Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects

Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the "accumulation" of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection.

A weighted FDR procedure under discrete and heterogeneous null distributions

Multiple testing (MT) with false discovery rate (FDR) control has been widely conducted in the "discrete paradigm" where p-values have discrete and heterogeneous null distributions. However, in this scenario existing FDR procedures often lose some power and may yield unreliable inference, and for this scenario there does not seem to be an FDR procedure that partitions hypotheses into groups, employs data-adaptive weights and is nonasymptotically conservative. We propose a weighted p-value-based FDR procedure, "weighted FDR (wFDR) procedure" for short, for MT in the discrete paradigm that efficiently adapts to both heterogeneity and discreteness of p-value distributions. We theoretically justify the nonasymptotic conservativeness of the wFDR procedure under independence, and show via simulation studies that, for MT based on p-values of binomial test or Fisher's exact test, it is more powerful than six other procedures. The wFDR procedure is applied to two examples based on discrete data, a drug safety study, and a differential methylation study, where it makes more discoveries than two existing methods.

Cholesterol Sulfotransferase SULT2B1b Modulates Sensitivity to Death Receptor Ligand TNFα in Castration-Resistant Prostate Cancer

Cholesterol sulfotransferase, SULT2B1b, has been demonstrated to modulate both androgen receptor activity and cell growth properties. However, the mechanism(s) by which SULT2B1b alters these properties within prostate cancer cells has not been described. Furthermore, specific advantages of SULT2B1b expression in prostate cancer cells are not understood. In these studies, single-cell mRNA sequencing was conducted to compare the transcriptomes of SULT2B1b knockdown (KD) versus Control KD LNCaP cells. Over 2,000 differentially expressed genes were identified along with alterations in numerous canonical pathways, including the death receptor signaling pathway. The studies herein demonstrate that SULT2B1b KD increases TNFα expression in prostate cancer cells and results in NF-κB activation in a TNF-dependent manner. More importantly, SULT2B1b KD significantly enhances TNF-mediated apoptosis in both TNF-sensitive LNCaP cells and TNF-resistant C4-2 cells. Overexpression of SULT2B1b in LNCaP cells also decreases sensitivity to TNF-mediated cell death, suggesting that SULT2B1b modulates pathways dictating the TNF sensitivity capacity of prostate cancer cells. Probing human prostate cancer patient datasets further supports this work by providing evidence that SULT2B1b expression is inversely correlated with TNF-related genes, including TNF, CD40LG, FADD, and NFKB1. Together, these data provide evidence that SULT2B1b expression in prostate cancer cells enhances resistance to TNF and may provide a growth advantage. In addition, targeting SULT2B1b may induce an enhanced therapeutic response to TNF treatment in advanced prostate cancer. IMPLICATIONS: These data suggest that SULT2B1b expression enhances resistance to TNF and may promote prostate cancer.

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.

Environmental Regulation of Heterosis in the Allopolyploid Arabidopsis suecica

Allopolyploids are organisms possessing more than two complete sets of chromosomes from two or more species and are frequently more vigorous than their progenitors. To address the question why allopolyploids display hybrid vigor, we compared the natural allopolyploid Arabidopsis suecica to its progenitor species Arabidopsis thaliana and Arabidopsis arenosa. We measured chlorophyll content, CO2 assimilation, and carbohydrate production under varying light conditions and found that the allopolyploid assimilates more CO2 per unit chlorophyll than either of the two progenitor species in high intensity light. The increased carbon assimilation corresponds with greater starch accumulation, but only in strong light, suggesting that the strength of hybrid vigor is dependent on environmental conditions. In weaker light A. suecica tends to produce as much primary metabolites as the better progenitor. We found that gene expression of LIMIT DEXTRINASE1, a debranching enzyme that cleaves branch points within starch molecules, is at the same level in the allopolyploid as in the maternal progenitor A. thaliana and significantly more expressed than in the paternal progenitor A. arenosa. However, expression differences of β-amylases and GLUCAN-WATER DIKINASE1 were not statistically significantly elevated in the allopolyploid over progenitor expression levels. In contrast to allopolyploids, autopolyploid A. thaliana showed the same photosynthetic rate as diploids, indicating that polyploidization alone is likely not the reason for enhanced vigor in the allopolyploid. Taken together, our data suggest that the magnitude of heterosis in A. suecica is environmentally regulated, arises from more efficient photosynthesis, and, under specific conditions, leads to greater starch accumulation than in its progenitor species.

MAGI: Methylation analysis using genome information

By incorporating annotation information into the analysis of next-generation sequencing DNA methylation data, we provide an improvement in performance over current testing procedures. Methylation analysis using genome information (MAGI) is applicable for both unreplicated and replicated data, and provides an effective analysis for studies with low sequencing depth. When compared with current tests, the annotation-informed tests provide an increase in statistical power and offer a significance-based interpretation of differential methylation.

Next-generation transcriptome sequencing of the premenopausal breast epithelium using specimens from a normal human breast tissue bank

Introduction

Our efforts to prevent and treat breast cancer are significantly impeded by a lack of knowledge of the biology and developmental genetics of the normal mammary gland. In order to provide the specimens that will facilitate such an understanding, The Susan G. Komen for the Cure Tissue Bank at the IU Simon Cancer Center (KTB) was established. The KTB is, to our knowledge, the only biorepository in the world prospectively established to collect normal, healthy breast tissue from volunteer donors. As a first initiative toward a molecular understanding of the biology and developmental genetics of the normal mammary gland, the effect of the menstrual cycle and hormonal contraceptives on DNA expression in the normal breast epithelium was examined.

