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Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours –to do the exact same work but faster– could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: . [ABSTRACT FROM AUTHOR]

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through “flexible” clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, “rigid” clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions. [ABSTRACT FROM AUTHOR]

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment. [ABSTRACT FROM AUTHOR]

We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the model’s energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations. [ABSTRACT FROM AUTHOR]

Common bean (Phaseolus vulgaris L.) is an important high protein crop grown worldwide. North Dakota and Minnesota are the largest producers of common beans in the USA, but crop production is threatened by soybean cyst nematode (SCN; Heterodera glycines Ichinohe) because most current cultivars are susceptible. Greenhouse screening data using SCN HG type 0 from 317 plant introductions (PI’s) from the USDA core collection was used to conduct a genome wide association study (GWAS). These lines were divided into two subpopulations based on principal component analysis (Middle American vs. Andean). Phenotypic results based on the female index showed that accessions could be classified as highly resistant (21% and 27%), moderately resistant (51% and 48%), moderately susceptible (27% and 22%) and highly susceptible (1% and 3%) for Middle American and Andean gene pools, respectively. Mixed models with two principal components (PCs) and kinship matrix for Middle American genotypes and Andean genotypes were used in the GWAS analysis using 3,985 and 4,811 single nucleotide polymorphic (SNP) markers, respectively which were evenly distributed across all 11 chromosomes. Significant peaks on Pv07, and Pv11 in Middle American and on Pv07, Pv08, Pv09 and Pv11 in Andean group were found to be associated with SCN resistance. Homologs of soybean rhg1, a locus which confers resistance to SCN in soybean, were identified on chromosomes Pv01 and Pv08 in the Middle American and Andean gene pools, respectively. These genomic regions may be the key to develop SCN-resistant common bean cultivars. [ABSTRACT FROM AUTHOR]

Genome-wide proximity ligation based assays such as Hi-C have revealed that eukaryotic genomes are organized into structural units called topologically associating domains (TADs). From a visual examination of the chromosomal contact map, however, it is clear that the organization of the domains is not simple or obvious. Instead, TADs exhibit various length scales and, in many cases, a nested arrangement. Here, by exploiting the resemblance between TADs in a chromosomal contact map and densely connected modules in a network, we formulate TAD identification as a network optimization problem and propose an algorithm, MrTADFinder, to identify TADs from intra-chromosomal contact maps. MrTADFinder is based on the network-science concept of modularity. A key component of it is deriving an appropriate background model for contacts in a random chain, by numerically solving a set of matrix equations. The background model preserves the observed coverage of each genomic bin as well as the distance dependence of the contact frequency for any pair of bins exhibited by the empirical map. Also, by introducing a tunable resolution parameter, MrTADFinder provides a self-consistent approach for identifying TADs at different length scales, hence the acronym "Mr" standing for Multiple Resolutions. We then apply MrTADFinder to various Hi-C datasets. The identified domain boundaries are marked by characteristic signatures in chromatin marks and transcription factors (TF) that are consistent with earlier work. Moreover, by calling TADs at different length scales, we observe that boundary signatures change with resolution, with different chromatin features having different characteristic length scales. Furthermore, we report an enrichment of HOT (high-occupancy target) regions near TAD boundaries and investigate the role of different TFs in determining boundaries at various resolutions. To further explore the interplay between TADs and epigenetic marks, as tumor mutational burden is known to be coupled to chromatin structure, we examine how somatic mutations are distributed across boundaries and find a clear stepwise pattern. Overall, MrTADFinder provides a novel computational framework to explore the multi-scale structures in Hi-C contact maps. [ABSTRACT FROM AUTHOR]

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal. [ABSTRACT FROM AUTHOR]

African rice gall midge (AfRGM) is one of the most destructive pests of irrigated and lowland African ecologies. This study aimed to identify the quantitative trait loci (QTL) associated with AfRGM pest incidence and resistance in three independent bi-parental rice populations (ITA306xBW348-1, ITA306xTOG7106 and ITA306xTOS14519), and to conduct meta QTL (mQTL) analysis to explore whether any genomic regions are conserved across different genetic backgrounds. Composite interval mapping (CIM) conducted on the three populations independently uncovered a total of 28 QTLs associated with pest incidence (12) and pest severity (16). The number of QTLs per population associated with AfRGM resistance varied from three in the ITA306xBW348-1 population to eight in the ITA306xTOG7106 population. Each QTL individually explained 1.3 to 34.1% of the phenotypic variance. The major genomic region for AfRGM resistance had a LOD score and R2 of 60.0 and 34.1% respectively, and mapped at 111 cM on chromosome 4 (qAfrGM4) in the ITA306xTOS14519 population. The meta-analysis reduced the number of QTLs from 28 to 17 mQTLs, each explaining 1.3 to 24.5% of phenotypic variance, and narrowed the confidence intervals by 2.2 cM. There was only one minor effect mQTL on chromosome 1 that was common in the TOS14519 and TOG7106 genetic backgrounds; all other mQTLs were background specific. We are currently fine-mapping and validating the major effect genomic region on chromosome 4 (qAfRGM4). This is the first report in mapping the genomic regions associated with the AfRGM resistance, and will be highly useful for rice breeders. [ABSTRACT FROM AUTHOR]

