Genetic Analysis of Complex Traits in Alfalfa (Medicago Sativa L.)

Genetic Analysis of Complex Traits in Alfalfa (Medicago Sativa L.)
Author: Joseph Gary Robins
Publisher:
Total Pages: 260
Release: 2004
Genre:
ISBN:

The genetic structure of complex agronomic traits in alfalfa (Medicago sativa) is not well understood. By crossing the subspecies M. sativa subsp. falcata and M. sativa subsp. sativa, a fullsib F1 population was created from which a genetic linkage map of each parental genome was developed using RFLP and SSR markers. These maps include simplex, duplex, and simplex-simplex alleles along with a number of alleles exhibiting segregation distortion. The inclusion of these more complicated segregation ratios resulted in greater saturation of the genome, a better convergence to eight consensus linkage groups, and a more realistic view of regions of the genome that may not behave normally due to segregation distortion than would have been possible by only using simplex alleles as has been done previously. The population was clonally propagated and grown at three field locations with phenotypic data collected over three years for various agronomic traits, including biomass production, forage height, and forage regrowth. Combining the marker data with the phenotypic data, markers were identified from each parental genome that were associated with these traits, suggesting that both major germplasm sources of cultivated alfalfa contain alleles that may contribute to improved alfalfa cultivars. These results provide a much better understanding of the genomic regions underlying these traits and are an important start in efforts aimed at the use of marker-assisted selection for the improvement of alfalfa cultivars.

Understanding Complex Traits in Alfalfa Through Transcriptomics, Genomics, and Proteomics

Understanding Complex Traits in Alfalfa Through Transcriptomics, Genomics, and Proteomics
Author: Atit Parajuli
Publisher:
Total Pages: 0
Release: 2023
Genre: Alfalfa
ISBN:

Alfalfa (Medicago sativa L.) is a perennial, outcrossing legume crop predominantly grown for hay, silage, or pasture. Genetic improvement in Alfalfa in terms of hay yield is still comparable to 30 years ago. Under a variety of growing conditions, forage yield in Alfalfa is stymied by biotic and abiotic stresses including heat, salt, drought, and disease. To overcome such stresses, Alfalfa uses a differential gene expression pathway which is under the control of transcription factors that contribute to tolerance of stresses. The Alfalfa breeding program is mainly focused on developing synthetic varieties through recurrent phenotypic selection exploiting additive genetic effects. The production of hybrid Alfalfa breeding programs uses synthetic varieties as the most feasible means for genetic gain. High heterozygosity of the plants and severe inbreeding depression upon selfing precludes the development of inbred lines for hybrid production. However, quantifying inbreeding depression through fitness and vigor traits expressed as weak and strong plants can help map these traits using association study. Identifying these genetic variants paves the way for the elimination of deleterious alleles and eventually the development of inbred alfalfa lines for hybrid production. However, genetic regions identified through association study do not always translate to actual functional proteins as they are not always linked to genes or genetic variants responsible for traits of interest. As the protein's biological function is strongly dependent on its 3D structure, associating proteins directly with phenotype could help assess the effect of mutation on protein function. To understand the role of transcription factors in stress tolerance, we identified and performed transcriptome analysis of Basic-leucine zipper (bZIP) transcription factors that have played a critical role in regulating growth and development and mediating the responses to abiotic stress in several species, including Arabidopsis thaliana, Oryza sativa, Lotus japonicus, and Medicago truncatula. We identified 237 bZIP genes that were differentially expressed in response to ABA, cold, drought, and salt stresses, indicating a likely role in abiotic stress signaling and/or tolerance. These expressions were further validated through RT-qPCR analysis. Next, a genome-wide association study was performed to map genetic loci associated with Alfalfa for plant vigor trait using 534 plants collected from three locations (Washington, Wisconsin, and Utah) over three generations of selfing. These plants were selected based on plant health of strong and weak within the same line. A total of 11 genetic loci were identified using 588,136 Single nucleotide polymorphisms (SNPs). Gene ontology analysis of significant loci associated them with genes involved in stress response, defense responses against pathogens, and plant reproduction. Finally, we attempted the first-ever association study between features from alphafold predicted 3D structure of protein and phenotype, to link non-synonymous mutation to phenotypes. We used 154 genes, including significant genes from the GWAS study, after filtering 591,919 SNPs, to predict protein 3D structures that identified the five significant GWAS hits. However, two more genes with the lowest p-values (Nod 19, Cytochrome P450) were also identified which play key roles in plant growth and development and also in stress tolerance. This association study is a promising way to narrow down causal mutations from SNP GWAS through stringent filtering of SNPs.

