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To the Editor: In the papers1,2 by Xu and Jin, the genetic structure of Uyghurs was described by 8150 ancestry-informative markers (AIMs). These markers estimated the admixture rate of the Uyghur population to be around 50% East Asian ancestry by comparing Uyghurs to East Asians and Europeans. However, we suspect that the estimate of Xu and Jin may be considerably biased by insufficient reference population coverage. In their study, Xu and Jin used the STRUCTURE program3,4 as the major method for estimating the admixture rate; however, results from the STRUCTURE program are strictly the probabilities of assignment to different estimated clusters and therefore are influenced by both the marker selection and the populations used to estimate allele frequencies in those other clusters.

Objective: The tagging approach appears as a promising tool to test the association of genetic variants with complex traits such as disease susceptibility or drug response. However, since tag markers are selected only on the basis of inter-marker LD properties, regardless of any phenotypes, it remains unclear to what extent they can be useful to predict variable drug responses, once typed in clinical material. We undertook a study to provide further insights into the usefulness of the tagging approach for selecting phenotype-associated markers relevant to drug response. Methods: Several tagging methods were applied to the genotyping data of two drug-metabolizing enzymes, NAT2 and CYP2D6, and the ability of the selected tagging markers to predict the individual metabolizer status was empirically evaluated. We also assessed the impact of LD levels, tagging thresholds and allele frequencies on tagging efficiency. Results: We found that the functional variation was adequately represented by the selected tagging markers, these latter providing a classification accuracy for the individual metabolizer status close to the maximal 100% value observed with the entire set of polymorphisms. Conclusion: The tagging approach is an interesting approach to select candidate gene markers predictive of drug response in pharmacogenomic studies.

Primary Audience: Feed Mill Managers, Feed Mill Quality Control Managers, Nutritionists SUMMARY An experiment was conducted evaluating several markers to determine mix uniformity. Treatment diet was a corn-soybean meal-based diet formulated for broiler chicks fed from 0 to 17 d posthatch. Dietary nutrients or tracers evaluated included the following: 1) DL-Met (99%), 2) L-Lys-HCl (78%), 3) CP, 4) mixing salt (chloride ion), 5) P, 6) Mn, 7) Fe particles (#40 Red, count), 8) Fe particles (#40 Red, absorbance), 9) Fe particles (RF-Blue Lake), 10) roxarsone, and 11) semduramicin. All minor and microingredients were individually hand-weighed and added to the mixer to insure accuracy and were added at the same location for all treatments. Diets were mixed using a double ribbon mixer for 3 different mix times (0.5, 2.5, and 5.0 min). Overall, from 0.5 to 5.0 min, all markers evaluated showed a numerical reduction in percentage of CV. Crude protein and P should not be considered as markers, because many different components in the batch of feed contribute some level of CP or P, and results can be confounding. DL-Methionine (99%) and L-Lys-HCl (78%) were the only markers that statistically reduced over time and had aC V< 10% (23.86 to 9.47% and 19.75 to 8.70%, respectively). These data suggest that mixer uniformity results can be influenced by the particular marker that is chosen for mixer uniformity analysis.

An optimal and individualized treatment protocol based on accurate diagnosis is urgently required for the adequate treatment of patients. For this purpose, it is important to develop a sophisticated algorithm that can manage large amount of data, such as gene expression data from DNA microarray, for optimal and individualized diagnosis. Especially, marker gene selection is essential in the analysis of gene expression data. In the present study, we developed the combination method of projective adaptive resonance theory and boosted fuzzy classifier with SWEEP operator method for model construction and marker selection. And we applied this method to microarray data of acute leukemia and brain tumor. The method enabled the selection of 14 important genes related to the prognosis of the tumor. In addition, we proposed improved reliability index for cancer diagnostic prediction of blinded subjects. Based on the index, the discriminated group with over 90% prediction accuracy was separated from the others. PART-BFCS with improved RIBFCS method does not only show high performance, but also has the feature of reliable prediction further. This result suggests that PART-BFCS with improved RIBFCS method has the potential to function as a new method of class prediction for diagnosis of patients.

Castrate-resistant prostate cancer (CRPC), the most life-threatening form of prostate cancer, has recently been the focus of many successful new treatments. Contemporary trials highlight the heterogeneous prognosis of CRPC as overall survival times vary greatly across different patient sub-groups. As presented in BMC Medicine, Wang et al. identify a blood-based prognostic signature in CRPC. Their approach is notable for discovery and validation of a four-gene model based on a whole-blood expression signature sampled from three distinct clinical cohorts. Further, the marker selection process incorporates an understanding of biological pathways expressed in myeloid or lymphoid cells which may provide some insight into host-tumor interactions as reflected in the peripheral blood. While the study includes a multivariate analysis accounting for many important clinical variables, larger datasets with more complete clinical information and sufficient follow-up are needed to confirm the independent significance of the four-gene expression model in a way which may better inform the care of CRPC patients.Please see related article: http://www.biomedcentral.com/1741-7015/13/201 .

