Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Exploring impact of recombination landscapes on breeding outcomes

Plant breeding relies on crossing-over to create novel combinations of alleles needed to confer increased productivity and other desired traits in new varieties. However, crossover (CO) events are rare, as usually only one or two of them occur per chromosome in each generation. In addition, COs are not distributed evenly along chromosomes. In plants with large genomes, which includes most crops, COs are predominantly formed close to chromosome ends, and there are few COs in the large chromosome swaths around centromeres. This situation has created interest in engineering CO landscape to improve breeding efficiency. Methods have been developed to boost COs globally by altering expression of anti-recombination genes and increase CO rates in certain chromosome parts by changing DNA methylation patterns. In addition, progress is being made to devise methods to target COs to specific chromosome sites. We review these approaches and examine using simulations whether they indeed have the capacity to improve efficiency of breeding programs. We found that the current methods to alter CO landscape can produce enough benefits for breeding programs to be attractive. They can increase genetic gain in recurrent selection and significantly decrease linkage drag around donor loci in schemes to introgress a trait from unimproved germplasm to an elite line. Methods to target COs to specific genome sites were also found to provide advantage when introgressing a chromosome segment harboring a desirable quantitative trait loci. We recommend avenues for future research to facilitate implementation of these methods in breeding programs.

Quantitative Genetic Analysis of Interactions in the Pepper-Phytophthora capsici Pathosystem

The development of pepper cultivars with durable resistance to the oomycete Phytophthora capsici has been challenging due to differential interactions between the species that allow certain pathogen isolates to cause disease on otherwise resistant host genotypes. Currently, little is known about the pathogen genes involved in these interactions. To investigate the genetic basis of P. capsici virulence on individual pepper genotypes, we inoculated sixteen pepper accessions, representing commercial varieties, sources of resistance, and host differentials, with 117 isolates of P. capsici, for a total of 1,864 host-pathogen combinations. Analysis of disease outcomes revealed a significant effect of inter-species genotype-by-genotype interactions, although these interactions were quantitative rather than qualitative in scale. Isolates were classified into five pathogen subpopulations, as determined by their genotypes at over 60,000 single-nucleotide polymorphisms (SNPs). While absolute virulence levels on certain pepper accessions significantly differed between subpopulations, a multivariate phenotype reflecting relative virulence levels on certain pepper genotypes compared with others showed the strongest association with pathogen subpopulation. A genome-wide association study (GWAS) identified four pathogen loci significantly associated with virulence, two of which colocalized with putative RXLR effector genes and another with a polygalacturonase gene cluster. All four loci appeared to represent broad-spectrum virulence genes, as significant SNPs demonstrated consistent effects regardless of the host genotype tested. Host genotype-specific virulence variants in P. capsici may be difficult to map via GWAS with all but excessively large sample sizes, perhaps controlled by genes of small effect or by multiple allelic variants that have arisen independently. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs

The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.

Breedbase: a digital ecosystem for modern plant breeding

Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.

Genomic Prediction Strategies for Dry-Down-Related Traits in Maize

For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24-0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set.

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.

Simulation of sugar kelp (Saccharina latissima) breeding guided by practices to accelerate genetic gains

Though Saccharina japonica cultivation has been established for many decades in East Asian countries, the domestication process of sugar kelp (Saccharina latissima) in the Northeast United States is still at its infancy. In this study, by using data from our breeding experience, we will demonstrate how obstacles for accelerated genetic gain can be assessed using simulation approaches that inform resource allocation decisions. Thus far, we have used 140 wild sporophytes that were sampled in 2018 from the northern Gulf of Maine to southern New England. From these sporophytes, we sampled gametophytes and made and evaluated over 600 progeny sporophytes from crosses among the gametophytes in 2019 and 2020. The biphasic life cycle of kelp gives a great advantage in selective breeding as we can potentially select both on the sporophytes and gametophytes. However, several obstacles exist, such as the amount of time it takes to complete a breeding cycle, the number of gametophytes that can be maintained in the laboratory, and whether positive selection can be conducted on farm-tested sporophytes. Using the Gulf of Maine population characteristics for heritability and effective population size, we simulated a founder population of 1,000 individuals and evaluated the impact of overcoming these obstacles on rate of genetic gain. Our results showed that key factors to improve current genetic gain rely mainly on our ability to induce reproduction of the best farm-tested sporophytes, and to accelerate the clonal vegetative growth of released gametophytes so that enough gametophyte biomass is ready for making crosses by the next growing season. Overcoming these challenges could improve rates of genetic gain more than 2-fold. Future research should focus on conditions favorable for inducing spring reproduction, and on increasing the amount of gametophyte tissue available in time to make fall crosses in the same year.

Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava

Introduction

Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression.

Methods

To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava.

Results

Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction.

Conclusion

We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.

Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava

Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades; however, genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in nonmodel species where resources are less abundant. Genotype imputation makes it possible to infer whole-genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The practical haplotype graph (PHG) is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the PHG to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The PHG achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross-validation testing. The PHG showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.

Quantitative dissection of color patterning in the foliar ornamental coleus

Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.

Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize

In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10-30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program

Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia

Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

Empirical Comparison of Tropical Maize Hybrids Selected Through Genomic and Phenotypic Selections

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%-23% higher grain yield under WW and 51%-52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates.

Comparison of computational models for assessing conservation of gene expression across species

Assessing conservation/divergence of gene expression across species is important for the understanding of gene regulation evolution. Although advances in microarray technology have provided massive high-dimensional gene expression data, the analysis of such data is still challenging. To date, assessing cross-species conservation of gene expression using microarray data has been mainly based on comparison of expression patterns across corresponding tissues, or comparison of co-expression of a gene with a reference set of genes. Because direct and reliable high-throughput experimental data on conservation of gene expression are often unavailable, the assessment of these two computational models is very challenging and has not been reported yet. In this study, we compared one corresponding tissue based method and three co-expression based methods for assessing conservation of gene expression, in terms of their pair-wise agreements, using a frequently used human-mouse tissue expression dataset. We find that 1) the co-expression based methods are only moderately correlated with the corresponding tissue based methods, 2) the reliability of co-expression based methods is affected by the size of the reference ortholog set, and 3) the corresponding tissue based methods may lose some information for assessing conservation of gene expression. We suggest that the use of either of these two computational models to study the evolution of a gene's expression may be subject to great uncertainty, and the investigation of changes in both gene expression patterns over corresponding tissues and co-expression of the gene with other genes is necessary.

Effect of diet supplementation on the expression of bovine genes associated with Fatty Acid synthesis and metabolism

Conjugated linoleic acids (CLA) are of important nutritional and health benefit to human. Food products of animal origin are their major dietary source and their concentration increases with high concentrate diets fed to animals. To examine the effects of diet supplementation on the expression of genes related to lipid metabolism, 28 Angus steers were fed either pasture only, pasture with soybean hulls and corn oil, pasture with corn grain, or high concentrate diet. At slaughter, samples of subcutaneous adipose tissue were collected, from which RNA was extracted. Relative abundance of gene expression was measured using Affymetrix GeneChip Bovine Genome array. An ANOVA model nested within gene was used to analyze the background adjusted, normalized average difference of probe-level intensities. To control experiment wise error, a false discovery rate of 0.01 was imposed on all contrasts. Expression of several genes involved in the synthesis of enzymes related to fatty acid metabolism and lipogenesis such as stearoyl-CoA desaturase (SCD), fatty acid synthetase (FASN), lipoprotein lipase (LPL), fatty-acyl elongase (LCE) along with several trancription factors and co-activators involved in lipogenesis were found to be differentially expressed. Confirmatory RT-qPCR was done to validate the microarray results, which showed satisfactory correspondence between the two platforms. Results show that changes in diet by increasing dietary energy intake by supplementing high concentrate diet have effects on the transcription of genes encoding enzymes involved in fat metabolism which in turn has effects on fatty acid content in the carcass tissue as well as carcass quality. Corn supplementation either as oil or grain appeared to significantly alter the expression of genes directly associated with fatty acid synthesis.

