D. L. Johnson

Bio: D. L. Johnson is an academic researcher from Livestock Improvement Corporation. The author has contributed to research in topics: Dairy cattle & Sire. The author has an hindex of 9, co-authored 11 publications receiving 1223 citations.

Quantifying the effect of thermal environment on production traits in three breeds of dairy cattle in New Zealand

Abstract: In this study, the effects of hot and cold conditions throughout New Zealand were assessed from 1990 to 2002 using daily milk yield, fat and protein concentration records from 19 201 spring calving first lactation cows comprising Holstein Friesian (HF), New Zealand Jersey (NZJ) and crossbred CA HF and l A NZJ; HF x NZJ). The effect of hot and cold conditions on test day records were assessed using a 3‐day average of temperature‐humidity index (THI) and a 3‐day average cold stress index (CSI), which includes the effects of temperature, rain and wind. Hot conditions were associated with reductions in milk and milksolids yields, and fat and protein concentrations in all breeds. Reductions greater than 10 g of milksolids day1 per unit increase in 3 ‐day average THI, started to occur at 3‐day average THI of 68 inHF, 69 inHF x NZJ, and 75 inNZJ cattle. Temperature‐humidity indices of 68 and 75 are approximately equivalent to temperatures of 21 and 25.5°C, respectively at 75% humidity. Fat and protein c...

A unified approach to utilize phenotypic, full pedigree and genomic information for a genetic evaluation of Holstein final score

Abstract: A relationship matrix in a genetic evaluation system was augmented for incorporation of genomic information to create a single-step procedure. The procedure was applied to a national evaluation for final score in U.S. Holsteins. Computing was 2% longer than the traditional evaluation. Accuracies and biases of prediction of young bulls were affected by scaling of the genomic relationship matrix. With “optimal” scaling, reliabilities were higher and the inflation of EBV was lower compared to a multi-step approach. Accurate genomic evaluations can be obtained by modifying the relationship matrices in current evaluation systems. Advantages of the single-step procedure include a dramatic simplification of computations, ability to use more complicated models (including multi-trait), increased resistance to selection bias introduced by use of genomic evaluations, and improved accuracy for ungenotyped animals.

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Abstract: Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

Social and economic impacts of climate

Abstract: For centuries, thinkers have considered whether and how climatic conditions-such as temperature, rainfall, and violent storms-influence the nature of societies and the performance of economies. A multidisciplinary renaissance of quantitative empirical research is illuminating important linkages in the coupled climate-human system. We highlight key methodological innovations and results describing effects of climate on health, economics, conflict, migration, and demographics. Because of persistent "adaptation gaps," current climate conditions continue to play a substantial role in shaping modern society, and future climate changes will likely have additional impact. For example, we compute that temperature depresses current U.S. maize yields by ~48%, warming since 1980 elevated conflict risk in Africa by ~11%, and future warming may slow global economic growth rates by ~0.28 percentage points per year. In general, we estimate that the economic and social burden of current climates tends to be comparable in magnitude to the additional projected impact caused by future anthropogenic climate changes. Overall, findings from this literature point to climate as an important influence on the historical evolution of the global economy, they should inform how we respond to modern climatic conditions, and they can guide how we predict the consequences of future climate changes.

Genome-wide association mapping including phenotypes from relatives without genotypes.

Abstract: A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.