Methods

Using normal breast tissue from 20 premenopausal donors to KTB, the changes in the mRNA of the normal breast epithelium as a function of phase of the menstrual cycle and hormonal contraception were assayed using next-generation whole transcriptome sequencing (RNA-Seq).

Results

In total, 255 genes representing 1.4% of all genes were deemed to have statistically significant differential expression between the two phases of the menstrual cycle. The overwhelming majority (221; 87%) of the genes have higher expression during the luteal phase. These data provide important insights into the processes occurring during each phase of the menstrual cycle. There was only a single gene significantly differentially expressed when comparing the epithelium of women using hormonal contraception to those in the luteal phase.

Conclusions

We have taken advantage of a unique research resource, the KTB, to complete the first-ever next-generation transcriptome sequencing of the epithelial compartment of 20 normal human breast specimens. This work has produced a comprehensive catalog of the differences in the expression of protein-coding genes as a function of the phase of the menstrual cycle. These data constitute the beginning of a reference data set of the normal mammary gland, which can be consulted for comparison with data developed from malignant specimens, or to mine the effects of the hormonal flux that occurs during the menstrual cycle.

Cytokinins secreted by Agrobacterium promote transformation by repressing a plant myb transcription factor

Agrobacterium-mediated transformation is the most widely used technique for generating transgenic plants. However, many crops remain recalcitrant. We found that an Arabidopsis myb family transcription factor (MTF1) inhibited plant transformation susceptibility. Mutating MTF1 increased attachment of several Agrobacterium strains to roots and increased both stable and transient transformation in both susceptible and transformation-resistant Arabidopsis ecotypes. Cytokinins from Agrobacterium tumefaciens decreased the expression of MTF1 through activation of the cytokinin response regulator ARR3. Mutating AHK3 and AHK4, genes that encode cytokinin-responsive kinases, increased the expression of MTF1 and impaired plant transformation. Mutant mtf1 plants also had increased expression of AT14A, which encodes a putative transmembrane receptor for cell adhesion molecules. Plants overexpressing AT14A exhibited increased susceptibility to transformation, whereas at14a mutant plants exhibited decreased attachment of bacteria to roots and decreased transformation, suggesting that AT14A may serve as an anchor point for Agrobacteria. Thus, by promoting bacterial attachment and transformation of resistant plants and increasing such processes in susceptible plants, treating roots with cytokinins may help engineer crops with improved features or yield.

Non-trisomic homeobox gene expression during craniofacial development in the Ts65Dn mouse model of Down syndrome

Trisomy 21 in humans causes cognitive impairment, craniofacial dysmorphology, and heart defects collectively referred to as Down syndrome. Yet, the pathophysiology of these phenotypes is not well understood. Craniofacial alterations may lead to complications in breathing, eating, and communication. Ts65Dn mice exhibit craniofacial alterations that model Down syndrome including a small mandible. We show that Ts65Dn embryos at 13.5 days gestation (E13.5) have a smaller mandibular precursor but a normal sized tongue as compared to euploid embryos, suggesting a relative instead of actual macroglossia originates during development. Neurological tissues were also altered in E13.5 trisomic embryos. Our array analysis found 155 differentially expressed non-trisomic genes in the trisomic E13.5 mandible, including 20 genes containing a homeobox DNA binding domain. Additionally, Sox9, important in skeletal formation and cell proliferation, was upregulated in Ts65Dn mandible precursors. Our results suggest trisomy causes altered expression of non-trisomic genes in development leading to structural changes associated with DS. Identification of genetic pathways disrupted by trisomy is an important step in proposing rational therapies at relevant time points to ameliorate craniofacial abnormalities in DS and other congenital disorders.

Dynamic clustering of gene expression

It is well accepted that genes are simultaneously involved in multiple biological processes and that genes are coordinated over the duration of such events. Unfortunately, clustering methodologies that group genes for the purpose of novel gene discovery fail to acknowledge the dynamic nature of biological processes and provide static clusters, even when the expression of genes is assessed across time or developmental stages. By taking advantage of techniques and theories from time frequency analysis, periodic gene expression profiles are dynamically clustered based on the assumption that different spectral frequencies characterize different biological processes. A two-step cluster validation approach is proposed to statistically estimate both the optimal number of clusters and to distinguish significant clusters from noise. The resulting clusters reveal coordinated coexpressed genes. This novel dynamic clustering approach has broad applicability to a vast range of sequential data scenarios where the order of the series is of interest.

Differential expression--the next generation and beyond

RNA-sequencing (RNA-seq) technologies have not only pushed the boundaries of science, but also pushed the computational and analytic capacities of many laboratories. With respect to mapping and quantifying transcriptomes, RNA-seq has certainly established itself as the approach of choice. However, as the complexities of experiments continue to grow, there is still no standard practice that allows for design, processing, normalization, efficient dimension reduction and/or statistical analysis. With this in mind, we provide a brief review of some of the key challenges that are general to all RNA-seq experiments, namely experimental design, statistical analysis and dimensionality reduction.

