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The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense heritability (h2 = 0.3, 0.5, and 0.8) and four amounts of QTLs controlling the trait (8, 40, 80, and 120). All scenarios included non-additive effects due to dominance and additive–additive epistasis. The individuals' genomic estimated breeding values (GEBV) were predicted via MARS and compared against the GBLUP method, whose models were additive, additive–dominant, and additive–epistatic. In addition, a linkage disequilibrium study between markers and QTL was performed. Linkage maps highlighted the QTL and molecular markers identified by the methodologies under study. MARS showed superior results to the GBLUP models regarding predictive ability for traits controlled by 8 loci, and results were similar for traits controlled by more than 40 loci. Moreover, the use of MARS, together with a linkage disequilibrium study of the trait, can help to elucidate the traits' genetic architecture. Therefore, MARS showed potential to improve genomic prediction, especially for oligogenic traits or traits controlled by approximately 40 QTLs, while enabling the elucidation of the genetic architecture of traits. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Microsatellite markers are widely used molecular markers for genetic studies, but choosing the right set involves a challenging trade-off between effectiveness and cost. The research aims to enhance the widely used ant colony optimization algorithm by integrating marker effectiveness indicators. By considering the genetic properties of the markers such as the polymorphic information content, the study seeks to determine the suitable way to select a reduced set of microsatellites. The approach addresses the accuracy–cost trade-off, aiding genetic assessments, breeding, and conservation efforts with cost-effective solutions. This research provides valuable insights into real-world genetic studies, including breeding programs and conservation initiatives. Microsatellites are polymorphic and cost-effective. Optimizing reduced microsatellite panels using heuristic algorithms eases budget constraints in genetic diversity and population genetic assessments. Microsatellite marker efficiency is strongly associated with its polymorphism and is quantified as the polymorphic information content (PIC). Nevertheless, marker selection cannot rely solely on PIC. In this study, the ant colony optimization (ACO) algorithm, a widely recognized optimization method, was adopted to create an enhanced selection scheme for refining microsatellite marker panels, called the PIC–ACO selection scheme. The algorithm was fine-tuned and validated using extensive datasets of chicken (Gallus gallus) and Chinese gorals (Naemorhedus griseus) from our previous studies. In contrast to basic optimization algorithms that stochastically initialize potential outputs, our selection algorithm utilizes the PIC values of markers to prime the ACO process. This increases the global solution discovery speed while reducing the likelihood of becoming trapped in local solutions. This process facilitated the acquisition of a cost-efficient and optimized microsatellite marker panel for studying genetic diversity and population genetic datasets. The established microsatellite efficiency metrics such as PIC, allele richness, and heterozygosity were correlated with the actual effectiveness of the microsatellite marker panel. This approach could substantially reduce budgetary barriers to population genetic assessments, breeding, and conservation programs. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non‐linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Partial least squares discriminant analysis (PLS‐DA) was applied to the integrated dataset for origin classification. RESULTS: Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS‐DA predicted origin poorly at the continental and Central American regional levels. Non‐linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively. CONCLUSION: The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision‐tree‐based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: ImputAccur is a software tool to measure genotype-imputation accuracy. Imputation of untyped markers is a standard approach in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy for imputed genotypes is fundamental. Several accuracy measures have been proposed, but unfortunately, they are implemented on different platforms, which is impractical. Results: With ImputAccur, the accuracy measures info, Iam-hiQ and r2-based indices can be derived from standard output files of imputation software. Sample/probe and marker filtering is possible. This allows e.g. accurate marker filtering ahead of data analysis. Conclusions: The source code (Python version 3.9.4), a standalone executive file, and example data for ImputAccur are freely available at https://gitlab.gwdg.de/kolja.thormann1/imputationquality.git. [ABSTRACT FROM AUTHOR]

