Phenotypic Datasets Research Articles

Holistic and component plant phenotyping using temporal image sequence

BackgroundImage-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored.ResultsA set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska–Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1.ConclusionThe central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.

Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders

Neurodevelopmental disorders are associated with atypical development and maturation of brain networks. A recent focus on human connectomics research and the growing popularity of open science initiatives has created the ideal climate in which to make real progress toward understanding the neurobiology of disorders affecting youth. Here we outline future directions for neuroscience researchers examining brain networks in neurodevelopmental disorders, highlighting gaps in the current literature. We emphasize the importance of leveraging large neuroimaging and phenotypic data sets recently made available to the research community, and we suggest specific novel methodological approaches, including analysis of brain dynamics and structural connectivity, that have the potential to produce the greatest clinical insight. Transdiagnostic approaches will also become increasingly necessary as the Research Domain Criteria framework put forth by the National Institute of Mental Health permeates scientific discourse. During this exciting era of big data and increased computational sophistication of analytic tools, the possibilities for significant advancement in understanding neurodevelopmental disorders are limitless.

FungiDB: An Integrated Bioinformatic Resource for Fungi and Oomycetes.

FungiDB (fungidb.org) is a free online resource for data mining and functional genomics analysis for fungal and oomycete species. FungiDB is part of the Eukaryotic Pathogen Genomics Database Resource (EuPathDB, eupathdb.org) platform that integrates genomic, transcriptomic, proteomic, and phenotypic datasets, and other types of data for pathogenic and nonpathogenic, free-living and parasitic organisms. FungiDB is one of the largest EuPathDB databases containing nearly 100 genomes obtained from GenBank, Aspergillus Genome Database (AspGD), The Broad Institute, Joint Genome Institute (JGI), Ensembl, and other sources. FungiDB offers a user-friendly web interface with embedded bioinformatics tools that support custom in silico experiments that leverage FungiDB-integrated data. In addition, a Galaxy-based workspace enables users to generate custom pipelines for large-scale data analysis (e.g., RNA-Seq, variant calling, etc.). This review provides an introduction to the FungiDB resources and focuses on available features, tools, and queries and how they can be used to mine data across a diverse range of integrated FungiDB datasets and records.

Arachidonic Acid Metabolism Pathway Is Not Only Dominant in Metabolic Modulation but Associated With Phenotypic Variation After Acute Hypoxia Exposure.

Background: The modulation of arachidonic acid (AA) metabolism pathway is identified in metabolic alterations after hypoxia exposure, but its biological function is controversial. We aimed at integrating plasma metabolomic and transcriptomic approaches to systematically explore the roles of the AA metabolism pathway in response to acute hypoxia using an acute mountain sickness (AMS) model.Methods: Blood samples were obtained from 53 enrolled subjects before and after exposure to high altitude. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and RNA sequencing were separately performed for metabolomic and transcriptomic profiling, respectively. Influential modules comprising essential metabolites and genes were identified by weighted gene co-expression network analysis (WGCNA) after integrating metabolic information with phenotypic and transcriptomic datasets, respectively.Results: Enrolled subjects exhibited diverse response manners to hypoxia. Combined with obviously altered heart rate, oxygen saturation, hemoglobin, and Lake Louise Score (LLS), metabolomic profiling detected that 36 metabolites were highly related to clinical features in hypoxia responses, out of which 27 were upregulated and nine were downregulated, and could be mapped to AA metabolism pathway significantly. Integrated analysis of metabolomic and transcriptomic data revealed that these dominant molecules showed remarkable association with genes in gas transport incapacitation and disorders of hemoglobin metabolism pathways, such as ALAS2, HEMGN. After detailed description of AA metabolism pathway, we found that the molecules of 15-d-PGJ2, PGA2, PGE2, 12-O-3-OH-LTB4, LTD4, LTE4 were significantly up-regulated after hypoxia stimuli, and increased in those with poor response manner to hypoxia particularly. Further analysis in another cohort showed that genes in AA metabolism pathway such as PTGES, PTGS1, GGT1, TBAS1 et al. were excessively elevated in subjects in maladaptation to hypoxia.Conclusion: This is the first study to construct the map of AA metabolism pathway in response to hypoxia and reveal the crosstalk between phenotypic variation under hypoxia and the AA metabolism pathway. These findings may improve our understanding of the advanced pathophysiological mechanisms in acute hypoxic diseases and provide new insights into critical roles of the AA metabolism pathway in the development and prevention of these diseases.

