Evaluating dimensionality reduction for genomic prediction

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data

Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS)

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E

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

Omental whirl associated with bilateral inguinal hernia: a case report

INTRODUCTION: Torsion of the omentum is a rare cause of abdominal pain. It is clinically similar to common causes of acute surgical abdomen and is often diagnosed during surgery. Inguinal hernia is a common condition but not frequently related with torsion of the omentum. CASE PRESENTATION: A 40-year-old Caucasian man came to our emergency department with abdominal pain of the left quadrant and abdominal distension for 2 days. His medical history included an untreated left inguinal hernia in the last year. Computed tomography revealed densification of mesocolon with left omentum "whirl" component and other signs of omental torsion. During an exploratory laparoscopy, a wide twist of his omentum with necrotic alterations that extended to the bilateral inguinal hernial content was observed. Omentectomy and surgical repair of bilateral inguinal hernia were performed. CONCLUSIONS: Torsion of the omentum is a rare entity and usually presents a diagnostic challenge. The use of abdominal computed tomography can help diagnosing torsion of the omentum preoperatively and, thus, prevents a surgical approach. Nonetheless, some cases of torsion of the omentum require surgical repair. Accordingly, a laparoscopic approach is minimally invasive and efficient in performing omentectomy.(undefined

DNA Extraction Method Development for Solid Tissues

Although germline variation testing is traditionally performed using DNA obtained from blood or other liquid samples, determining somatic variation in cancer samples requires DNA extraction directly from tissues. Additionally, epigenetic markers, such as 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are tissue-specific and change in selected disease states. However, several substances present in tissues are known to inhibit downstream reactions, including polymerase chain reaction PCR). For this project, we are assessing the quantity and quality of DNA obtained from extractions of various vital organs using 30 different commercially available DNA extraction kits to determine optimal kits for each tissue

Genomic-enabled prediction model with genotype × environment interaction in elite chickpea lines

Genomic selection (GS) allows safe phenotyping and reduces cost and shortening selection cycles. Incorporating of genotype × environment (G×E) interactions in genomic prediction models improves the predictive ability of lines performance across environments and in target environments. Phenotyping data on a set of 320 elite chickpea breeding lines on different traits (e.g., plant height, days to maturity, and seed yield), from three consecutive years for two different treatments at two locations were recorded. These lines were genotyped on DArTseq(1.6K) and Genotyping- by-Sequencing (GBS; 89K SNPs) platforms. Five different models were fitted, four of which included genomic information as main effects (baseline model) and/or G×E interactions. Three different cross-validation schemes that mimic real scenarios that breeders might face on fields were considered to assess the predictive ability of the models (CV2: incomplete field trials; CV1: newly developed lines; and CV0: new previously untested environments). Different prediction models gave different results for the different traits; however, some interesting patterns were observed. For CV1, analyzing yield seed interaction models improved baseline counterparts on an average between 55 and 92% using DArT and DArT combined with GBS data, respectively [between 9 and 112% for all traits]. While for CV2 these improvements varied b tween 65 and 102% [between 8 and 130% remaining traits]. In CV0, no clear advantage was observed considering the interaction term. These results suggest that GS models hold potential for breeder’s applications on chickpea cultivar improvements

School closures during the 2009 influenza pandemic: national and local experiences

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Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut