Selection for Silage Yield and Composition Did Not Affect Genomic Diversity Within the Wisconsin Quality Synthetic Maize Population

Maize silage is forage of high quality and yield, and represents the second most important use of maize in the United States. The Wisconsin Quality Synthetic (WQS) maize population has undergone five cycles of recurrent selection for silage yield and composition, resulting in a genetically improved population. The application of high-density molecular markers allows breeders and geneticists to identify important loci through association analysis and selection mapping, as well as to monitor changes in the distribution of genetic diversity across the genome. The objectives of this study were to identify loci controlling variation for maize silage traits through association analysis and the assessment of selection signatures and to describe changes in the genomic distribution of gene diversity through selection and genetic drift in theWQS recurrent selection program. We failed to find any significant marker-trait associations using the historical phenotypic data from WQS breeding trials combined with 17,719 high-quality, informative single nucleotide polymorphisms. Likewise, no strong genomic signatures were left by selection on silage yield and quality in the WQS despite genetic gain for these traits. These results could be due to the genetic complexity underlying these traits, or the role of selection on standing genetic variation. Variation in loss of diversity through drift was observed across the genome. Some large regions experienced much greater loss in diversity than what is expected, suggesting limited recombination combined with small populations in recurrent selection programs could easily lead to fixation of large swaths of the genome

Fostering Active Learning in an International Joint Classroom: A Case Study

Engaging students in an international online setting that is interdisciplinary and culturally diverse is a challenge. A joint classroom between German and Ugandan universities used a formative assessment approach paired with active learning elements to foster individual and peer learning in an international virtual setting. A survey at three different times across the semester explored students’ perceptions towards the value of the active learning activities and evaluated how perceptions changed over time. Overall, students enjoyed the diverse active learning activities and perceived value toward their success in class. This was more pronounced and unidirectional for individual tasks than it was for group work. In addition to the findings of the structured survey, observation and feedback indicated that other elements contributed to effective course delivery. These included clear and frequent communication to the students from the primary instructor, prompt feedback from the instructor on graded exercises, such as a reflective learning diary and ungraded quizzes, and student confidence that sincere effort would achieve a good grade

An R Framework for the Partitioning of Linkage Disequilibrium between and Within Populations

Patterns of linkage disequilibrium (LD) across the genome result from a myriad of contributing factors including selection and genetic drift. Natural selection can increase LD near individually selected loci, or it can influence LD between epistatically selected groups of loci. Statistics have previously been derived which compare levels of linkage disequilibrium in subpopulations relative to the total population. These statistics may be leveraged to identify loci that may be under selection or epistatic selection. This is a powerful approach, but to date no framework exists to support its use on a genome-wide scale. We present ohtadstats, an R package designed to facilitate the implementation of Ohta’s D statistics in a variety of use cases. Statistics calculated by this package can be used to determine whether a locus is under selection or not, and can provide insight into the nature of the selection that is taking place (hard sweep or epistatic selection). This package is available on the Comprehensive R Archive Network (CRAN).   Funding statement: This research was supported by funding from the USDA Agricultural Research Service. PFP is funded by the University of Missouri Life Sciences Fellowship and a training grant from the National Institute of Health (T32GM008396)

Predation and infanticide influence ideal free choice by a parrot occupying heterogeneous tropical habitats

The ideal free distribution (IFD) predicts that organisms will disperse to sites that maximize their fitness based on availability of resources. Habitat heterogeneity underlies resource variation and influences spatial variation in demography and the distribution of populations. We relate nest site productivity at multiple scales measured over a decade to habitat quality in a box-nesting population of Forpus passerinus (green-rumped parrotlets) in Venezuela to examine critical IFD assumptions. Variation in reproductive success at the local population and neighborhood scales had a much larger influence on productivity (fledglings per nest box per year) than nest site or female identity. Habitat features were reliable cues of nest site quality. Nest sites with less vegetative cover produced greater numbers of fledglings than sites with more cover. However, there was also a competitive cost to nesting in high-quality, low-vegetative cover nest boxes, as these sites experienced the most infanticide events. In the lowland local population, water depth and cover surrounding nest sites were related with F. passerinus productivity. Low vegetative cover and deeper water were associated with lower predation rates, suggesting that predation could be a primary factor driving habitat selection patterns. Parrotlets also demonstrated directional dispersal. Pairs that changed nest sites were more likely to disperse from poor-quality nest sites to high-quality nest sites rather than vice versa, and juveniles were more likely to disperse to, or remain in, the more productive of the two local populations. Parrotlets exhibited three characteristics fundamental to the IFD: habitat heterogeneity within and between local populations, reliable habitat cues to productivity, and active dispersal to sites of higher fitness