Cashmere is regarded as a specialty and luxury fiber due to its scarcity and high economic value. For fiber quality assessment, it is technically very challenging to distinguish and quantify the cashmere fiber from yak or wool fibers because of their highly similar physical appearance and substantial protein sequence homology. To address this issue, we propose a workflow combining untargeted and targeted proteomics strategies for selecting, verifying and quantifying biomarkers for cashmere textile authentication. Untargeted proteomic surveys were first applied to identify 174, 157, and 156 proteins from cashmere, wool and yak fibers, respectively. After marker selection at different levels, peptides turned out to afford much higher selectivity than proteins for fiber species discrimination. Subsequently, parallel reaction monitoring (PRM) methods were developed for ten selected peptide markers. The PRM-based targeted analysis of peptide markers enabled accurate determination of fiber species and cashmere percentages in different fiber mixtures. Furthermore, collective use of these peptide makers allowed us to discriminate and quantify cashmere fibers in commercial finished fabrics that have undergone heavy chemical treatments. Cashmere proportion measurement in fabric samples using our proteomic approach was in good agreement with results from traditional light microscopy, yet our method can be more readily standardized to become an objective and robust assay for assessing authenticity of fibers and textiles. We anticipate that the proteomic strategies presented in our study could be further implicated in discovery of quality trait markers for other products containing highly homologous proteomes.

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms.

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA-B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene ontology biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the central nervous system malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes.

TEZ9799 Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2015. Kaynakça (s. 133-149) var. xv, 151 s. : res. (bzs. rnk.), tablo ; 29 cm. Bu çalışmada, melezleme ıslahı yöntemi ile Şarka hastalığına dayanıklı şeftali, nektarin ve kayısı bireyleri elde etmek amaçlanmıştır. Erik beneklenme virüsünün (Plum pox virus) sebep olduğu “Şarka hastalığı,” sert çekirdekli meyvelerin en önemli hastalığıdır. Bu hastalık hemen hemen tüm sert çekirdeklilerde görülmekte, ancak ekonomik olarak özellikle kayısı, erik ve şeftalilerde önemli zararlara yol açmaktadır. Şarka ileriki yıllarda ülkemiz içinde tehdit oluşturabileceğinden yürütülen bu araştırmada, Türkiye için önemli olan yerli kayısı çeşitler ile ticari öneme sahip şeftali ve nektarin çeşitlerinde melezleme ıslahı yolu ile Şarka hastalığına karşı dayanıklı çeşitlerin elde edilmesi amaçlanmıştır. Bunun için Yerli kayısı çeşitleri Hacıhaliloğlu ve Kabaaşı ile Şarka’ya dayanıklı olduğu bilinen yabancı kayısı çeşitleri; Stark Early Orange, Rojo Pasion, Murciana, ayrıca Prunus davidiana klonu P1908 (şeftali) çeşitleri ile melezlemeler yapılmıştır. Şeftalilerde ise, ticari şeftali ve nektarin çeşitleri olan Flored ve Carolina dayanıklı genotiplerden Stark Early Orange ve P1908 (şeftali) çeşitleri melezlenmiştir. Elde edilen melez bireylerde markır seleksiyonu ile ilgili genin varlığı araştırılmış, dayanıklı olduğu varsayılan genotipler tespit edilmiştir. Toplam 12 kombinasyona (FLxSEO, FLxP1908, CLxSEO, CLx1908, KAxSEO, KAxP1908, HHxSEO, HHxP1908, MRxHH, MRxKA, RPxSEO, RPxKA) ait toplam 365 melez bireyde SSR markırları (P GS1.21, PGS1.24 ve ZP002) ile yapılan PCR testlerinde 138 melez bireyin gelecekte yapılacak çalışmalar için umut veren genotip olarak varsayılacağı tespit edilmiştir. This study aimed to obtain resistant peach, nectarin and apricot genotypes to “Sharka” disease with cross breeding method. Plum pox virus (PPV) causing Sharka disease is the most important viralagent for stone fruits. This disease is most harmful to apricot, plum and peach trees.In this study, local apricot varieties Hacıhaliloğlu and Kabaaşı, were crossed with foreign apricot varieties Stark Early Orange, Rojo Pasion, Murciana and P 1908 (peach clone from Prunus davidiana) known to be resistant to PPV. For peaches, commercial peach varieties Flored and Carolina were also crossed with Stark Early Orange (apricot) and P 1908 clone (peach). A total of 365 genotypes from crossing among 12 combinations (FLxSEO, FLxP1908, CLxSEO, CLx1908, KAxSEO, KAxP1908, HHxSEO, HHxP1908, MRxHH, MRxKA, RPxSEO, RPxKA were tested with SSR markers (P GS1.21, PGS1.24 ve ZP002). Approximately 138 genotypes PPV resistance were expected to be candidate for PPV resistance in future studies. Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir. Proje No: ZF2013D4.