Comparison of two output-coding strategies for multi-class tumor classification using gene expression data and Latent Variable Model as binary classifier

Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO) combined with Latent Variable Model (LVM) is used. Results from the proposed One Versus One (OVO) outputcoding strategy is compared with the results obtained from the generalized One Versus All (OVA) method and their efficiencies of using them for multi-class tumor classification have been studied. This comparative study was done using two microarray gene expression data: Global Cancer Map (GCM) dataset and brain cancer (BC) dataset. Primary feature selection was based on fold change and penalized t-statistics. Evaluation was conducted with varying feature numbers. The OVO coding strategy worked quite well with the BC data, while both OVO and OVA results seemed to be similar for the GCM data. The selection of output coding methods for combining binary classifiers for multi-class tumor classification depends on the number of tumor types considered, the discrepancies between the tumor samples used for training as well as the heterogeneity of expression within the cancer subtypes used as training data.

Human embryonic stem cell-derived mesoderm-like epithelium transitions to mesenchymal progenitor cells

Human embryonic stem cells (hESC) have the potential to produce all of the cells in the body. They are able to self-renew indefinitely, potentially making them a source for large-scale production of therapeutic cell lines. Here, we developed a monolayer differentiation culture that induces hESC (WA09 and BG01) to form epithelial sheets with mesodermal gene expression patterns (BMP4, RUNX1, and GATA4). These E-cadherin+ CD90low cells then undergo apparent epithelial-mesenchymal transition for the derivation of mesenchymal progenitor cells (hESC-derived mesenchymal cells [hES-MC]) that by flow cytometry are negative for hematopoietic (CD34, CD45, and CD133) and endothelial (CD31 and CD146) markers, but positive for markers associated with mesenchymal stem cells (CD73, CD90, CD105, and CD166). To determine their functionality, we tested their capacity to produce the three lineages associated with mesenchymal stem cells and found they could form osteogenic and chondrogenic, but not adipogenic lineages. The derived hES-MC were able to remodel and contract collagen I lattice constructs to an equivalent degree as keloid fibroblasts and were induced to express alpha-smooth muscle actin when exposed to transforming growth factor (TGF)-beta1, but not platelet derived growth factor-B (PDGF-B). These data suggest that the derived hES-MC are multipotent cells with potential uses in tissue engineering and regenerative medicine and for providing a highly reproducible cell source for adult-like progenitor cells.

Ant colony optimization as a method for strategic genotype sampling

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.

The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification

The use of gene expression data to diagnose complex diseases represents an exciting area of medicine; however, such data sets are often noisy, requiring the selection of feature subsets to obtain maximum classification accuracy. Due to the high dimensions of many expression data sets, filter-based methods are commonly used, but often yield inconsistent results. Optimization algorithms can outperform filter methods, but often require preselection of features to achieve good results. To address the problems of many commonly used feature selection methods, the ant colony algorithm (ACA) is proposed for use on data sets with large numbers of features. The ACA is an optimization algorithm capable of incorporating prior information, allowing it to search the sample space more efficiently than other optimization methods. When applied to several high-dimensional data sets, the ACA was able to identify small subsets of highly predictive and biologically relevant genes without the need for extensive preselection of features. Using the selected genes to train a latent variable model yielded substantial increases in prediction accuracy when compared to several rank-based methods and results obtained in previous studies. The superiority of the ACA algorithm was validated through simulation.

A statistical and biological approach for identifying misdiagnosis of incipient Alzheimer patients using gene expression data

A latent-threshold model and misclassification algorithm were implemented to examine potential misdiagnosis among 16 Alzheimer's disease (AD) subjects using gene expression data. Results obtained without invoking the misclassification algorithm showed limited predictive power of the model. When the misclassification algorithm was invoked, four subjects were identified as being potentially misdiagnosed. Results obtained after adjustment of the AD status of these four samples showed a significant increase in the model's predictive ability. Mixed model analysis detected no AD related genes as differentially expressed when using original classifications; conversely, multiple AD genes were identified using the new classifications. These results suggest that this algorithm can identify misclassified subjects which, in turn, can increase power to predict disease status and identify disease related genes.