Potato virus Y (PVY) impacts potato production worldwide by reducing potato yield, tuber quality, and successful certification of seed. Extreme resistance to PVY is conferred by three genes (Ryadg, Rysto, and Rychc) which have been introgressed from wild species into cultivated varieties. Both Rysto and Ryadg are used in many breeding programs worldwide, but Rychc is found in only a limited number of varieties and previously had been absent in varieties and germplasm representative of the russet market class that is widely grown in North America. Five russet breeding clones were identified as having the molecular marker associated with Rychc. Confirmation of the presence of Rychc was established following field and greenhouse screening of the clones for resistance to PVY with no infection by the virus being observed. These clones also have been characterized as having resistance to late blight and cold-induced sweetening of tubers and all are male-fertile as well. The unique assemblage of resistances in this russet germplasm will be of value to the potato breeding community in the development of russet potato varieties, especially with respect to Rychc which previously has not been reported as being present in this potato market class. [ABSTRACT FROM AUTHOR]

The addition of cattle health and immunity traits to genomic selection indices holds promise to increase individual animal longevity and productivity, and decrease economic losses from disease. However, highly variable genomic loci that contain multiple immune-related genes were poorly assembled in the first iterations of the cattle reference genome assembly and underrepresented during the development of most commercial genotyping platforms. As a consequence, there is a paucity of genetic markers within these loci that may track haplotypes related to disease susceptibility. By using hierarchical assembly of bacterial artificial chromosome inserts spanning 3 of these immune-related gene regions, we were able to assemble multiple full-length haplotypes of the major histocompatibility complex, the leukocyte receptor complex, and the natural killer cell complex. Using these new assemblies and the recently released ARS-UCD1.2 reference, we aligned whole-genome shotgun reads from 125 sequenced Holstein bulls to discover candidate variants for genetic marker development. We selected 124 SNPs, using heuristic and statistical models to develop a custom genotyping panel. In a proof-of-principle study, we used this custom panel to genotype 1,797 Holstein cows exposed to bovine tuberculosis (bTB) that were the subject of a previous GWAS study using the Illumina BovineHD array. Although we did not identify any significant association of bTB phenotypes with these new genetic markers, 2 markers exhibited substantial effects on bTB phenotypic prediction. The models and parameters trained in this study serve as a guide for future marker discovery surveys particularly in previously unassembled regions of the cattle genome. [ABSTRACT FROM AUTHOR]

Marker sets used in US dairy genomic predictions were previously expanded by including high-density (HD) or sequence markers with the largest effects for Holstein breed only. Other non-Holstein breeds lacked enough HD genotyped animals to be used as a reference population at that time, and thus were not included in the genomic prediction. Recently, numbers of non-Holstein breeds genotyped using HD panels reached an acceptable level for imputation and marker selection, allowing HD genomic prediction and HD marker selection for Holstein plus 4 other breeds. Genotypes for 351,461 Holsteins, 347,570 Jerseys, 42,346 Brown Swiss, 9,364 Ayrshires (including Red dairy cattle), and 4,599 Guernseys were imputed to the HD marker list that included 643,059 SNP. The separate HD reference populations included Illumina BovineHD (San Diego, CA) genotypes for 4,012 Holsteins, 407 Jerseys, 181 Brown Swiss, 527 Ayrshires, and 147 Guernseys. The 643,059 variants included the HD SNP and all 79,254 (80K) genetic markers and QTL used in routine national genomic evaluations. Before imputation, approximately 91 to 97% of genotypes were unknown for each breed; after imputation, 1.1% of Holstein, 3.2% of Jersey, 6.7% of Brown Swiss, 4.8% of Ayrshire, and 4.2% of Guernsey alleles remained unknown due to lower density haplotypes that had no matching HD haplotype. The higher remaining missing rates in non-Holstein breeds are mainly due to fewer HD genotyped animals in the imputation reference populations. Allele effects for up to 39 traits were estimated separately within each breed using phenotypic reference populations that included up to 6,157 Jersey males and 110,130 Jersey females. Correlations of HD with 80K genomic predictions for young animals averaged 0.986, 0.989, 0.985, 0.992, and 0.978 for Jersey, Ayrshire, Brown Swiss, Guernsey, and Holstein breeds, respectively. Correlations were highest for yield traits (about 0.991) and lowest for foot angle and rear legs–side view (0.981and 0.982, respectively). Some HD effects were more than twice as large as the largest 80K SNP effect, and HD markers had larger effects than nearby 80K markers for many breed-trait combinations. Previous studies selected and included markers with large effects for Holstein traits; the newly selected HD markers should also improve non-Holstein and crossbred genomic predictions and were added to official US genomic predictions in April 2020. [ABSTRACT FROM AUTHOR]

