Linkage disequilibrium between STRPs and SNPs across the human genome

Patterns of linkage disequilibrium (LD) reveal the action of evolutionary processes and provide crucial information for association mapping of disease genes. Although recent studies have described the landscape of LD among single nucleotide polymorphisms (SNPs) from across the human genome, associations involving other classes of molecular variation remain poorly understood. In addition to recombination and population history, mutation rate and process are expected to shape LD. To test this idea, we measured associations between short-tandem-repeat polymorphisms (STRPs), which can mutate rapidly and recurrently, and SNPs in 721 regions across the human genome. We directly compared STRP-SNP LD with SNP-SNP LD from the same genomic regions in the human HapMap populations. The intensity of STRP-SNP LD, measured by the average of D', was reduced, consistent with the action of recurrent mutation. Nevertheless, a higher fraction of STRP-SNP pairs than SNP-SNP pairs showed significant LD, on both short (up to 50 kb) and long (cM) scales. These results reveal the substantial effects of mutational processes on LD at STRPs and provide important measures of the potential of STRPs for association mapping of disease genes.     

Linkage disequilibrium in wild mice

Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD) in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1) Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2) they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3) LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits.     

Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes

There is currently tremendous interest in the possibility of using genome-wide association mapping to identify genes responsible for natural variation, particularly for human disease susceptibility. The model plant Arabidopsis thaliana is in many ways an ideal candidate for such studies, because it is a highly selfing hermaphrodite. As a result, the species largely exists as a collection of naturally occurring inbred lines, or accessions, which can be genotyped once and phenotyped repeatedly. Furthermore, linkage disequilibrium in such a species will be much more extensive than in a comparable outcrossing species. We tested the feasibility of genome-wide association mapping in A. thaliana by searching for associations with flowering time and pathogen resistance in a sample of 95 accessions for which genome-wide polymorphism data were available. In spite of an extremely high rate of false positives due to population structure, we were able to identify known major genes for all phenotypes tested, thus demonstrating the potential of genome-wide association mapping in A. thaliana and other species with similar patterns of variation. The rate of false positives differed strongly between traits, with more clinal traits showing the highest rate. However, the false positive rates were always substantial regardless of the trait, highlighting the necessity of an appropriate genomic control in association studies.     

Mapping disease genes: family-based association studies.

With recent rapid advances in mapping of the human genome, including highly polymorphic and closely linked markers, studies of marker associations with disease are increasingly relevant for mapping disease genes. The use of nuclear-family data in association studies was initially developed to avoid possible ethnic mismatching between patients and randomly ascertained controls. The parental marker alleles not transmitted to an affected child or never transmitted to an affected sib pair form the so-called AFBAC (affected family-based controls) population. In this paper, the theoretical foundation of the AFBAC method is proved for any single-locus model of disease and for any nuclear family-based ascertainment scheme. In a random-mating population, when there is a marker association with disease, the AFBAC population provides an unbiased estimate of the overall population (control) marker alleles when the recombination fraction (theta) between the marker and disease genes is sufficiently small that it can be taken as zero (theta = 0). With population stratification, only marker associations present in the subpopulations will be detected with family-based analyses. Of more importance, however, is the fact that, when theta not equal to 0, differences between transmitted parental (patient) marker allele frequencies and non- or never-transmitted parental marker allele frequencies (implying a marker association with disease) can only be observed for marker genes linked to a disease gene (theta < 1/2). Thus, associations of unlinked marker loci with disease at the population level, caused by population stratification, migration, or admixture, are eliminated. This validates the use of family-based association tests as an appropriate strategy for mapping disease genes.     

Prospects for admixture mapping of complex traits

Admixture mapping extends to human populations the principles that underlie linkage analysis of an experimental cross. For detecting genes that contribute to ethnic variation in disease risk, admixture mapping has greater statistical power than family-linkage studies. In comparison with association studies, admixture mapping requires far fewer markers to search the genome and is less affected by allelic heterogeneity. Statistical-analysis programs for admixture mapping are now available, and a genomewide panel of markers for admixture mapping in populations formed by West African-European admixture has been assembled. Some of the remaining technical challenges include the ability to ensure that the statistical methods are robust and to develop marker panels for other admixed populations. Where admixed populations and panels of markers informative for ancestry are available, admixture mapping can be applied to localize genes that contribute to ethnic variation in any measurable trait.     