Meta-analysis combines affymetrix microarray results across laboratories

With microarray technology becoming more prevalent in recent years, it is now common for several laboratories to employ the same microarray technology to identify differentially expressed genes that are related to the same phenomenon in the same species. Although experimental specifics may be similar, each laboratory will typically produce a slightly different list of statistically significant genes, which calls into question the validity of each gene list (i.e. which list is best). A statistically-based meta-analytic approach to microarray analysis systematically combines results from the different laboratories to provide a single estimate of the degree of differential expression for each gene. This approach provides a more precise view of genes that are of significant interest, while simultaneously allowing for differences between laboratories. The widely-used Affymetrix oligonucleotide array and its software are of particular interest because the results are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the utility of such an approach in combining microarray results across laboratories.

Gene expression analysis at the intersection of ploidy and hybridity in maize

Heterosis and polyploidy are two important aspects of plant evolution. To examine these issues, we conducted a global gene expression study of a maize ploidy series as well as a set of tetraploid inbred and hybrid lines. This gene expression analysis complements an earlier phenotypic study of these same materials. We find that ploidy change affects a large fraction of the genome, albeit at low levels; gene expression changes rarely exceed 2-fold and are typically not statistically significant. The most common gene expression profile we detected is greater than linear increase from monoploid to diploid, and reductions from diploid to triploid and from triploid to tetraploid, a trend that mirrors plant stature. When examining heterosis in tetraploid maize lines, we found a large fraction of the genome impacted but the majority of changes were not statistically significant at 2-fold or less. Non-additive expression was common in the hybrids, and the extent of non-additivity increased both in number and magnitude from duplex to quadruplex hybrids. Overall, we find that gene expression trends mirror observations from the phenotypic studies; however, obvious mechanistic connections remain unknown.

Analysis of gene expression in resynthesized Brassica napus Allopolyploids using arabidopsis 70mer oligo microarrays

Background

Studies in resynthesized Brassica napus allopolyploids indicate that homoeologous chromosome exchanges in advanced generations (S_5∶6) alter gene expression through the loss and doubling of homoeologous genes within the rearrangements. Rearrangements may also indirectly affect global gene expression if homoeologous copies of gene regulators within rearrangements have differential affects on the transcription of genes in networks.

Methodology/Principal Findings

We utilized Arabidopsis 70mer oligonucleotide microarrays for exploring gene expression in three resynthesized B. napus lineages at the S_0∶1 and S_5∶6 generations as well as their diploid progenitors B. rapa and B. oleracea. Differential gene expression between the progenitors and additive (midparent) expression in the allopolyploids were tested. The S_5∶6 lines differed in the number of genetic rearrangements, allowing us to test if the number of genes displaying nonadditive expression was related to the number of rearrangements. Estimates using per-gene and common variance ANOVA models indicated that 6–15% of 26,107 genes were differentially expressed between the progenitors. Individual allopolyploids showed nonadditive expression for 1.6–32% of all genes. Less than 0.3% of genes displayed nonadditive expression in all S_0∶1 lines and 0.1–0.2% were nonadditive among all S_5∶6 lines. Differentially expressed genes in the polyploids were over-represented by genes differential between the progenitors. The total number of differentially expressed genes was correlated with the number of genetic changes in S_5∶6 lines under the common variance model; however, there was no relationship using a per-gene variance model, and many genes showed nonadditive expression in S_0∶1 lines.

Conclusions/Significance

Few genes reproducibly demonstrated nonadditive expression among lineages, suggesting few changes resulted from a general response to polyploidization. Furthermore, our microarray analysis did not provide strong evidence that homoeologous rearrangements were a determinant of genome-wide nonadditive gene expression. In light of the inherent limitations of the Arabidopsis microarray to measure gene expression in polyploid Brassicas, further studies are warranted.

Epigenomic consequences of immortalized plant cell suspension culture

Plant cells grown in culture exhibit genetic and epigenetic instability. Using a combination of chromatin immunoprecipitation and DNA methylation profiling on tiling microarrays, we have mapped the location and abundance of histone and DNA modifications in a continuously proliferating, dedifferentiated cell suspension culture of Arabidopsis. We have found that euchromatin becomes hypermethylated in culture and that a small percentage of the hypermethylated genes become associated with heterochromatic marks. In contrast, the heterochromatin undergoes dramatic and very precise DNA hypomethylation with transcriptional activation of specific transposable elements (TEs) in culture. High throughput sequencing of small interfering RNA (siRNA) revealed that TEs activated in culture have increased levels of 21-nucleotide (nt) siRNA, sometimes at the expense of the 24-nt siRNA class. In contrast, TEs that remain silent, which match the predominant 24-nt siRNA class, do not change significantly in their siRNA profiles. These results implicate RNA interference and chromatin modification in epigenetic restructuring of the genome following the activation of TEs in immortalized cell culture.

Extending the modified bayesian information criterion (mBIC) to dense markers and multiple interval mapping

SUMMARY

The modified version of Bayesian Information Criterion (mBIC) is a relatively simple model selection procedure that can be used when locating multiple interacting quantitative trait loci (QTL). Our earlier work demonstrated the statistical properties of mBIC for situations where the average genetic map interval is at least 5 cM. In this work mBIC is adapted to genome searches based on a dense map and, more importantly, to the situation where consecutive QTL and interactions are located by multiple interval mapping. Easy to use formulas for the extended mBIC are given. A simulation study, as well as the analysis of real data, confirm the good properties of the extended mBIC.

