High-dimensional Phenotypic Data Research Articles

123 Phenomics enabled quantitative genetic modeling of complex traits

Abstract With the development of high-throughput technologies, particularly for genomics and phenomics, biology has become a large-scale, data-rich field. High-throughput phenotyping (HTP) is an innovative technology that integrates advances in engineering or computer science into agriculture to optimize production while ensuring sustainability. In particular, the use of image-based HTP technologies allows us to collect a large number of phenotypes in an automated manner with less human labor and reduced costs, allowing us to efficiently evaluate phenotypes that were previously difficult to measure manually. The advent of HTP, coupled with the wealth of genomic data generated by next-generation sequencing technologies, provides new and exciting resources for studying complex traits. These new technologies also bring new challenges to quantitative genetics, namely the development of robust statistical frameworks that can accommodate spatial, temporal, heterogeneous, and high-dimensional phenotypic data. However, the impact of HTP-derived traits on genetic analysis in animal breeding and genetics has been limited. In this talk some real data examples of quantitative genetic modeling of image-derived traits will be presented.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40–60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

431 Awardee Talk: Phenomics and Phenotyping in Animal Breeding

Abstract Genomic prediction is now a mature technology for animal breeding and genetics and many breeders’ associations and livestock, and poultry genetics companies have adopted high-through genotyping for routine breeding value prediction and for assisting in selection decisions. In this context, more research efforts in breeding and genetics are focusing more on genome annotation and phenotyping. In animal breeding, the term phenomics is almost exchangeable with high-throughput phenotyping and automated phenotyping, i.e., the recording of high dimensional phenotypic data on an organism-wide scale. High throughput phenotyping is achieved through using a variety of sensors. The sensors generate raw data that must be captured, processed, cleaned, and modeled to produce meaningful phenotypes. Animal breeders are used to working with large amounts of data. However, high throughput phenomics usually produces data that may quickly overwhelm storage and processing capabilities of even the most prepared data analysis teams. Moreover, the black-box nature of many algorithms used to process raw sensor data into actionable phenotypes usually require thorough validation and checking. Animal breeders may build on prior lessons learned processing and modeling high-throughput genotypes and traditional phenotypes and we also need to adopt new practices commonly used in the field of machine learning and computer science. Finally, high throughput phenomics will produce novel phenotypes that will require different modeling or revisiting traditional models. In this talk I will share my views on all these points, and I will illustrate them using literature data and data from my own research related to computer vision, modeling of social genetic effects and modeling of social interactions.

Features Selection in Statistical Classification of High Dimensional Image Derived Maize (<i>Zea Mays</i> L.) Phenomic Data

Phenotyping has advanced with the application of high throughput phenotyping techniques such automated imaging. This has led to derivation of large quantities of high dimensional phenotypic data that could not have been achieved using manual phenotyping in a single run. Hence, the need for parallel development of statistical techniques that can appropriately handle such large and/or high dimensional data set. Moreover, there is need to come up with a statistical criteria for selecting the best image derived phenotypic features that can be used as best predictors in modelling plant growth. Information on such criteria is limited. The objective of this study is to apply feature importance, feature selection with Shapley values and LASSO regression techniques to find the subset of features with the highest predictive power for subsequent use in modelling maize plant growth using high-dimensional image derived phenotypic data. The study compared the statistical power of these features extraction methods by fitting an XGBoost model using the best features from each selection method. The image derived phenomic data was obtained from Leibniz Institute of Plant Genetics and Crop Plant Research, -Gatersleben, Germany. Data analysis was performed using R-statistical software. The data was subjected to data imputation using k Nearest Neighbours technique. Features extraction was performed using feature importance, Shapley values and LASSO regression. The Shapley values extracted 25 phenotypic features, feature importance extracted 31 features and LASSO regression extracted 12 features. Of the three techniques, the feature importance criterion emerged the best feature selection technique, followed by Shapley values and LASSO regression, respectively. The study demonstrated the potential of using feature importance as a selection technique in reduction of input variables in of high dimensional growth data set.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system

