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

Understanding Complex Traits in Alfalfa Through Transcriptomics, Genomics, and Proteomics
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Book Synopsis Understanding Complex Traits in Alfalfa Through Transcriptomics, Genomics, and Proteomics by : Atit Parajuli

Download or read book Understanding Complex Traits in Alfalfa Through Transcriptomics, Genomics, and Proteomics written by Atit Parajuli and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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