One of the grand challenges in biology today is the ability to link genotype to phenotype. Phenotypes are the basic form of traits of an organism. Phenotypes, in the form of diseases, can arise when mutations occur at certain loci. Recent advances in genomic technologies have enabled the scientific community to generate genotypic information for hundreds of species. Moreover, methods for linking genotype to phenotype using genome-wide association mapping are continuously improving. As a result of these two endeavors, the first landmark studies using this association-mapping technique from an entire range of organisms have surfaced. These studies demonstrate the power of combining genomics with population genetics for the study of phenotypic diversity. The ultimate goal of genome-wide association studies is to identify the casual genetic variant for a particular trait. While remarkable progress is being made in identifying some casual variants, many remain elusive. One possible source of naturally occurring casual variants that is currently being overlooked are epigenetic alleles (epialleles). These alleles often go undetected in a population because their defining signatures are not as obvious as a change in DNA sequence, like genetic mutations, which can easily be identified by sequencing-based technologies. Epialleles often contain changes in their DNA methylation status. Very few epialleles have been identified across kingdoms, which is likely a result of the difficulty in their identification. This proposal aims to use naturally occurring Arabidopsis thaliana strains that have been isolated from a broad range of geographically distributed regions of the planet for the identification of population-wide epialleles. The short-term goals of this proposal are to identify these unique alleles by whole-genome bisulfite sequencing technologies. Upon computational identification of these unique alleles, proposed experiments aim to understand their origins and their modes of inheritance, in addition to their impact on phenotypic variation. To accomplish these goals, the candidate will continue to learn computational biology methods as well as principles in quantitative genetics through collaboration with experts in these respective fields. The successful completion of this proposed work will reveal the degree of epiallelic variation within a species paving the way for extending these studies into human populations. This work will attempt to determine the basis for epiallelic variation using association-mapping techniques. This approach will allow the mapping of genomic regions that are linked to the presence of an epiallele. One major goal of this work is to determine the impact that epialleles identified by these high-throughput techniques have on phenotypic variation. Demonstrating that epialleles identified in this population actually contribute to the phenotype variation will be of utmost importance to increasing awareness of naturally occurring epialleles and their potential phenotypic consequences in human biology. Finally, successful training in genomics, computational biology and quantitative genetics will give me a strong foundation for beginning an independent research program focused on natural epigenetic variation.

Public Health Relevance

Genome sequences for thousands of individuals are being analyzed to determine the total genetic variation present within a species and to determine the impact this variation has on phenotypic variation. Missing from these efforts are the identification of naturally occurring epigenetic variants, which have already been demonstrated to contribute to human disease and development. Therefore, identifying these epialleles in a systemic manner and understanding their specific roles and behavior is essential for a comprehensive understanding of phenotypic variation.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Transition Award (R00)
Project #
5R00GM100000-03
Application #
8733709
Study Section
No Study Section (in-house review) (NSS)
Program Officer
Janes, Daniel E
Project Start
2012-09-15
Project End
2016-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Georgia
Department
Type
DUNS #
City
Athens
State
GA
Country
United States
Zip Code
30602
Niederhuth, Chad E; Schmitz, Robert J (2017) Putting DNA methylation in context: from genomes to gene expression in plants. Biochim Biophys Acta 1860:149-156
Bewick, Adam J; Schmitz, Robert J (2017) Gene body DNA methylation in plants. Curr Opin Plant Biol 36:103-110
Stathopoulou, Athanasia; Chhetri, Jyoti B; Ambrose, John C et al. (2017) A novel requirement for DROSHA in maintenance of mammalian CG methylation. Nucleic Acids Res 45:9810
Hofmeister, Brigitte T; Lee, Kevin; Rohr, Nicholas A et al. (2017) Stable inheritance of DNA methylation allows creation of epigenotype maps and the study of epiallele inheritance patterns in the absence of genetic variation. Genome Biol 18:155
Yu, Ping; Ji, Lexiang; Lee, Kevin J et al. (2016) Subsets of Visceral Adipose Tissue Nuclei with Distinct Levels of 5-Hydroxymethylcytosine. PLoS One 11:e0154949
Hohos, Natalie M; Lee, Kevin; Ji, Lexiang et al. (2016) DNA cytosine hydroxymethylation levels are distinct among non-overlapping classes of peripheral blood leukocytes. J Immunol Methods 436:1-15
Basenko, Evelina Y; Kamei, Masayuki; Ji, Lexiang et al. (2016) The LSH/DDM1 Homolog MUS-30 Is Required for Genome Stability, but Not for DNA Methylation in Neurospora crassa. PLoS Genet 12:e1005790
Bewick, Adam J; Ji, Lexiang; Niederhuth, Chad E et al. (2016) On the origin and evolutionary consequences of gene body DNA methylation. Proc Natl Acad Sci U S A 113:9111-6
Kawakatsu, Taiji; Stuart, Tim; Valdes, Manuel et al. (2016) Unique cell-type-specific patterns of DNA methylation in the root meristem. Nat Plants 2:16058
Panda, Kaushik; Ji, Lexiang; Neumann, Drexel A et al. (2016) Full-length autonomous transposable elements are preferentially targeted by expression-dependent forms of RNA-directed DNA methylation. Genome Biol 17:170

Showing the most recent 10 out of 23 publications