Joint longitudinal modeling of age of dam and age of animal for growth traits in beef cattle

Two methods to jointly model age of dam (AOD) and age of animal in random regression analyses of growth in Gelbvieh cattle were examined. The first method (M1) was analogous to the multiple-trait analysis and consisted of AOD as a nested class variable and a cubic polynomial regression on age nested within birth, weaning, and yearly weights. The second method (M2) used two-dimensional splines, with age knots at 150, 205, 270, 340, and 390 d. The AOD knots were placed at 725, 1,464, and 2,189 d. These selected knots were used to form a two-dimensional grid containing 15 knots, each representing a specific age and AOD combination. A data set containing Gelbvieh growth records was split along contemporary groups into two data sets. Data set 1 contained 316,078 records and was used for prediction by mixed-model equations. Data set 2 contained 164,167 records and was used for cross validation. In the complete data set, only 90 and 30% of animals with birth weight had records on weaning and yearling weights, respectively. Models were evaluated based on R2, average squared error (ASE), percent bias, and plots of solutions. The ASE for weights associated with birth weight, weaning weight, and yearling weight for M1 were 15, 505, and 703 kg2. With M2, large jumps in fixed-effect estimates were observed outside the two-dimensional grid. To eliminate this problem, weighted one-dimensional splines were used for extrapolation beyond the two-dimensional grid. For M2 with weighted spline extrapolation, the ASE were 15, 542, and 777 kg2 for birth weight, weaning weight, and yearling weight, respectively. Creation of optimal two-dimensional splines is difficult when data are clustered. Despite such difficulties, the two-dimensional spline was capable of jointly and continuously modeling AOD and age of animal.

Estimation of nutrient requirements using broken-line regression analysis1

We evaluated and compared various broken-line regression models and SAS (SAS Inst. Inc., Cary, NC) procedures for estimating nutrient requirements from nutrient dose response data. We used the SAS (Version 9) procedures NLIN and NLMixed and the response data of Parr et al. (2003), who evaluated the isoleucine requirement of growing swine. The SAS NLIN was used to fit 2 different broken-line regression models: a simple 2 straight-line, one-breakpoint model and a quadratic broken-line model in which the response below the single breakpoint was quadratic; there was a plateau above the breakpoint. The latter was fit using 2 different approaches in NLIN. We also used SAS NLMixed to fit 3 different broken-line models: the 2 straight-line, one-breakpoint model that included a random component for the plateau; the quadratic broken-line model that included a random component for the plateau; and the quadratic broken-line model that included random components for both the plateau and the slope of the curve below the requirement. The best fit (greater adjusted R2; least log likelihood) was achieved using SAS NLMixed and the quadratic model with a random component for asymptote included in the model. Model descriptions, SAS code, and output are presented and discussed. Additionally, we provide other examples of possible models and discuss approaches to handling difficult-to-fit data.

Environmental heat stress does not reduce blood ionized calcium concentration in hens acclimated to elevated temperatures

Changes in blood concentrations of ionized calcium and total calcium were measured in broiler breeder hens (42 wk old) relative to egg cycle and environmental temperatures. Two environmental temperature treatments were used: 1) temperature cycled daily from a low of 10 C at 0300 h to a high of 25 C at 1600 h; and 2) temperature cycled from a low of 21 C at 0300 h to a high of to a high of 39 C at 1600 h. Serial blood samples were collected from five laying hens per temperature treatment via cutaneous ulnar vein cannula beginning at the time of oviposition and every 4 h thereafter until the next oviposition. Neither blood concentration of ionized calcium nor total plasma calcium was affected by temperature. Results suggest that the supply of calcium available in blood for shell deposition is not diminished in hens acclimated to high environmental temperatures.

