Wisconsin diversity panel phenotypes: spoken descriptions of plants and supporting data.

OBJECTIVES: Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. DATA DESCRIPTION: Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants' (whose personal data are protected for ethical reasons) and volunteers' observation transcripts, volunteers' audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University's Agricultural Engineering and Agronomy Research Farms.

The maize Ga1-s allele confers protection against ga1 pollen in popcorn and dent corn.

Because corn pollen can be carried great distances by wind, maintaining genetic purity of corn grain is challenging. The challenge is substantially reduced in popcorn, which carries the Ga1-s allele preventing pollination by ga1 plants, which include the vast majority of non-popcorn commercial maize varieties in the U.S.. Ga1-s can be transferred into dent corn but the effectiveness of the Ga1-s allele in popcorn and dent corn has never been compared, which is important because each are regulated differently regarding GMO contamination. We compared pollen exclusion of commercial popcorn hybrids, Ga1-s dent corn hybrids and normal dent corn hybrids for their ability to exclude ga1 pollen using a sensitive field-based assay. While both popcorn and Ga1-s dent corn had significantly better pollen exclusion than normal dent corn, popcorn was significantly better than Ga1-s dent corn on average. Some Ga1-s dent hybrids excluded as well or better than some popcorn lines suggesting that identification of hybrids comparable to popcorn is possible. The information in this study will support revised gene purity regulations potentially decreasing costs and increasing genetic purity of organic corn.

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize.

Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

Inheritance of 4-hydroxyphenylpyruvate dioxygenase inhibitor herbicide resistance in an Amaranthus tuberculatus population from Iowa, USA.

Waterhemp (Amaranthus tuberculatus (Moq.) J.D. Sauer) is a weed prevalent in the Midwest United States and can cause yield losses up to 74% in maize (Zea mays L.) and 56% in soybean (Glycine max (L.) Merr.). An important adaptive trait commonly found in waterhemp is the ability to evolve herbicide resistance and waterhemp populations have evolved resistance to six herbicide sites of action. In 2011, two waterhemp populations were discovered resistant to p-hydroxyphenylpyruvate-dioxygenase (HPPD, EC 1.13.11.27) inhibitor herbicides. We reciprocally crossed a known HPPD-resistant waterhemp population with a known HPPD-susceptible waterhemp population and then intermated the F1 families to established a pseudo-F2 generation. We challenged the parent, F1 and pseudo-F2 generations against four HPPD-inhibiting herbicide rates (mesotrione). Our results suggest the HPPD-resistance trait is polygenic. Furthermore, the number of genes involved with the herbicide resistance increase at higher herbicide rates. These data indicated at least one dominant allele at each major locus is required to confer HPPD herbicide resistance in waterhemp. Using different waterhemp populations and methodologies, this study confirms the reported "complex" HPPD resistance inheritance while providing new information in the response of HPPD-resistant waterhemp to HPPD herbicides.

Selection Signatures Underlying Dramatic Male Inflorescence Transformation During Modern Hybrid Maize Breeding.

Inflorescence capacity plays a crucial role in reproductive fitness in plants, and in production of hybrid crops. Maize is a monoecious species bearing separate male and female flowers (tassel and ear, respectively). The switch from open-pollinated populations of maize to hybrid-based breeding schemes in the early 20th century was accompanied by a dramatic reduction in tassel size, and the trend has continued with modern breeding over the recent decades. The goal of this study was to identify selection signatures in genes that may underlie this dramatic transformation. Using a population of 942 diverse inbred maize accessions and a nested association mapping population comprising three 200-line biparental populations, we measured 15 tassel morphological characteristics by manual and image-based methods. Genome-wide association studies identified 242 single nucleotide polymorphisms significantly associated with measured traits. We compared 41 unselected lines from the Iowa Stiff Stalk Synthetic (BSSS) population to 21 highly selected lines developed by modern commercial breeding programs, and found that tassel size and weight were reduced significantly. We assayed genetic differences between the two groups using three selection statistics: cross population extended haplotype homozogysity, cross-population composite likelihood ratio, and fixation index. All three statistics show evidence of selection at genomic regions associated with tassel morphology relative to genome-wide null distributions. These results support the tremendous effect, both phenotypic and genotypic, that selection has had on maize male inflorescence morphology.

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.

BACKGROUND: Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. RESULTS: To address this challenge, we have developed a Microrarray PowerAtlas. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC). CONCLUSION: This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

BACKGROUND: Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. RESULTS: Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. CONCLUSION: HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

The Genomic Impacts of Drift and Selection for Hybrid Performance in Maize.

Although maize is naturally an outcrossing organism, modern breeding utilizes highly inbred lines in controlled crosses to produce hybrids. The U.S. Department of Agriculture's reciprocal recurrent selection experiment between the Iowa Stiff Stalk Synthetic (BSSS) and the Iowa Corn Borer Synthetic No. 1 (BSCB1) populations represents one of the longest running experiments to understand the response to selection for hybrid performance. To investigate the genomic impact of this selection program, we genotyped the progenitor lines and >600 individuals across multiple cycles of selection using a genome-wide panel of ∼40,000 SNPs. We confirmed previous results showing a steady temporal decrease in genetic diversity within populations and a corresponding increase in differentiation between populations. Thanks to detailed historical information on experimental design, we were able to perform extensive simulations using founder haplotypes to replicate the experiment in the absence of selection. These simulations demonstrate that while most of the observed reduction in genetic diversity can be attributed to genetic drift, heterozygosity in each population has fallen more than expected. We then took advantage of our high-density genotype data to identify extensive regions of haplotype fixation and trace haplotype ancestry to single founder inbred lines. The vast majority of regions showing such evidence of selection differ between the two populations, providing evidence for the dominance model of heterosis. We discuss how this pattern is likely to occur during selection for hybrid performance and how it poses challenges for dissecting the impacts of modern breeding and selection on the maize genome.

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

A design and statistical perspective on microarray gene expression studies in nutrition: the need for playful creativity and scientific hard-mindedness.

OBJECTIVES: Our purpose is to highlight some of the past and potential future uses of microarray in nutrition research, while also commenting on some aspects of the design conduct and analysis of microarray data that will leave to improved data quality. METHODS: In this review article we outline some of the aspects of microarray experimentation that must be considered before and during these experiments. These topics include: identification of the experiment's objective (hypothesis), the experimental design, sample size, statistical analysis, data verification, data handling, and experimental interpretation. RESULTS: In order to illustrate the principles we outline in this article we use the methods to layout the design of a microarray experiment to study one aspect of the observation that a diet high in soy is associated with lower rates of breast cancer. CONCLUSIONS: Microarrays are a very powerful tool for studying virtually every nutrition-related disease and trait and can provide valuable insights that are not obtainable with other techniques. However, unless nutrition researchers conduct their studies with scientific hard-mindedness, the studies will be of lower power at least if not completely misleading.

Empirical Bayes estimation of gene-specific effects in micro-array research.

Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http://www.soph.uab.edu/ssg.asp?id=1087.