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Restoring the normal structure and function of injured tendons is one of the biggest challenges in orthopedics and sports medicine department. The discovery of tendon-derived stem cells (TDSCs) provides a novel perspective to treat tendon injuries, which is expected to be an ideal seed cell to promote tendon repair and regeneration. Because of the lack of specific markers, the identification of tenocytes and TDSCs has not been conclusive in thein vitrostudy of tendons. In addition, the morphology of tendon derived cells is similar, and the comparison and identification of tenocytes and TDSCs are insufficient, which causes some obstacles to thein vitrostudy of tendon. In this review, the characteristics of tenocytes and TDSCs are summarized and compared based on some existing research results (mainly in terms of biomarkers), and a potential marker selection for identification is suggested. It is of profound significance to further explore the mechanism of biomarkersin vivoand to find more specific markers.

Background and ObjectivesLongitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD).MethodsWe use data from the 2 largest cohort imaging studies in HD—284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)—to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control.ResultsOur model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years).DiscussionOur findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

ACS Sensors, 4 (2), ISSN:2379-3694

Objective: to study the lethal indicators a Hereford x black and motley bulls depending on genotypes of genes of a tireoglobulin (TG5) and a miostatin (MSTN). Methods: genetic, biological, and zootechnical. Exert impact on production efficiency of production of livestock production a set of factors, one of the most considerable is genetic the potential of the animals used in breeding work. The majority of economically significant indicators such as meat efficiency have the polygenic nature and can be defined by many genes, for example, miostatin (MSTN), tireoglobulin (TG). Marker selection as an additional method can become the powerful instrument of selection selection of animals. Thus, genetic progress in achievement of a definite purpose in herd accelerates several times in comparison with traditional methods of selection. For production of meat (beef) use animals of all breeds of cattle, however most more effectively use forages and animals of highly specialized meat breeds transform them to the most high-quality meat. Main results. As a result of research in the population bulls with MSTNBBTGTT genotypes all lethal indicators were higher in comparison with animals of genotypes of MSTNAATGCC. They surpassed animals with alternative genotypes in the mass of pair ink on 26,1 kg or 9,4% (Р

Milk freshness is an important parameter for both consumers' health and quality of milk-based products. Up to now there have been neither analytical methods nor specific parameters to uniquely define milk freshness from a complete and univocal chemical perspective. In this study, 8 molecules were selected and identified as responsible for milk aging, using a liquid chromatography-high-resolution mass spectrometry approach followed by chemometric data elaboration. For model setup and marker selection, 30 high-quality pasteurized fresh milk samples were collected directly from the production site and analyzed immediately and after storage at 2 to 8°C for 7 d. The markers were then validated by challenging the model with a set of 10 milk samples, not previously analyzed. Our results demonstrated that the markers identified within this study can be successfully used for the correct classification of non-fresh milk samples, complementing and successfully enhancing parallel evaluations obtainable through sensory measures.

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value &lt, 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within thesingleCellTKR package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

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.

