USDA ARS Corn Breeding

The United States Department of AgricultureAgricultural Research Service (USDA ARS) evaluated 5,412 experimental corn research plots at the Southeast Research Farm in 2010 representing three research projects within USDA ARS: Germplasm Enhancement of Maize (GEM). The objective of the GEM project is to increase the diversity of U.S. maize germplasm utilized by producers, global end-users, and consumers. The mission is accomplished though a collaborative effort between USDA-ARS and both public and private research scientists. Genetic Analysis of Selection Response in Maize Populations. The objective of this project is to develop more efficient strategies to increase maize production. The primary emphasis is on understanding the genetics of adaptation to high plant density. Breeding High-Quality Corn for LowInput and Organic Farming Systems. The primary objective of this project is to develop germplasm for low-input and organic farming systems through conventional breeding

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 [1]. 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

Maize Cultivar Performance under Diverse Organic Production Systems

Maize (Zea mays L.) performance can vary widely between different production systems. The need for high-performing hybrids for organic systems with wide adaptation to various macroenvironments is becoming increasingly important. The goal of this study was to characterize inbred lines developed by distinct breeding programs for their combining ability and hybrid yield performance across diverse organic environments. Parent lines were selected from five different breeding programs to give a sample of publically available germplasm with potential for use in organic production systems with expired plant variety protection (Ex-PVP) and current commercial inbreds as benchmarks. A North Carolina Design II mating design was used to produce all possible cross combinations between seven lines designated as males and seven lines designated as females. A significantly positive general combining ability for the female inbred UHF134 suggests that it performs well in hybrid combination. Significant general combining ability was not observed for any male inbred line in this study. Several significantly positive specific combining abilities suggest that nonadditive genetic effects play an important role in determining yield in this germplasm. Further analysis revealed that hybrids containing either an Ex-PVP line or a commercial inbred line were on average superior to hybrids containing only inbreds developed by the cooperators of this study. This demonstrates the utility of testing inbreds from diverse sources when developing hybrids for organic production systems

Interval Estimation in a Finite Mixture Model: Modeling P-values in Multiple Testing Applications

The performance of interval estimates in a uniform-beta mixture model is evaluated using three computational strategies. Such a model has found use when modeling a distribution of P-values from multiple testing applications. The number of P-values and the closeness of a parameter to the boundary of its space both play a role in the precision of parameter estimates as does the “nearness” of the beta-distribution component to the uniform distribution. Three computational strategies are compared for computing interval estimates with each one having advantages and disadvantages for cases considered here