The Alfalfa Genome

The Alfalfa Genome
Author: Long-Xi Yu
Publisher: Springer Nature
Total Pages: 296
Release: 2021-07-17
Genre: Science
ISBN: 3030744663

This book is the first comprehensive compilation of deliberations on whole genome sequencing of the diploid and tetraploid alfalfa genomes including sequence assembly, gene annotation, and comparative genomics with the model legume genome, functional genomics, and genomics of important agronomic characters. Other chapters describe the genetic diversity and germplasm collections of alfalfa, as well as development of genetic markers and genome-wide association and genomic selection for economical important traits, genome editing, genomics, and breeding targets to address current and future needs. Altogether, the book contains about 300 pages over 16 chapters authored by globally reputed experts on the relevant field in this crop. This book is useful to the students, teachers, and scientists in the academia and relevant private companies interested in genetics, breeding, pathology, physiology, molecular genetics and breeding, biotechnology, and structural and functional genomics. The work is also useful to seed and forage industries.

Genetic Data Analysis for Plant and Animal Breeding

Genetic Data Analysis for Plant and Animal Breeding
Author: Fikret Isik
Publisher: Springer
Total Pages: 409
Release: 2017-09-09
Genre: Science
ISBN: 3319551779

This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.

Dissecting the Genetic Basis of Various Adaptation Traits in Alfalfa Using QTL Mapping

Dissecting the Genetic Basis of Various Adaptation Traits in Alfalfa Using QTL Mapping
Author: Laxman Adhikari
Publisher:
Total Pages: 354
Release: 2018
Genre:
ISBN:

Fall dormancy (FD) and winter hardiness (WH) influence seasonal yield, stand persistence, and latitudinal adaptation of alfalfa (Medicago sativa L.). Selection of dormant alfalfa genotypes with higher WH has been a common practice. This research was carried out to dissect the genetic basis of FD and WH through quantitative trait loci (QTL) mapping and explore the potential of incorporating WH in non-dormant alfalfa. Other traits, including time of flowering (TOF), spring yield (SY), cumulative summer biomass (CSB), and leaf rust resistance were also evaluated. An F1 population was derived for linkage analysis and QTL mapping by crossing a dormant winter-hardy cultivar (3010) with a non-dormant cold-sensitive cultivar (CW 1010) Genotyping-by-sequencing was used for single nucleotide polymorphism (SNP) marker discovery. Dormancy and WH were evaluated according to NAAIC protocols. We mapped 45 FD and 35 WH QTLs on the genetic linkage maps of both parents. More than 70% of the FD QTLs did not share genomic locations with WH QTLs, suggesting that the two traits are inherited separately. This study also showed that using late autumn to early winter regrowth height is more reliable than early autumn in estimating alfalfa dormancy in southern environments with mild-winters. The QTL markers with higher phenotypic effects (R2) can be used in marker-assisted selection (MAS) of non-dormant alfalfa with improved WH. Incorporating WH in non-dormant alfalfa can ensure forage production in late autumn and early winter to minimize the forage gaps. In this research, we mapped a total of 25 QTLs for TOF, 17 QTLs for SY, six QTLs for CSB, and eight QTLs for leaf rust resistance in the same alfalfa population. Four TOF QTLs were detected in corresponding genomic positions of flowering QTLs of M. truncatula reported previously. The multiple QTLs detected for leaf rust resistance suggests that alfalfa resistance to the rust pathogen is polygenic. The QTL markers identified in this study constitute an important addition to alfalfa genomic resources and can be validated in populations with diverse genetic backgrounds and in multiple environments for potential use in MAS.

Hands-On Ensemble Learning with Python

Hands-On Ensemble Learning with Python
Author: George Kyriakides
Publisher: Packt Publishing Ltd
Total Pages: 284
Release: 2019-07-19
Genre: Computers
ISBN: 178961788X

Combine popular machine learning techniques to create ensemble models using Python Key FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.

Genetically Engineered Crops

Genetically Engineered Crops
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 607
Release: 2017-01-28
Genre: Science
ISBN: 0309437385

Genetically engineered (GE) crops were first introduced commercially in the 1990s. After two decades of production, some groups and individuals remain critical of the technology based on their concerns about possible adverse effects on human health, the environment, and ethical considerations. At the same time, others are concerned that the technology is not reaching its potential to improve human health and the environment because of stringent regulations and reduced public funding to develop products offering more benefits to society. While the debate about these and other questions related to the genetic engineering techniques of the first 20 years goes on, emerging genetic-engineering technologies are adding new complexities to the conversation. Genetically Engineered Crops builds on previous related Academies reports published between 1987 and 2010 by undertaking a retrospective examination of the purported positive and adverse effects of GE crops and to anticipate what emerging genetic-engineering technologies hold for the future. This report indicates where there are uncertainties about the economic, agronomic, health, safety, or other impacts of GE crops and food, and makes recommendations to fill gaps in safety assessments, increase regulatory clarity, and improve innovations in and access to GE technology.