The objective to be achieved with the search for molecular markers of low cost and good reproducibility for the selection and genetic characterization of passion fruit was to select ISSR (Inter Simple Sequence Repeat) primers for different unimproved varieties and cultivars of Passiflora, in order to subsidize molecular genetic studies. To achieve this goal, 23 ISSR starters were tested for eight unimproved varieties and 12 cultivars of Passiflora spp. The amplifications were obtained by electrophoresis on 2% agarose gel, photocumented under transilluminator with ultraviolet light. The ISSR primers showed satisfactory results. Among the 23 primers, 16 obtained values above 60% of efficiency when classified as good (presence of two marks or more). As a consequence of the results presented, it can be attested that such primers are suitable for studies of polymorphism, structure and genetic diversity in species of the Passiflora genus, in addition to assisting conservation and breeding programs. As espécies do gênero Passiflora são amplamente distribuídas no território brasileiro, sendo algumas delas utilizadas na indústria, nos setores de ornamentação, farmacêutico e alimentício. Concomitante ao interesse econômico, o gênero, possui importância ecológica, devido à grande diversidade de espécies silvestres. Desta forma, o conhecimento molecular do maracujazeiro traz benefícios para os programas de melhoramento e conservação dessas espécies. Com finalidade de produzir dados e gerar subsídios em estudos genéticos de maracujazeiro, foram caracterizados e selecionados 23 iniciadores ISSR (Inter Simple Sequence Repeat) para 8 espécies de Passiflora spp. e 12 cultivares (BRS). Foi utilizado gel de agarose a 2% e luz ultravioleta para amplificação e visualização dos produtos amplificados. Os iniciadores ISSR apresentaram resultados satisfatórios, dentre os 23 iniciadores, 16 obtiveram valores acima de 60% de eficiência na classificação como bom (presença de 2 marcas ou mais). Em decorrência dos resultados apresentados, pode-se atestar que, tais iniciadores são adequados para estudos de polimorfismo, estrutura e diversidade genética em espécies do gênero Passiflora, além de, auxiliar programas de conservação e melhoramento.

The large number of existing databases provides a freely available independent source of information with a considerable potential to increase the likelihood of identifying genes for complex diseases. We developed a flexible framework for integrating such heterogeneous databases into novel large scale genetic studies and implemented the methods in a freely-available, user-friendly R package called MIND. For each marker, MIND computes the posterior probability that the marker has effect in the novel data collection based on the information in all available data. MIND 1) relies on a very general model, 2) is based on the mathematical formulas that provide us with the exact value of the posterior probability, and 3) has good estimation properties because of its very efficient parameterization. For an existing data set, only the ranks of the markers are needed, where ties among the ranks are allowed. Through simulations, cross-validation analyses involving 18 GWAS, and an independent replication study of 6,544 SNPs in 6,298 samples we show that MIND 1) is accurate, 2) outperforms marker selection for follow up studies based on p-values, and 3) identifies effects that would otherwise require replication of over 20 times as many markers.AUTHOR SUMMARYThe large number of existing databases provides a freely available independent source of information with a considerable potential to increase the likelihood of identifying genes for complex diseases. We developed a flexible framework for integrating such heterogeneous databases into novel large scale genetic studies and implemented the methods in a freely-available, user-friendly R package called MIND. For each marker, MIND computes an estimate of the (posterior) probability that the marker has effect in the novel data collection based on the information in all available data. For an existing data set, only the ranks of the markers are needed to be known, where ties among the ranks are allowed. MIND 1) relies on a realistic model that takes confounding effects into account, 2) is based on the mathematical formulas that provide us with the exact value of the posterior probability, and 3) has good estimation properties because of its very efficient parameterization. Simulation, validation, and a replication study in independent samples show that MIND is accurate and greatly outperforms marker selection without using existing data sets.

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance., Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.

Erratum in: PLoS One. 2020 Apr 23;15(4):e0232422 3232481; We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Objective To investigate whether previously curated chronic lymphocytic leukemia (CLL) risk genes could be leveraged in gene marker selection for the diagnosis and prediction of CLL. Methods A CLL genetic database (CLL_042017) was developed through a comprehensive CLL-gene relation data analysis, in which 753 CLL target genes were curated. Expression values for these genes were used for case-control classification of four CLL datasets, with a sparse representation-based variable selection (SRVS) approach employed for feature (gene) selection. Results were compared with outcomes obtained by using analysis of variance (ANOVA)-based gene selection approaches. Results For each of the four datasets, SRVS selected a subset of genes from the 753 CLL target genes, resulting in significantly higher classification accuracy, compared with randomly selected genes (100%, 100%, 93.94%, 89.39%). The SRVS method outperformed ANOVA in terms of classification accuracy. Conclusion Gene markers selected from the 753 CLL genes could enable significantly greater accuracy in the prediction of CLL. SRVS provides an effective method for gene marker selection.