Interaction of feeding time and temperature and their relationship to performance of the broiler breeder hen

Experiments with broiler breeder hens were undertaken to determine effect of feeding time and environmental temperature on various production variables, body weight, and feed consumption. Two temperature treatments were used: low cyclic temperature (10 to 25 C), and high cyclic temperature (21 to 39 C). The three feeding treatments were: fed one daily meal either at 0700 h (Treatment 1) or 1800 h (Treatment 2), or one-half the daily amount at 0700 h and the other half at 1800 h (Treatment 3). In another experiment, hens were assigned to feeding times of either 0700 or 1800 h. Feeding time and temperature did not markedly affect rate of egg production; however, hens at high temperature fed two meals per day produced the fewest eggs. High temperature caused significant reductions in egg weight, specific gravity, and shell thickness. Feeding time and temperature had no effect on time of oviposition, ovulation, or the transit time of the egg through the oviduct. Significant body weight loss occurred in hens at high temperature and fed at 0700 h. Both high temperature and feeding one-half of the daily feed at 0700 and the other half at 1800 h caused a reduction in feed consumption.

Relative biological availability of manganese from manganese proteinate, manganese sulfate, and manganese monoxide in broilers reared at elevated temperatures

The relative biological availabilities of Mn from Mn proteinate, MnSO4, and MnO were compared under two different environmental conditions. Commercial broilers were reared in brooder batteries between Days 1 and 21 and fed diets containing 0, 1,000, 2,000, or 3,000 mg supplemental Mn/kg diet. On Day 22, birds were transferred to individual cages in two environmental chambers maintaining the same dietary Mn sources and supplemental levels. The temperature in one chamber cycled between 18 and 23.9 C (thermoneutral, TN), and in the other chamber cycled between 23.9 and 35 C (heat distress, HD). Birds in the HD environment were exposed to 8 h of 23.9 C, 4 h of 23.9 to 35 C, 4 h of 35 C, and 8 h of 35 to 23.9 C. Tibia Mn increased linearly (P < .05) with level of supplementation when measured on Days 22 and 47. Based on ratios of slopes from multiple linear regression analysis of bone Mn on Mn intake from various sources, the biological availabilities of Mn proteinate and MnO relative to MnSO4 (100%) were 120 and 91%, respectively, in 21-d-old chicks. In 49-d-old birds, corresponding relative biological availabilities of Mn from proteinate and oxide were 125 and 83%, respectively, in birds reared under TN, and 145 and 82%, respectively, for HD birds.

Light and feed management of broiler breeders reared under short versus natural day length

A series of four experiments was conducted to determine the response of meat-type breeder pullets to various light and feed management systems during the rearing, developing, and laying periods. Pullets reared under a short-day (8 h) lighting regimen entered lay at a significantly younger age and lighter body weight than did pullets reared under natural day length. When reared under short days, pullets subjected to light and feed stimulation earlier than 18 wk of age entered lay at a slightly younger age and significantly heavier body weight and produced fewer eggs through 45 wk of age. When pullets were reared under natural day length and photostimulated at 14 wk of age, incremental reductions in age (from 24 to 15 wk of age) at which feed stimulation was initiated resulted in proportionate reductions in age at first egg but had no effect on body weight at first egg. Results suggest that egg production by commercial meat-type females is optimum when managed to begin lay at 24 wk of age. Pullets reared under short days will enter lay at 24 wk if photostimulation is begun at 18 wk and feed stimulation is initiated not later than 24 wk. Pullets reared under natural day length will enter lay at 24 wk if photostimulation is begun at 14 wk and feed stimulation is initiated at 15 wk.

Effects of ad libitum versus restricted feeding on body composition and egg production of broiler breeders

A 64-wk experiment was conducted in which the effects of ad libitum vs. restricted feeding were studied during the pullet-layer transition and laying periods. Broiler breeder females were reared through 23 wk of age according to the breeder's recommendation. At 24 wk of age the following four treatments were imposed: T1, fed restricted amounts of feed according to the breeder's management guide; T2, feed restricted from 24 to 32 wk of age (as in T1), then fed ad libitum; T3, fed ad libitum from 24 to 64 wk of age; and T4, fed ad libitum from 24 to 32 wk of age, then restricted to 85% of the average 24 to 32-wk consumption. Birds fed ad libitum during the pullet-layer transition period (T3 + T4) consumed an average of 37 g more feed per day and reached sexual maturity 14 days earlier than restricted birds (T1 + T2). Live body weight, carcass weight, carcass weight corrected to zero fat content, and percentage carcass fat were all significantly higher at first egg in ad libitum vs. restricted birds, but these differences were small. Birds fed ad libitum during lay (T2 + T3) produced more eggs, achieved a higher peak percentage hen-day production, consumed more feed, and contained more body fat at last egg than hens restricted during lay (T1 + T4). However, neither mortality, feed efficiency, egg weight, egg fertility, nor egg specific gravity was affected by treatment.

