Tag: plant breeding genomics

ANOVA

Sample Size Estimation

Population Structure and Linkage

Association Analysis

Layout Specific Analyses

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This presentation describes how to handle data generated by a field-based sensor array.

Part 1

Part 2

Part 3

Full Recording

Software Links

QGIS: A Free and Open Source Geographic Information System

HTP Geoprocessor: A plugin for QGIS

ASReml: Data analysis software designed for fitting linear mixed models

PROSAIL: The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model

Presenters

Michael Gore is an associate professor of molecular breeding and genetics for nutritional quality at Cornell University in Ithaca, NY, where he is a member of the faculty in the Department of Plant Breeding and Genetics. He holds a BS and MS from Virginia Tech in Blacksburg, Virginia, and a PhD from Cornell University. Before joining the faculty at Cornell, he worked as a Research Geneticist with the USDA-ARS at the Arid-Land Agricultural Research Center in Maricopa, AZ. His expertise is in the field of quantitative genetics and genomics, especially the genetic dissection of metabolic traits. He has also contributed to the development and application of field-based, high-throughput phenotyping tools for plant breeding and genetics research. He teaches two short courses at the Tucson Winter Plant Breeding Institute in Tucson, Arizona, and serves on the editorial boards of Crop Science and Theoretical and Applied Genetics. His career accomplishments in plant breeding and genetics earned him the National Association of Plant Breeders Early Career Scientist Award in 2012 and the American Society of Plant Biologists Early Career Award in 2013. 

Kelly Thorp is a Research Agricultural Engineer with USDA-ARS in Maricopa, Arizona. He holds a BS and MS from the University of Illinois at Urbana-Champaign and a PhD from Iowa State University. His research focuses primarily on the development and application of informational technologies for monitoring cropping systems and understanding cropping system processes.  Areas of expertise include remote sensing, cropping system simulation modeling, and geographic information systems.  Application areas for these technologies include crop water and nitrogen status assessment, precision agriculture, management of nitrogen fertilizer, irrigation and drainage water management, field-based plant phenomics, and development of new bioenergy crops.  He serves as an associate editor for Transactions of the ASABE and Applied Engineering in Agriculture.

See Part 1: Developing and Using a Sensor Array

Related Publications

Andrade-Sanchez Pedro, Gore Michael A., Heun John T., Thorp Kelly R., Carmo-Silva A. Elizabete, French Andrew N., Salvucci Michael E., White Jeffrey W. (2013) Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology.

A. Elizabete Carmo-Silva, Michael A. Gore, Pedro Andrade-Sanchez, Andrew N. French, Doug J. Hunsaker, Michael E. Salvucci (2012) Decreased CO₂ availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany.83:1-11. ISSN 0098-8472, 10.1016/j.envexpbot.2012.04.001.

Jeffrey W. White, Pedro Andrade-Sanchez, Michael A. Gore, Kevin F. Bronson, Terry A. Coffelt, Matthew M. Conley, Kenneth A. Feldmann, Andrew N. French, John T. Heun, Douglas J. Hunsaker, Matthew A. Jenks, Bruce A. Kimball, Robert L. Roth, Robert J. Strand, Kelly R. Thorp, Gerard W. Wall, Guangyao Wang (2012) Field-based phenomics for plant genetics research. Field Crops Research, Volume 133: 101-112, ISSN 0378-4290, 10.1016/j.fcr.2012.04.003.

Thorp, K.R., Wang, G., West, A.L., Moran, M.S., Bronson, K.F., White, J.W., Mon, J.  2012.  Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models.  Remote Sensing of Environment. 124:224-233.

Jeffrey W. White, Pedro Andrade-Sanchez, Michael A. Gore, Kevin F. Bronson, Terry A. Coffelt, Matthew M. Conley, Kenneth A. Feldmann, Andrew N. French, John T. Heun, Douglas J. Hunsaker, Matthew A. Jenks, Bruce A. Kimball, Robert L. Roth, Robert J. Strand, Kelly R. Thorp, Gerard W. Wall, Guangyao Wang (2012) Field-based phenomics for plant genetics research. Field Crops Research, Volume 133: 101-112, ISSN 0378-4290, 10.1016/j.fcr.2012.04.003.

Recommended Reading

Barnett V, Lewis T (1994). Outliers in Statistical Data, 3rd edition, John Wiley, New York, NY, USA.

Belsley DA, Kuh E, Welsch RE (2004). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley-Interscience, Hoboken, NJ, USA. 

Box GEP, Cox DR (1964). An analysis of Transformations. J. Roy. Stat. Soc. B. Met. 26: 211–252.

Cook RD, and Weisberg S (1982) Residuals and Influence in Regression, Chapman & Hall, New York, NY, USA

Endelman JB (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Gen 4: 250-255.

Henderson CR (2004). Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, Ontario, Canada.

Holland JB, Nyguist WE, Cervantes-Martınez CT (2003). Estimating and interpreting heritability for plant breeding: an update. In Janick J (ed) Plant Breeding Reviews. John Wiley and Sons: Hoboken, New Jersey, USA.

Hung H-Y, Browne CJ, Guill KE, Coles N, Eller M, Garcia A et al (2011). The relationship between parental genetic or phenotypic divergence and progeny variation in the maize Nested Association Mapping population. Heredity 108:490-499. 

Jia Y, Jannink J-L (2012). Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192: 1513-1522.

Jiang C, Zeng ZB (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140: 1111-1127.

Korte A, Vilhjalmsson BJ, Segura V, Platt A, Long Q, Nordborg M (2012). A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet 44: 1066-1071.

Kutner MH, Nachtsheim CJ, Neter J, Li W (2004). Applied Linear Statistical Models, 4th ed. McGraw-Hill, Boston, MA, USA.

Li H, Ye G, Wang J (2007). A modified algorithm for the improvement of composite interval mapping. Genetics 175: 361-374.

Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ et al (2012). GAPIT: genome association and prediction integrated tool.
Bioinformatics 28: 2397-2399.

McCullagh P and Nelder JA (1989). Generalized Linear Models, Second Edition. Chapman & Hall, New York, NY, USA.

Osborne JW, Overbay A (2004). The power of outliers (and why researchers should always check for them). Practical Assessment, Research & Evaluation, 9(6).

Segura V, Vilhjalmsson BJ, Platt A, Korte A, Seren U, Long Q et al (2012). An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44: 825-830.

Wu W-R, Li W-M, Tang D-Z, Lu H-R, Worland AJ (1999). Time-related mapping of quantitative trait loci underlying tiller number in rice. Genetics 151: 297-303.

Funding Statement

Development of this resource was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, and Dry Bean Root Health East Africa, Cotton Incorporated and United States Department of Agriculture – Agricultural Research Service (USDA-ARS).  Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.

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Original day/time: February 6, 2014 at 1:00pm Eastern Time (-05:00 GMT)

Resources from the Webinar

Databases

• GenBank
• Phytophthora-id.org
• Fusarium-ID database

Field identification

• http://beanipm.pbgworks.org
• http://legume.ipmpipe.org
• Soilborne Root and Stem Diseases of Dry Beans in Nebraska EC1869 

Metagenomics

• Qiime
• Mothur

Find all upcoming and archived webinars »

About the Presenter

Martin Chlivers is a visiting Assistant Professor at Michigan State University.  He studies diseases of field crops, and the biology and genetics of fungal and oomycete organisms that cause disease. He utilizes both classic techniques such as culturing causal agents and the latest technologies, such as next generation sequencing of metagenomes, to improve disease management by understanding the organisms, factors, and host-pathogen interactions that drive disease.

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Solanaceae Coordinated Agricultural Project: Dedicated to the Improvement of Potato and Tomato

Presentor

Allen Van Deynze
Univeristy of California Davis Seed Biotechnology Center 

Link to Presentation PDF

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(Figure 1) Flow of information from collecting genotypic and phenotypic data to mapping to graphical genotyping and quantitative trait locus analysis. Figure credit: Heather Merk. Image credits: Genotyping, Allen Van Deynze, UC Davis; Mapping, Scott Wolfe, The Ohio State University; Graphical Genotyping, Nancy Huarachi, The Ohio State University; Phenotyping, David Francis, The Ohio State University; QTL Analysis, Hamid Ashrafi, UC Davis.

