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Title:Three studies in marker-assisted plant breeding
Author(s):Labroo, Marlee Rose
Director of Research:Studer, Anthony J
Doctoral Committee Chair(s):Studer, Anthony J
Doctoral Committee Member(s):Bohn, Martin O; Clark, Lindsay V; Diers, Brian W; Rutkoski, Jessica E
Department / Program:Crop Sciences
Discipline:Crop Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):mapping-by-sequencing
maize
rice
genomic prediction
breeding simulation
quantitative genetics
genomics
Abstract:Effective breeding for crop improvement has the potential to increase food security, improve environmental health, and contribute to economic development. Here, we present three studies which further our understanding of marker-assisted plant breeding and plant genetics. First, we fine-map a recessive Mendelian mutation in maize which confers reduced leaf area, rdla, to a 153-Kb region on chromosome 4 with a mapping-by-sequencing approach. We find that the rdla mutation reduces total leaf area and per-leaf area in terms of both leaf length and leaf width, and has further pleiotropic effects of reduced plant height and reduced basal stem circumference. We find no evidence of changes in specific leaf weight, a proxy for leaf thickness. Characterization and mapping of the rdla gene is of basic interest in understanding the genetic control of maize leaf traits. We also speculate that rdla may useful in breeding maize varieties tolerant of high planting densities and initiate a marker-assisted backcrossing program of rdla into modern Expired Plant Variety Protection maize cultivars. Second, we phenotype F1 rice hybrids of existing male-sterile lines at the International Rice Research Institute and highly stress-tolerant male lines used in development of Green Super Rice varieties. We select the F1 hybrids for yield performance and their parents with outstanding general combining ability for yield by genomic best linear unbiased prediction. We also find that parental genetic distance is negatively correlated with mid-parent heterosis and performance of progeny for most traits, including yield, in the population of study, which supports other bodies of evidence that increased parental genetic distance does not always result in improved progeny performance. We hypothesize that the outstanding F1 hybrids identified are likely to be highly stress-tolerant. However, based on the prediction accuracies observed in our study, we recommend obtaining better estimates of parental general combining ability by expanding the training set before relying on genomic prediction to conduct reciprocal recurrent genomic selection for stress-tolerant hybrid rice. Third, we compare the effects of using overlapping and discrete selection generations in recurrent mass phenotypic, genomic, and genomic optimum contribution selection by simulation of fifty breeding cycles. We find that discrete generations outperform overlapping generations in terms of mean genetic value under phenotypic selection. This is because selected plants are more likely to have positive error variances if phenotyped only once, and overlapping selection generations allow error to propagate as individuals erroneously believed to have exceptional breeding values are repeatedly selected. In genomic selection, estimates of genetic value are updated even if individuals are phenotyped once as relatives in the population are phenotyped, and overlapping and discrete selections perform similarly with genomic and genomic optimum contribution selection. However, it appears that overlapping selection might allow slightly better optimization of inbreeding and mean genetic value in optimum contribution selection. As such, we recommend maintaining discrete generations in phenotypic mass selection programs in which individuals are only phenotyped once. In genomic mass selection, we observed no clear benefit to allowing generations to overlap.
Issue Date:2020-07-17
Type:Thesis
URI:http://hdl.handle.net/2142/108719
Rights Information:Copyright 2020 Marlee Labroo
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08


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