Despite the various existing studies about nonsynonymous single nucleotide polymorphisms (nsSNPs), genome-wide studies based on nsSNPs are rare. NsSNPs alter amino acid sequences, affect protein structure and function, and have deleterious effects. By predicting the deleterious effect of nsSNPs, we determined the total risk score per individual. Additionally, the machine learning technique was utilized to find an optimal nsSNP subset that best explains the complete nsSNP effect. A total of 16,100 nsSNPs were selected as the best representatives among 89,519 regressed nsSNPs. In the gene ontology analysis encompassing the 16,100 nsSNPs, DNA metabolic process, chemokine- and immune-related, and reproduction were the most enriched terms. We expect that our risk score prediction and nsSNP marker selection will contribute to future development of extant genome-wide association studies and breeding science more broadly. [ABSTRACT FROM AUTHOR]

Marker-assisted backcrossing is a powerful method for developing new cultivars. To develop genomic-wide markers, genotyping-by-sequencing (GBS) can be an efficient method. However, unrefined low-quality markers and missing data between markers can contribute to hampering the marker selection process, particularly in multi-way crosses. In this study, we aimed to calculate the recovery rate of offspring individuals and minimize errors that occur among a large number of markers. Initially, missing data were imputed by comparing samples using the k-nearest neighbor (k-NN) algorithm. Thereafter, low-quality single-nucleotide polymorphisms (SNPs) were corrected by applying the graphical representation method based on the k-NN algorithm in order of the SNPs in a chromosome designed for a multi-parental population. Four-way cross and double-backcrossed tomato BC1F1 (230 lines) and BC2F1 (96 lines) populations were genotyped by GBS. The genotype of samples of the BC1F1 and BC2F1 populations was determined based on the parental haplotype. Thus, the method of visualizing the genotype of offspring individuals, generated via crosses of multiple parents, not only improves estimation of the recovery rate but also facilitates easier selection in breeding programs. [ABSTRACT FROM AUTHOR]

Analysis of a large number of markers is crucial in both genome-wide association studies (GWAS) and genome-wide selection (GWS). However there are two methodological issues that restrict statistical analysis: high dimensionality (p≫n) and multicollinearity. Although there are methodologies that can be used to fit models for data with high dimensionality (eg, the Bayesian Lasso), a big problem that can occurs in this cases is that the predictive ability of the model should perform well for the individuals used to fit the model, but should not perform well for other individuals, restricting the applicability of the model. This problem can be circumvent by applying some selection methodology to reduce the number of markers (but keeping the markers associated with the phenotypic trait) before adjusting a model to predict GBVs. We revisit a tournament-based strategy between marker samples, where each sample has good statistical properties for estimation: n>p and low collinearity. Such tournaments are elaborated using multiple linear regression to eliminate markers. This method is adapted from previous works found in the literature. We used simulated data as well as real data derived from a study with SNPs in beef cattle. Tournament strategies not only circumvent the p≫n issue, but also minimize spurious associations. For real data, when we selected a few more than 20 markers, we obtained correlations greater than 0.70 between predicted Genomic Breeding Values (GBVs) and phenotypes in validation groups of a cross-validation scheme; and when we selected a larger number of markers (more than 100), the correlations exceeded 0.90, showing the efficiency in identifying relevant SNPs (or segregations) for both GWAS and GWS. In the simulation study, we obtained similar results. [ABSTRACT FROM AUTHOR]

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments. [ABSTRACT FROM AUTHOR]