The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging.

The Komodo monitor (Varanus komodoensis) is a classic example of a species that has been fragmented into small isolated populations over a limited range. This species, classified as endangered under Appendix I of the Convention on the International Trade of Endangered Species. We describe 10 novel species-specific microsatellite loci characterized in representatives from three of the endemic island populations. One locus, 12HDZ820 appears to be fixed in one population at an allele size not found in the other two. This microsatellite suite should be helpful in augmenting the marker selection currently available for Komodo Monitor population studies.

Several studies of haplotype structures in the human genome in various populations have found that the human chromosomes are structured such that each chromosome can be divided into many blocks, within which there is limited haplotype diversity. In addition, only a few genetic markers in a putative block are needed to capture most of the diversity within a block. There has been no systematic empirical study of the effects of sample size and marker set on the identified block structures and representative marker sets, however. The purpose of this study was to conduct a detailed empirical study to examine such impacts. Towards this goal, we have analysed three representative autosomal regions from a large genome-wide study of haplotypes with samples consisting of African-Americans and samples consisting of Japanese and Chinese individuals. For both populations, we have found that the sample size and marker set have significant impact on the number of blocks and the total number of representative markers identified. The marker set in particular has very strong impacts, and our results indicate that the marker density in the original datasets may not be adequate to allow a meaningful characterisation of haplotype structures. In general, we conclude that we need a relatively large sample size and a very dense marker panel in the study of haplotype structures in human populations.

In recent years, many molecular biomarkers have been discovered that are capable of distinguishing tumors from normal tissue. Among the different types of markers, DNA methylation markers stand out for their potential to provide a unique combination of specificity, sensitivity, high information content, and applicability to a wide variety of clinical specimens. Methylation markers are particularly suited for situations where sensitive detection is necessary, such as when tumor DNA is either scarce or diluted by excess normal DNA. One of the most widely used methods for measuring methylation levels, methylation-specific PCR (MSP), has been proved to be a very effective tool in situations requiring sensitive detection. The addition of fluorogenic probes makes these assays more informative, quantitative, and suitable for a clinical format. The field of sensitive detection is not limited to MSP; hence, an alternative methylation-sensitive amplification is discussed. PCR-based methylation assays have been applied to the detection of tumor DNA in a variety of body fluids, including serum, plasma, urine, sputum, and lavage fluids. In many cases, the sensitivity and specificity of these detection assays has been impressive, but important technological issues remain in areas such as sample preparation, assay design, and marker selection. Once these technical concerns have been addressed, the sensitive detection of methylation will provide a powerful diagnostic and prognostic tool, especially for the early detection of preneoplastic and neoplastic lesions.

The mapping of complex traits is one of the most important and central areas of human genetics today. Recent attention has been focused on genome scans using a large number of marker loci. Because complex traits are typically caused by multiple genes, the common approaches of mapping them by testing markers one after another fail to capture the substantial information of interactions among disease loci. Here we propose a backward haplotype transmission association (BHTA) algorithm to address this problem. The algorithm can administer a screening on any disease model when case-parent trio data are available. It identifies the important subset of an original larger marker set by eliminating the markers of least significance, one at a time, after a complete evaluation of its importance. In contrast with the existing methods, three major advantages emerge from this approach. First, it can be applied flexibly to arbitrary markers, regardless of their locations. Second, it takes into account haplotype information; it is more powerful in detecting the multifactorial traits in the presence of haplotypic association. Finally, the proposed method can potentially prove to be more efficient in future genomewide scans, in terms of greater accuracy of gene detection and substantially reduced number of tests required in scans. We illustrate the performance of the algorithm with several examples, including one real data set with 31 markers for a study on the Gilles de la Tourette syndrome. Detailed theoretical justifications are also included, which explains why the algorithm is likely to select the ‘correct’ markers.