Extraction of phenotypic traits from taxonomic descriptions for the tree of life using natural language processing.

Premise of the StudyPhenotypic data sets are necessary to elucidate the genealogy of life, but assembling phenotypic data for taxa across the tree of life can be technically challenging and prohibitively time consuming. We describe a semi‐automated protocol to facilitate and expedite the assembly of phenotypic character matrices of plants from formal taxonomic descriptions. This pipeline uses new natural language processing (NLP) techniques and a glossary of over 9000 botanical terms.Methods and ResultsOur protocol includes the Explorer of Taxon Concepts (ETC), an online application that assembles taxon‐by‐character matrices from taxonomic descriptions, and MatrixConverter, a Java application that enables users to evaluate and discretize the characters extracted by ETC. We demonstrate this protocol using descriptions from Araucariaceae.ConclusionsThe NLP pipeline unlocks the phenotypic data found in taxonomic descriptions and makes them usable for evolutionary analyses.

Vavilov wheat accessions provide useful sources of resistance to tan spot (syn. yellow spot) of wheat

Host genetic resistance is the most effective and sustainable means of managing tan spot or yellow spot of wheat. The disease is becoming increasingly problematic due to the adoption of minimum tillage practices, evolution of effector‐mediated pathogenicity, and widespread cultivation of susceptible cultivars from a narrow genetic base. This highlights the importance of broadening the diversity of resistance factors in modern breeding germplasm. This study explored 300 genetically diverse wheat accessions, originally sourced from the N. I. Vavilov Institute of Plant Genetic Resources (VIR), St Petersburg, Russia. The collection was screened for resistance to tan spot at seedling and adult stage under controlled conditions, and in the field across 2 years. The phenotypic datasets, coupled with ToxA bioassay screening, identified a number of accessions with useful sources of resistance. Seedling disease response corresponded well with ToxA sensitivity (r = 0.49, P < 0.000), but not adult responses (r = −0.02 to −0.19, P < 0.002), and overall reactions to ToxA appeared to show poor correspondence with disease response at the adult stage. ToxA‐insensitive accessions were generally found resistant across different growth stages (all‐stage resistance, ASR) in all experiments (seedling and adult stage under controlled conditions and field). ToxA‐sensitive accessions that were susceptible at seedling stage, but resistant at both adult‐plant stages, were deemed to carry adult‐plant resistance (APR). This study provides detailed information on the degree of tan spot resistance in the Vavilov wheat collection and discusses strategies to harness these sources to boost the diversity of resistance factors in modern wheat breeding germplasm.

Abstract TP161: The Cerebrovascular Disease Knowledge Portal: an Open Access Data Resource to Accelerate Genomic Discoveries in Stroke

Introduction: High throughput genotyping technologies and large collaborative consortia have revolutionized the field of medical genetics. Open data access is the final barrier capitalizing fully on the opportunities available in stroke genetics research. The International Stroke Genetics Consortium (ISGC) has created the Cerebrovascular Disease Knowledge Portal (CDKP), a platform to explore and access genetic data related to cerebrovascular diseases. Methods: Funded by the NIH, the CDKP has been developed by the ISGC and the American Heart Association (AHA) Institute for Precision Cardiovascular Medicine. The CDKP seeks to democratize access to genomic data and potentiate stroke genomics research by providing open access to genetic, phenotypic and imaging data on patients with stroke. Within the CDKP, data are aggregated, integrated, and harmonized according to a pre-specified standardized pipeline. Any institution or investigator working with stroke genomic data is welcome to deposit their data or use available data. Results: The CDKP houses two types of data, for different regulatory and analytical needs: summary level data and individual level data. The CDKP offers three main features: (1) a web-based graphical user interphase that allows the exploration of genomic information through a wide menu of integrated tools for analysis and data visualization; (2) a repository of full sets of genome-wide summary statistics produced by published landmark studies in the field; and (3) a repository of individual level data, accessible through a secure cloud working space provided by the AHA Platform for Precision Medicine. The CDKP can be accessed at www.cerebrovascularportal.org . Conclusion: The CDKP advances the ISGC’s goal of liberal data sharing in stroke genomics and other areas of cardiovascular research that may benefit from genomic analyses. In the future, phenotypic datasets can be added to further enrich sharing of non-genetic data as well.

The use of plant models in deep learning: an application to leaf counting in rosette plants

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

Pathway-based analysis of genome-wide association study of circadian phenotypes.