To secure the world’s food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts.

The article provides the results of breeding work fulfilled by the FSB SI Agricultural Research Center “Donskoy”, in the course of which the new productive varieties Virasan and Pimet well adapted to the climate and soil of the south of the Rostov Region were successfully developed and handed over for the State Variety Testing. The early ripening rice variety Virasan was developed from the hybridization of the early ripening variety Virazh and the average ripening, energetically growing variety Kuban 3. The vegetation period of the variety is 110 days. The plants are of low height (76 cm). The compact erected panicles are 14 cm of height with 112 kernels of 7.5 mm length and 4.0 mm width on average. 1000 kernels weight is 31.4 g. In the competitive testing, the average productivity of Virasan during 3 years was 6.66 t/ha, which is 0.50 t/ha more than the standard variety Kontakt. The medium-ripening variety Pimet is developed from the crossing of the hybrids C101-A-51« Boyarin and C101-Lac x Virage. With the help of marker selection, it was possible to obtain forms having three blast resistant genes Pi-1, Pi-2, Pi-33 in the homozygous state. The vegetation period from sowing to maturation is 124 days. The variety is of medium height with 88 cm. A panicle of 17.5 cm length forms 162 oval kernels of 8.9 mm length and 3.7 mm width on average. 1000 kernels weight is 31.6 g on average. The productivity of the variety ‘Pimet’ was 9.57 t/ha in the competitive variety testing on average, which exceeded productivity of the standard variety Yuzhanin by 1.13 t/ha.

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.

The assessment of kidney function is a cornerstone in the clinical management and health of the patient. Although the kidneys perform many physiologic functions and are essential for maintaining homeostasis, kidney function is typically evaluated, quantitated, and understood using the glomerular filtration rate (GFR). Although GFR can be directly measured using a variety of externally administered glomerular filtration markers, in general practice, the GFR is usually estimated (eGFR) using endogenous markers that are cleared primarily by kidney filtration. Common situations exist where the GFR needs to be measured (mGFR) in order to proceed with care. This manuscript will review laboratory challenges in the assessment of GFR. Key points to consider when implementing a mGFR testing protocol are the following: marker selection, clearance methodology (urinary vs solely plasma measurements of filtration marker), sample collection, number of samples to collect, staff required, and analytical measurement technology for the filtration marker selected. We suggest those wanting to implement mGFR testing examine site-specific institutional resources along with patient population and proceed with the approaches best suited for their clinical needs and laboratory resources available.

Cadmium (Cd) is a toxic heavy metal that causes severe health issues in humans. Cd accumulates in the human body when foods produced in Cd-contaminated fields are eaten. Therefore, soil remediation of contaminated fields is necessary to provide safe foods. Rice is one of the primary candidates for phytoremediation. There is a genotypic variation of Cd concentration in the shoots and grains of rice. Using the world rice core collection, ‘Jarjan’, ‘Anjana Dhan’, and ‘Cho-ko-koku’ were observed with a significantly higher level of Cd accumulation in the shoots and grains. Moreover, OsHMA3, a heavy metal transporter, was identified as a responsive gene of quantitative trait locus (QTL) for high Cd concentration in the shoots of these three varieties likewise. However, it is difficult to apply practical phytoremediation to these varieties because of their unfavorable agricultural traits, such as shatter and easily lodged. New rice varieties and lines were bred for Cd phytoremediation using OsHMA3 as a DNA marker selection. All of them accumulated Cd in the shoots equal to or higher than ‘Cho-ko-koku’ with improved cultivation traits. Therefore, they can be used for practical Cd phytoremediation.

Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line.