High-density map of short tandem repeats across the human major histocompatibility complex

The human genome contains one short tandem repeat (STR) roughly every 2,000 base pairs. They are particularly useful markers for gene mapping and disease association studies due to their high degree of polymorphism and ubiquitous frequency throughout the genome. The major histocompatibility complex (MHC) has been the focus of many disease association studies, and the recent availability of the entire sequence of the complex has logarithmically expanded the density of potential markers for fine mapping disease loci. Here we present a complete assessment of the available STRs within a 3.8-Mb genomic segment encompassing the MHC. Of 443 potential STRs identified by computer analysis and tested for variation in a single sample containing pooled DNA from 36 individuals, 249 polymorphic STRs located throughout the complex were identified. The class of repeat (di-, tri-, etc.), precise nucleotide position, position relative to known genes, PCR conditions, and D6S numbers for the 249 polymorphic STRs are provided as a resource for selecting appropriate markers to use in future studies of MHC molecular genetics and disease association.     

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.     

Analysis of dyslexia candidate genes in the Raine cohort representing the general Australian population

Several genes have been suggested as dyslexia candidates. Some of these candidate genes have been recently shown to be associated with literacy measures in sample cohorts derived from the general population. Here, we have conducted an association study in a novel sample derived from the Australian population (the Raine cohort) to further investigate the role of dyslexia candidate genes. We analysed markers, previously reported to be associated with dyslexia, located within the MRPL19/C2ORF3, KIAA0319, DCDC2 and DYX1C1 genes in a sample of 520 individuals and tested them for association with reading and spelling measures. Association signals were detected for several single nucleotide polymorphisms (SNPs) within DYX1C1 with both the reading and spelling tests. The high linkage disequilibrium (LD) we observed across the DYX1C1 gene suggests that the association signal might not be refined by further genetic mapping.     

Association studies under general disease models

There is great expectation that the levels of association found between genetic markers and disease status will play a role in the location of disease genes. This expectation follows from regarding association as being proportional to linkage disequilibrium and therefore inversely related to recombination value. For disease genes with more than two alleles, the association measure is instead a weighted average of linkage disequilibria, with the weights depending on allele frequencies and genotype susceptibilities at the disease loci. There is no longer a simple relationship, even in expectation, with recombination. We adopt a general framework to examine association mapping methods which helps to clarify the nature of case-control and transmission/disequilibrium-type tests and reveals the relationship between measures of association and coefficients of linkage disequilibrium. In particular, we can show the consequences of additive and nonadditive effects at the trait locus on the behavior of these tests. These concepts have a natural extension to marker haplotypes. The association of two-locus marker haplotypes with disease phenotype depends on a weighted average of three-locus disequilibria (two markers with each disease locus). It is likely that these two-marker analyses will provide additional information in association mapping studies.     

Estimation of population structure in coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] using allozyme and microsatellite markers

Characterizing population structure using neutral markers is an important first step in association genetic studies in order to avoid false associations between phenotypes and genotypes that may arise from non-selective demographic factors. Population structure was studied in a wide sample of ∼1,300 coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] trees from Washington and Oregon. This sample is being used for association mapping between cold hardiness and phenology phenotypes and single-nucleotide polymorphisms in adaptive-trait candidate genes. All trees were genotyped for 25 allozyme and six simple sequence repeat (SSR) markers using individual megagametophytes. Population structure analysis was done separately for allozyme and SSR markers, as well as for both datasets combined. The parameter of genetic differentiation (θ or F _ST) was standardized to take into account high within-population variation in the SSR loci and to allow comparison with allozyme loci. Genetic distance between populations was positively and significantly correlated with geographic distance, and weak but significant clinal variation was found for a few alleles. Although the STRUCTURE simulation analysis inferred the same number of populations as used in this study and as based on previous analysis of quantitative adaptive trait variation, clustering among populations was not significant. In general, results indicated weak differentiation among populations for both allozyme and SSR loci (θ _s = 0.006–0.059). The lack of pronounced population subdivision in the studied area should facilitate association mapping in this experimental population, but we recommend taking the STRUCTURE analysis and population assignments for individual trees (Q-matrix) into account in association mapping.     