TEZ9527 Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2014. Kaynakça (s. 199-207) var. xiii, 209 s. : res. (bzs. rnk.), tablo ; 29 cm. Bu araştırma, soğuklama gereksinimi yüksek, geç mevsim turfanda yetiştiriciliğine uygun şeftali ve nektarin çeşitleri geliştirmek amacıyla yürütülmüştür. Geç olgunlaşan nektarin çeşitleri Venüs ve Stark Red Gold ile Üstün şeftali çeşidinin melezlenmesi sonucunda VxÜ kombinasyonundan 61, SRGxÜ kombinasyonundan 115 birey elde edilmiştir. Elde edilen 176 bireyin morfolojik ve fenolojik özellikleri incelenmiş, pomolojik analizleri yapılmıştır. Ayrıca bu iki popülasyonda, BPPCT009, MA014, MA040 ve STS-OPAG8 SSR primerleri ile bazı pomolojik özellikler karşılaştırılarak F1 bireylerinde markır seleksiyonunun etkinliği araştırılmıştır. VxÜ kombinasyonundaki bireyler arasında tartılı derecelendirmede en yüksek puanı alan şeftali genotipleri VxÜ-55, VxÜ-41, VxÜ-34, VxÜ-14, VxÜ-1, VxÜ-13, VxÜ-24, VxÜ-26; nektarin genotipleri VxÜ-31, VxÜ-42, VxÜ-53, VxÜ-15 olmuştur. SRGxÜ kombinasyonundaki bireyler arasından en yüksek puanı alan şeftali genotipleri SRGxÜ-101, SRGxÜ-28, SRGxÜ-88, SRGxÜ-84, SRGxÜ-36, SRGxÜ-57, SRGxÜ-23, SRGxÜ-92, SRGxÜ-93, nektarin genotipi SRGxÜ-5 olmuştur. VxÜ ve SRGxÜ populasyonlarında, MA040 ve STS-OPAG8 SSR primerleri kullanılarak yapılan PCR analizinde DNA çoğalması elde edilememiştir. BPPCT009 ve MA014SSR primerleri ise VxÜ ve SRGxÜ populasyonlarının meyve şekli ve yarmalık özelliklerinin belirlenmesinde yetersiz kalmıştır. This experiment was carried out to develop high chilling, late ripening peach and nectarine cultivars. Late cultivars Venus and Stark Red Gold nectarines were individually crossed with cv. Ustun peach, resulting 61 genotypes from VxU combination and 115 genotypes from SRGxU combination. At total of 176 genotypes were investigated for the morphological and phenological characteristics, then analyzed for pomological features. Some of pomological characters were compared by using BPPCT009, MA014, MA040 and STS-OPAG8 SSR primers to investigate the effectiveness of the marker selection in F1 genotypes. Due to Weighted Ranking method, VxU-55, VxU-41, VxU-34, VxU-14, VxU-1, VxU-13, VxU-24, VxU-26 peach genotypes and VxU-31, VxU-42, VxU-53, VxU-15 nectarine genotypes in VxU combination gave the highest points. In SRGxU combination, SRGxU-101, SRGxU-28, SRGxU-88, SRGxU-84, SRGxU-36, SRGxU-57, SRGxU-23, SRGxU-92, SRGxU-93 peach and SRGxU-5 nectarine genotype gave the highest points same scaling as well. No amplification was obtained from the PCR reactions among VxU and SRGxU populations analyzed by MA040 and STS-OPAG8 SSR primers. BPPCT009 and MA014SSR primers were also insufficient to determine fruit shape and free stone characteristics of VxU and SRGxU populations.

Background Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures. Results MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome. Conclusions MPAgenomics provides an easy tool to analyze data from SNP arrays in R. The R-package MPAgenomics is available on CRAN.

Summary Generalization of the polymorphism information content (PIC) index to represent marker informativeness (MI) for a three-generation F2 design requires that two additional sources of non-informativeness be added to the PIC formula: the probability of matings between like-heterozygous F1 individuals, of which one is non-informative; and that of matings between like-heterozygous F1 individuals, which are both fully informative but where line of origin of the same alleles is reciprocal. Given the dense marker-maps currently available for some species, this F2 informativeness parameter constitutes the natural criterion for marker selection in F2 designs, and two computer programs to predict MI from grandparental marker-genotypes were developed for an F2 population originating from two divergent selection lines of outbred mice (F ~ 0.2). A total of 403 markers had been genotyped for the F0 grandparents (na 31), and 14 markers had also been genotyped in the complete pedigree including 559 F2 individuals. One program was based on assumptions of random-mating (RM), while the other (PED) accounted for the pedigreed mating structure. For the 403 markers, the correlation between MI from RM and from PED was 0.95, and the average deviation between the two predictions was 0.005 MI units (MI ranged from 0 to 1). Correlations between predicted and realized MI for the 14 fully genotyped markers were 0.97 for PED and 0.94 for RM, while the corresponding average of deviations between predicted and actual values were 0.01 and 0.04, respectively. Absolute deviations from realized MI never exceeded 0.09 and 0.16 for PED and RM, respectively. Simulated optimization of the mating system to maximize average MI of 28 markers on one chromosome led to improvements in the range of 15‐20% average MI (0.07‐0.09 MI units). The degree of relative advantage conferred by the F2 generalization of the PIC index over the traditional index was found to be of minor significance.