Analysis of gene expression profiles in response to Sclerotinia sclerotiorum in Brassica napus

Sclerotinia sclerotiorum is a necrotrophic plant pathogen which causes serious disease in agronomically important crop species. The molecular basis of plant defense to this pathogen is poorly understood. We investigated gene expression changes associated with S. sclerotiorum infection in a partially resistant and a susceptible genotype of oilseed Brassica napus using a whole genome microarray from Arabidopsis. A total of 686 and 1,547 genes were found to be differentially expressed after infection in the resistant and susceptible genotypes, respectively. The number of differentially expressed genes increased over infection time with the majority being up-regulated in both genotypes. The putative functions of the differentially expressed genes included pathogenesis-related (PR) proteins, proteins involved in the oxidative burst, protein kinase, molecule transporters, cell maintenance and development, abiotic stress, as well as proteins with unknown functions. The gene regulation patterns indicated that a large part of the defense response exhibited as a temporal and quantitative difference between the two genotypes. Genes associated with jasmonic acid (JA) and ethylene signal transduction pathways were induced, but no salicylic acid (SA) responsive genes were identified. Candidate defense genes were identified by integration of the early response genes in the partially resistant line with previously mapped quantitative trait loci (QTL). Expression levels of these genes were verified by Northern blot analyses. These results indicate that genes encoding various proteins involved in diverse roles, particularly WRKY transcription factors and plant cell wall related proteins may play an important role in the defense response to S. sclerotiorum disease.

Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis

The genetic architecture of transcript-level variation is largely unknown. The genetic determinants of transcript-level variation were characterized in a recombinant inbred line (RIL) population (n = 211) of Arabidopsis thaliana using whole-genome microarray analysis and expression quantitative trait loci (eQTL) mapping of transcript levels as expression traits (e-traits). Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts/e-traits were regulated by cis-eQTL, and many trans-eQTL mapped to hotspots that regulated hundreds to thousands of e-traits. Several thousand eQTL of large phenotypic effect were detected, but almost all (93%) of the 36,871 eQTL were associated with small phenotypic effects (R(2) < 0.3). Many transcripts/e-traits were controlled by multiple eQTL with opposite allelic effects and exhibited higher heritability in the RILs than their parents, suggesting nonadditive genetic variation. To our knowledge, this is the first large-scale global eQTL study in a relatively large plant mapping population. It reveals that the genetic control of transcript level is highly variable and multifaceted and that this complexity may be a general characteristic of eukaryotes.

Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets

Using a unique microarray platform for cytosine methylation profiling, the DNA methylation landscape of the human genome was monitored at more than 21,000 sites, including 79% of the annotated transcriptional start sites (TSS). Analysis of an oligodendroglioma derived cell line LN-18 revealed more than 4000 methylated TSS. The gene-centric analysis indicated a complex pattern of DNA methylation exists along each autosome, with a trend of increasing density approaching the telomeres. Remarkably, 2% of CpG islands (CGI) were densely methylated, and 17% had significant levels of 5 mC, whether or not they corresponded to a TSS. Substantial independent verification, obtained from 95 loci, suggested that this approach is capable of large scale detection of cytosine methylation with an accuracy approaching 90%. In addition, we detected large genomic domains that are also susceptible to DNA methylation reinforced inactivation, such as the HOX cluster on chromosome 7 (CH7). Extrapolation from the data suggests that more than 2000 genomic loci may be susceptible to methylation and associated inactivation, and most have yet to be identified. Finally, we report six new targets of epigenetic inactivation (IRX3, WNT10A, WNT6, RARalpha, BMP7 and ZGPAT). These targets displayed cell line and tumor specific differential methylation when compared with normal brain samples, suggesting they may have utility as biomarkers. Uniquely, hypermethylation of the CGI within an IRX3 exon was correlated with over-expression of IRX3 in tumor tissues and cell lines relative to normal brain samples.

Identification of QTLs controlling gene expression networks defined a priori

Background

Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.

Results

We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara.

Conclusion

This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.

High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis

Expression microarrays hybridized with RNA can simultaneously provide both phenotypic (gene expression) and genotypic (marker) data. We developed two types of genetic markers from Affymetrix GeneChip expression data to generate detailed haplotypes for 148 recombinant inbred lines (RILs) derived from Arabidopsis thaliana accessions Bayreuth and Shahdara. Gene expression markers (GEMs) are based on differences in transcript levels that exhibit bimodal distributions in segregating progeny, while single feature polymorphism (SFP) markers rely on differences in hybridization to individual oligonucleotide probes. Unlike SFPs, GEMs can be derived from any type of DNA-based expression microarray. Our method identifies SFPs independent of a gene's expression level. Alleles for each GEM and SFP marker were ascertained with GeneChip data from parental accessions as well as RILs; a novel algorithm for allele determination using RIL distributions capitalized on the high level of genetic replication per locus. GEMs and SFP markers provided robust markers in 187 and 968 genes, respectively, which allowed estimation of gene order consistent with that predicted from the Col-0 genomic sequence. Using microarrays on a population to simultaneously measure gene expression variation and obtain genotypic data for a linkage map will facilitate expression QTL analyses without the need for separate genotyping. We have demonstrated that gene expression measurements from microarrays can be leveraged to identify polymorphisms across the genome and can be efficiently developed into genetic markers that are verifiable in a large segregating RIL population. Both marker types also offer opportunities for massively parallel mapping in unsequenced and less studied species.

