Abstract Number:
2530
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Michelle Bang (1), Duo Jiang (1), John Fowler (1), Zuzana Vejlupkova (1)
Institutions:
(1) Oregon State University, Corvallis, OR
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
We are interested in assessing ~300 maize genes, selected based on genomic data, for mutations that affect the biological fitness of maize pollen. For each gene, a 1:1 mix of mutant and wild-type pollen is crossed onto a non-mutant ear. In an offspring maize ear, any deviation from a 1:1 proportion between wildtype and mutant kernels would suggest that the associated mutation changes the fitness of the pollen. To detect genes that affect fitness, a generalized linear model (GLM) is used to test if mutations significantly deviated from the 1:1 proportion. The model assumes a quasibinomial distribution to account for variation across maize ears. For the 30 mutations found to reduce fitness, we also investigate the idea that altered pollen fitness will result in a non-uniform spatial distribution of mutant/wildtype kernels on an ear. A spatial analysis using GLM is therefore conducted on each fitness-altering allele to test for non-random spatial patterns of mutant versus wild-type kernels on a maize ear, such as a gradient effect. Consistent with our motivating idea, results identify several alleles that produce a non-random spatial pattern.
Keywords:
Generalized Linear Models|Genotype-phenotype|Quasi-binomial regression|Biological fitness|Spatial Pattern|
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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