scholarly journals Haplotype analysis of data from UAV imagery of rice MAGIC population for the trait dissection of biomass and plant architecture

2020 ◽  
Author(s):  
Daisuke Ogawa ◽  
Toshihiro Sakamoto ◽  
Hiroshi Tsunematsu ◽  
Noriko Kanno ◽  
Yasunori Nonoue ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Early Stage ◽  
Association Studies ◽  
Image Data ◽  
Shoot Biomass ◽  
Genome Wide Association Studies ◽  
Vegetation Fraction ◽  
High Throughput Phenotyping ◽  
Uav Images

Abstract Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four QTL for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTL. Further genetic characterization revealed the relationships between these QTL and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stage of rice in the field provides insight into plant growth, architecture, final biomass and yield.

scholarly journals High-resolution genome-wide functional dissection of transcriptional regulatory regions in human

2017 ◽  
Author(s):  
Xinchen Wang ◽  
Liang He ◽  
Sarah Goggin ◽  
Alham Saadat ◽  
Li Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Association Studies ◽  
Regulatory Elements ◽  
Regulatory Sequence ◽  
Genome Wide Association Studies ◽  
Sequence Motifs ◽  
High Definition ◽  
Regulatory Regions ◽  
Genome Wide

AbstractGenome-wide epigenomic maps revealed millions of regions showing signatures of enhancers, promoters, and other gene-regulatory elements1. However, high-throughput experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited in their scale and length of regions tested. Here, we present a new method, HiDRA (High-Definition Reporter Assay), that overcomes these limitations by combining components of Sharpr-MPRA2 and STARR-Seq3 with genome-wide selection of accessible regions from ATAC-Seq4. We used HiDRA to test ~7 million DNA fragments preferentially selected from accessible chromatin in the GM12878 lymphoblastoid cell line. By design, accessibility-selected fragments were highly overlapping (up to 370 per region), enabling us to pinpoint driver regulatory nucleotides by exploiting subtle differences in reporter activity between partially-overlapping fragments, using a new machine learning model SHARPR2. Our resulting maps include ~65,000 regions showing significant enhancer function and enriched for endogenous active histone marks (including H3K9ac, H3K27ac), regulatory sequence motifs, and regions bound by immune regulators. Within them, we discover ~13,000 high-resolution driver elements enriched for regulatory motifs and evolutionarily-conservednucleotides, and help predict causal genetic variants underlying disease from genome-wide association studies. Overall, HiDRA provides a general, scalable, high-throughput, and high-resolution approach for experimental dissection of regulatory regions and driver nucleotides in the context of human biology and disease.

scholarly journals PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records

2020 ◽  
Vol 27 (11) ◽  
pp. 1675-1687
Author(s):  
Neil S Zheng ◽  
QiPing Feng ◽  
V Eric Kerchberger ◽  
Juan Zhao ◽  
Todd L Edwards ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Knowledge Base ◽  
High Throughput ◽  
Association Studies ◽  
Research Quality ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Health Records ◽  
High Throughput Phenotyping ◽  
Electronic Health

Abstract Objective Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. Materials and Methods PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. Results In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online. Conclusions PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals High Resolution Ancestry Deconvolution for Next Generation Genomic Data

2021 ◽  
Author(s):  
Helgi Hilmarsson ◽  
Arvind S. Kumar ◽  
Richa Rastogi ◽  
Carlos D. Bustamante ◽  
Daniel Mas Montserrat ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Association Studies ◽  
Training Data ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Next Generation ◽  
Genome Data ◽  
Computational Performance ◽  
Genetic Risk Prediction

ABSTRACTAs genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

scholarly journals High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 374 ◽  
Author(s):  
Anna Dziewulska ◽  
Aneta Dobosz ◽  
Agnieszka Dobrzyn
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Islets ◽  
High Throughput ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genomic Landscape ◽  
A Genome ◽  
Next Generation Sequencing Ngs

Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.

Abstract 144: Zebrafish Larvae: A Model System for Early Stage Atherosclerosis?

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Manoj K Bandaru ◽  
Petter Ranefall ◽  
Anastasia Emmanouilidou ◽  
Tiffany Klingström ◽  
Lingjie Tao ◽  
...  
Keyword(s):  
Early Stage ◽  
Association Studies ◽  
Oxidized Ldl ◽  
Control Diet ◽  
High Energy ◽  
Model System ◽  
Genome Wide Association Studies ◽  
Lipid Deposition ◽  
Zebrafish Larvae ◽  
Starting Point

Objectives: Published results show that overfeeding zebrafish larvae on a high-cholesterol diet (HCD) can result in hypercholesterolemia and sub-endothelial lipid deposition in macrophages and other cell types. However, results are so far based on small samples, and the atherogenic response has been heterogeneous. We aim to use zebrafish larvae for large-scale, CRISPR-Cas9-based genetic screens, using results from genome wide association studies for coronary heart disease as a starting point. Firstly however, we need to ensure the model system is appropriate and robust. Therefore, we examined the effect of a high-energy diet (HED) and HCD on vascular lipid deposition in a larger number of larvae (n=241). Methods: Starting at 5 days post fertilization (dpf), ~30 larvae/tank were fed 2x/day on: 1) 5 mg control diet (CD; n=33); 2) 15 mg control diet (HED; n=90); or 3) 15 mg control diet enriched with 4% cholesterol (HCD; n=94). At 14-17 dpf, larvae were soaked in monodansylpentane cadaverase - a lipid staining dye - for 45 min, before imaging the dorsal aorta and caudal vein with a Leica SP5 confocal microscope. We used a custom written script in Cell Profiler to quantify the surface area of lipid deposits in the vasculature. Results: Manual annotation of vascular lipid deposition in 30 images (10 randomly selected images per dietary condition) allowed us to calculate the sensitivity (36%) and specificity (71%) of the Cell Profiler script. Subsequent analyses showed that HED (p=0.004) and HCD (p=0.001) fed larvae have significantly more vascular lipid deposition than CD fed larvae after adjusting for age, batch and vessel length. There was no difference in vascular lipid deposition between HED and HCD fed larvae (p=0.11). Discussion and conclusion: Our results confirm that zebrafish larvae represent a promising model system for early-stage atherosclerosis. In addition, they show that enriching the diet with cholesterol is not required to prompt atherogenesis. Future directions: In the next few months, we will examine if overfeeding also triggers vascular infiltration by macrophages, neutrophils and oxidized LDL cholesterol, and if atherogenesis can be prevented or reduced by treating larvae with statins and/or ezetimibe, using our new, automated imaging setup.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

2019 ◽  
Vol 132 (6) ◽  
pp. 1705-1720 ◽  
Author(s):  
Jin Sun ◽  
Jesse A. Poland ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Philomin Juliana ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Early Stage ◽  
Wheat Grain ◽  
High Throughput Phenotyping ◽  
Selection For

scholarly journals Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Austin A. Dobbels ◽  
Aaron J. Lorenz
Keyword(s):  
Iron Deficiency ◽  
Data Collection ◽  
Data Processing ◽  
High Throughput ◽  
Image Data ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Iron Deficiency Chlorosis ◽  
High Throughput Phenotyping ◽  
Aircraft System

In the original article [1], under the subheading “Image data processing”, last paragraph, last sentence that reads as “The least …… data collection” was incorrectly published. The correct sentence should read as “Least-significant differences (P < 0.20) were calculated for all 36 trials on both ground-based and UAS-image based scores for both dates of data collection.” The original article has been corrected.

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