Agronomy and Horticulture Department

 

First Advisor

James C. Schnable

Date of this Version

Summer 8-1-2019

Citation

Zhikai Liang, “New Approaches to Use Genomics, Field Traits, and High-throughput Phenotyping for Gene Discovery in Maize (Zea mays)” PhD thesis, University of Nebraska, Lincoln, 2019.

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy, Major: Agronomy, Under the Supervision of Professor James C. Schnable. Lincoln, Nebraska: August, 2019

Copyright 2019 Zhikai Liang

Abstract

Maize is one of major crop species over the world. With lots of genetic resources and genomic tools, maize also serves as a model species to understand genetic diversity, facilitate the development of trait extraction algorithms and map candidate functional genes. Since the first version of widely used B73 reference genome was released, independent research groups in the maize community propagated seeds themselves for further research purposes. However, unexpected or occasional contamination may happen during this process. The first study in this thesis used public RNA-seq data of B73 from 27 research groups across three countries for calling single nucleotide polymorphisms (SNP). Those SNPs were applied for investigating the distance of 27 maize B73 samples from the reference genome and three major clades were defined for determining their original sources. On the other side, maize is a plant with clear plant architecture. The second study was to employ the high-throughput plant phenotyping to dissect plant phenotypes using computer vision methods. A total of 32 maize inbreds distributed from the Genomes to Fields project were captured images in daily by 4 types of cameras (RGB, Hyperspectral, Fluorescence and Thermal-IR) for approximate 1 month. Differences between computer vision measurements and manual measurements about the plant fresh biomass were evaluated. Broad-sense heritability was estimated for extracted measurements from images. The expanded types of plant phenotype from the perspective of imaging provided a broader range of opportunities for connecting phenotypic variants with genetic variants. The third study utilized the phenome-wide variants in maize Goodman-Buckler 282 association panel to scan and associate with genetic variants of annotated genes along the maize genome. Genes detected by the proposed model, Genome-Phenome Wide Association Study (GPWAS), are significantly different from conventional GWAS detected genes. GPWAS genes tend to be more functionally conserved and more similar as classical maize mutants with known functions. Results from these researches assist to answer question about the genetic purity of same maize genotype. Methods developed in this thesis can also provide the valuable reference for trait discoveries from images and candidate functional gene identification using a broad set of phenotypes.

Adviser: James C. Schnable

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