Comparison of classification methods for detecting associations between SNPs and chick mortality

Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories (2, 3, 4, 5 and 10) and three classification algorithms (naïve Bayes classifiers, Bayesian networks and neural networks) were compared, using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naïve Bayes classifier, coupled with discretization into two or three categories generated the SNP subset with greatest predictive ability. Further, an alternative categorization scheme, which used only two extreme portions of the empirical distribution of mortality rates, was considered. This scheme selected SNPs with greater predictive ability than those chosen by the methods described previously. Use of extreme samples seems to enhance the ability of feature selection procedures to select influential SNPs in genetic association studies

Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts

The impact of extent of genetic relatedness on accuracy of genome-enabled predictions was assessed using a dairy cattle population and alternative cross-validation (CV) strategies were compared. The CV layouts consisted of training and testing sets obtained from either random allocation of individuals (RAN) or from a kernel-based clustering of individuals using the additive relationship matrix, to obtain two subsets that were as unrelated as possible (UNREL), as well as a layout based on stratification by generation (GEN). The UNREL layout decreased the average genetic relationships between training and testing animals but produced similar accuracies to the RAN design, which were about 15% higher than in the GEN setting. Results indicate that the CV structure can have an important effect on the accuracy of whole-genome predictions. However, the connection between average genetic relationships across training and testing sets and the estimated predictive ability is not straightforward, and may depend also on the kind of relatedness that exists between the two subsets and on the heritability of the trait. For high heritability traits, close relatives such as parents and full-sibs make the greatest contributions to accuracy, which can be compensated by half-sibs or grandsires in the case of lack of close relatives. However, for the low heritability traits the inclusion of close relatives is crucial and including more relatives of various types in the training set tends to lead to greater accuracy. In practice, CV designs should resemble the intended use of the predictive models, e.g., within or between family predictions, or within or across generation predictions, such that estimation of predictive ability is consistent with the actual application to be considered

Repro Money: An Extension Program to Improve Dairy Farm Reproductive Performance

A farmer-directed, team-based Extension program (Repro Money) was developed and executed by the University of Wisconsin–Madison Department of Dairy Science in collaboration with University of Wisconsin–Extension. The goal of the Repro Money program was to help Wisconsin dairy farmers increase reproductive performance and profitability through identification of areas for improvement and implementation of action plans. For the 40 Wisconsin dairy farms that completed the Repro Money program, mean 21-day pregnancy rate increased by 2 percentage points, which was estimated to result in an economic net gain of $31 per cow per year. Extension professionals can apply similar team-based programs to tackle multifaceted, interrelated problems that may be only partially addressed by other, more traditional programming

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production

Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations

RenSeq is a NB-LRR (nucleotide binding-site leucine-rich repeat) gene-targeted, Resistance gene enrichment and sequencing method that enables discovery and annotation of pathogen resistance gene family members in plant genome sequences. We successfully applied RenSeq to the sequenced potato Solanum tuberosum clone DM, and increased the number of identified NB-LRRs from 438 to 755. The majority of these identified R gene loci reside in poorly or previously unannotated regions of the genome. Sequence and positional details on the 12 chromosomes have been established for 704 NB-LRRs and can be accessed through a genome browser that we provide. We compared these NB-LRR genes and the corresponding oligonucleotide baits with the highest sequence similarity and demonstrated that ~80% sequence identity is sufficient for enrichment. Analysis of the sequenced tomato S. lycopersicum ‘Heinz 1706’ extended the NB-LRR complement to 394 loci. We further describe a methodology that applies RenSeq to rapidly identify molecular markers that co-segregate with a pathogen resistance trait of interest. In two independent segregating populations involving the wild Solanum species S. berthaultii (Rpi-ber2) and S. ruiz-ceballosii (Rpi-rzc1), we were able to apply RenSeq successfully to identify markers that co-segregate with resistance towards the late blight pathogen Phytophthora infestans. These SNP identification workflows were designed as easy-to-adapt Galaxy pipelines

Genomic evaluations with many more genotypes

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.</p> <p>Methods</p> <p>Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.</p> <p>Results</p> <p>Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.</p> <p>Conclusions</p> <p>Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.</p

A Primer on High-Throughput Computing for Genomic Selection

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl, and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general-purpose computation on a graphics processing unit provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin–Madison, which can be leveraged for genomic selection, in terms of central processing unit capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general-purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to realized genetic gain). Eventually, HTC may change our view of data analysis as well as decision-making in the post-genomic era of selection programs in animals and plants, or in the study of complex diseases in humans

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Model Evaluation Guidelines for Geomagnetic Index Predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.Plain Language SummaryOne aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.Key PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd