methods for genotypic data from unrelated individuals. METHODS: We compared the performance of three haplotype inference methods that are currently in use--HAPLOTYPER, hap, and PHASE--by applying them to a large data set from unrelated individuals with known haplotypes. We also applied these methods to coalescent-based simulation studies using both constant size and exponential growth models. The performance of these methods, along with that of the expectation-maximization algorithm, was further compared in the context of an association study. RESULTS: While the algorithm implemented in the software PHASE was found to be the most accurate in both real and simulated data comparisons, all four methods produced good results in the association study.     

Methods for assessing gene content diversity of KIR with examples from a global set of populations

A number of statistical methods are widely used to describe allelic variation at specific genetic loci and its implication on the evolutionary history of these loci. Although the methods were developed primarily to study allelic variation at loci that are virtually always present in the genome, they are often applied to data of gene content variation (i.e., presence/absence of multiple homologous genes) at the killer cell immunoglobulin-like receptor (KIR) gene cluster. In this paper, we discuss methodological issues involved in the analysis of gene content variation data in the KIR region and also its covariation with polymorphism at the human leukocyte antigen class I loci, which encode ligands for KIR. A comparison of several statistical methods and measures (gene frequency, haplotype frequency, and linkage disequilibrium estimation) using the Centre d’Etude du Polymorphisme Humain data will be provided using KIR haplotypes that have been determined by segregation analysis, noting the strengths and weaknesses of the methods when only the presence/absence data is considered. Finally, application of these methods to a set of globally distributed populations is described (see Single et al., Nat Genet 39:1114–1119, 2007) in order to illustrate the challenges faced when inferring the joint effects of natural selection and demographic history on these immune-related genes.     

Haplotype hitchhiking promotes trait coselection in Brassica napus

Local haplotype patterns surrounding densely spaced DNA markers with significant trait associations can reveal information on selective sweeps and genome diversity associated with important crop traits. Relationships between haplotype and phenotype diversity, coupled with analysis of gene content in conserved haplotype blocks, can provide insight into coselection for nonrelated traits. We performed genome‐wide analysis of haplotypes associated with the important physiological and agronomic traits leaf chlorophyll and seed glucosinolate content, respectively, in the major oilseed crop species Brassica napus. A locus on chromosome A01 showed opposite effects on leaf chlorophyll content and seed glucosinolate content, attributed to strong linkage disequilibrium (LD) between orthologues of the chlorophyll biosynthesis genes EARLY LIGHT‐INDUCED PROTEIN and CHLOROPHYLL SYNTHASE, and the glucosinolate synthesis gene ATP SULFURYLASE 1. Another conserved haplotype block, on chromosome A02, contained a number of chlorophyll‐related genes in LD with orthologues of the key glucosinolate biosynthesis genes METHYLTHIOALKYMALATE SYNTHASE‐LIKE 1 and 3. Multigene haplogroups were found to have a significantly greater contribution to variation for chlorophyll content than haplotypes for any single gene, suggesting positive effects of additive locus accumulation. Detailed reanalysis of population substructure revealed a clade of ten related accessions exhibiting high leaf chlorophyll and low seed glucosinolate content. These accessions each carried one of the above‐mentioned haplotypes from A01 or A02, generally in combination with further chlorophyll‐associated haplotypes from chromosomes A05 and/or C05. The phenotypic rather than pleiotropic correlations between leaf chlorophyll content index and seed GSL suggest that LD may have led to inadvertent coselection for these two traits.     

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.     

Detecting Local Haplotype Sharing and Haplotype Association

A novel haplotype association method is presented, and its power is demonstrated. Relying on a statistical model for linkage disequilibrium (LD), the method first infers ancestral haplotypes and their loadings at each marker for each individual. The loadings are then used to quantify local haplotype sharing between individuals at each marker. A statistical model was developed to link the local haplotype sharing and phenotypes to test for association. We devised a novel method to fit the LD model, reducing the complexity from putatively quadratic to linear (in the number of ancestral haplotypes). Therefore, the LD model can be fitted to all study samples simultaneously, and, consequently, our method is applicable to big data sets. Compared to existing haplotype association methods, our method integrated out phase uncertainty, avoided arbitrariness in specifying haplotypes, and had the same number of tests as the single-SNP analysis. We applied our method to data from the Wellcome Trust Case Control Consortium and discovered eight novel associations between seven gene regions and five disease phenotypes. Among these, GRIK4, which encodes a protein that belongs to the glutamate-gated ionic channel family, is strongly associated with both coronary artery disease and rheumatoid arthritis. A software package implementing methods described in this article is freely available at http://www.haplotype.org.     