Disease diagnosis using cell-free DNA (cfDNA) has been an active research field recently. Most existing approaches perform diagnosis based on the detection of sequence variants on cfDNA; thus, their applications are limited to diseases associated with high mutation rate such as cancer. Recent developments start to exploit the epigenetic information on cfDNA, which could have substantially wider applications. In this work, we provide thorough reviews and discussions on the statistical method developments and data analysis strategies for using cfDNA epigenetic profiles, in particular DNA methylation, to construct disease diagnostic models. We focus on two important aspects: marker selection and prediction model construction, under different scenarios. We perform simulations and real data analysis to compare different approaches, and provide recommendations for data analysis. [ABSTRACT FROM AUTHOR]

Quality control (QC) of germplasm identity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotyping methods have been developed from a very limited panel of markers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and to measure heterogeneity. The optimal marker selection strategies combined the selection of markers with high minor allele frequency, sampling of clustered SNP in proportion to marker cluster distance and selecting markers that maintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5% contamination in the entry, and approximately a 98% probability to detect a 2% contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved in maize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species. [ABSTRACT FROM AUTHOR]

The algebraic multigrid (AMG) method is used to solve linear systems of equations on a series of progressively coarser grids and has recently attracted significant attention for image segmentation due to its high efficiency and robustness. In this paper, a novel spectral-spatial classification method for hyperspectral images based on the AMG method and hierarchical segmentation (HSEG) algorithm is proposed. Our method consists of the following steps. First, the AMG method is applied to hyperspectral imagery to construct a multigrid structure of fine-to-coarse grids based on the anisotropic diffusion partial differential equation (PDE). The vertices in the multigrid structure are then considered as the initial seeds (markers) for growing regions and are clustered to obtain a sequence of segmentation results. In the next step, a maximum vote decision rule is employed to combine the pixel-wise classification map and the segmentation maps. Finally, a final classification map is produced by choosing the optimal grid level to extract representative spectra. Experiments based on three different types of real hyperspectral datasets with different resolutions and contexts demonstrate that our method can obtain 3.84%-13.81% higher overall accuracies than the SVM classifier. The performance of our method was further compared to several marker-based spectral-spatial classification methods using objective quantitative measures and a visual qualitative evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated framework 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 algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. [ABSTRACT FROM PUBLISHER]

The 6th International Barcode of Life Conference (Guelph, Canada, 18-21 August 2015), themed Barcodes to Biomes, showcases the latest developments in DNA barcoding research and its diverse applications. The meeting also provides a venue for a global research community to share ideas and to initiate collaborations. All plenary and contributed abstracts are being published as an open-access special issue of Genome. Here, I use a comparison with the 3rd Conference (Mexico City, 2009) to highlight 10 recent and emerging trends that are apparent among the contributed abstracts. One of the outstanding trends is the rising proportion of abstracts that focus upon multiple socio-economically important applications of DNA barcoding, including studies of agricultural pests, quarantine and invasive species, wildlife forensics, disease vectors, biomonitoring of ecosystem health, and marketplace surveys evaluating the authenticity of seafood products and medicinal plants. Other key movements include the use of barcoding and metabarcoding approaches for dietary analyses--and for studies of food webs spanning three or more trophic levels--as well as the spread of next-generation sequencing methods in multiple contexts. In combination with the rising taxonomic and geographic scope of many barcoding iniatives, these developments suggest that several important questions in biology are becoming tractable. "What is this specimen on an agricultural shipment?", "Who eats whom in this whole food web?", and even "How many species are there?" are questions that may be answered in time periods ranging from a few years to one or a few decades. The next phases of DNA barcoding may expand yet further into prediction of community shifts with climate change and improved management of biological resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the analysis of omics data, integrative analysis provides an effective way of pooling information across multiple datasets or multiple correlated responses, and can be more effective than single dataset (response) analysis. Multiple families of integrative analysis methods have been proposed in the literature. The current review focuses on the penalization methods. Special attention is paid to sparse meta-analysis methods that pool summary statistics across datasets, and integrative analysis methods that pool raw data across datasets. We discuss their formulation and rationale. Beyond 'standard' penalized selection, we also review contrasted penalization and Laplacian penalization that accommodate finer data structures. The computational aspects, including computational algorithms and tuning parameter selection, are examined. This review concludes with possible limitations and extensions. WIREs Comput Stat 2015, 7:99-108. doi: 10.1002/wics.1322 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