Threonine requirement of the broiler chick as affected by protein level and source

The threonine requirement of the young broiler chick was estimated in two experiments. In Experiment 1, a threonine-deficient corn-peanut meal-amino acid mixture was included in a basal diet at levels equivalent to 15 and 20% crude protein. In Experiment 2, a purified crystalline amino acid mixture, also limiting in threonine, was included in a basal diet at levels equivalent to 10.98 and 18.3% crude protein. When expressed as a percent of diet, the estimated threonine requirements increased as dietary protein content increased. However, when expressed as a percent of dietary protein, the estimated threonine requirements were not significantly affected by dietary protein content, and were similar for the two experiments. It was concluded that the threonine requirement is 3.7% of dietary crude protein, and is little affected by dietary protein content.

Effects of dietary crude protein, electrolyte balance, and photoperiod on growth of broiler chickens

Three experiments were conducted with broiler chicks to study the effects of dietary electrolyte balance, dietary crude protein level, and photoperiod on performance. Increasing the electrolyte balance (Na + K -Cl) from about 200 meq/kg to 350 meq/kg or more improved gain and feed consumption of chicks fed high protein (28.6%) diets but depressed gain and feed consumption of chicks fed low protein (14.3%) diets. Neither sex nor photoperiod affected to nature or magnitude of the crude protein X electrolyte balance interaction, although birds housed under 16L:8D had significantly higher concentrations of plasma ammonia than did those housed under 23L:1D. Chicks fed diets containing a high vs. a low electrolyte balance had significantly higher concentrations of plasma uric acid and significantly lower kidney asparaginase activities. Electrolyte effects on kidney asparaginase activity were not affected by diet crude protein content. Results indicate that the electrolyte balance that provides for optimum growth is dependent upon dietary crude protein concentration. Further, diet electrolyte effects on metabolic acid-base homeostasis are not related to their effects on growth.

Effect of feed restriction on growth, body composition, and egg production of broiler females through 68 weeks of age

Effects of ad libitum and restricted (50% of ad libitum) feeding on performance of female broilers were assessed in a 68-week experiment. The treatments imposed were AA, fed ad libitum throughout; RR, fed restricted amounts of feed throughout; RA restricted through 24 weeks of age and ad libitum thereafter; and AR, fed ad libitum through 24 weeks and restricted thereafter. Average age at first egg was delayed by 17 days in RA hens compared with AA hens. Average body weight at first egg was 3.9 kg for RA birds and 4.5 kg for AA birds. Peak production was higher for RA birds (71 vs. 59%), but age at peak production was similar for both AA and RA hens. Through 68 weeks, AR birds produced 50% more eggs than AA birds (159 vs. 106). Cumulative number of eggs produced for RR and AR birds were 37 and 47, respectively. Mortality was similar for RR, RA, and AR birds but was approximately fourfold greater in AA birds. At 68 weeks of age, live body, carcass, abdominal fat pad, and estimated fat-free carcass weights were similar for AA and RA birds. Although abdominal fat as a percent of carcass weight was similar for AR and RR birds, average live weight, carcass weight, and fat-free carcass weight were higher for AR than RR birds at 68 weeks.

Utilization of energy for maintenance and gain in broilers and Leghorns at two ages

Regression equations describing the relationship between energy gain and metabolizable energy intake per unit metabolic weight were derived for 8- to 22-day-old and 28- to 42-day-old broilers and 14- to 28-day-old and 28- to 42-day-old Leghorns. The estimated maintenance energy requirements were lower in the 28- to 42-day-old birds than in the younger birds, but the decreases were small. Maintenance energy requirements of Leghorns were 36% less than those of broilers. Age had no effect on the efficiency of energy utilization above maintenance in broilers. However, the efficiency of energy utilization above maintenance in Leghorns was 43% lower in the older birds. This was correlated with a lower percentage fat gain in the older Leghorns. The percentage fat gain in broilers was substantially higher in the older birds, but this was not correlated with changes in energetic efficiency.