Genomewide nonadditive gene regulation in Arabidopsis allotetraploids

Polyploidy has occurred throughout the evolutionary history of all eukaryotes and is extremely common in plants. Reunification of the evolutionarily divergent genomes in allopolyploids creates regulatory incompatibilities that must be reconciled. Here we report genomewide gene expression analysis of Arabidopsis synthetic allotetraploids, using spotted 70-mer oligo-gene microarrays. We detected >15% transcriptome divergence between the progenitors, and 2105 and 1818 genes were highly expressed in Arabidopsis thaliana and A. arenosa, respectively. Approximately 5.2% (1362) and 5.6% (1469) genes displayed expression divergence from the midparent value (MPV) in two independently derived synthetic allotetraploids, suggesting nonadditive gene regulation following interspecific hybridization. Remarkably, the majority of nonadditively expressed genes in the allotetraploids also display expression changes between the parents, indicating that transcriptome divergence is reconciled during allopolyploid formation. Moreover, >65% of the nonadditively expressed genes in the allotetraploids are repressed, and >94% of the repressed genes in the allotetraploids match the genes that are expressed at higher levels in A. thaliana than in A. arenosa, consistent with the silencing of A. thaliana rRNA genes subjected to nucleolar dominance and with overall suppression of the A. thaliana phenotype in the synthetic allotetraploids and natural A. suecica. The nonadditive gene regulation is involved in various biological pathways, and the changes in gene expression are developmentally regulated. In contrast to the small effects of genome doubling on gene regulation in autotetraploids, the combination of two divergent genomes in allotetraploids by interspecific hybridization induces genomewide nonadditive gene regulation, providing a molecular basis for de novo variation and allopolyploid evolution.

eae36, a locus on mouse chromosome 4, controls susceptibility to experimental allergic encephalomyelitis in older mice and mice immunized in the winter

Genetic factors are believed to contribute to multiple sclerosis (MS) susceptibility; however, strong evidence implicating intrinsic and environmental factors in the etiopathogenesis of MS also exists. Susceptibility to experimental allergic encephalomyelitis (EAE), the principal animal model of MS, is also influenced by nongenetic factors, including age and season at immunization. This suggests that age- and season-by-gene interactions exist and that different susceptibility loci may influence disease as a function of the two parameters. In this study, linkage analysis based on genome exclusion mapping was carried out using age and season at immunization restricted cohorts of (B10.S x SJL/J) F2 intercross mice in an effort to identify such linkages. Significant linkage of EAE to eae4 and eae5 was detected with 6- to 12-week-old and summer cohorts. In contrast, significant linkage of EAE to eae4 and eae5 was not detected with the >12-week-old and winter/spring populations. Rather, significant linkage to D4Mit203 at 128.50 Mb on chromosome 4 was detected with animals that were >12 weeks old at the time of immunization or were immunized in the winter. This previously unidentified locus has been designated eae36. These results support the existence of age- and season-by-gene-specific interactions in the genetic control of susceptibility to autoimmune inflammatory disease of the central nervous system and suggest that late-onset MS may be immunogenetically distinct.

Genomic survey of gene expression diversity in Arabidopsis thaliana

Differential gene expression controls variation in numerous plant traits, such as flowering time and plant/pest interactions, but little is known about the genomic distribution of the determinants of transcript levels and their associated variation. Affymetrix ATH1 GeneChip microarrays representing 22,810 genes were used to survey the transcriptome of seven Arabidopsis thaliana accessions in the presence and absence of exogenously applied salicylic acid (SA). These accessions encompassed approximately 80% of the moderate- to high-frequency nucleotide polymorphisms in Arabidopsis. A factorial design, consisting of three biological replicates per accession for the two treatments at three time points (4, 28, and 52 hr post-treatment), and a total of 126 microarrays were used. Between any pair of Arabidopsis accessions, we detected on average 2234 genes (ranging from 1428 to 3334) that were significantly differentially expressed under the conditions of this experiment, using a split-plot analysis of variance. Upward of 6433 genes were differentially expressed between at least one pair of accessions. These results suggest that analysis of additional genetic, developmental, and environmental conditions may show that a significant fraction of the Arabidopsis genome is differentially expressed. Examination of sequence diversity demonstrated a significant positive association with diversity in gene expression.

Biased estimators of quantitative trait locus heritability and location in interval mapping

In many empirical studies, it has been observed that genome scans yield biased estimates of heritability, as well as genetic effects. It is widely accepted that quantitative trait locus (QTL) mapping is a model selection procedure, and that the overestimation of genetic effects is the result of using the same data for model selection as estimation of parameters. There are two key steps in QTL modeling, each of which biases the estimation of genetic effects. First, test procedures are employed to select the regions of the genome for which there is significant evidence for the presence of QTL. Second, and most important for this demonstration, estimates of the genetic effects are reported only at the locations for which the evidence is maximal. We demonstrate that even when we know there is just one QTL present (ignoring the testing bias), and we use interval mapping to estimate its location and effect, the estimator of the effect will be biased. As evidence, we present results of simulations investigating the relative importance of the two sources of bias and the dependence of bias of heritability estimators on the true QTL heritability, sample size, and the length of the investigated part of the genome. Moreover, we present results of simulations demonstrating the skewness of the distribution of estimators of QTL locations and the resulting bias in estimation of location. We use computer simulations to investigate the dependence of this bias on the true QTL location, heritability, and the sample size.

