Source: PURDUE UNIVERSITY submitted to NRP
BIOINFORMATICS AND PLANT GENOME STRUCTURE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
0182893
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 1, 2003
Project End Date
Sep 30, 2008
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
PURDUE UNIVERSITY
(N/A)
WEST LAFAYETTE,IN 47907
Performing Department
AGRONOMY
Non Technical Summary
The findings will contribute to improved agricultural productivity. The fruits of this research will include: (1) new statistical and data handling techniques for integrating genetic, phenotypic, and extensive metabolic and ionic profiles (2) improved understanding of genetic controls of phenotypic responses to environmental changes, and (3) improved understanding of stress resistance in maize, which could be used for development of improved cultivars.
Animal Health Component
20%
Research Effort Categories
Basic
50%
Applied
20%
Developmental
30%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011510108033%
2031510108033%
2061510108034%
Knowledge Area
206 - Basic Plant Biology; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1510 - Corn;

Field Of Science
1080 - Genetics;
Goals / Objectives
The goals of this proposal are to 1) reduce the dimensionality of multilevel complex metabolic data in a biologically meaningful and statistically succinct way, 2) model phenotypic data as a function of the metabolic data, and 3) Apply these techniques to real experimental data.
Project Methods
This project will model the interface between genes, the environment, stress, metabolism, cell wall structures, and phenotypic plasticity. Experiments will center around two types of maize populations. Population I is a set of 64 near-isogenic lines (NILs) in a common B73 background into which different chromosomal segments of inbred line Tx303 were introgressed via marker-assisted selection. Population II (bm-NILs) is a set of near-isogenic brown midrib (bm) mutants. The TB-NILs in population I contain relatively large segments of DNA from the Tx303 donor line, so that allelic variations throughout much of the genome are sampled, but the identity of the specific genes contained in the introgressed DNA is not (yet) very well characterized. In contrast, the bm-NILs in population II contain few, but very well-defined mutations. The TB-NILs have been evaluated under high and low population density stresses in North Carolina in 2001 and 2002, and Indiana in 2002. Preliminary analysis indicates significant differences in flowering time, anthesis-silk interval, plant height, and number of ears per plant among NILs. We propose to grow the two populations under low density (45,000 plants per hectare) and high density (90,000 plants per hectare) in Indiana with different soil types and climate patterns. Phenotypic measurements will be taken on plants in the field: height and developmental stage at 4 and 8 weeks after planting, flowering and silking time, anthesis-silking interval, height at flowering, presence of ears, ear height, number of ears, ear weight, and kernel number. Multivariate statistical techniques will be used, e.g., discriminant, factor, and principle components analysis, and clustering. Factor analysis will be used to assess the structure of the data. When underlying relationships are completely unknown, we will use an exploratory mode to help reveal structure. When a theoretical foundation exists, factors will be specified in advance and confirmatory factor analysis used to verify and validate the relative importance of the underlying measured variables. Whichever mode is used, the resulting outcome is a set of factor scores. The factor scores can be used as predictor variables in a regression model. Once the dimensionality of the data has been reduced, it will be connected to outcomes of interest such as yield. The number of models and sub-models will depend upon the number of factors identified, and the number and types of different data to be integrated. To organize the model space into logical units for searching, we will develop automated mechanisms for the enumeration of models and the organization of these models into a hierarchical format. Many commonly used state-of-the-art methods for model-selection belong to the broad category of penalized maximum likelihood criteria. For each model class the likelihood is penalized by the dimensionality of the model class to preclude the maximum likelihood estimates from overfitting the data. At least four such penalized maximum likelihood criteria will be considered: Aikake Information Criterion, Bayes Information Criterion, Minimum Description Length, and Minimum Message Length.

Progress 10/01/03 to 09/30/08

Outputs
We have implemented the cell means model as a way of imporving model selection strategies for improving QTL detection. We have conceptualized the central dogma of geneticss into some simple statistical models tht can be used to help approach systems biology in a direct way. We have partnered with SAS institute, the nations largest staistical software company, to develop a new procedure forQTL mapping for binary triats called PROC BTL. We haved eveloped two JAva applets that implement the statistical methods developed.

Impacts
This project is expected to show some interesting impacts in the understanding of pleiotropy and epistasis. Significant progress has been made in shifting from an exploratory paradigm, where data from new technology is mined without regard to hypotheses to hypotheis driven model construction.

