The effects of genotype-by-environment interactions on body condition score across three winter supplemental feed environments in a composite beef cattle breed in Montana.

Cattle operations in the Northern Great Plains region of the United States face extreme cold weather conditions and require nutritional supplementation over the winter season in order for animals to maintain body condition. In cow-calf operations, body condition scores (BCS) measured at calving and breeding have been shown to be associated with several economically important health and fertility traits, so maintenance of BCS is both an animal welfare and economic concern. A low-to-medium heritability has been found for BCS when measured across various production stages, indicating a large environmental influence but sufficient genetic basis for selection. The present study evaluated BCS measured prior to calving (late winter) and breeding (early summer) under three winter supplementation environments in a multitrait linear mixed model. Traits were discretized by winter supplementation and genetic correlations between environments were considered a reflection of evidence for genotype-by-environment interactions between BCS and diet. Winter supplementation treatments were fed October through April and varied by range access and protein content: 1) feedlot environment with approximately 15% crude protein (CP) corn/silage diet, 2) native rangeland access with 1.8 kg of an 18% CP pellet supplement, and 3) native rangeland access with a self-fed 50% CP and mineral supplement. A total of 2,988 and 2,353 records were collected across multiple parities on 1,010 and 800 individuals for prebreeding and precalving BCS, respectively. Heifers and cows came from a composite beef cattle breed developed and maintained by the USDA Fort Keogh Livestock and Range Research Laboratory near Miles City, Montana. Genetic correlations between treatments 1 and 2, 1 and 3, and 2 and 3 were 0.98, 0.78, and 0.65 and 1.00, 0.98, and 0.99 for precalving and prebreeding BCS, respectively. This provides moderate evidence of genotype-by-environment interactions for precalving BCS under treatment 3 relative to treatments 1 and 2, but no evidence for genotype-by-environment interactions for prebreeding BCS. Treatment 3 differed substantially in CP content relative to treatments 1 and 2, indicating that some animals differ in their ability to maintain BCS up to spring calving across a protein gradient. These results indicate the potential for selection of animals with increased resilience under cold weather conditions and high protein, restricted energy diets to maintain BCS.

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite "fuzzy" ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. FST scores, and p-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1-50 k SNPs ranged from -0.4-11.7 and -0.3-3.8% higher than FST and p-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both FST scores and p-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.

Thrombin as a Potential Proxy to Select for Horn Fly Abundance in Beef Cattle.

Horn flies are a major nuisance to cattle and induce significant economic losses. Fly abundance varies within and across breeds and genetic analyses have shown sufficient genetic variation to permit selection. A major bottleneck for selecting against horn fly abundance is the complexity of measuring fly attraction phenotypes. Easy-to-measure proxy phenotypes could be an attractive option to indirectly estimate fly abundance. In the current study, thrombin was investigated as a potential proxy to assess fly abundance. Fly counts and blood samples were collected on 355 cows. Pearson correlation between subjective fly count and thrombin was -0.13, indicating a decrease in fly abundance with the increase in thrombin concentration. When thrombin was discretized into three classes, there was a 22% difference in fly count between the top and bottom classes. Heritability estimates of thrombin were 0.38 and 0.39 using linear and threshold models, respectively. The correlation between estimated thrombin breeding values and fly count was around -0.18. There was a noticeably lower density of high fly counts among animals with high breeding values for thrombin. These results indicate that thrombin could be used in combination with other biological factors to estimate fly abundance and as a proxy for selection against fly abundance.

Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1.

BACKGROUND: Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. RESULTS: We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. CONCLUSION: Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

A Bayesian approach for analysis of ordered categorical responses subject to misclassification.

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.

Correction to: High density marker panels, SNPs prioritizing and accuracy of genomic selection.

CORRECTION TO: BMC GENETICS (2018) 19:4 DOI: 10.1186/S12863-017-0595-2: The original version of this article [1], published on 5 January 2018, contained 3 formatting errors. In this Correction the affected parts of the article are shown. The original article has been updated.

High density marker panels, SNPs prioritizing and accuracy of genomic selection.

BACKGROUND: The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. RESULTS: In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. CONCLUSIONS: These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.