Advancements in the use of genome‐wide markers have provided unprecedented opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost‐effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)‐based high‐throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time‐series manner. In this experiment, 233 inbred lines from the maize (
- Award ID(s):
- 1954556
- NSF-PAR ID:
- 10403616
- Date Published:
- Journal Name:
- Frontiers in Plant Science
- Volume:
- 13
- ISSN:
- 1664-462X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Zea mays L.) diversity panel were grown in the field under different nitrogen treatments. Unmanned aerial vehicle images were collected during different plant developmental stages throughout the growing season. A workflow for extracting plot‐level images, filtering images to remove nonfoliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the workflow, about 100,000 plot‐level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield‐related traits. The genome‐wide association study was performed, resulting inn = 29 unique genomic regions associated with image extracted traits from two or more of the 12 total time points. A candidate geneZm00001d031997 , a maize homolog of theArabidopsis HCF244 (high chlorophyll fluorescence 244 ), located underneath the leading single nucleotide polymorphisms of the canopy coverage associated signals were repeatedly detected under both nitrogen conditions. The plot‐level time‐series phenotypic data and the trait‐associated genes provide great opportunities to advance plant science and to facilitate plant breeding. -
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Abstract Mungbean (
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Abstract Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 $$\%$$ % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer’s Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.more » « less
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