Publications

  • Verhoeven K, Jean-Luc Jannink, and L.M. McIntyre 2006 Using mating designs to uncover QTL and the genetic architecture of complex traits Heredity (96) 139-149.
  • Hammes U, Daniel P. Schachtman, R. Howard Berg, Erik Nielsen, Wolfgang Koch, LM. McIntyre, and Christopher G. Taylor 2005 Nematode induced changes of transporter gene expression in Arabidopsis roots Molecular Plant-Microbe Interactions 18(12):1247-1257
  • Gonzalo, M J.B. Holland, T. Vyn and L.M. McIntyre 2006 Mapping Density Response in Maize: A Direct Approach for Testing Genotype and Treatment Interactions Genetics
  • Coffman C., R Doerge, K.Simonsen, K. Nichols, C. Duarte, R Wolfinger, L.M. McIntyre 2005 Model Selection in Binary Trait Locus Mapping. Genetics 170:1281-1297
  • Verhoeven K., K. Simonsen, L.M. McIntyre. 2005. Implementing false discovery rate control: increasing your power Oikos 108: 643-647


Progress 10/01/04 to 09/30/05

Outputs
Analysis of quantitative trait loci (QTL) affecting complex traits is often pursued in single-cross experiments. For most purposes, including breeding, some assessment is desired of the generalizability of the QTL findings and of the overall genetic architecture of the trait. Single-cross experiments provide a poor basis for these purposes, as comparison across experiments is hampered by segregation of different allelic combinations among different parents and by context-dependent effects of QTL. To overcome this problem, we combined the benefits of QTL analysis (to identify genomic regions affecting trait variation) and classic diallel analysis (to obtain insight in the general inheritance of the trait) by analyzing multiple mapping families that are connected via shared parents. We first provide a theoretical derivation of main (general combining ability, GCA) and interaction (specific combining ability, SCA) effects on F2 family means relative to variance components in a randomly-mating reference populations. Then, using computer simulations to generate F2 families derived from 10 inbred parents in different partial diallel designs, we show that QTL can be detected and that the residual among-family variance can be analyzed using standard diallel analyses methods in order to reveal the presence and the mode of action (in terms of GCA and SCA) of undetected polygenes. Given a fixed experiment size (total number of individuals), we demonstrate that QTL detection and estimation of the genetic architecture of polygenic effects are competing goals which should be explicitly accounted for in experimental design. Our approach provides a general strategy for exploring the genetic architecture as well as the QTL underlying variation in quantitative traits.

Impacts
This project is expected to show some interesting impacts in the understanding of pleiotropy and epistasis. Significant progress has been made in shifting from an exploratory paradigm, where data from new technology is mined without regard to hypotheses to hypotheis driven model construction.

Publications

  • Verhoeven K., K. Simonsen, L.M. McIntyre. 2005. Implementing false discovery rate control: increasing your power Oikos 108: 643-647.
  • Coffman C., R Doerge, K.Simonsen, K. Nichols, C. Duarte, R Wolfinger, L.M. McIntyre 2005 Model Selection in Binary Trait Locus Mapping. Genetics 170:1281-1297
  • Coffman, C., M. L. Wayne , S. V. Nuzhdin , L.A. Higgins , L. M. McIntyre 2005 Identification of co-regulated transcripts affecting male body size in Drosophila. Genome Biology 6 (6): Art. No. R53


Progress 10/01/03 to 09/29/04

Outputs
As pointed out in the Nature Genetics article ``Studying complex biological systems using multifactorial perturbation'' (Jansen 2003), the real promise of genomic technology, specifically microarray technologies, will be realized when used in concert with classic genetic approaches. Broadening this view even further, I anticipate the vast potential to provide even more information by examining genomic technology in a larger context through the combination of information and results across common experiments and technologies. Ground breaking experimental work by many scientists is currently underway using genomic technology and genetic information in complex situations. My focus has been to develop and expand statistical methods that focus on hypothesis driven research using genomic technology. This past year I have focused on use of factor analysis for genomic and ionomic data. In both cases, biological model interpretation is afacilitated.

Impacts
This project is expected to show some interesting impacts in the understanding of pleiotropy and epistasis. Significant progress has been made in shifting from an exploratory paradigm, where data from new technology is mined without regard to hypotheses to hypotheis driven model construction.

Publications

  • Brett Lahner, Jiming Gong, Mehrzad Mahmoudian, Ellen L Smith, Khush B Abid, Elizabeth E Rogers, Mary L Guerinot, Jeffrey F Harper, John M Ward, Lauren McIntyre, Julian I Schroeder & David E Salt 2003. Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana Nature Biotechnology 21(10):pp1215-1221
  • Pirttila, A. M., L. M. McIntyre, G. A., Payne and C. P. Woloshuk. 2004 Comparison of expression profiles associate gene tags with fumonisin biosynthesis in wild type and fcc1 mutant of Fusarium verticillioides Fungal Genetics and Biology 41(6): 647-656
  • Simonsen, K, L M. McIntyre, 2004 Using alpha wisely: Improving power to detect multiple QTL. Statistical Applications in Genetics and Molecular Biology Vol: 3 No Article 1
  • Pedra, JHF, L. M. McIntyre, M.E. Scharf and B. Pittendrigh 2004 Genome wide transcription profile of field and laboratory selected DDT resistant Drosophila PNAS 101(18):7034-7039
  • Hong Li, A K. Singh, L. M. McIntyre and A. Sherman 2004 Differential gene expression in response to hydrogen peroxide and the putative PerR regulon of Synechocystis sp. Strain PCC 6803 J. of Bacteriology 186 (11): 3331-3345
  • Sergey V. Nuzhdin, Marta L. Wayne, Kristy L. Harmon, and L. M. McIntyre 2004 Common pattern of evolution of gene expression level and protein sequence in Drosophila Molecular Biology and Evolution 21(7):1308-17


Progress 10/01/03 to 11/03/03

Outputs
This is a new project and we are getting things started. Collaborations are in place and we are sending samples to Puerto Rico for generation advancement this winter. We expect to have the first batch of material ready for evaluation this summer.