A TNF region haplotype offers protection from typhoid fever in Vietnamese patients

The genomic region surrounding the TNF locus on human chromosome 6 has previously been associated with typhoid fever in Vietnam (Dunstan et al. in J Infect Dis 183:261–268, 2001). We used a haplotypic approach to understand this association further. Eighty single nucleotide polymorphisms (SNPs) spanning a 150 kb region were genotyped in 95 Vietnamese individuals (typhoid case/mother/father trios). A subset of data from 33 SNPs with a minor allele frequency of >4.3% was used to construct haplotypes. Fifteen SNPs, which tagged the 42 constructed haplotypes were selected. The haplotype tagging SNPs (T1–T15) were genotyped in 380 confirmed typhoid cases and 380 Vietnamese ethnically matched controls. Allelic frequencies of seven SNPs (T1, T2, T3, T5, T6, T7, T8) were significantly different between typhoid cases and controls. Logistic regression results support the hypothesis that there is just one signal associated with disease at this locus. Haplotype-based analysis of the tag SNPs provided positive evidence of association with typhoid (posterior probability 0.821). The analysis highlighted a low-risk cluster of haplotypes that each carry the minor allele of T1 or T7, but not both, and otherwise carry the combination of alleles *12122*1111 at T1–T11, further supporting the one associated signal hypothesis. Finally, individuals that carry the typhoid fever protective haplotype *12122*1111 also produce a relatively low TNF-α response to LPS.     

HaploBlockFinder: haplotype block analyses

Recent studies have unveiled discrete block-like structures of linkage disequilibrium (LD) in the human genome. We have developed a set of computer programs to analyze the block-like LD structures (haplotype blocks) based on haplotype data. Three definitions of haplotype block are supported, including minimal LD range, no historic recombination, and chromosome coverage. Tagged SNPs that uniquely distinguish common haplotypes are identified. A greedy algorithm was used to improve the efficiency. Two separate utilities were also provided to assist visual inspection of haplotype block structure and pattern of linkage disequilibrium. AVAILABILITY: A web interface for the HaploBlockFinder is available at http://cgi.uc.edu/cgi-bin/kzhang/haploBlockFinder.cgi the source codes are also freely available on the web site.     

Complex high-resolution linkage disequilibrium and haplotype patterns of single-nucleotide polymorphisms in 2.5 Mb of sequence on human chromosome 21.

One approach to identify potentially important segments of the human genome is to search for DNA regions with nonrandom patterns of human sequence variation. Previous studies have investigated these patterns primarily in and around candidate gene regions. Here, we determined patterns of DNA sequence variation in 2.5 Mb of finished sequence from five regions on human chromosome 21. By sequencing 13 individual chromosomes, we identified 1460 single-nucleotide polymorphisms (SNPs) and obtained unambiguous haplotypes for all chromosomes. For all five chromosomal regions, we observed segments with high linkage disequilibrium (LD), extending from 1.7 to>81 kb (average 21.7 kb), disrupted by segments of similar or larger size with no significant LD between SNPs. At least 25% of the contig sequences consisted of segments with high LD between SNPs. Each of these segments was characterized by a restricted number of observed haplotypes,with the major haplotype found in over 60% of all chromosomes. In contrast, the interspersed segments with low LD showed significantly more haplotype patterns. The position and extent of the segments of high LD with restricted haplotype variability did not coincide with the location of coding sequences. Our results indicate that LD and haplotype patterns need to be investigated with closely spaced SNPs throughout the human genome, independent of the location of coding sequences, to reliably identify regions with significant LD useful for disease association studies.     

Assessment of linkage disequilibrium by the decay of haplotype sharing, with application to fine-scale genetic mapping.

Linkage disequilibrium (LD) is of great interest for gene mapping and the study of population history. We propose a multilocus model for LD, based on the decay of haplotype sharing (DHS). The DHS model is most appropriate when the LD in which one is interested is due to the introduction of a variant on an ancestral haplotype, with recombinations in succeeding generations resulting in preservation of only a small region of the ancestral haplotype around the variant. This is generally the scenario of interest for gene mapping by LD. The DHS parameter is a measure of LD that can be interpreted as the expected genetic distance to which the ancestral haplotype is preserved, or, equivalently, 1/(time in generations to the ancestral haplotype). The method allows for multiple origins of alleles and for mutations, and it takes into account missing observations and ambiguities in haplotype determination, via a hidden Markov model. Whereas most commonly used measures of LD apply to pairs of loci, the DHS measure is designed for application to the densely mapped haplotype data that are increasingly available. The DHS method explicitly models the dependence among multiple tightly linked loci on a chromosome. When the assumptions about population structure are sufficiently tractable, the estimate of LD is obtained by maximum likelihood. For more-complicated models of population history, we find means and covariances based on the model and solve a quasi-score estimating equation. Simulations show that this approach works extremely well both for estimation of LD and for fine mapping. We apply the DHS method to published data sets for cystic fibrosis and progressive myoclonus epilepsy.     