In this paper, we developed an integrated system for bio-data analysis and a visualization tool by applying data mining technology. This system was written in MATLAB. This platform provides a high performance and facilitated flow of information among the appropriate analyses. This system included marker selection, data visualization and marker evaluation, which have been developed on the basis of the MATLAB. This system is tailor-made to the early diagnosis of overran cancer.

A total of 185 informative sons from nine heterozygous grandsires were used to study the associations of alleles A and B of the β-LG locus and milk production using the granddaughter design. The average informativeness of the β-LG locus for the nine heterozygous grandsires was 61%. The β-LG locus was found to be significantly associated with milk protein percentage (P ≤ 0.05) across families and (P ≤ 0.01) in one family. The A allele was associated with higher protein percentage EBV (0.096%) than the B allele in that family. There was no association between β-LG and other production traits (milk, fat and protein yields and, fat and percentages). The β-LG locus has a potential use in marker-assisted selection. However, since the association between markers and a given trait is not found in all families, within-family marker selection might be more appropriate due to linkage disequilibrium. Key words: β-lactoglobulin, Canadian Holstein, milk production traits, granddaughter design

SummaryIn cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the ‘large d, small n’ characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the accelerated failure time model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group minimax concave penalty approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.

Background Angelicae Sinensis Radix (ASR) and Chuanxiong Rhizoma (CR) can be treated with wine to promote their biological functions in Chinese medicine. Both ASR and CR contain similar volatile chemicals that could be altered after wine treatment. This study aims to identify the differential chemical profiles and to select marker chemicals of ASR and CR before and after wine treatment. Methods Chemical analyses were carried out by gas chromatography-triple quadrupole mass spectrometry (GC-QQQ-MS/MS) coupled with multivariate statistical analysis. Characterization of the compositions of essential oils was performed by automated matching to the MS library and comparisons of their mass spectra (NIST08 database). For ferulic acid, butylphthalide, Z-butylidenephthalide, senkyunolide A and Z-ligustilide, the mass spectrometer was operated in electron ionization mode, the selection reaction monitoring mode was used and an evaluation of the stability and sensitivity of the chromatographic system was performed for the tested extraction. Results Principal component analysis (PCA) simultaneously distinguished ASR and CR from different forms. Ferulic acid, Z-butylidenephthalide, Z-ligustilide, butylphthalide and senkyunolide A were screened by PCA loading plots and can be used as chemical markers for discrimination among different groups of samples. Conclusion Different chemical profiles of ASR and CR after wine treatment could be identified by GC-QQQ-MS/MS. The five marker chemicals selected by PCA, namely ferulic acid, butylphthalide, Z-butylidenephthalide, senkyunolide A and Z-ligustilide, were sufficient to distinguish between the crude and corresponding wine-treated forms of ASR and CR.

In cancer research, genomic studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Integrative analysis simultaneously analyzes multiple datasets and can be more effective than the analysis of single datasets and classic meta-analysis. In many existing integrative analyses, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. In practice, datasets may have been generated in studies that differ in patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. Here we explore the heterogeneity model, which assumes that different datasets may have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) models to describe survival. A weighted least squares approach is adopted for estimation. For marker selection, penalization-based methods are examined. These methods have intuitive formulations and can be computed using effective group coordinate descent algorithms. Analysis of three lung cancer prognosis datasets with gene expression measurements demonstrates the merit of heterogeneity model and proposed methods.

[Abstract] In this protocol, determination of seed paternity by microsatellite markers in Nicotiana attenuata is described. However, this does not include a protocol for the novel marker selection/identification, but rather exploits the markers generated for a closely related species N. tabacum (Bindler et al., 2007). This is a high-throughput protocol optimized and streamlined for one skilled person to process 384 (96 x 4) seeds in 5 days, from DNA isolation (from seedlings) to paternity assessment by microsatellite genotype data.

