Pregnancy rate and first-service conception rate in Angus heifers

The objective of this project was to determine the genetic control of conception rate, or pregnancy percentage in Angus beef heifers. Producers from 6 herds in 5 states provided 3,144 heifer records that included breeding dates, breeding contemporary groups, service sires, and pregnancy check information. Two hundred fourteen sires of the heifers were represented; with 104 sires having less than 5 progeny, and 14 sires having greater than 50 progeny. These data were combined with performance and pedigree information, including actual and adjusted birth weights, weaning weights, and yearling weights, from the American Angus Association database. Heifer pregnancy rate varied from 75 to 95% between herds, and from 65 to 100% between sires, with an overall pregnancy rate of 93%, measured as the percentage of heifers pregnant at pregnancy check after the breeding season. Pregnancy was analyzed as a threshold trait with an underlying continuous distribution. A generalized linear animal model, using a relationship matrix, was fitted. This model included the fixed effects of contemporary group, age of dam, and first AI service sire, and the covariates of heifer age at the beginning of breeding, adjusted birth weight, adjusted weaning weight, and adjusted yearling weight. The relationship matrix included 4 generations of pedigree. The heritability of pregnancy and first-service conception rates on the underlying scale was 0.13 ± 0.07 and 0.03 ± 0.03, respectively. Estimated breeding values for pregnancy rate on the observed scale ranged from −0.02 to 0.05 for sires of heifers. Including growth traits with pregnancy rate as 2-trait analyses did not change the heritability of pregnancy rate. As expected for a reproductive trait, the heritability of pregnancy rate was low. Because of its low heritability, genetic improvement in fertility by selection on heifer pregnancy rate would be expected to be slow

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Structure of an Engineered β-Lactamase Maltose Binding Protein Fusion Protein: Insights into Heterotropic Allosteric Regulation

Engineering novel allostery into existing proteins is a challenging endeavor to obtain novel sensors, therapeutic proteins, or modulate metabolic and cellular processes. The RG13 protein achieves such allostery by inserting a circularly permuted TEM-1 β-lactamase gene into the maltose binding protein (MBP). RG13 is positively regulated by maltose yet is, serendipitously, inhibited by Zn2+ at low µM concentration. To probe the structure and allostery of RG13, we crystallized RG13 in the presence of mM Zn2+ concentration and determined its structure. The structure reveals that the MBP and TEM-1 domains are in close proximity connected via two linkers and a zinc ion bridging both domains. By bridging both TEM-1 and MBP, Zn2+ acts to “twist tie” the linkers thereby partially dislodging a linker between the two domains from its original catalytically productive position in TEM-1. This linker 1 contains residues normally part of the TEM-1 active site including the critical β3 and β4 strands important for activity. Mutagenesis of residues comprising the crystallographically observed Zn2+ site only slightly affected Zn2+ inhibition 2- to 4-fold. Combined with previous mutagenesis results we therefore hypothesize the presence of two or more inter-domain mutually exclusive inhibitory Zn2+ sites. Mutagenesis and molecular modeling of an intact TEM-1 domain near MBP within the RG13 framework indicated a close surface proximity of the two domains with maltose switching being critically dependent on MBP linker anchoring residues and linker length. Structural analysis indicated that the linker attachment sites on MBP are at a site that, upon maltose binding, harbors both the largest local Cα distance changes and displays surface curvature changes, from concave to relatively flat becoming thus less sterically intrusive. Maltose activation and zinc inhibition of RG13 are hypothesized to have opposite effects on productive relaxation of the TEM-1 β3 linker region via steric and/or linker juxtapositioning mechanisms

Developing 1D nanostructure arrays for future nanophotonics

There is intense and growing interest in one-dimensional (1-D) nanostructures from the perspective of their synthesis and unique properties, especially with respect to their excellent optical response and an ability to form heterostructures. This review discusses alternative approaches to preparation and organization of such structures, and their potential properties. In particular, molecular-scale printing is highlighted as a method for creating organized pre-cursor structure for locating nanowires, as well as vapor–liquid–solid (VLS) templated growth using nano-channel alumina (NCA), and deposition of 1-D structures with glancing angle deposition (GLAD). As regards novel optical properties, we discuss as an example, finite size photonic crystal cavity structures formed from such nanostructure arrays possessing highQand small mode volume, and being ideal for developing future nanolasers

A Two-Stage Approximation for Analysis of Mixture Genetic Models in Large Pedigrees

Information from cosegregation of marker and QTL alleles, in addition to linkage disequilibrium (LD), can improve genomic selection. Variance components linear models have been proposed for this purpose, but accommodating dominance and epistasis is not straightforward with them. A full-Bayesian analysis of a mixture genetic model is favorable in this respect, but is computationally infeasible for whole-genome analyses. Thus, we propose an approximate two-step approach that neglects information from trait phenotypes in inferring ordered genotypes and segregation indicators of markers. Quantitative trait loci (QTL) fine-mapping scenarios, using high-density markers and pedigrees of five generations without genotyped females, were simulated to test this strategy against an exact full-Bayesian approach. The latter performed better in estimating QTL genotypes, but precision of QTL location and accuracy of genomic breeding values (GEBVs) did not differ for the two methods at realistically low LD. If, however, LD was higher, the exact approach resulted in a slightly higher accuracy of GEBVs. In conclusion, the two-step approach makes mixture genetic models computationally feasible for high-density markers and large pedigrees. Furthermore, markers need to be sampled only once and results can be used for the analysis of all traits. Further research is needed to evaluate the two-step approach for complex pedigrees and to analyze alternative strategies for modeling LD between QTL and markers

In situ Fe K-edge X-ray absorption fine structure of a pyrite electrode in a Li/polyethylene oxide (LiClO{sub 4})/FeS{sub 2} battery environment

Electronic and structural properties of materials generated by the reduction and subsequent oxidation of pyrite in a lithium-based solid polymer electrolyte have been examined by in situ fluorescence Fe K-edge X-ray absorption fine structure (XAFS) in a FeS{sub 2}/Li battery environment. The XAFS results obtained are consistent with the formation of metallic iron as one of the products of the full (4-electron) discharge, in agreement with information reported in other laboratories. Extended X-ray absorption fine structure (EXAFS) data reveal that a subsequent 2-electron or 4-electron recharge generates a species with a Fe-S bond distance identical to that of pyrite, d(Fe-S) = 2.259 {angstrom}, with no other clearly detectable interactions due to more distant atoms. Based on the similarities between the metrical parameters and other features in the X-ray absorption near edge structure (XANES), the ferrous sites in these species appear to be tetrahedrally coordinated, as in chalcopyrite (CuFeS{sub 2}), for which d(Fe-S) is 2.257 {angstrom}, and, thus, different than in Li{sub 2} FeS{sub 2}, a material that exhibits longer Fe-S distances

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

<div><p>Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. <i>Approximate Bayesian computation</i> (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.</p></div

Daily average temperature and solar radiation at Champaign, Illinois in 2012 and 2013.

<p>The thick grey line shows a smoothed curve.</p

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype-environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed

Predicted vs. observed grain yield of 1500 DH lines in testing set for prediction methods CGM-WGP (top row) and GBLUP (bottom row).

<p>The estimation environment was 2012. Results shown are from a representative example data set. In this example, the accuracy for observed environment predictions was 0.83 (CGM-WGP) and 0.69 (GBLUP). For new environment predictions it was 0.39 (CGM-WGP) and 0.11 (GBLUP).</p