Life Sciences Commons^™

Unraveling Prevalence And Effects Of Deleterious Mutations In Maize Elite Lines Across Decades Of Modern Breeding, Shichao Sun, Baobao Wang, Changyu Li, Gen Xu, Jinliang Yang, Matthew B. Hufford, Jeffrey Ross-Ibarra, Haiyang Wang, Li Wang

Department of Agronomy and Horticulture: Faculty Publications

Future breeding is likely to involve the detection and removal of deleterious alleles, which are mutations that negatively affect crop fitness. However, little is known about the prevalence of such mutations and their effects on phenotypic traits in the context of modern crop breeding. To address this, we examined the number and frequency of deleterious mutations in 350 elite maize inbred lines developed over the past few decades in China and the United States. Our findings reveal an accumulation of weakly deleterious mutations and a decrease in strongly deleterious mutations, indicating the dominant effects of genetic drift and purifying selection …

Enhancing Resolution Of Natural Methylome Reprogramming Behavior In Plants, Robersy Sanchez, Xiaodong Yang, Hardik Kundariya, Jose R. Barreras, Yashitola Wamboldt, Sally A. Mackenzie

Department of Agronomy and Horticulture: Faculty Publications

We have developed a novel methylome analysis procedure, Methyl-IT, based on information thermodynamics and signal detection. Methylation analysis involves a signal detection problem, and the method was designed to discriminate methylation regulatory signal from background noise induced by thermal fluctuations. Comparison with three commonly used programs and various available datasets to furnish a comparative measure of resolution by each method is included. To confirm results, methylation analysis was integrated with RNAseq and network enrichment analyses. Methyl-IT enhances resolution of genome methylation behavior to reveal network-associated responses, offering resolution of gene pathway influences not attainable with previous methods.

Substantial Contribution Of Genetic Variation In The Expression Of Transcription Factors To Phenotypic Variation Revealed By Erd-Gwas, Hung-Ying Lin, Qiang Liu, Xiao Li, Jinliang Yang, Sanzhen Liu, Yinlian Huang, Michael J. Scanlon, Dan Nettleton, Patrick S. Schnable

Department of Agronomy and Horticulture: Faculty Publications

Background: There are significant limitations in existing methods for the genome-wide identification of genes whose expression patterns affect traits.

Results: The transcriptomes of five tissues from 27 genetically diverse maize inbred lines were deeply sequenced to identify genes exhibiting high and low levels of expression variation across tissues or genotypes. Transcription factors are enriched among genes with the most variation in expression across tissues, as well as among genes with higher-than-median levels of variation in expression across genotypes. In contrast, transcription factors are depleted among genes whose expression is either highly stable or highly variable across genotypes. We developed a …