Sleepiness affects normal social life, which attracts more and more attention. Circadian phenotypes contribute to obvious individual differences in susceptibility to sleepiness. We aimed to identify candidate single nucleotide polymorphisms (SNPs) which may cause circadian phenotypes, elucidate the potential mechanisms, and generate corresponding SNP-gene-pathways. A genome-wide association studies (GWAS) dataset of circadian phenotypes was utilized in the study. Then, the Identify Candidate Causal SNPs and Pathways analysis was employed to the GWAS dataset after quality control filters. Furthermore, genotype-phenotype association analysis was performed with HapMap database. Four SNPs in three different genes were determined to correlate with usual weekday bedtime, totally providing seven hypothetical mechanisms. Eleven SNPs in six genes were identified to correlate with usual weekday sleep duration, which provided six hypothetical pathways. Our results demonstrated that fifteen candidate SNPs in eight genes played vital roles in six hypothetical pathways implicated in usual weekday bedtime and six potential pathways involved in usual weekday sleep duration.

Functional Modeling of Plant Growth Dynamics

Recent advances in automated plant phenotyping have enabled the collection of time series measurements from the same plants of a wide range of traits at different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change among different plant genotypes and different treatment conditions. Two widely used methods of modeling growth with time are pointwise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Pointwise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth; however, these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant life cycle sampled, these assumptions will not always hold true. We have developed a different approach—functional ANOVA—which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compared and validated this approach using data from an experiment measuring the growth of two maize (Zea mays L. ssp. mays) genotypes under two water availability treatments during a 21‐d period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits with time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values with time, functional ANOVA also estimates the derivatives of these curves, e.g., growth rates, simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves among plants phenotyped on non‐overlapping days with little reduction in estimation accuracy. This means that functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.

Identifying the Presence of Prostate Cancer in Individuals with PSA Levels

Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml−1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml−1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features (CD8+CD45RA−CD27−CD28− (CD8+ Effector Memory cells); CD4+CD45RA−CD27−CD28− (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD3−CD19+ (B cells); CD3+CD56+CD8+CD4+ (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.

The early Cambrian fossil embryo Pseudooides is a direct-developing cnidarian, not an early ecdysozoan.

Early Cambrian Pseudooides prima has been described from embryonic and post-embryonic stages of development, exhibiting long germ-band development. There has been some debate about the pattern of segmentation, but this interpretation, as among the earliest records of ecdysozoans, has been generally accepted. Here, we show that the ‘germ band’ of P. prima embryos separates along its mid axis during development, with the transverse furrows between the ‘somites’ unfolding into the polar aperture of the ten-sided theca of Hexaconularia sichuanensis, conventionally interpreted as a scyphozoan cnidarian; co-occurring post-embryonic remains of ecdysozoans are unrelated. We recognize H. sichuanensis as a junior synonym of P. prima as a consequence of identifying these two form-taxa as distinct developmental stages of the same organism. Direct development in P. prima parallels the co-occuring olivooids Olivooides, and Quadrapyrgites and Bayesian phylogenetic analysis of a novel phenotype dataset indicates that, despite differences in their tetra-, penta- and pseudo-hexa-radial symmetry, these hexangulaconulariids comprise a clade of scyphozoan medusozoans, with Arthrochites and conulariids, that all exhibit direct development from embryo to thecate polyp. The affinity of hexangulaconulariids and olivooids to extant scyphozoan medusozoans indicates that the prevalence of tetraradial symmetry and indirect development are a vestige of a broader spectrum of body-plan symmetries and developmental modes that was manifest in their early Phanerozoic counterparts.

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.

New Method Application for Marker-Trait Association Studies in Plants: Partial Least Square Regression Aids Detection of Simultaneous Correlations.