Barley yellow dwarf viruses (BYDVs) are responsible for the disease barley yellow dwarf (BYD) and affect many cereals including oat (Avena sativa L.). Until recently, the molecular marker technology in oat has not allowed for many marker-trait association studies to determine the genetic mechanisms for tolerance. A genome-wide association study (GWAS) was performed on 428 spring oat lines using a recently developed high-density oat single nucleotide polymorphism (SNP) array as well as a SNP-based consensus map. Marker-trait associations were performed using a Q-K mixed model approach to control for population structure and relatedness. Six significant SNP-trait associations representing two QTL were found on chromosomes 3C (Mrg17) and 18D (Mrg04). This is the first report of BYDV tolerance QTL on chromosome 3C (Mrg17) and 18D (Mrg04). Haplotypes using the two QTL were evaluated and distinct classes for tolerance were identified based on the number of favorable alleles. A large number of lines carrying both favorable alleles were observed in the panel. [ABSTRACT FROM AUTHOR]

Introduction: Evidence for linkage and association of obesity-related quantitative traits to chromosome 1q43 has been reported in the Quebec Family Study (QFS) and in populations of Caribbean Hispanic ancestries yet no specific candidate locus has been replicated to date. Methods: Using a set of 1,902 single nucleotide polymorphisms (SNPs) genotyped in 525 African American (AA) and 391 European American (EA) women enrolled in the NIEHS uterine fibroid study (NIEHS-UFS), we generated a fine association map for the body mass index (BMI) across a 2.3 megabase-long interval delimited by RGS7 (regulator of G-protein signaling 7) and PLD5 (Phospholipase D, member 5). Multivariable-adjusted linear regression models were fitted to the data to evaluate the association in race-stratified analyses and meta-analysis. Results: The strongest associations were observed in a recessive genetic model and peaked in the 3’ end of RGS7 at intronic rs261802 variant in the AA group (p = 1.0 x 10−4) and in meta-analysis of AA and EA samples (p = 9.0 x 10−5). In the EA group, moderate associations peaked at rs6429264 (p = 2.0 x 10−3) in the 2 Kb upstream sequence of RGS7. In the reference populations for the European ancestry in the 1,000 genomes project, rs6429264 occurs in strong linkage disequilibrium (D’ = 0.94) with rs1341467, the strongest candidate SNP for total body fat in QFS that failed genotyping in the present study. Additionally we report moderate associations at the 3’ end of PLD5 in meta-analysis (3.2 x 10−4 ≤ p ≤ 5.8 x 10−4). Conclusion: We report replication data suggesting that RGS7, a gene abundantly expressed in the brain, might be a putative body fat QTL on human chromosome 1q43. Future genetic and functional studies are required to substantiate our observations and to potentially link them to the neurobehavioral phenotypes associated with the RGS7 region. [ABSTRACT FROM AUTHOR]

Grain length is an important quantitative trait in rice (Oryza sativa L.) that influences both grain yield and exterior quality. Although many quantitative trait loci (QTLs) for grain length have been identified, it is still unclear how different alleles from different QTLs regulate grain length coordinately. To explore the mechanisms of QTL combination in the determination of grain length, five mapping populations, including two F2 populations, an F3 population, an F7 recombinant inbred line (RIL) population, and an F8 RIL population, were developed from the cross between the U.S. tropical japonica variety ‘Lemont’ and the Chinese indica variety ‘Yangdao 4’ and grown under different environmental conditions. Four QTLs (qGL-3-1, qGL-3-2, qGL-4, and qGL-7) for grain length were detected using both composite interval mapping and multiple interval mapping methods in the mapping populations. In each locus, there was an allele from one parent that increased grain length and another allele from another parent that decreased it. The eight alleles in the four QTLs were analyzed to determine whether these alleles act additively across loci, and lead to a linear relationship between the predicted breeding value of QTLs and phenotype. Linear regression analysis suggested that the combination of eight alleles determined grain length. Plants carrying more grain length-increasing alleles had longer grain length than those carrying more grain length-decreasing alleles. This trend was consistent in all five mapping populations and demonstrated the regulation of grain length by the four QTLs. Thus, these QTLs are ideal resources for modifying grain length in rice. [ABSTRACT FROM AUTHOR]