Genomic Prediction of Complex Phenotypes Using Genic Similarity Based Relatedness Matrix

Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information

This study aims to compare the accuracy of genomic estimated breeding values (GEBV) estimated using a genomic best linear unbiased prediction (GBLUP) method and GEBV estimates incorporating prior marker information from a genome-wide association study (GWAS) for the weaning weight trait in highland Merino sheep. The objective is to provide theoretical and technical support for improving the accuracy of genomic selection. The study used a population of 1007 highland Merino ewes, with the weaning weight at 3 months as the target trait. The population was randomly divided into two groups. The first group was used for GWAS analysis to identify significant markers, and the top 5%, top 10%, top 15%, and top 20% markers were selected as prior marker information. The second group was used to estimate genetic parameters and compare the accuracy of GEBV predictions using different prior marker information. The accuracy was obtained using a five-fold cross-validation. Finally, both groups were subjected to cross-validation. The study's findings revealed that the heritability of the weaning weight trait, as calculated using the GBLUP model, ranged from 0.122 to 0.394, with corresponding prediction accuracies falling between 0.075 and 0.228. By incorporating prior marker information from GWAS, the heritability was enhanced to a range of 0.125 to 0.407. The inclusion of the top 5% to top 20% significant SNPs from GWAS results as prior information into GS showed potential for improving the accuracy of predicting genomic breeding value.

Forecasting trait responses in novel environments to aid seed provenancing under climate change

Revegetation projects face the major challenge of sourcing optimal plant material. This is often done with limited information about plant performance and increasingly requires factoring resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide seed provenancing, but trait values expected under novel conditions are often unknown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits-days-to-bolting and fecundity-across the species' European range. Prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Variation and genetic control of individual recombination rates in Norwegian Red dairy cattle

Meiotic recombination is an important evolutionary mechanism that breaks up linkages between loci and creates novel haplotypes for selection to act upon. Understanding the genetic control of variation in recombination rates is therefore of great interest in both natural and domestic breeding populations. In this study, we used pedigree information and medium-density (∼50K) genotyped data in a large cattle (Bos taurus) breeding population in Norway (Norwegian Red cattle) to investigate recombination rate variation between sexes and individual animals. Sex-specific linkage mapping showed higher rates in males than in females (total genetic length of autosomes = 2,492.9 cM in males and 2,308.9 cM in females). However, distribution of recombination along the genome showed little variation between males and females compared with that in other species. The heritability of autosomal crossover count was low but significant in both sexes (h² = 0.04 and 0.09 in males and females, respectively). We identified 2 loci associated with variation in individual crossover counts in female, one close to the candidate gene CEP55 and one close to both MLH3 and NEK9. All 3 genes have been associated with recombination rates in other cattle breeds. Our study contributes to the understanding of how recombination rates are controlled and how they may vary between closely related breeds as well as between species.

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R² value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.

Genomic Prediction: Progress and Perspectives for Rice Improvement

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.

Incorporating Omics Data in Genomic Prediction

In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.

From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data

In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.

The Genetic Basis of Tomato Aroma

Tomato (Solanum lycopersicum L.) aroma is determined by the interaction of volatile compounds (VOCs) released by the tomato fruits with receptors in the nose, leading to a sensorial impression, such as "sweet", "smoky", or "fruity" aroma. Of the more than 400 VOCs released by tomato fruits, 21 have been reported as main contributors to the perceived tomato aroma. These VOCs can be grouped in five clusters, according to their biosynthetic origins. In the last decades, a vast array of scientific studies has investigated the genetic component of tomato aroma in modern tomato cultivars and their relatives. In this paper we aim to collect, compare, integrate and summarize the available literature on flavour-related QTLs in tomato. Three hundred and 5ifty nine (359) QTLs associated with tomato fruit VOCs were physically mapped on the genome and investigated for the presence of potential candidate genes. This review makes it possible to (i) pinpoint potential donors described in literature for specific traits, (ii) highlight important QTL regions by combining information from different populations, and (iii) pinpoint potential candidate genes. This overview aims to be a valuable resource for researchers aiming to elucidate the genetics underlying tomato flavour and for breeders who aim to improve tomato aroma.

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

Optimizing genomic prediction model given causal genes in a dairy cattle population

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge. Although several plausible results have been obtained from model modification or strategy optimization, most of them were validated in a specific empirical population with a limited variety of genetic architecture for complex traits. An alternative approach is to use simulated genetic architecture with known causal genes (e.g., simulated causative SNP) to evaluate different GP models with given causal genes. Our objectives were to (1) evaluate the performance of GP under a variety of genetic architectures with a subset of known causal genes and (2) compare different GP models modified by highlighting causal genes and different strategies to weight causal genes. In this study, we simulated pseudo-phenotypes under a variety of genetic architectures based on the real genotypes and phenotypes of a dairy cattle population. Besides classical genomic best linear unbiased prediction, we evaluated 3 modified GP models that highlight causal genes as follows: (1) by treating them as fixed effects, (2) by treating them as a separate random component, and (3) by combining them into the genomic relationship matrix as random effects. Our results showed that highlighting the known causal genes, which explained a considerable proportion of genetic variance in the GP models, increased the predictive accuracy. Combining all given causal genes into the genomic relationship matrix was the optimal strategy under all the scenarios validated, and treating causal genes as a separate random component is also recommended, when more than 20% of genetic variance was explained by known causal genes. Moreover, assigning differential weights to each causal gene further improved the predictive accuracy.

Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations

Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F₃ progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F₃ progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.