Consider the integrative analysis of genetic data with multiple correlated response variables. The goal is to identify important gene-environment (G × E) interactions along with main gene and environment effects that are associated with the responses. The homogeneity and heterogeneity models can be adopted to describe the genetic basis of multiple responses. To accommodate possible nonlinear effects of some environment effects, a multi-response partially linear varying coefficient model is assumed. Penalization is adopted for marker selection. The proposed penalization method can select genetic variants with G × E interactions, no G × E interactions, and no main effects simultaneously. It adopts different penalties to accommodate the homogeneity and heterogeneity models. The proposed method can be effectively computed using a coordinate descent algorithm. Simulation study and the analysis of Health Professionals Follow-up Study, which has two correlated continuous traits, SNP measurements and multiple environment effects, show superior performance of the proposed method over its competitors. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the analysis of cancer studies with high-dimensional genomic measurements, integrative analysis provides an effective way of pooling information across multiple heterogeneous datasets. The genomic basis of multiple independent datasets, which can be characterized by the sets of genomic markers, can be described using the homogeneity model or heterogeneity model. Under the homogeneity model, all datasets share the same set of markers associated with responses. In contrast, under the heterogeneity model, different studies have overlapping but possibly different sets of markers. The heterogeneity model contains the homogeneity model as a special case and can be much more flexible. Marker selection under the heterogeneity model calls for bi-level selection to determine whether a covariate is associated with response in any study at all as well as in which studies it is associated with responses. In this study, we consider two minimax concave penalty-based penalization approaches for marker selection under the heterogeneity model. For each approach, we describe its rationale and an effective computational algorithm. We conduct simulations to investigate their performance and compare with the existing alternatives. We also apply the proposed approaches to the analysis of gene expression data on multiple cancers. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influences of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral–spatial) using standard quantitative criteria and visual qualitative evaluation. [ABSTRACT FROM AUTHOR]

Background: Marker-assisted introgression currently represents the most widely spread application of DNA markers as an aid to selection in plant breeding. New barley germplasm should be supplemented by genes that facilitate growth and development under stressful conditions. The homology search against known genes is a fundamental approach to identify genes among the generated sequences. This procedure can be utilized for SNP search in genes of predicted function of interest and associated gene ontology (GO). Results: Backcross breeding enhanced by marker selection may become a powerful method to transfer one or a few genes controlling a specific trait. In the study, the integrated approach of combining phenotypic selection with marker assisted backcross breeding for introgression of LTP2 gene, in the background of semi-dwarf spring barley cultivar, was employed. This study discusses the efficiency of molecular marker application in backcrossing targeted on the selected gene. Conclusions: BC6 lines developed in this study can serve as a unique and adequate plant material to dissect the role of LTP2 gene. Due to its role in lipid transfer, the LTP2 may be crucial in lipidome modification in response to abiotic stress. [ABSTRACT FROM AUTHOR]

Abstract: Genetic mapping is one of the key steps in positional cloning. The traditional mapping strategies typically require to genotype a set of markers that are nearly evenly or randomly distributed across the genome or a region of interest. Such “grid” strategies work with low efficiency. We propose an improved mapping strategy by integrating the principle of one-dimensional optimization and information on physical map into the standard mapping procedure used in experimental populations. Computer simulations based on a set of empirical data suggest that our new procedure can reduce the number of markers required for genotyping to less than one-fourth of that of the standard procedure. An illustrative application also demonstrates a pronounced reduction of the burden in genotyping. The proposed strategy offers a quick and cost-effective access to the target gene for positional cloning without any extra expense except for making use of genomic sequence data. A Microsoft Excel spreadsheet, for performing easy calculations described in this article, is available on request from the authors. [Copyright &y& Elsevier]

