Student Workshop: Built by Bama, Anne Larimer, Addie Ziegler, and Logan Thompson

If you are looking for ways to improve your professional development opportunities within your chapter, let us be an inspiration! At The University of Alabama, our chapter mission is to cultivate exceptional leaders and build better information systems professionals. We embody this by providing our members countless opportunities to improve their skills and be more prepared for a professional environment. We are very proud of the network we have created and the leaders we are sending into the world, and we believe that our processes should be shared. In this workshop we will discuss our organization structure, events we are proud of, and how our passion is helping create the next generation of IS professionals. For our 349 members, this looks like resume books, mock interviews, specialized affinity meetings, and corporate partnerships. Our processes and dedication have resulted in a 98% placement rate for our members, and we can’t wait to share it with our AIS community

Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction

Acción : diario de Teruel y su provincia: Año III Número 633 - (11/12/34)

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.</p

Molecular Epidemiology of Canine Parvovirus, Europe

Canine parvovirus (CPV), which causes hemorrhagic enteritis in dogs, has 3 antigenic variants: types 2a, 2b, and 2c. Molecular method assessment of the distribution of the CPV variants in Europe showed that the new variant CPV-2c is widespread in Europe and that the viruses are distributed in different countries

Brd1 Gene in Maize Encodes a Brassinosteroid C-6 Oxidase

The role of brassinosteroids in plant growth and development has been well-characterized in a number of plant species. However, very little is known about the role of brassinosteroids in maize. Map-based cloning of a severe dwarf mutant in maize revealed a nonsense mutation in an ortholog of a brassinosteroid C-6 oxidase, termed brd1, the gene encoding the enzyme that catalyzes the final steps of brassinosteroid synthesis. Homozygous brd1–m1 maize plants have essentially no internode elongation and exhibit no etiolation response when germinated in the dark. These phenotypes could be rescued by exogenous application of brassinolide, confirming the molecular defect in the maize brd1-m1 mutant. The brd1-m1 mutant plants also display alterations in leaf and floral morphology. The meristem is not altered in size but there is evidence for differences in the cellular structure of several tissues. The isolation of a maize mutant defective in brassinosteroid synthesis will provide opportunities for the analysis of the role of brassinosteroids in this important crop system

Advances in plant phenomics: From data and algorithms to biological insights

The measurement of the characteristics of living organisms is re- ferred to as phenotyping (Singh et al., 2016). While the use of phe- notyping in plant biology and genetics can be traced back at least to Gregor Mendel sorting and counting peas by shape and pod color 160 years ago, addressing current questions in plant biology, genet- ics, and breeding often requires increasingly precise phenotyping of a wide range of traits. Accurate phenotyping has played a role in both novel discoveries about the fundamental biology of plants and the development of improved crop varieties around the world. With the advent of inexpensive genotyping tools, crop functional genomics has entered the “big data” era, but efficient large-scale phenotyping is still an impediment hindering plant functional genomics. The precise measurement of plant traits both throughout the growth cycle and across environments is expensive and labor intensive. A convergence of interdisciplinary efforts has led to the development of new technologies for nondestructive phenotyping in plants to measure large numbers of traits accurately with higher throughput (Close and Last, 2011). Improvements in imaging and automation, as well as in data processing and analytics, are helping to fill significant gaps in efforts to employ these new technologies to connect genetic variation with phenotypes (Yang et al., 2020). In recent years, plant phenomics research has transitioned from the development of methods and molecular genetic analysis of model plants in controlled environments toward accelerated efforts for applications in plant breeding, association studies, and stress phenotyping in crops grown under complex field conditions (Costaet al., 2018). In this special issue, “Advances in Plant Phenomics: From Data and Algorithms to Biological Insights,” we present six papers that capture plant phenomics extending to multiple scales, from field-wide traits, to individual plots or plants, to specific gene interactions. In the context of field-scale image acquisition and processing, one of the first challenges that must be addressed in drone-based imaging of agricultural fields is turning free-flown images acquired over an area into a single mosaic image from which phenotypes can be extracted. Current methods rely mostly on the ability to locate each pixel in space, requiring costly global positioning systems (GPS) and/or inertial measurement units (IMU) to track the posi- tion of ground control points relative to the image acquisition de- vice. These approaches are computationally taxing, demand larger data storage, and require the purchase of software licenses, lead- ing to a high barrier of entry. Aktar et al. (2020) have developed a method called Video Mosaicking and summariZation (VMZ) to provide an alternative pipeline that is faster, less computationally de- manding, and much cheaper to implement. The authors show that compared to other methods, VMZ not only works faster but also produces mosaics with superior quality. This work, demonstrated here in maize, begins to democratize drone-based phenotyping for large- and small-scale field researchers across multiple species

Advances in plant phenomics: From data and algorithms to biological insights

The measurement of the characteristics of living organisms is referred to as phenotyping (Singh et al., 2016). While the use of phenotyping in plant biology and genetics can be traced back at least to Gregor Mendel sorting and counting peas by shape and pod color 160 years ago, addressing current questions in plant biology, genetics, and breeding often requires increasingly precise phenotyping of a wide range of traits. Accurate phenotyping has played a role in both novel discoveries about the fundamental biology of plants and the development of improved crop varieties around the world. With the advent of inexpensive genotyping tools, crop functional genomics has entered the “big data” era, but efficient large-scale phenotyping is still an impediment hindering plant functional genomics. The precise measurement of plant traits both throughout the growth cycle and across environments is expensive and labor intensive. A convergence of interdisciplinary efforts has led to the development of new technologies for nondestructive phenotyping in plants to measure large numbers of traits accurately with higher throughput (Close and Last, 2011). Improvements in imaging and automation, as well as in data processing and analytics, are helping to fill significant gaps in efforts to employ these new technologies to connect genetic variation with phenotypes (Yang et al., 2020). In recent years, plant phenomics research has transitioned from the development of methods and molecular genetic analysis of model plants in controlled environments toward accelerated efforts for applications in plant breeding, association studies, and stress phenotyping in crops grown under complex field conditions (Costa et al., 2018). In this special issue, “Advances in Plant Phenomics: From Data and Algorithms to Biological Insights,” we present six papers that capture plant phenomics extending to multiple scales, from field-wide traits, to individual plots or plants, to specific gene interactions