Epigenomic mapping in Arabidopsis using tiling microarrays

In addition to genetic information, chromosomes transmit epigenetic information from cell to cell during division, and sometimes from generation to generation. While genetic information is encoded directly in the DNA sequence, epigenetic information is not, although it is usually associated with specific chromosomal regions. Epigenetic modifications in plants include cytosine methylation as well as modification of histones and other chromosomal proteins. Small interfering RNA play major roles in targeting these modifications to specific regions. Genomic tiling microarrays are powerful tools for analysing epigenetic information, and we review their application in building epigenomic maps in the model plant, Arabidopsis.

Model selection in binary trait locus mapping

Quantitative trait locus (QTL) mapping methodology for continuous normally distributed traits is the subject of much attention in the literature. Binary trait locus (BTL) mapping in experimental populations has received much less attention. A binary trait by definition has only two possible values, and the penetrance parameter is restricted to values between zero and one. Due to this restriction, the infinitesimal model appears to come into play even when only a few loci are involved, making selection of an appropriate genetic model in BTL mapping challenging. We present a probability model for an arbitrary number of BTL and demonstrate that, given adequate sample sizes, the power for detecting loci is high under a wide range of genetic models, including most epistatic models. A novel model selection strategy based upon the underlying genetic map is employed for choosing the genetic model. We propose selecting the "best" marker from each linkage group, regardless of significance. This reduces the model space so that an efficient search for epistatic loci can be conducted without invoking stepwise model selection. This procedure can identify unlinked epistatic BTL, demonstrated by our simulations and the reanalysis of Oncorhynchus mykiss experimental data.

Adding confidence to gene expression clustering

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.

Changes in gene expression within the nucleus accumbens and striatum following sexual experience

Sexual experience, like repeated drug use, produces long-term changes including sensitization in the nucleus accumbens and dorsal striatum. To better understand the molecular mechanisms underlying the neuroadaptations following sexual experience, we employed a DNA microarray approach to identify genes differentially expressed between sexually experienced and sexually naive female hamsters within the nucleus accumbens and dorsal striatum. For 6 weeks, a stimulus male was placed in the home cage of one-half of the hormonally primed, ovariectomized female hamsters. On the seventh week, the two experimental groups were subdivided, with one half paired with a stimulus male. In comparison with sexually naive animals, sexually experienced hamsters receiving a stimulus male on week 7 exhibited an increase in a large number of genes. Conversely, sexually experienced female hamsters not receiving a stimulus male on week 7 exhibited a reduction in the expression of many genes. For directional changes and the categories of genes regulated by the experimental conditions, data were consistent across the nucleus accumbens and dorsal striatum. However, the specific genes exhibiting changes in expression were disparate. These experiments, among the first to profile genes regulated by female sexual behavior, will provide insight into the mechanisms by which both motivated behaviors and drugs of abuse induce long-term changes in the mesolimbic and nigrostriatal dopamine pathways.

Modifying the Schwarz Bayesian information criterion to locate multiple interacting quantitative trait loci

The problem of locating multiple interacting quantitative trait loci (QTL) can be addressed as a multiple regression problem, with marker genotypes being the regressor variables. An important and difficult part in fitting such a regression model is the estimation of the QTL number and respective interactions. Among the many model selection criteria that can be used to estimate the number of regressor variables, none are used to estimate the number of interactions. Our simulations demonstrate that epistatic terms appearing in a model without the related main effects cause the standard model selection criteria to have a strong tendency to overestimate the number of interactions, and so the QTL number. With this as our motivation we investigate the behavior of the Schwarz Bayesian information criterion (BIC) by explaining the phenomenon of the overestimation and proposing a novel modification of BIC that allows the detection of main effects and pairwise interactions in a backcross population. Results of an extensive simulation study demonstrate that our modified version of BIC performs very well in practice. Our methodology can be extended to general populations and higher-order interactions.

Combining Affymetrix microarray results

BACKGROUND

As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.

RESULTS

We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.

CONCLUSION

The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.

A model selection-based interval-mapping method for autopolyploids

While extensive progress has been made in quantitative trait locus (QTL) mapping for diploid species, similar progress in QTL mapping for polyploids has been limited due to the complex genetic architecture of polyploids. To date, QTL mapping in polyploids has focused mainly on tetraploids with dominant and/or codominant markers. Here, we extend this view to include any even ploidy level under a dominant marker system. Our approach first selects the most likely chromosomal marker configurations using a Bayesian selection criterion and then fits an interval-mapping model to each candidate. Profiles of the likelihood-ratio test statistic and the maximum-likelihood estimates (MLEs) of parameters including QTL effects are obtained via the EM algorithm. Putative QTL are then detected using a resampling-based significance threshold, and the corresponding parental configuration is identified to be the underlying parental configuration from which the data are observed. Although presented via pseudo-doubled backcross experiments, this approach can be readily extended to other breeding systems. Our method is applied to single-dose restriction fragment autotetraploid alfalfa data, and the performance is investigated through simulation studies.

