Using a Generalized Linear Model to Reveal Spatial Pattern in Plant Reproduction

2530 

Contributed Abstract 

Poster 

Michelle Bang (1), Duo Jiang (1), John Fowler (1), Zuzana Vejlupkova (1)

(1) Oregon State University, Corvallis, OR

Duo Jiang  
Oregon State University

John Fowler  
Oregon State University

Zuzana Vejlupkova  
Oregon State University

Michelle Bang  
Oregon State University

Michelle Bang  
N/A

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.

Generalized Linear Models|Genotype-phenotype|Quasi-binomial regression|Biological fitness|Spatial Pattern|

Section on Statistics in Genomics and Genetics

Miscellaneous