Introduction to Association Genetics

Mapping and QTL Analysis

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Part 1: Semi-Parallel Linear Regression

Part one explains GWA analysis in a loop using lm and lsfit functions and semi-parallel computations of linear regression with covariates.  Also explains how to handle missing phenotype and SNP data.

Part 2: Semi- Parallel Logisitic Regression

Part two explains semi-parallel logisitic regression in R based on iteratively reweighted least squares (equivalent to glm), with and without covariates.

Part 3: Efficient Data Access

Part three explains how to convert the SNP matrix from a text file to an array-oriented binary file using the Ncdf and ff packages.  Array-oriented binary files allow efficient access to blocks (columns) of SNPs by SNP, as opposed to by individual/line (rows).

Full Recording

​Download R and Individual R Packages

www.r-project.org

R Packages Specific to this Tutorial

ncdf: Interface to Unidata netCDF data files
ff: memory-efficient storage of large data on disk and fast access functions

R Codes Available

https://bitbucket.org/ksikorska/gwasp

About the Presenter

Karolina Sikorska received a Master’s degree in Mathematics from the Gdansk University of Technology, Poland, with a specialization in financial mathematics.  In 2009 she started  her PhD project in the Department of Biostatistics, Erasmus Medical Centre in Rotterdam.  Her research is related to fast computations in genome-wide association studies.  Her work is focused on developing new methodology and algorithms which significantly speed up computations in GWAS for simple models, such as linear and logistic regression, as well as, mixed models for analyzing longitudinal data. She is also interested in improving tools for efficient data access in GWAS framework.

Related Publications

Sikorska, K., Lesaffre, E., Groenen, P. F., & Eilers, P. H. (2013). GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics, 14(1), 166.

Sikorska, K., Rivadeneira, F., Groenen, P. J., Hofman, A., Uitterlinden, A. G., Eilers, P. H., & Lesaffre, E. (2013). Fast linear mixed model computations for genome‐wide association studies with longitudinal data. Statistics in Medicine, 32(1), 165-180.

Funding Statement

Development of this resource was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, Dry Bean Root Health East Africa, and the Erasmus Medical Center  Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.

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Solanaceae Coordinated Agricultural Project: Dedicated to the Improvement of Potato and Tomato

Link to Presentation PDF

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Introduction to Crossing and Selection

Marker Assisted Selection

Genome-Wide Selection

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Part 1 of 3: The utility of GAPIT, installation of RStudio and use of the GAPIT manual

Part 2 of 3: Sample data, necessary packages, uploading data, GAPIT functions and GWAS output

Part 3 of 3: Demonstration of GAPIT GWAS using RStudio

Full Recording: The use of GAPIT in RStudio for GWAS

Software Downloads

• www.r-project.org
• www.rstudio.com

Additional Resources

• GAPIT – Genome Association and Prediction Integrated Tool
• GAPIT User Manual (highly recommended)

About the Presenter

Duke Pauli is a graduate student at Montana State University. His work primarily focuses on the development of new malting barley varieties for the Montana production region using genome-wide association mapping to identify novel beneficial alleles that can be incorporated into barley breeding programs. His work also focuses on the implementation of genomic selection and understanding the utility of genomic selection on regional plant breeding programs.

Photo Credit: Modified from Mina Talajoor

Recommending Reading

Zhiwu Zhang, Elhan Ersoz, Chao-Qiang Lai, Rory J Todhunter, Hemant K Tiwari, Michael A Gore, Peter J Bradbury, Jianming Yu, Donna K Arnett, Jose M Ordovas, & Edward S Buckler (2010) Adaptation of Mixed Linear Model for Genome-Wide Association Studies. Nature Genetics 42: 355-360.

Alexander E. Lipka, Feng Tian, Qishan Wang, Jason Peiffer, Meng Li, Peter J. Bradbury, Michael A. Gore, Edward S. Buckler, and Zhiwu Zhang (2012) GAPIT: Genome Association and Prediction Integrated Tool. Bioinformatics. doi:10.1093/bioinformatics/bts444

Funding Statement

Development of this resource was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, Dry Bean Root Health East Africa, the Triticeae Coordinated Agriculture Project, and the Barley Coordinated Agriculture Project.  Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.

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