Methods for genome-wide analysis of gene expression changes in polyploids

Polyploidy is an evolutionary innovation, providing extra sets of genetic material for phenotypic variation and adaptation. It is predicted that changes of gene expression by genetic and epigenetic mechanisms are responsible for novel variation in nascent and established polyploids (Liu and Wendel, 2002; Osborn et al., 2003; Pikaard, 2001). Studying gene expression changes in allopolyploids is more complicated than in autopolyploids, because allopolyploids contain more than two sets of genomes originating from divergent, but related, species. Here we describe two methods that are applicable to the genome-wide analysis of gene expression differences resulting from genome duplication in autopolyploids or interactions between homoeologous genomes in allopolyploids. First, we describe an amplified fragment length polymorphism (AFLP)--complementary DNA (cDNA) display method that allows the discrimination of homoeologous loci based on restriction polymorphisms between the progenitors. Second, we describe microarray analyses that can be used to compare gene expression differences between the allopolyploids and respective progenitors using appropriate experimental design and statistical analysis. We demonstrate the utility of these two complementary methods and discuss the pros and cons of using the methods to analyze gene expression changes in autopolyploids and allopolyploids. Furthermore, we describe these methods in general terms to be of wider applicability for comparative gene expression in a variety of evolutionary, genetic, biological, and physiological contexts.

The Collaborative Cross, a community resource for the genetic analysis of complex traits

The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

Reversible histone acetylation and deacetylation mediate genome-wide, promoter-dependent and locus-specific changes in gene expression during plant development

Histone acetylation and deacetylation activate or repress transcription, yet the physiological relevance of reversible changes in chromatin structure and gene expression is poorly understood. We have shown that disrupting the expression of AtHD1 that encodes a putative Arabidopsis thaliana histone deacetylase induces a variety of developmental abnormalities. However, causal effects of the AtHD1 disruption on chromatin structure and gene expression are unknown. Using Arabidopsis spotted oligo-gene microarray analysis, here we report that >7% of the transcriptome was up- or downregulated in A. thaliana plants containing a T-DNA insertion in AtHD1 (athd1-t1), indicating that AtHD1 provides positive and negative control of transcriptional regulation. Remarkably, genes involved in ionic homeostasis and protein synthesis were ectopically expressed, whereas genes in ionic homeostasis, protein transport, and plant hormonal regulation were repressed in athd1-t1 leaves or flowers, suggesting a role of AtHD1 in developmental and environmental regulation of gene expression. Moreover, defective AtHD1 induced site-specific and reversible acetylation changes in H3-Lys9, H4-Lys12, and H4 tetra-lysines (residues 5, 8, 12, and 16) in homozygous recessive and heterozygous plants. Transcriptional activation was locus specific and often associated with specific acetylation sites in the vicinity of promoters, whereas gene repression did not correlate with changes in histone acetylation or correlated directly with H3-Lys9 methylation but not with DNA methylation. The data suggest that histone acetylation and deacetylation are promoter dependent, locus specific, and genetically reversible, which provides a general mechanism for reversible gene regulation responsive to developmental and environmental changes.

The development of an Arabidopsis model system for genome-wide analysis of polyploidy effects

Arabidopsis is a model system not only for studying numerous aspects of plant biology, but also for understanding mechanisms of the rapid evolutionary process associated with genome duplication and polyploidization. Although in animals interspecific hybrids are often sterile and aneuploids are related to disease syndromes, both Arabidopsis autopolyploids and allopolyploids occur in nature and can be readily formed in the laboratory, providing an attractive system for comparing changes in gene expression and genome structure among relatively 'young' and 'established' or 'ancient' polyploids. Powerful reverse and forward genetics in Arabidopsis offer an exceptional means by which regulatory mechanisms of gene and genome duplication may be revealed. Moreover, the Arabidopsis genome is completely sequenced; both coding and non-coding sequences are available. We have developed spotted oligo-gene and chromosome microarrays using the complete Arabidopsis genome sequence. The oligo-gene microarray consists of ~26 000 70-mer oligonucleotides that are designed from all annotated genes in Arabidopsis, and the chromosome microarray contains 1 kb genomic tiling fragments amplified from a chromosomal region or the complete sequence of chromosome 4. We have demonstrated the utility of microarrays for genome-wide analysis of changes in gene expression, genome organization and chromatin structure in Arabidopsis polyploids and related species.

Sensitivity of 70-mer oligonucleotides and cDNAs for microarray analysis of gene expression in Arabidopsis and its related species

Synthetic oligonucleotides (oligos) represent an attractive alternative to cDNA amplicons for spotted microarray analysis in a number of model organisms, including Arabidopsis, C. elegans, Drosophila, human, mouse and yeast. However, little is known about the relative effectiveness of 60-70-mer oligos and cDNAs for detecting gene expression changes. Using 192 pairs of Arabidopsis thaliana cDNAs and corresponding 70-mer oligos, we performed three sets of dye-swap experiments and used analysis of variance (anova) to compare sources of variation and sensitivities for detecting gene expression changes in A. thaliana, A. arenosa and Brassica oleracea. Our major findings were: (1) variation among different RNA preparations from the same tissue was small, but large variation among dye-labellings and slides indicates the need to replicate these factors; (2) sources of variation were similar for experiments with all three species, suggesting these feature types are effective for analysing gene expression in related species; (3) oligo and cDNA features had similar sensitivities for detecting expression changes and they identified a common subset of significant genes, but results from quantitative RT-PCR did not support the use of one over the other. These findings indicate that spotted oligos are at least as effective as cDNAs for microarray analyses of gene expression. We are using oligos designed from approximately 26,000 annotated genes of A. thaliana to study gene expression changes in Arabidopsis and Brassica polyploids.