Background The rapid increase in number of sequenced genomes for species across of the tree of life is revealing a diverse suite of orthologous genes that could potentially be employed to inform molecular phylogenetic studies that encompass broader taxonomic sampling. Optimal usage of this diversity of loci requires user-friendly tools to facilitate widespread cost-effective locus prioritization for phylogenetic sampling. The Townsend (2007) phylogenetic informativeness provides a unique empirical metric for guiding marker selection. However, no software or automated methodology to evaluate sequence alignments and estimate the phylogenetic informativeness metric has been available. Results Here, we present PhyDesign, a platform-independent online application that implements the Townsend (2007) phylogenetic informativeness analysis, providing a quantitative prediction of the utility of loci to solve specific phylogenetic questions. An easy-to-use interface facilitates uploading of alignments and ultrametric trees to calculate and depict profiles of informativeness over specified time ranges, and provides rankings of locus prioritization for epochs of interest. Conclusions By providing these profiles, PhyDesign facilitates locus prioritization increasing the efficiency of sequencing for phylogenetic purposes compared to traditional studies with more laborious and low capacity screening methods, as well as increasing the accuracy of phylogenetic studies. Together with a manual and sample files, the application is freely accessible at http://phydesign.townsend.yale.edu.

Summarization: The purpose of this study is to identify reliable sets of metabolic markers that provide accurate classification of complex brain tumors and facilitate the process of clinical diagnosis. Several ratios of metabolites are tested alone or in combination with imaging markers. A wrapper feature selection and classification methodology is studied, employing Fisher's criterion for ranking the markers. The set of extracted markers that express statistical significance is further studied in terms of biological behavior with respect to the brain tumor type and grade. The outcome of this study indicates that the proposed method by exploiting the intrinsic properties of data can actually reveal reliable and biologically relevant sets of metabolic markers, which form an important adjunct toward a more accurate type and grade discrimination of complex brain tumors Παρουσιάστηκε στο: Measurement Science and Technology

Gene marker selection from gene expression profiles has been extensively investigated in the Bioinformatics literature. The aim is usually to find a compact set of genes potentially correlated to a particular disease, which can then be candidate targets for new drugs and treatments. Available gene expression data sets are often noisy and sparse, having a low number of patient samples, for which a high number of expressed genes is recorded. These characteristics may pose challenges in finding proper gene markers. Using some available gene expression data sets for cancer diagnosis, we experimentally try to understand the influence of their sparsity in the performance of two popular gene marker selection methods.

A redundant flexible set of 44 skin markers has been developed to study human gait. In practice, only subsets of the marker system are used for individual gait studies. Software structure allows extreme flexibility in specific marker selection sites. Analysis of identical kinematic studies with different marker sets demonstrates the inherent inaccuracies of rotational wands and skin marker sets that have been commonly used for motion analysis.

Development of high-throughput technologies makes it possible to survey the whole genome. Genomic studies have been extensively conducted, searching for markers with predictive power for prognosis of complex diseases such as cancer, diabetes and obesity. Most existing statistical analyses are focused on developing marker selection techniques, while little attention is paid to the underlying prognosis models. In this article, we review three commonly used prognosis models, namely the Cox, additive risk and accelerated failure time models. We conduct simulation and show that gene identification can be unsatisfactory under model misspecification. We analyze three cancer prognosis studies under the three models, and show that the gene identification results, prediction performance of all identified genes combined, and reproducibility of each identified gene are model-dependent. We suggest that in practical data analysis, more attention should be paid to the model assumption, and multiple models may need to be considered.

International audience; A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a minimum spanning forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixelwise classification is performed, and the most reliable classified pixels are chosen as markers. Each classification-derived marker is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Furthermore, the classification map is refined using the results of a pixelwise classification and a majority voting within the spatially connected regions. Experimental results are presented for three hyperspectral airborne images. The use of different dissimilarity measures for the construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.

Surgical navigation assists in endoscopic surgeries by enabling surgeons to see concealed lesions and surrounding organs. Successful surgical navigation depends on accurate registration between a medical image and a patient. For accurate point-based registration, it is important to determine the matching order and positions of the markers correctly. It is particularly difficult to determine the order and positions when part of the markers cannot be located on the patient's body or when they cannot be identified in the images.By using the automatic marker-matching option of the proposed tool, an optimum registration result can be obtained even with the partial loss of markers. In addition, this tool provides an intuitive marker selection interface that displays the registration error of each marker pair in different colors.The efficiency of the described tool in terms of the registration accuracy and time has been confirmed in more than 70 clinical applications. The fiducial registration errors were 1.28 + or - 1.09 mm in ear, nose, and throat surgery and 3.55 + or - 1.30 mm in liver tumor ablation therapy.The proposed automatic matching scheme with marker selection interface was particularly effective where the markers were partly lost or incorrectly identified.