Impacts
This project is expected to show some interesting impacts in the understanding of pleiotropy and epistasis.

Publications

  • No publications reported this period


Progress 10/01/01 to 09/30/02

Outputs
Work on the plant growth modeling continues. This summer we grew 64 lines in 3 replications at two locations and measured them for height and developmental stage every week. We also recorded flowering time, silking time and final height. Data from this summers experiment will be used to expand QTL mapping methodology and develop new statistical strategies for modeling plant growth.

Impacts
This research provides a more precise prediction of grain yield, an understanding of vegetative growth characteristics on plant yield and statistical models to describe data on corn growth.

Publications

  • Vermerris, W., K. Thompson, L. McIntyre, and J. Axtell. 2002. Evidence for an evolutionarily conserved interaction between cell wall biosynthesis and flowering in maize and sorghum. BMC Evol. Biol. 2(1). http://www.biomedcentral.com/1471-2148/2/2.
  • Vermerris, W., K. Thompson, and L. McIntyre. 2002. The maize Brown midrib locus affects cell wall composition and plant development in a dose dependent manner. Heredity 88(6):450-7.
  • G. Bepler, A. Gautam, L. McIntyre, A. F. Beck, D. Chervinsky, Y.C. Kim, D. Pitterle, and A. Hyland. 2002 Prognostic Significance of Molecular Genetic Aberrations on Chromosome Segment 11p15.5 in Non-Small-Cell Lung CancerJ Clin. Oncol. 20(5):1353-60


Progress 10/01/00 to 09/30/01

Outputs
Work on the development of a multiple gene model has been completed. This fully parametric model will enable the model selection and estimation of recombination and penetrance for multiple binary trait loci. Simulation studies show that under many conditions the number of causal loci can be identified. Work on the modeling of plant growth and development has begun. This modeling is complex as it involves serial autocorrelation of measurements and traits. Inbred lines differing in development are being studied and measured and these data used to inform the modeling process. Work on medical applications of statistical methodology has resulted in two publications this year.

Impacts
The statistical methodology developed as a part of this project is anticipated to have broad usefulness in plant and animal breeding as well as potential applications in human disease.

Publications

  • Coffman, C., R. Doerge, and L. McIntyre. 2001. Compound hypothesis testing in QTL analysis. Abstract, ENAR, March 2001.
  • K. Simonsen, and L. McIntyre. 2001. Multiple Testing Issues for Linked Genetic Markers. Abstract. ENAR, March 2001.
  • L. McIntyre, K. O'Briant, C. McBride, G. Bepler (2001) Rater Agreement and the Utility of the Mutagen-Induced Chromosome Damage Assay. Anticancer Research 21:605-610.
  • V. G. Fowler, Jr., L. McIntyre, M. R. Yeaman, G. E. Peterson, L. B. Reller, G. Corey, D. Wray, A. S. Bayer (2000) In Vitro Resistance to Thrombin-induced Platelet Microbicidal Protein in Isolates of Staphylococcus aureus from Endocarditis Patients Correlates with an Intravascular Device Source. Journal of Infectous Disease(182:1251-4)
  • Abstracts Coffman, C., R. Doerge, and L. McIntyre. 2001. Are single marker models for QTL detection really less powerful than two marker models? Compound hypotheses for QTL analysis, Plant and Animal Genome, January 2001.
  • Coffman, C., R. Doerge, K. Simonsen, and L. McIntyre. 2001. Detection and localization of multiple binary trait loci in experimental populations. Eastern North American Regional Conference of the Boimeterics Society (ENAR), March 2001.
  • Vermerris, W., K. Thompson, and L. McIntyre. 2001. A closer look at an allegedly recessive mutation: evidence for dose effects at the brown midrib 1 locus. Maize Meeting, March 2001.


Progress 10/01/99 to 09/30/00

Outputs
We continue to work on methods to handle complex trait data. We have been focusing on non-normally distributed traits and impacts of these on statistical inference. Initial results are promising. A single gene probability model has been developed and progress is being made on an extention to multiple genes.

Impacts
This work will bring QTL methodology to the next level by incorporating important agronomic traits such as disease resistance and insect resistance.

Publications

  • L. McIntyre, C. Coffman, R. Doerge. (2000) Detection and Location of Single Binary Trait Loci in Experimental Populations. In press. Genetical Research.