In this work, we investigated the suitability of performing partial least square regression (PLSR) on genotype-phenotype datasets to identify marker-trait associations. We utilized data collected on a cotton (Gossypium hirsutum L.) recombinant inbred line (RIL) mapping population that was evaluated under contrasting irrigation treatments, well-watered and water-limited conditions, in a hot, arid environment in 2012. Two phenotypic data sets were used in combination with the genetic data which consisted of 841 marker loci assigned to 117 linkage groups. The first dataset contained canopy traits that were gathered using a mobile, high-throughput phenotyping platform and included canopy temperature (CT), normalized difference vegetation index (NDVI), and canopy height (CHT) with leaf area index (LAI) being derived from NDVI and CHT measurements. The second phenotypic data set consisted of 14 elemental concentration measurements corresponding to the following elements: P, K, Ca, Mn, Fe, Zn, Ni, Cu, As, Co, Rb, Mo, S, and Mg. To conduct the PSLR analyses we used the “pls” and “pls depot” available in R statistical software version 3.2.4. The PLSR bi plot from the analysis of the first dataset showed that three (LAI, NDVI, and CHT) out of the four canopy traits were highly correlated, and by using multivariate analysis of variance (MANOVA), we detected 22 significant (p<0.01) marker-trait associations for the four traits. In contrast to the canopy trait analysis, our PLSR bi plot for the second dataset showed varying correlations for each of the 14 traits. Because of the lack of distinct trait similarities, MANOVA was not an ideal option to test for marker-trait associations so we implemented a jackknife re sampling technique. Jackknife re sampling failed to detect significant marker effects for several of the 14 elemental concentration traits. Thus, our future work aims to test other re sampling techniques such as boot straping for traits that do not exhibit high correlation. Overall, PLSR was a very informative way to comprehend data structure, displaying correlations within markers, within traits, and between marker and traits in one bi plot. Further studies are still needed to leverage detection of additional variance in correlated datasets and to prevent spurious results. To the best of our knowledge, this is the first time PLSR has been reported in such a context.

A post-gene silencing bioinformatics protocol for plant-defence gene validation and underlying process identification: case study of the Arabidopsis thaliana NPR1

BackgroundAdvances in forward and reverse genetic techniques have enabled the discovery and identification of several plant defence genes based on quantifiable disease phenotypes in mutant populations. Existing models for testing the effect of gene inactivation or genes causing these phenotypes do not take into account eventual uncertainty of these datasets and potential noise inherent in the biological experiment used, which may mask downstream analysis and limit the use of these datasets. Moreover, elucidating biological mechanisms driving the induced disease resistance and influencing these observable disease phenotypes has never been systematically tackled, eliciting the need for an efficient model to characterize completely the gene target under consideration.ResultsWe developed a post-gene silencing bioinformatics (post-GSB) protocol which accounts for potential biases related to the disease phenotype datasets in assessing the contribution of the gene target to the plant defence response. The post-GSB protocol uses Gene Ontology semantic similarity and pathway dataset to generate enriched process regulatory network based on the functional degeneracy of the plant proteome to help understand the induced plant defence response. We applied this protocol to investigate the effect of the NPR1 gene silencing to changes in Arabidopsis thaliana plants following Pseudomonas syringae pathovar tomato strain DC3000 infection. Results indicated that the presence of a functionally active NPR1 reduced the plant’s susceptibility to the infection, with about 99% of variability in Pseudomonas spore growth between npr1 mutant and wild-type samples. Moreover, the post-GSB protocol has revealed the coordinate action of target-associated genes and pathways through an enriched process regulatory network, summarizing the potential target-based induced disease resistance mechanism.ConclusionsThis protocol can improve the characterization of the gene target and, potentially, elucidate induced defence response by more effectively utilizing available phenotype information and plant proteome functional knowledge.

Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection.

Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat diversity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen. Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a diversity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The diversity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker-trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r 2=0.7), suggested a high allelic fixation. Subsequent haplotype analysis for this region found seven haplotype variants, of which two were strongly associated with LR resistance at seedling stage. Similarly, analysis of an APR QTL on chromosome 7B revealed 22 variants, of which 4 were associated with resistance atthe adult plant stage. Furthermore, most of the tested lines in the diversity panel carried 10 or more combined resistance-associated marker alleles, highlighting the potential of allele stacking for long-lasting resistance.

Finding loci associated to partial resistance to white pine blister rust in sugar pine (Pinus lambertiana Dougl.).

White pine blister rust (WPBR) is an exotic disease threatening five-needle pines in North America. In spite of its relatively recent introduction, some five-needle pines such as sugar pine (Pinus lambertiana) have developed both complete (major) gene resistance and partial (quantitative) resistance to WPBR. While significant effort has been dedicated to clone and locate the position of the major gene of WPBR resistance in sugar pine, the genetic basis of quantitative resistance remains largely unknown in all Strobus pines. In this work, we took a preliminary approach to identify potential genotype × phenotype associations using the results of long-term survival and symptoms of infection in both experimental and applied breeding populations. Our study found significant associations between several genes and WPBR disease symptoms such as normal active cankers and blights, important symptoms in the development of partial resistance. No significant associations were found with percentage of survival, probably due to the complex inheritance of the disease and long time to infection. With this study, we hope to lay the ground for further genome-wide association studies using large phenotypic data sets in sugar pine and other Strobus pines.