Saliency-based marker-controlled watershed method was proposed to detect and segment phytoplankton cells from microscopic images of non-setae species. This method first improved IG saliency detection method by combining saturation feature with colour and luminance feature to detect cells from microscopic images uniformly and then produced effective internal and external markers by removing various specific noises in microscopic images for efficient performance of watershed segmentation automatically. The authors built the first benchmark dataset for cell detection and segmentation, including 240 microscopic images across multiple phytoplankton species with pixel-wise cell regions labelled by a taxonomist, to evaluate their method. They compared their cell detection method with seven popular saliency detection methods and their cell segmentation method with six commonly used segmentation methods. The quantitative comparison validates that their method performs better on cell detection in terms of robustness and uniformity and cell segmentation in terms of accuracy and completeness. The qualitative results show that their improved saliency detection method can detect and highlight all cells, and the following marker selection scheme can remove the corner noise caused by illumination, the small noise caused by specks, and debris, as well as deal with blurred edges.

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient’s biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

Six upland cotton (Gossypium hirsutum L.) germplasm lines, M Rk-Rn 1(Reg. No. GP-1015, PI 678938), M Rk-Rn 2 (Reg. No. GP-1016, PI 678939), M Rk-Rn 3 (Reg. No. GP-1017, PI 678940), M Rk-Rn 4 (Reg. No. GP-1018, PI 678941), M Rk-Rn 5 (Reg. No. GP-1019, PI 678942), and M Rk-Rn 6 (Reg. No. GP-1020, PI 78943), with resistance to root-knot nematode [Meloidogyne incognita (Kofoid and White) Chitwood], and reniform nematode (Rotylenchulus reniformis Linford and Oliveria) were jointly released by the USDA-ARS and the Mississippi Agricultural and Forestry Experiment Station in 2016. The source of resistance to root-knot nematode was M-240 RNR (PI 592511) and to reniform nematode was M713 Ren1 (PI 665928). The two lines were crossed and plants were selected using markers with subsequent crosses to ‘Sure-Grow 747’ (PI 656375), followed by marker selection. The germplasm lines were selected using simple sequence repeat (SSR) markers CIR 316 and BNL 3661 that are linked to root-knot resistance and GH 132, BNL 3279, and BNL 569 that are linked to reniform resistance genes. Egg production of root-knot and reniform nematodes was suppressed significantly from the susceptible check Sure-Grow 747 in growth chamber tests. In addition to resistance to both nematodes, these lines, which also exhibit differences for agronomic and fiber traits such as length, strength, and micronaire, should be valuable to cotton breeding programs. The successful use of these specific SSR markers further validates their use in selection of nematode-resistant plants in segregating generations.

To overcome the limitations of conventional analysis of male fertility in animals and humans, proteomic studies have been performed to develop fertility-related biomarkers for prognosis and diagnosis of male fertility. However, the studies were focused on specific species or breeds. Therefore, a study is required to validate whether fertility-related markers would apply to other breeds in pigs. In this study, previously developed fertility-related biomarkers from Landrace were validated to use for prognosis of male fertility in commercially available breeds. Expression level of eight biomarkers in non-capacitated and capacitated (C) spermatozoa from Yorkshire and Duroc boars was analyzed. And then, to explore the validity of these markers for prognosis of male fertility, i.e. litter size, artificial insemination was performed. Among them, RAB2A (NC) and UQCRC1 (NC) turned out to be highest efficient markers for Yorkshire. RAB2A (C) was most efficient marker for Duroc. Average litter size has increased as much as 1.41 live born after prediction using eight fertility-related biomarkers in Yorkshire. In addition, average 2.52 litter size was increased after prediction using eight fertility-related biomarkers in Duroc. Average litter sizes were especially highly increased after prediction of fertility using RAB2A (NC) in Yorkshire (1.57 piglets) and TPI (NC) in Duroc (3.14 piglets), respectively. As a result, all biomarkers were significantly correlated with litter size. However, overall accuracy to predict litter size in three breeds was different in response with each marker. Average litter size after artificial insemination was also significantly affected by marker selection. Therefore, this study suggests that developed fertility-related markers may be used for prognosis and diagnosis of male fertility irrespective of breed. However, selection of efficient markers for breeds should be considered to obtain more accurate and efficient outcomes.