The effect of light regime on growth and pattern of body fat accretion of broiler chickens

A seven week serial slaughter experiment was conducted with male and female broiler chickens to determine the effect of photoperiod on body growth rate and pattern and rate of fat deposition. The two light regimes compared were constant light and a 16 hour light:8 hour dark photoperiod. Males grown under the 16L:8D photoperiod consumed less feed and grew less rapidly than males under constant light. The slower rate of growth was apparently due entirely to less feed consumption, since feed efficiency of males was unaffected by light treatment. Females reared under 16L:8D also grew less rapidly than under constant light. However, the difference was much more pronounced than in males, and was not due to reduced feed consumption, rather to less efficient feed utilization. Females grown under 16L.8D exhibited a slower rate of increase in percent daily fat gain than did females grown under constant light. At seven weeks of age, total body fat content of females averaged 12% less when grown under 16L:8D. Rat and pattern of fat gain in males was unaffected by light treatment. The incidence of leg abnormalities was substantially lower for both sexes reared under 16L:8D.

Potassium-induced changes in muscle free amino acid concentrations in chicks

Two experiments with purified crystalline amino acid diets were conducted to determine the effect of dietary potassium level on chick breast muscle free amino acid concentrations. In both experiments, concentrations of muscle free glutamine, lysine and arginine increased as dietary potassium increased from 0.12 to 0.18% and decreased as dietary potassium exceeded 0.18%. As dietary potassium increased, muscle potassium concentration increased linearly with an equivalent linear decrease in muscle sodium concentration. Peak concentration of muscle free glutamine, lysine and arginine occurred in muscle with a K+:Na+ ratio of approximately 1:4. Concentrations of the three amino acids were less in muscle with K4:Na+ ratios either less than or greater than 1.4. Breast muscle concentrations of free histidine decreased slightly, while muscle free carnosine increased substantially when dietary potassium exceeded 0.18%.

Amino acid utilization as influenced by antibacterial and anticoccidial drugs

Utilization of amino acids, sulfur-containing amino acid (SAA) in particular, is little affected by antibiotic and anticoccidial compounds. Coccidiosis (i.e., Eimeria acervulina infection) likewise seems to have little effect on SAA utilization. Copper sulfate, a commonly used antibacterial-antifungal compound (used at levels of 100-250 mg/kg diet), interacts with SAA. Hence, at upper levels of copper ingestion (i.e., 250 mg/kg and higher), copper binds SH compounds such as cysteine and reduced glutathione. Dietary SAA requirements are increased in both chicks and rats by dietary copper levels of 250 or 500 mg/kg. Hepatic copper deposition is enhanced by copper feeding and also by E. acervulina infection. These two effects, moreover, appear to be additive. The organic arsenic compound, roxarsone, interacts with SAA also, but in a different way. Thus, whereas added dietary cysteine partially ameliorates copper toxicity due to the binding of copper by cysteine-SH with subsequent excretion, roxarsone toxicity (i.e., 500 mg/kg diet) is exacerbated by supplemental cysteine.

Kidney arginase activity in chicks fed diets containing deficient or excessive concentrations of lysine, arginine, histidine, or total nitrogen

Three experiments were conducted with 1 to 2-week-old chicks fed purified crystalline amino acid diets. Kidney arginase activity increased substantially when total dietary nitrogen exceeded the dietary requirement for maximal chick weight gain. Single deficiencies of either histidine or lysine in nitrogen-adequate diets also resulted in marked increases in enzyme level. Arginine deficiency resulted in a slight increase in arginase activity, but the magnitude of the increase appeared to have little effect on efficiency of arginine utilization. Further, the response could be prevented by making lysine and arginine equally limiting. Increasing the lysine:arginine ratio to two in a nitrogen-deficient diet did not increase arginase activity. The same ratio in a arginine- and nitrogen-adequate diet resulted in a twofold increase in arginase activity accompanied by reductions in rate and efficiency of weight gain.