Summarization: Many scientific works in the field of bioinformatics and marker selection deal with the problem of deriving a gene signature with significant statistical properties without paying much attention on the biological aspect of the produced result. In this paper we asses the problem of revealing possible significant knowledge which might be hidden under a given gene signature, using previous biological information provided through gene ontologies and pathways. Presented on

Summarization: The problem of marker selection in DNA microarray analysis has been mostly addressed by linear methods. RFE-SVM is such a representative method where a linear kernel is used as the basic tool to address the problem. On the other hand a single neuron is known to be a linear estimator. In this study we explore such a single neuron to address the problem of marker selection. Παρουσιάστηκε στο: Annual International Conference of the IEEE Engineering in Medicine and Biology Society

In the course of breeding, a number of genetic resources have been used to investigate the effect of the overexpressed allelic form of the Bx7 high molecular weight glutenin encoded by Glu-B1 on dough strength, stability and extensibility. Biochemical marker selection was carried out using RP-HPLC on breeding lines in the F3 –F4 and F5 –F7 generations, developed using parental lines overexpressing storage proteins, in order to detect the overexpression of the Bx7 HMW glutenin subunit. In early generations lines were selected that had mean values of dough strength (R_max) and area under the curve (A) substantially exceeding those recorded for the original set of breeding material using Kieffer’s Texture Analyser. The values of R_max rose from 16.0 to 23.3 and those of A from 984 to 1403 on average in the selected lines. Correlation analysis indicated that a medium strong, significant correlation was found for the resistance and stability of the dough and the area under the curve. The results of rheological analysis on selected lines overexpressing Bx7 show that the ratio of genotypes with good breadmaking quality increased during both periods of selection, but breeding for HMW glutenin overexpression alone is not sufficient for an improvement in breadmaking quality. It can be concluded that overexpressed allelic forms could be useful means of breeding for improvements in traits influencing technological quality, especially dough strength and stability. Compared to genetic resources with high protein content, these overexpressed forms do not make a significant contribution to increases in protein content and dough extensibility

DNA micro-array analysis allows us to study the expression level of thousands of genes simultaneously on a single experiment. The problem of marker selection has been extensively studied but in this paper we also consider the quality of the selected markers. Thus, we address the problem of selecting a small subset of genes that would be adequate enough to discriminate between the two classes of interest in classification, while preserving self-similar characteristics to allow closed clustering within each class.

Background: The studies of complex traits project new challenges to current methods that evaluate association between genotypes and a specific trait. Consideration of possible interactions among loci leads to overwhelming dimensions that cannot be handled using current statistical methods. Methods: In this article, we evaluate a multi-marker screening algorithm – the backward genotype-trait association (BGTA) algorithm for case-control designs, which uses unphased multi-locus genotypes. BGTA carries out a global investigation on a candidate marker set and automatically screens out markers carrying diminutive amounts of information regarding the trait in question. To address the ‘too many possible genotypes, too few informative chromosomes’ dilemma of a genomic-scale study that consists of hundreds to thousands of markers, we further investigate a BGTA-based marker selection procedure, in which the screening algorithm is repeated on a large number of random marker subsets. Results of these screenings are then aggregated into counts that the markers are retained by the BGTA algorithm. Markers with exceptional high counts of returns are selected for further analysis. Results and Conclusion: Evaluated using simulations under several disease models, the proposed methods prove to be more powerful in dealing with epistatic traits. We also demonstrate the proposed methods through an application to a study on the inflammatory bowel disease.

Building predictive models for genomic mining requires feature selection, as an essential preliminary step to reduce the large number of variable available. Feature selection is a process to select a subset of features which is the most essential for the intended tasks such as classification, clustering or regression analysis. In gene expression microarray data, being able to select a few genes not only makes data analysis efficient but also helps their biological interpretation. Microarray data has typically several thousands of genes (features) but only tens of samples. Problems which can occur due to the small sample size have not been addressed well in the literature. Our aim is to discuss some issues on feature selection in microarray data in order to select the most predictive genes. We compare classical approaches based on statistical tests with a new approach based on marker selection. Finally, we compare the best predictive model with a model derived from a boosting method.

One of the grand challenges of human genetics to systematically map by gene-association susceptibility genes for complex diseases is underway. High-throughput genotyping platforms have been developed; a comprehensive map of human genetic variation (HapMap) to guide efficient marker selection is imminent and many researchers have assembled suitable cohorts of patients. Expectations are understandably high and it is timely to review the promise and pitfalls of this strategy.

Reliability in written examinations is taken very seriously by examination boards and candidates alike. Within general education many factors influence reliability including variations between markers, within markers, within candidates and within teachers. Mechanisms designed to overcome, or at least minimize, the impact of such variables are detailed. Methods of establishing reliability are also explored in the context of a range of assessment situations. In written tests of general practice within the Membership of the Royal College of General Practitioner (MRCGP) examination considerable effort has put been put into achieving acceptable levels of reliability. Current mechanisms designed to ensure high reliability are described and related to the evolution of the written component of the examination. In addition to description of marker selection and training, question development including construct a detailed example of specific and generic marking schedules is provided. Examination results for the Written Paper of the MRCGP from 1998 to 2003 are reported including Cronbach's alpha coefficients and standard error of measurements, mean scores (and SD) and pass rates. In addition individual discrimination scores for each question in the October 2002 paper are shown. Consistent high reliability of the written component of the MRCGP examination provides valuable lessons in terms of selection, training and monitoring of markers as well as practical methods of moderating factors affecting candidate variability. The challenge for examination developers is to carry these important lessons forward into a modernized assessment structure of UK general practice.