The nature and identification of quantitative trait loci: a community's view

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Transfer of T-DNA and Vir proteins to plant cells by Agrobacterium tumefaciens induces expression of host genes involved in mediating transformation and suppresses host defense gene expression

Agrobacterium tumefaciens is a plant pathogen that incites crown gall tumors by transferring to and expressing a portion of a resident plasmid in plant cells. Currently, little is known about the host response to Agrobacterium infection. Using suppressive subtractive hybridization and DNA macroarrays, we identified numerous plant genes that are differentially expressed during early stages of Agrobacterium-mediated transformation. Expression profiling indicates that Agrobacterium infection induces plant genes necessary for the transformation process while simultaneously repressing host defense response genes, thus indicating successful utilization of existing host cellular machinery for genetic transformation purposes. A comparison of plant responses to different strains of Agrobacterium indicates that transfer of both T-DNA and Vir proteins modulates the expression of host genes during the transformation process.

Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis

BACKGROUND

It has been reported in the quantitative trait locus (QTL) literature that when testing for QTL location and effect, the statistical power supporting methodologies based on two markers and their estimated genetic map is higher than for the genetic map independent methodologies known as single marker analyses. Close examination of these reports reveals that the two marker approaches are more powerful than single marker analyses only in certain cases. Simulation studies are a commonly used tool to determine the behavior of test statistics under known conditions. We conducted a simulation study to assess the general behavior of an intersection test and a two marker test under a variety of conditions. The study was designed to reveal whether two marker tests are always more powerful than intersection tests, or whether there are cases when an intersection test may outperform the two marker approach.We present a reanalysis of a data set from a QTL study of ovariole number in Drosophila melanogaster.

RESULTS

Our simulation study results show that there are situations where the single marker intersection test equals or outperforms the two marker test. The intersection test and the two marker test identify overlapping regions in the reanalysis of the Drosophila melanogaster data. The region identified is consistent with a regression based interval mapping analysis.

CONCLUSION

We find that the intersection test is appropriate for analysis of QTL data. This approach has the advantage of simplicity and for certain situations supplies equivalent or more powerful results than a comparable two marker test.

Understanding mechanisms of novel gene expression in polyploids

Polyploidy has long been recognized as a prominent force shaping the evolution of eukaryotes, especially flowering plants. New phenotypes often arise with polyploid formation and can contribute to the success of polyploids in nature or their selection for use in agriculture. Although the causes of novel variation in polyploids are not well understood, they could involve changes in gene expression through increased variation in dosage-regulated gene expression, altered regulatory interactions, and rapid genetic and epigenetic changes. New research approaches are being used to study these mechanisms and the results should provide a more complete understanding of polyploidy.

Sex-specific quantitative trait loci govern susceptibility to Theiler's murine encephalomyelitis virus-induced demyelination

Susceptibility to Theiler's murine encephalomyelitis virus-induced demyelination (TMEVD), a mouse model for multiple sclerosis (MS), is genetically controlled. Through a mouse-human comparative mapping approach, identification of candidate susceptibility loci for MS based on the location of TMEVD susceptibility loci may be possible. Composite interval mapping (CIM) identified quantitative trait loci (QTL) controlling TMEVD severity in male and female backcross populations derived from susceptible DBA/2J and resistant BALBc/ByJ mice. We report QTL on chromosomes 1, 5, 15, and 16 affecting male mice. In addition, we identified two QTL in female mice located on chromosome 1. Our results support the existence of three linked sex-specific QTL on chromosome 1 with opposing effects on the severity of the clinical signs of TMEV-induced disease in male and female mice.

Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments

MOTIVATION

We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques.

RESULTS

After analyzing two experimental data sets containing control array data, we were able to determine the statistical power available for the detection of significant differential expression given differing levels of replication. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.

Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints

In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhizosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high-dimensional binary vectors. We address the problem of modeling and variable selection in high-dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.

Genetic control of susceptibility to experimental Lyme arthritis is polygenic and exhibits consistent linkage to multiple loci on chromosome 5 in four independent mouse crosses

C3H/He mice infected with Borrelia burgdorferi develop severe arthritis and are high antibody responders, while infected C57BL/6 and BALB/c mice develop mild arthritis and less robust humoral responses. Genetic analysis using composite interval mapping (CIM) on reciprocal backcross populations derived from C3H/HeN and C57BL/6N or C3H/HeJ and BALB/cAnN mice identified 12 new quantitative trait loci (QTL) linked to 10 murine Lyme disease phenotypes. These QTL reside on chromosomes 1, 2, 4, 6, 7, 9, 10, 12, 14, 15, 16, and 17. A reanalysis of an F(2) intercross between C57BL/6N and C3H/HeN mice using CIM identified two new QTL on chromosomes 4 and 15 and confirmed the location of seven previously identified loci. Two or more experimental crosses independently verified six QTL controlling phenotypes after B. burgdorferi infection. Additionally, Bb2 on chromosome 5 was reproduced in four experimental populations and was linked to the candidate locus Cora1. Evidence of four distinct QTL residing within the 30-cM region of chromosome 5 encompassing the previously mapped Bb2 and Bb3 loci was shown by CIM. Interestingly, some alleles contributing to susceptibility to Lyme arthritis were derived from C57BL/6N and BALB/cAnN mice, showing that disease-resistant strains harbor susceptibility alleles.

Detection and localization of a single binary trait locus in experimental populations

The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.