Efficiency of haplotype frequency estimation when nuclear family information is included

In genetic studies the haplotype structure of the regarded population is expected to carry important information. Experimental methods to derive haplotypes, however, are expensive and none of them has yet become standard methodology. On the other hand, maximum likelihood haplotype estimation from unphased individual genotypes may incur inaccuracies. We therefore investigated the relative efficiency of haplotype frequency estimation when nuclear family information is included compared to estimation from experimentally derived haplotypes. Efficiency was measured in terms of variance ratios of the estimates. The variances were derived from the binomial distribution for experimentally derived haplotypes, and from the Fisher information matrix corresponding to the general likelihood function of the haplotype frequency parameters, including family information. We subsequently compared these variance ratios to the variance ratios for the case of estimation from individual genotypes. We found that the information gained from a single child compensates missing phase information to a high degree, resulting in estimates almost as reliable as those derived from observed haplotypes. Thus, if children have already been genotyped for other reasons, it is highly recommendable to include them into the estimation. If child information is not already present, it depends on the number of loci and the haplotype diversity if it is useful to genotype a single child just to reduce phase ambiguity. In general, if the number of loci is less than or equal to three or if the number of haplotypes with a frequency >5% is less than or equal to four, haplotype estimation from individuals is quite good already and the improvement gained from a single child can not compensate the genotyping effort for it. On the other hand, under scenarios with many loci and high haplotype diversity, haplotype frequency estimation from trios can be more efficient than haplotype frequency estimation from individuals also on a per genotype base.     

Efficient haplotype block recognition of very long and dense genetic sequences

Background

The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n ²) time and memory complexity.

Results

We derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ⁺⁺, has only Θ(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ⁺⁺ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D ^′ variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ⁺⁺ can support genome-wide haplotype association studies.

Conclusions

The MIG ⁺⁺ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.     

Patterns of linkage disequilibrium and haplotype distribution in disease candidate genes

Background

The adequacy of association studies for complex diseases depends critically on the existence of linkage disequilibrium (LD) between functional alleles and surrounding SNP markers.

Results

We examined the patterns of LD and haplotype distribution in eight candidate genes for osteoporosis and/or obesity using 31 SNPs in 1,873 subjects. These eight genes are apolipoprotein E (APOE), type I collagen α1 (COL1A1), estrogen receptor-α (ER-α), leptin receptor (LEPR), parathyroid hormone (PTH)/PTH-related peptide receptor type 1 (PTHR1), transforming growth factor-β1 (TGF-β1), uncoupling protein 3 (UCP3), and vitamin D (1,25-dihydroxyvitamin D₃) receptor (VDR). Yin yang haplotypes, two high-frequency haplotypes composed of completely mismatching SNP alleles, were examined. To quantify LD patterns, two common measures of LD, D' and r², were calculated for the SNPs within the genes. The haplotype distribution varied in the different genes. Yin yang haplotypes were observed only in PTHR1 and UCP3. D' ranged from 0.020 to 1.000 with the average of 0.475, whereas the average r² was 0.158 (ranging from 0.000 to 0.883). A decay of LD was observed as the intermarker distance increased, however, there was a great difference in LD characteristics of different genes or even in different regions within gene.

Conclusion

The differences in haplotype distributions and LD patterns among the genes underscore the importance of characterizing genomic regions of interest prior to association studies.     

Two haplotypes of resistance gene analogs have been conserved during evolution at the leaf rust resistance locus Lr10in wild and cultivated wheat

The isolation of genes of agronomic interest such as disease resistance genes is a central issue in wheat research. A good knowledge of the organization and evolution of the genome can greatly help in defining the best strategies for efficient gene isolation. So far, very few wheat disease resistance loci have been studied at the molecular level and little is known about their evolution during polyploidization and domestication. In this study, we have analyzed the haplotype structure at loci orthologous to the leaf rust resistance locus Lr10 in hexaploid wheat which spans 350 kb in diploid wheat. Two haplotypes (H1, H2) were defined by the presence (H1) or the absence (H2) of two different resistance gene analogs (rga1, rga2) at this locus on chromosome 1AS. Both haplotypes were found in a collection of 113 wild and cultivated diploid and polyploid wheat lines and they do not reflect phylogenetic relationships. This indicates an ancient origin for this disease resistance locus and the independent conservation of the two haplotypes throughout the evolution of the wheat genome. Finally, the coding regions of the H1 haplotype RGAs are extremely conserved in all the species. This suggests a selective pressure for maintaining the structural and functional configuration of this haplotype in wheat. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s10142-002-0051-9. Electronic Publication