BackgroundThe development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy.MethodsThe concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up.ResultsThe safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%).ConclusionPROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient’s acceptance of data sharing can be increased by patient-centric decision-making.Graphic abstract

238 Linking livestock phenomics and precision livestock farming

Abstract Numerous pre- and post-mortem factors, such as genotype, production system, growth promotants, diet, health events, stress, slaughter age and weight, carcass chilling, and ageing time, have been shown to impact beef production and final product quality. The objective of livestock phenomics is the systematic acquisition of high dimensional phenotypic data, which requires measuring phenomes as they change in response to genetic mutation and environmental influences. Due to the decrease in costs associated to genomics technology and related fields, researchers had to face the so called “phenomic gap”, a lack of sufficient, appropriate phenotypic data. Selecting phenotypes of interests, standardizing methodologies, developing high-throughput data collection systems, systematically recording environmental factors, and integrating bioinformatics are some of the challenges when developing a livestock phenomics program. Precision livestock farming aims at applying continuous, automated real-time monitoring systems to optimize livestock management. The information collected by these systems can be used to optimize individual animal health and welfare, reproductive traits, and productivity, as well as environmental influences. This approach requires the use of novel technologies and the management of large amounts of data. Multiple technologies and sensors are already being used, or have the potential, to monitor important individual traits. These two interdisciplinary fields share multiple objectives that could lead to significant synergies. The complexity of in-farm data collection varies depending on the species and production system, with beef cattle presenting specific challenges. In addition, data collection needs to continue after slaughter, as carcass and meat quality traits are influenced by in vivo practices, determine the final profitability of the system, and need to be taken into consideration to modify management practices. Integrating livestock phenomics and precision livestock farming approaches will lead to a faster development of both fields and an optimal use of resources.

Toward understanding of evolutionary constraints: experimental and theoretical approaches.

Although organisms have diversified remarkably through evolution, they do not exhibit unlimited variability. During evolution, the phenotypic changes do not occur at random; instead, they are directional and restricted by the constraints imposed on them. Despite the perceived importance of characterizing the unevenness of these changes, studies on evolutionary constraints have been primarily qualitative in nature. In this review, we focus on the recent studies of evolutionary constraints, which are based on the quantification of high-dimensional phenotypic and genotypic data. Furthermore, we present a theoretical analysis that enables us to predict evolutionary constraints on the basis of phenotypic fluctuation, modeled on the fluctuation-response relationship in statistical physics. The review lays emphasis on the tight interactions between experimental and theoretical analyses in evolutionary biology that will contribute to a better understanding of evolutionary constraints.

Understanding metabolic adaptation by using bacterial laboratory evolution and trans-omics analysis.

Many diseases such as metabolic syndrome, cancer, inflammatory diseases, and pathological phenomena can be understood as an adaptive reconstitution of the metabolic state (metabolic adaptation). One of the effective approaches to reveal the property of metabolic networks is using model organisms such as microorganisms that are easier to experiment with than higher organisms. Using the laboratory evolution approach, we can elucidate the evolutionary dynamics in various stress environments, which provide us an understanding of the metabolic adaptation. In addition, the integration of omics data and phenotypic data enables us to clarify the genetic and phenotypic alterations during adaptation. In this review, we describe our recent studies on bacterial laboratory evolution and the omics approach to clarify the stress adaptation process. We have also obtained high-dimensional phenotypic data using our automated culture system. By combining these genomic and transcriptomic data within high-throughput phenotypic data, we can discuss the complex trans-omics network of metabolic adaptation.

Reliable Phylogenetic Regressions for Multivariate Comparative Data: Illustration with the MANOVA and Application to the Effect of Diet on Mandible Morphology in Phyllostomid Bats.