Abstract: Objective: The problem of marker selection in DNA microarray analysis has been addressed so far by two basic types of approaches, the so-called filter and wrapper methods. Wrapper methods operate in a recursive fashion where feature (gene) weights are re-evaluated and dynamically changing from iteration to iteration, while in filter methods feature weights remain fixed. Our objective in this study is to show that the application of filter criteria in a recursive fashion, where weights are potentially adjusted from cycle to cycle, produces noticeable improvement on the generalization performance measured on independent test sets. Methods and materials: Toward this direction we explore the behavior of two well known and broadly accepted pattern recognition approaches namely the support vector machines (SVM) and a single linear neuron (LN), properly adapted to the problem of marker selection. Within this context we also show how the kernel ability of SVM could be employed in a practical manner to provide alternative ways to approach the problem of reliable marker selection. Results: We explore how the proposed approaches behave in two application domains (breast cancer and leukemia), achieving comparable or even better results than those reported in the related bibliography. An important advantage of these approaches is their ability to derive stable performance without deteriorating due to the complexity of the application domain. Validation is performed using internal leave one out (ILOO) and 10-fold cross validation as well as independent test set evaluation. Conclusions: Results show that the proposed methodologies achieve remarkable performance and indicate that applying filter criteria in a wrapper fashion (‘wrapper filtering criteria’) provides a useful tool for marker selection. The contribution of this study is threefold. First it provides a methodology to apply filter criteria in a wrapper way (which is a new approach), second it introduces a fundamental pattern recognition component namely the single neuron (which is a linear estimator) and explores its behavior on marker selection and third it demonstrates an approach to exploit the kernel ability of SVMs in a practical and effective manner. [Copyright &y& Elsevier]

The old Portuguese wheat landrace aggregate known as 'Barbela' shows good productivity under the low-fertility conditions often associated with acid soils. The use of genomic rye DNA, in combination with 45S rDNA and the repetitive sequences dpTa1 and pSc119.2 as probes, in two sequential in situ hybridization steps enabled the identification of all chromosomes in the 'Barbela' wheat lines and the detection of the introgression of rye-origin chromatin onto wheat chromosome arm 2DL in two of the lines. Amplification of microsatellite loci using published primer pairs showed that the distal segment of wheat chromosome 2DL, which was involved in the rye translocation, was deleted. The identification and characterization of small recombinant chromosome segments in wheat–rye lines may allow their use in plant breeding programmes. Their presence in farmer-maintained material demonstrates the importance of maintaining, characterizing, and collecting landrace material before valuable genetic combinations are lost as uniform commercial crops are introduced.Key words: biodiversity, in situ hybridization, microsatellites, plant breeding, recombination, alien chromosomes, marker selection.La vieille variété de pays amalgamée d'origine portugaise connue sous le nom de 'Barbela' montre une bonne productivité en conditions de faible fertilité, lesquelles sont souvent rencontrées dans des sols acides. L'emploi comme sondes de l'ADN génomique du seigle en combinaison avec l'ADNr 45S et les séquences répétitives dpTa1 ainsi que pSc119.2, lors d'hybridations in situ séquentielles, a permis d'identifier tous les chromosomes au sein des lignées du blé 'Barbela' et la détection d'une introgression de chromatine provenant du seigle au sein du bras chromosomique 2DL chez deux des lignées. L'amplification de microsatellites à l'aide d'amorces déjà caractérisées a montré que le segment distal du chromosome 2DL, celui impliqué dans la translocation avec le seigle, a été délété. L'identification et la caractérisation de courts segments chromosomiques recombinants dans des lignées blé–seigle pourraient ouvrir la voie à leur utilisation dans des programmes de sélection. Leur présence au sein de lignées retenues par les agriculteurs démontre l'importance du maintien, de la caractérisation et de la collection de variétés de pays avant que ne soient perdues des combinaisons génétiques de valeur avec l'introduction de cultures commerciales uniformes.Mots clés : biodiversité, hybridation in situ, microsatellite, amélioration génétique, recombinaison, chromosomes étrangers, sélection de marqueurs[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Background: High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner. Results: We introduce RankCorr, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RankCorr proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RankCorr to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. Conclusions: According to the metrics introduced in this work, RankCorr is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RankCorr is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RankCorr software is available for download at https://github.com/ahsv/RankCorrwith extensive documentation. [ABSTRACT FROM AUTHOR]

Background: Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.Methods: In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.Results: The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.Conclusions: We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important. [ABSTRACT FROM AUTHOR]