Investigating successive Australian barley breeding populations for stable resistance to leaf rust.

Genome-wide association studies of barley breeding populations identified candidate minor genes for pairing with the adult plant resistance gene Rph20 to provide stable leaf rust resistance across environments. Stable resistance to barley leaf rust (BLR, caused by Puccinia hordei) was evaluated across environments in barley breeding populations (BPs). To identify genomic regions that can be combined with Rph20 to improve adult plant resistance (APR), two BPs genotyped with the Diversity Arrays Technology genotyping-by-sequencing platform (DArT-seq) were examined for reaction to BLR at both seedling and adult growth stages in Australian environments. An integrated consensus map comprising both first- and second-generation DArT platforms was used to integrate QTL information across two additional BPs, providing a total of four interrelated BPs and 15 phenotypic data sets. This enabled identification of key loci underpinning BLR resistance. The APR gene Rph20 was the only active resistance region consistently detected across BPs. Of the QTL identified, RphQ27 on chromosome 6HL was considered the best candidate for pairing with Rph20. RphQ27 did not align or share proximity with known genes and was detected in three of the four BPs. The combination of RphQ27 and Rph20 was of low frequency in the breeding material; however, strong resistance responses were observed for the lines carrying this pairing. This suggests that the candidate minor gene RphQ27 can interact additively with Rph20 to provide stable resistance to BLR across diverse environments.

Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows

The objective of this study was to evaluate the improvement of the accuracy of estimated breeding values for ability to recycle after calving by using information of genomic markers and phenotypic information of correlated traits. The traits in this study were the interval from calving to first insemination (CFI), based on artificial insemination data, and the interval from calving to first high activity (CFHA), recorded from activity tags, which could better measure ability to recycle after caving. The phenotypic data set included 1,472,313 records from 820,218 cows for CFI, and 36,504 records from 25,733 cows for CFHA. The genomic information was available for 3,159 progeny-tested sires, which were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). Heritability estimates were 0.06 for the interval from calving to first insemination and 0.14 for the interval from calving to first high activity, and the genetic correlation between both traits was strong (0.87). Breeding values were obtained using 4 models: conventional single-trait BLUP; conventional multitrait BLUP with pedigree-based relationship matrix; single-trait single-step genomic BLUP; and multitrait single-step genomic BLUP model with joint relationship matrix combining pedigree and genomic information. The results showed that reliabilities of estimated breeding values (EBV) from single-step genomic BLUP models were about 40% higher than those from conventional BLUP models for both traits. Furthermore, using a multitrait model doubled the reliability of breeding values for CFHA, whereas no gain was observed for CFI. The best model was the multitrait single-step genomic BLUP, which resulted in a reliability of EBV 0.19 for CFHA and 0.14 for CFI. The results indicate that even though a relatively small number of records for CFHA were available, with genomic information and using multitrait model, the reliability of EBV for CFHA is acceptable. Thus, it is feasible to include CFHA in Nordic Holstein breeding evaluations to improve fertility performance.

A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium.

Rare copy number variants contribute significantly to the risk for schizophrenia, with the 22q11.2 locus consistently implicated. Individuals with the 22q11.2 deletion syndrome (22q11DS) have an estimated 25-fold increased risk for schizophrenia spectrum disorders, compared to individuals in the general population. The International 22q11DS Brain Behavior Consortium is examining this highly informative neurogenetic syndrome phenotypically and genomically. Here we detail the procedures of the effort to characterize the neuropsychiatric and neurobehavioral phenotypes associated with 22q11DS, focusing on schizophrenia and subthreshold expression of psychosis. The genomic approach includes a combination of whole-genome sequencing and genome-wide microarray technologies, allowing the investigation of all possible DNA variation and gene pathways influencing the schizophrenia-relevant phenotypic expression. A phenotypically rich data set provides a psychiatrically well-characterized sample of unprecedented size (n=1616) that informs the neurobehavioral developmental course of 22q11DS. This combined set of phenotypic and genomic data will enable hypothesis testing to elucidate the mechanisms underlying the pathogenesis of schizophrenia spectrum disorders.