It is possible to estimate the proportionate contributions of ancestral populations to admixed individuals or populations using genetic markers, but different loci and alleles vary considerably in the amount of information that they provide. Conventionally, the allele frequency difference between parental populations (d) has been used as the criterion to select informative markers. However, it is unclear how to use d for multiallelic loci, or populations formed by the mixture of more than two groups. Moreover, several other factors, including the actual ancestral proportions and the relative genetic diversities of the parental populations, affect the information provided by genetic markers. We demonstrate here that using d as the sole criterion for marker selection is inadequate, and we propose, instead, to use Fisher’s information, which is the inverse of the variance of the estimated ancestral contributions. This measure is superior because it is directly related to the precision of ancestry estimates. Although d is related to Fisher’s information, the relationship is neither linear nor simple, and the information can vary widely for markers with identical ds. Fortunately, Fisher’s information is easily computed and formally extends to the situation of multiple alleles and/or parental populations. We examined the distribution of information for SNP and microsatellite loci available in the public domain for a variety of model admixed populations. The information, on average, is higher for microsatellite loci, but exceptional SNPs exceed the best microsatellites. Despite the large number of genetic markers that have been identified for admixture analysis, it appears that information for estimating admixture proportions is limited, and estimates will typically have wide confidence intervals. & 2004 Wiley-Liss, Inc. n

Complex traits, by definition, are the pheonotypic outcome from multiple interacting genes. The traditional analysis of association studies on complex traits is to test one locus at a time, but a better approach is to analyze all markers simultaneously. We previously proposed a two-stage approach, first selecting the influential markers and then modeling main and interaction effects of these markers. Here we introduce alternative approaches to marker selection and discuss issues regarding analytical tools for disease gene mapping, marker selection, and statistical modeling.

DNA metabarcoding enables efficient characterization of species composition in environmental DNA or bulk biodiversity samples, and this approach is making significant and unique contributions in the field of ecology. In metabarcoding of animals, the cytochrome c oxidase subunit I (COI) gene is frequently used as the marker of choice because no other genetic region can be found in taxonomically verified databases with sequences covering so many taxa. However, the accuracy of metabarcoding datasets is dependent on recovery of the targeted taxa using conserved amplification primers. We argue that COI does not contain suitably conserved regions for most amplicon-based metabarcoding applications. Marker selection deserves increased scrutiny and available marker choices should be broadened in order to maximize potential in this exciting field of research.

Additional STR loci can be beneficial for a number of human identity, forensic casework, and DNA database applications. The marker selection and characterization process applied at NIST in developing these new loci and assays are described along with concordance testing results from non-overlapping PCR primers. A 23plex for simultaneous amplification of 22 autosomal STR loci and an amelogenin sex-typing assay is also demonstrated.

Motivation: An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria. Availability: The Comparative Marker Selection application suite is freely available as a GenePattern module. The GenePattern analysis environment is freely available at Contact: jgould@broad.mit.edu

Background The identification and validation of neuroprotective targets is of primary importance to develop Huntington9s disease (HD) therapeutics. This inherited neurodegenerative disease is extensively studied thanks to well characterised models that were developed in several species (invertebrates, mammals) and that recapitulate complementary components of HD pathogenesis. Genome wide analyses in these models have generated a large amount of data (dysregulated genes, modifier genes) with high potential for target and marker selection. Aims The comprehensive and unbiased integration of ‘omics data’ on HD may allow better decisions in candidate target selection to be reached. To this end, the network based analysis of large datasets is anticipated to be highly instructive. Methods We have designed a network based procedure for integrating data from different models of HD pathogenesis. We aimed at preserving useful information from individual screens and allowing testing for the probabilistic interdependencies of different datasets/variables. The core method is the spectral analysis of the data using large and integrated networks such as WormNet to gradually remove unreliable information. Results Our procedure extracts gene clusters that are highly interconnected, enriched in HD data and automatically annotated for their biological role and biomedical potential. Conclusion Preliminary results will be shown to illustrate how our data analysis procedure is able to identify biological processes/pathways/genes of high interest in HD. Further investigation will aim at developing analyses and making the resulting information publically available online.