Understanding what shapes species phenotypes over macroevolutionary timescales from comparative data often requires studying the relationship between phenotypes and putative explanatory factors or testing for differences in phenotypes across species groups. In phyllostomid bats for example, is mandible morphology associated to diet preferences? Performing such analyses depends upon reliable phylogenetic regression techniques and associated tests (e.g., phylogenetic Generalized Least Squares, pGLS, and phylogenetic analyses of variance and covariance, pANOVA, pANCOVA). While these tools are well established for univariate data, their multivariate counterparts are lagging behind. This is particularly true for high-dimensional phenotypic data, such as morphometric data. Here, we implement much-needed likelihood-based multivariate pGLS, pMANOVA, and pMANCOVA, and use a recently developed penalized-likelihood framework to extend their application to the difficult case when the number of traits $p$ approaches or exceeds the number of species $n$. We then focus on the pMANOVA and use intensive simulations to assess the performance of the approach as $p$ increases, under various levels of phylogenetic signal and correlations between the traits, phylogenetic structure in the predictors, and under various types of phenotypic differences across species groups. We show that our approach outperforms available alternatives under all circumstances, with greater power to detect phenotypic differences across species group when they exist, and a lower risk of improperly detecting nonexistent differences. Finally, we provide an empirical illustration of our pMANOVA on a geometric-morphometric data set describing mandible morphology in phyllostomid bats along with data on their diet preferences. Overall our results show significant differences between ecological groups. Our approach, implemented in the R package mvMORPH and illustrated in a tutorial for end-users, provides efficient multivariate phylogenetic regression tools for understanding what shapes phenotypic differences across species. [Generalized least squares; high-dimensional data sets; multivariate phylogenetic comparative methods; penalized likelihood; phenomics; phyllostomid bats; phylogenetic MANOVA; phylogenetic regression.].

Abstract 1179: Deep spatial immuno-profiling through high biomarker colocalization in FFPE tumor tissue sections

Abstract Background: The recent emergence of highly multiplexed immunohistochemistry (IHC) has the potential to revolutionize immuno-oncology and pathology research as it enables the identification of complex cell subtypes and their potential interactions in the tumor environment. Comprehensive classification of the different cell types in immuno-oncology presents a unique challenge as many of the relevant biomarkers are common to large subsets of cell types within a particular cell class. Therefore, a key feature for accurate phenotypic classification is the ability to detect a high number of colocalized biomarkers. Current IHC technologies offer a low subtyping level due to steric constraints, spectral overlap between dyes, or depletion of deposition sites. Alternative strategies using mass- or cleavable oligonucleotide- tags can potentially achieve high colocalization but are hindered by their low-throughput, high-cost, and elaborate post-acquisition image reconstruction. In this study, we demonstrate how the Ultivue® InSituPlex® technology enables researchers to reliably label and detect at least 4 markers in the same compartment (cytoplasmic or nuclear) on single cells in tumor tissue samples. Methods: Different FFPE tumor sections were labeled, imaged, and profiled for 15 targets using InSituPlex technology. Staining was carried out in a single workday on a Leica BOND RX autostainer using a cocktail of the 15 primary antibodies relevant to immuno-oncology. Commercially available multi-color fluorescence slide scanners were used to obtain whole slide images. The resulting images were then segmented and analyzed for different phenotypes and their spatial relationships using Indica Labs HALO® software. Additional dimensionality reduction algorithms were used to visualize the high-dimensional phenotypic data of cells with many overlapping biomarkers. Results: In this poster, we demonstrate the detection of at least 4 co-localized biomarkers on the same cell in FFPE tissue sections. Analysis of the high dimensional, spatially resolved data obtained from a 15-plex assay provided phenotypic information of different lymphocytes, macrophages, dendritic cells, antigen presenting cells, and tumor cell populations with multiple colocalized biomarkers. Conclusions: InSituPlex technology empowers pathology research through colocalization analysis of many markers on single cells over entire sections of tissue for accurate differentiation and subtyping of cell populations in the tumor microenvironment. As exploratory research in immuno-oncology expands, advancements in multiplex spatial profiling technologies promise to offer greater insights by providing a systems-level view of the biology. Citation Format: Abdul Mohammed, Douglas Wood, Mael Manesse. Deep spatial immuno-profiling through high biomarker colocalization in FFPE tumor tissue sections [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1179.

Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks.

Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change that develops over time, but it is difficult to prioritize phenotypes for measurement because it is unclear which biological changes should be considered aspects of aging and, further, which species and environments exhibit "real aging." Here, I propose a strategy to address this challenge: rather than classify phenotypes as "real aging" or not, conceptualize aging as the set of all age-dependent phenotypes and appreciate that this set and its underlying mechanisms may vary by population. Use automated phenotyping technologies to measure as many age-dependent phenotypes as possible within individuals over time, prioritizing organism-level (i.e., physiological) phenotypes in order to enrich for health relevance. Use those high-dimensional phenotypic data to construct dynamic networks that allow aging to be studied with unprecedented sophistication and rigor.

Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry.

Recent advances in single cell multi-omics methodologies significantly expand our understanding of cellular heterogeneity, particularly in the field of immunology. Today's state-of-the-art flow and mass cytometers can detect up to 50 parameters to comprehensively characterize the identity and function of individual cells within a heterogeneous population. As a consequence, the increasing number of parameters that can be detected simultaneously also introduces substantial complexity for the experimental setup and downstream data processing. However, this challenge in data analysis fostered the development of novel bioinformatic tools to fully exploit the high-dimensional data. These tools will eventually replace cumbersome serial, manual gating in the two-dimensional space driven by a priori knowledge, which still represents the gold standard in flow cytometric analysis, to meet the needs of the today's immunologist. To this end, we provide guidelines for a high-dimensional cytometry workflow including experimental setup, panel design, fluorescent spillover compensation, and data analysis.

Hierarchical classification of microorganisms based on high-dimensional phenotypic data.

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.

Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data.

The LIPGENE-SU.VI.MAX study, like many others, recorded high-dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory. A novel latent variable model that elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. A computationally efficient variable selection algorithm is incorporated, estimation is via a Gibbs sampling algorithm and an approximate BIC-MCMC criterion is developed to select the optimal model. Two clusters or sub-phenotypes ('healthy' and 'at risk') are uncovered. A small subset of variables is deemed discriminatory, which notably includes phenotypic and genotypic variables, highlighting the need to jointly consider both factors. Further, 7years after the LIPGENE-SU.VI.MAX data were collected, participants underwent further analysis to diagnose presence or absence of the MetS. The two uncovered sub-phenotypes strongly correspond to the 7-year follow-up disease classification, highlighting the role of phenotypic and genotypic factors in the MetS and emphasising the potential utility of the clustering approach in early screening. Additionally, the ability of the proposed approach to define the uncertainty in sub-phenotype membership at the participant level is synonymous with the concepts of precision medicine and nutrition. Copyright © 2017 John Wiley & Sons, Ltd.

A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data.

BackgroundThe widely used genetic pleiotropic analyses of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data.To overcome limitations of the traditional genetic pleiotropic analysis of multiple phenotypes, we develop sparse structural equation models (SEMs) as a general framework for a new paradigm of genetic analysis of multiple phenotypes. To incorporate both common and rare variants into the analysis, we extend the traditional multivariate SEMs to sparse functional SEMs. To deal with high dimensional phenotype and genotype data, we employ functional data analysis and the alternative direction methods of multiplier (ADMM) techniques to reduce data dimension and improve computational efficiency.ResultsUsing large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods. Simulations also demonstrate that the gene-based pleiotropic analysis has higher power than the single variant-based pleiotropic analysis. The proposed method is applied to exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) with 11 phenotypes, which identifies a network with 137 genes connected to 11 phenotypes and 341 edges. Among them, 114 genes showed pleiotropic genetic effects and 45 genes were reported to be associated with phenotypes in the analysis or other cardiovascular disease (CVD) related phenotypes in the literature.ConclusionsOur proposed sparse functional SEMs can incorporate both common and rare variants into the analysis and the ADMM algorithm can efficiently solve the penalized SEMs. Using this model we can jointly infer genetic architecture and casual phenotype network structure, and decompose the genetic effect into direct, indirect and total effect. Using large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3169-1) contains supplementary material, which is available to authorized users.

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.

BackgroundGenome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives.MethodsOur tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes.ResultsWe retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants.ConclusionsOur integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0261-8) contains supplementary material, which is available to authorized users.