A Primer on High-Throughput Computing for Genomic Selection

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl, and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general-purpose computation on a graphics processing unit provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin–Madison, which can be leveraged for genomic selection, in terms of central processing unit capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general-purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to realized genetic gain). Eventually, HTC may change our view of data analysis as well as decision-making in the post-genomic era of selection programs in animals and plants, or in the study of complex diseases in humans

The evolutionary history of wild, domesticated, and feral Brassica oleracea (Brassicaceae)

Understanding the evolutionary history of crops, including identifying wild relatives, helps to provide insight for conservation and crop breeding efforts. Cultivated Brassica oleracea has intrigued researchers for centuries due to its wide diversity in forms, which include cabbage, broccoli, cauliflower, kale, kohlrabi, and Brussels sprouts. Yet, the evolutionary history of this species remains understudied. With such different vegetables produced from a single species, B. oleracea is a model organism for understanding the power of artificial selection. Persistent challenges in the study of B. oleracea include conflicting hypotheses regarding domestication and the identity of the closest living wild relative. Using newly generated RNA-seq data for a diversity panel of 224 accessions, which represents 14 different B. oleracea crop types and nine potential wild progenitor species, we integrate phylogenetic and population genetic techniques with ecological niche modeling, archaeological, and literary evidence to examine relationships among cultivars and wild relatives to clarify the origin of this horticulturally important species. Our analyses point to the Aegean endemic B. cretica as the closest living relative of cultivated B. oleracea, supporting an origin of cultivation in the Eastern Mediterranean region. Additionally, we identify several feral lineages, suggesting that cultivated plants of this species can revert to a wild-like state with relative ease. By expanding our understanding of the evolutionary history in B. oleracea, these results contribute to a growing body of knowledge on crop domestication that will facilitate continued breeding efforts including adaptation to changing environmental conditions

MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken

Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO) and more recently by Medical Subject Headings (MeSH). Here we report a suite of MeSH packages for chicken in Bioconductor and illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published data sets that were employed represent (i) differentially expressed genes and (ii) candidate genes under selective sweep or epistatic selection. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis but also that MeSH is able to further enrich the representation of biological knowledge and often provide more interpretable results. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies as well as semantic similarities among selected genes. These yielded the similarity levels between significant functional terms, and the annotation of each gene yielded the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits

Predation and infanticide influence ideal free choice by a parrot occupying heterogeneous tropical habitats

The ideal free distribution (IFD) predicts that organisms will disperse to sites that maximize their fitness based on availability of resources. Habitat heterogeneity underlies resource variation and influences spatial variation in demography and the distribution of populations. We relate nest site productivity at multiple scales measured over a decade to habitat quality in a box-nesting population of Forpus passerinus (green-rumped parrotlets) in Venezuela to examine critical IFD assumptions. Variation in reproductive success at the local population and neighborhood scales had a much larger influence on productivity (fledglings per nest box per year) than nest site or female identity. Habitat features were reliable cues of nest site quality. Nest sites with less vegetative cover produced greater numbers of fledglings than sites with more cover. However, there was also a competitive cost to nesting in high-quality, low-vegetative cover nest boxes, as these sites experienced the most infanticide events. In the lowland local population, water depth and cover surrounding nest sites were related with F. passerinus productivity. Low vegetative cover and deeper water were associated with lower predation rates, suggesting that predation could be a primary factor driving habitat selection patterns. Parrotlets also demonstrated directional dispersal. Pairs that changed nest sites were more likely to disperse from poor-quality nest sites to high-quality nest sites rather than vice versa, and juveniles were more likely to disperse to, or remain in, the more productive of the two local populations. Parrotlets exhibited three characteristics fundamental to the IFD: habitat heterogeneity within and between local populations, reliable habitat cues to productivity, and active dispersal to sites of higher fitness