An entropy-based measure for QTL mapping using extreme samples of population

Quantitative trait locus (QTL) mapping can be accomplished through the method of selective genotyping, which is based on the differences of frequencies between an upper sample and a lower sample in population. However, amplifying the differences in marker allele frequencies in extreme samples may increase the probability for QTL mapping. Shannon entropy, which is a nonlinear function of allele frequencies, can be used to amplify the differences in marker allele frequencies. In this paper, we present a novel measure for linkage disequilibrium (LD) between a marker and single QTL, that is based on the comparison of the entropy and conditional entropy in a marker in extreme samples of population. This measure of LD between the marker and the trait locus can be used when the marker allele frequencies are known in the extreme samples of a population. We investigate the mapping performance in both analytic and simulation scenarios of a single QTL linked to a single marker. Our results show that the measure has very reasonable performance. In addition, a simulation study is performed on the basis of the haplotype frequencies of 10 SNPs of angiotensin-I converting enzyme (ACE) genes.     

Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach

Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map’s performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map’s performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow – from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.

     

Genetic analysis of the physiological responses to low boron stress in Arabidopsis thaliana

Boron (B) is an essential micronutrient for higher plants. There is wide genetic variation in the response to B deficiency among plant species and cultivars. The objective of this study was to identify quantitative trait loci (QTL) that control B efficiency in natural Arabidopsis accessions. The B efficiency coefficient (BEC) and seed yield under low B conditions (SYLB) were investigated by solution culture in two separate experiments in an Arabidopsis recombinant inbred line (RIL) population. Both of the traits studied exhibited high transgressive variation in the RIL population, and, in total, five and three QTL were identified for BEC and SYLB, respectively. Three of the five QTL, including the QTL, AtBE1-2, that has a large effect on the BEC, were found at the interval of the corresponding QTL for SYLB in both experiments. The close genetic relationship between BEC and SYLB was further confirmed by conditional QTL mapping in the RIL population and unconditional QTL mapping in an AtBE1-2-segregated F(2) population. Epistatic interactions for the tested traits were analysed, and were found to be widespread in the detected QTL of Arabidopsis in the RIL population. Comparison of the QTL interval for B efficiency with reported B-related genes showed that 10 B-related genes, together with one BOR1 homolog (BOR5, At1g74810) were located in the QTL region of AtBE1-2. These results suggest that natural variation in B efficiency in Arabidopsis has a complex molecular basis. They also provide a basis for fine mapping and cloning of the B-efficiency genes, with the ultimate aim of discovering the physiological mechanism of action of the genes.     

Simple sequence repeat markers useful for sorghum downy mildew (Peronosclerospora sorghi) and related species

Background

Association mapping, initially developed in human disease genetics, is now being applied to plant species. The model species Arabidopsis provided some of the first examples of association mapping in plants, identifying previously cloned flowering time genes, despite high population sub-structure. More recently, association genetics has been applied to barley, where breeding activity has resulted in a high degree of population sub-structure. A major genotypic division within barley is that between winter- and spring-sown varieties, which differ in their requirement for vernalization to promote subsequent flowering. To date, all attempts to validate association genetics in barley by identifying major flowering time loci that control vernalization requirement (VRN-H1 and VRN-H2) have failed. Here, we validate the use of association genetics in barley by identifying VRN-H1 and VRN-H2, despite their prominent role in determining population sub-structure.

Results

By taking barley as a typical inbreeding crop, and seasonal growth habit as a major partitioning phenotype, we develop an association mapping approach which successfully identifies VRN-H1 and VRN-H2, the underlying loci largely responsible for this agronomic division. We find a combination of Structured Association followed by Genomic Control to correct for population structure and inflation of the test statistic, resolved significant associations only with VRN-H1 and the VRN-H2 candidate genes, as well as two genes closely linked to VRN-H1 (HvCSFs1 and HvPHYC).