Inter-functional analysis of high-throughput phenotype data by non-parametric clustering and its application to photosynthesis.

Phenomics is the study of the properties and behaviors of organisms (i.e. their phenotypes) on a high-throughput scale. New computational tools are needed to analyze complex phenomics data, which consists of multiple traits/behaviors that interact with each other and are dependent on external factors, such as genotype and environmental conditions, in a way that has not been well studied. We deployed an efficient framework for partitioning complex and high dimensional phenotype data into distinct functional groups. To achieve this, we represented measured phenotype data from each genotype as a cloud-of-points, and developed a novel non-parametric clustering algorithm to cluster all the genotypes. When compared with conventional clustering approaches, the new method is advantageous in that it makes no assumption about the parametric form of the underlying data distribution and is thus particularly suitable for phenotype data analysis. We demonstrated the utility of the new clustering technique by distinguishing novel phenotypic patterns in both synthetic data and a high-throughput plant photosynthetic phenotype dataset. We biologically verified the clustering results using four Arabidopsis chloroplast mutant lines. Software is available at www.msu.edu/~jinchen/NPM. Supplementary data are available at Bioinformatics online. jinchen@msu.edu, kramerd8@cns.msu.edu or rongjin@cse.msu.edu.

Global Individual Ancestry Using Principal Components for Family Data

Studies of complex human diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In GWAS, the principal components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals, and if family structure is present and ignored, such substructures may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao [Biometrika 1992;79:631-641], taking into account only siblings' relatedness, and by Oualkacha et al. [Stat Appl Genet Mol Biol 2012, DOI: 10.2202/1544-6115.1711], taking into account large pedigrees and high-dimensional phenotype data. In this work, we extend these methods to estimate the global individual ancestry coefficients from PCs derived from different variance component matrix estimators using SNPs from two simulated data sets and two real data sets: the GENOA sibship data consisting of European and African-American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from the Affymetrix 6.0 chip. Our results show that the family structure plays an important role in the estimation of the global individual ancestry value for extended pedigrees but not for sibships.

R/qtlcharts: interactive graphics for quantitative trait locus mapping.

Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map quantitative trait loci (QTL) (genetic loci that influence quantitative traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl’s static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.

Environmental exposures during pregnancy: US National Children's Study formative research investigation

omics data sets with measured environmental contaminants and pregnancy outcome data. Study Design: As part of the NCSMain Study, 210 placentas from 10 regional centers were uniformly collected and stored with a priori protocols designed to preserve biologic material for future 'omics research. Subsequent DNA/RNA/miRNA/methylated DNA analysis, analytic chemistry for environmental exposures, and 3-D placental imaging and mapping were conducted by established experts from over 20 collaborating centers. High dimensional data sets were reduced and integrative analysis to determine the interrelationships among biologic measures was undertaken. RNA levels for all annotated transcripts (UCSC & Ensembl) were normalized to construct a weighted coexpression gene network. Leveraged variant information derived from placental RNA sequencing enabled construction of a weighted gene network of 20,000 most variable gene expression traits. 1 million SNPs and SNPs from 1000 genomes (14 million SNP genotypes per sample) were imputed across all samples, and placental eQTL were mapped to identify enriched loci at p b 0.00001. Results: A heretofore unparalleled data set was generated and analyzed to yield novel placental interaction networks (Figure). The placental co-expression network indicates strong structurally segregated regions of the placental molecular interactome, generating 20 modules, each containing between 30 and 4000 gene members. In testing for pathway enrichments across all modules, we uncovered not only thousands of loci modulating the expression of genes in the placenta, but one subnetwork (yellow nodes/red edges) that was enriched for pathways, SNPs, and toxins previously associated with autism and associated spectrumdisorders. Conclusion:Wedemonstrate for the first time that population-derived high dimensional placental multi'omics data can be integrated with systems biology approaches. These comprehensive molecular profiles,when taken in the context of other high-dimensional phenotypic data, promise to enhance our understanding of a variety of biological processes that are modulated in the placenta and in turn associate with later human disease.