Xanthophyll bioavailability in dehydrated turf grass

In a 16-day experiment involving 270 32-week-old laying hens, dehydrated turf grass (DTG) and corn gluten meal (CGM) were compared as xanthophyll sources for egg yolk pigmentation. Experimental diets were formulated to contain, 0, 5, 10, 15 and 20 mg total xanthophyll/kg diet, provided as either DTG or CGM. Neither egg production nor feed consumption was affected by diet. Egg yolk color dominant wavelength increased slightly with increasing dietary xanthophyll and did not differ with source. Egg yolk color excitation purity increased quadratically (P less than .001) with dietary xanthophyll. The response was similar for both DTG and CGM. It was concluded that the xanthophyll in DTG is 100% bioavailable relative to CGM.

Effect of high-level copper feeding on the sulfur amino acid need of chicks fed corn-soybean meal and purified crystalline amino acid diets

Supplementation of a corn-soybean meal diet with .05% methionine maximized gain and feed efficiency of three-week-old chicks fed copper at either 0 or 250 mg/kg. With copper present at 500 mg/kg, the supplemental methionine requirement was in excess of .10%. Liver copper stores of chicks fed copper at 250 mg/kg declined linearly as supplemental methionine level increased, but methionine level had no effect on liver copper when chicks were fed 500 mg/kg copper. Liver copper accumulation did not occur in chicks fed 250 mg/kg copper when supplemental sulfur was provided as a combination of .05% methionine and 1800 mg/kg sulfide. Irrespective of methionine level, sulfide addition reduced liver copper by one-half in birds fed 500 mg/kg copper. Chicks fed the purified diet were appreciably more susceptible to copper toxicity than those fed the corn-soybean meal diet. Chicks fed the purified diet without supplemental copper required .52% sulfur amino acids (SAA). Chicks fed copper at 250 and 500 mg/kg required .65 and .70 SAA, respectively, although gain and feed efficiency were still depressed when compared with the performance of the control birds. Higher levels of methionine failed to restore growth rates to those achieved with birds fed no supplemental copper. Supplementing the copper-containing diets with up to 1.6% cystine improved performance some but failed to completely overcome the growth depressing effect of copper at 500 mg/kg. Results of additional studies indicated that the purified diet containing .70% SAA and 500 mg/kg copper was nutritionally adequate and that the marked toxicity of copper when fed in the purified diet was due to the chemical nature of the diet rather than to any specific copper-induced nutritional deficiencies.

Studies on the utilization of lysinoalanine and lanthionine

Studies were conducted to determine the nutritional value of lysinoalanine for rats and chicks and the nutritional value of lanthionine for chicks. For each experiment, purified crystalline amino acid diets and crystalline lysinoalanine and lanthionine were employed. Results indicated that the lysine moiety of lysinoalanine is completely unavailable to the rat and only partially available to the chick. Of 10 rats fed lysinoalanine, seven developed nephrocytomegaly. The lanthionine studies indicated that the cysteine moiety of lanthionine is 32% available when fed as the racemic mixture and 52% available when fed as the L-DL-isomeric mixture. Thus, it appears that lysinoalanine formation in alkali-treated proteins results in a corresponding decrease in nutritionally available lysine. However, the lanthionine formed under similar conditions results in loss of only one-half of the corresponding level of nutritionally available cysteine.

Estimation of nutrient requirements from growth data

Two least squares methods of estimating nutrient requirements from growth data were compared. One method involved fitting a broken line by the method of least squares. The requirement was taken as the abscissa of the breakpoint in the curve. The other method involved fitting an appropriate exponential function to the growth data and estimating the requirement as the abscissa of the point on the fitted curve whose ordinate was 95% of the upper asymptote. For the nine sets of data studied, the broken line provided adequate fits for only six. The nonlinear models provided adequate fits for all the data studied. When both the broken line and the chosen nonlinear model provided adequate fits, the estimated requirements were nearly the same. However, the consistently good fits obtained with the nonlinear models suggest that this approach may generally be more useful.