Conclusion

We show that, after employing appropriate statistical methods to correct for population sub-structure, the genome-wide partitioning effect of allelic status at VRN-H1 and VRN-H2 does not result in the high levels of spurious association expected to occur in highly structured samples. Furthermore, we demonstrate that both VRN-H1 and the candidate VRN-H2 genes can be identified using association mapping. Discrimination between intragenic VRN-H1 markers was achieved, indicating that candidate causative polymorphisms may be discerned and prioritised within a larger set of positive associations. This proof of concept study demonstrates the feasibility of association mapping in barley, even within highly structured populations. A major advantage of this method is that it does not require large numbers of genome-wide markers, and is therefore suitable for fine mapping and candidate gene evaluation, especially in species for which large numbers of genetic markers are either unavailable or too costly.     

一个基于熵的对疾病基因精细定位的指数

针对人类疾病基因的精细定位,本文利用稠密的标记位点,通过比较标记的熵和条件熵,给出了一个基于熵的指数。该指数可以度量标记基因和性状位点间连锁不平衡(LD)程度。该指数的特性是它不依赖于标记基因的频率。同时它对应疾病易感位点(DSL)精细定位的哈迪-温伯格不平衡(HWD)指数。通过计算机模拟,文章调查了不同遗传参数下该指数的性质。模拟结果表明该指数用作疾病易感位点精细定位是有效的。     

An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH

Background  

Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations.     

Comparison of the power and accuracy of biallelic and microsatellite markers in population-based gene-mapping methods.

Because of their great abundance and amenability to fully automated genotyping, single-nucleotide polymorphisms (SNPs) and simple insertion/deletion are emerging as a new generation of markers for positional cloning. Although the efficiency and cost associated with the markers are important in the mapping of human disease genes, the power to detect the linkage between the marker and the disease locus, as well as the accuracy of the estimation of the map location of the disease gene, dictate the selection of the markers. Both the power and the accuracy depend not only on the type of the markers but also on other factors, such as the age of the disease mutation, the magnitude of the genetic effect, the marker-allele distribution in the population, mutation rates of marker loci, the frequency of the disease allele, the recombination fraction, and the methods for mapping the human disease genes. In this article, we develop a mathematical framework and the analytical formulas for calculation of the power and the accuracy and investigate the impact that the aforementioned factors have on the power and the accuracy, by using two population-based gene-mapping methods-likelihood-based linkage-disequilibrium mapping and the transmission/disequilibrium test, for both biallelic SNPs and microsatellites. These studies provide not only guidance in selection of the markers and in the design of the sample scheme for positional cloning but also insight into the biological bases of the mapping of human disease genes.     

Gene-centric genomewide association study via entropy

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.     

Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations

Linkage analysis and allelic association (also referred to as linkage disequilibrium) studies are two major approaches for mapping genes that control simple or complex traits in plants, animals, and humans. But these two approaches have limited utility when used alone, because they use only part of the information that is available for a mapping population. More recently, a new mapping strategy has been designed to integrate the advantages of linkage analysis and linkage disequilibrium analysis for genome mapping in outcrossing populations. The new strategy makes use of a random sample from a panmictic population and the open-pollinated progeny of the sample. In this article, we extend the new strategy to map quantitative trait loci (QTL), using molecular markers within the EM-implemented maximum-likelihood framework. The most significant advantage of this extension is that both linkage and linkage disequilibrium between a marker and QTL can be estimated simultaneously, thus increasing the efficiency and effectiveness of genome mapping for recalcitrant outcrossing species. Simulation studies are performed to test the statistical properties of the MLEs of genetic and genomic parameters including QTL allele frequency, QTL effects, QTL position, and the linkage disequilibrium of the QTL and a marker. The potential utility of our mapping strategy is discussed.     

Application of genome-wide single nucleotide polymorphism typing: simple association and beyond

The International HapMap Project and the arrival of technologies that type more than 100,000 SNPs in a single experiment have made genome-wide single nucleotide polymorphism (GW-SNP) assay a realistic endeavor. This has sparked considerable debate regarding the promise of GW-SNP typing to identify genetic association in disease. As has already been shown, this approach has the potential to localize common genetic variation underlying disease risk. The data provided from this technology also lends itself to several other lines of investigation; autozygosity mapping in consanguineous families and outbred populations, direct detection of structural variation, admixture analysis, and other population genetic approaches. In this review we will discuss the potential uses and practical application of GW-SNP typing including those above and beyond simple association testing.