Genomic Selection for Crop Improvement
Genomic Selection for Crop Improvement
JMP Genomics serves as your experimental design and analysis platform for crop-breeding programs. Biologists and breeders can employ linkage mapping and QTL analysis for experimental populations, and complex association methods for diversity populations to identify markers for pest and pathogen resistance.
Comprehensive predictive modeling reviews perform genomic selection with multiple traits of interest to improve breeding selection and accuracy, allowing insights that aren’t possible with classical phenotypic selection. These genomic selection tools uncover optimal combinations of markers to produce desirable traits, which in turn can be used to design and evaluate future crosses to produce healthier foods.
- Perform molecular marker-assisted selection to improve crop viability and performance.
- Utilize multi-trait predictive modeling reviews to optimize breeding programs.
- Evaluate potential crosses and simulate their progeny based on genomic selection model scoring to determine optimal breeding strategies for future performance.
JMP Genomics allows you to import variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, Plink text and binary files, and common output formats from SNP arrays. Once imported, choose from extensive association analysis options from simple case-control association to complex linear models supporting covariates, interactions and random effects.
Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, or identify genomic regions shared identical by state (IBS) between related or unrelated individuals. You can also analyze patterns of linkage disequilibrium, correct for population structure and discover SNP-SNP interactions. Create, compress and easily integrate relationship matrices into association tests to simultaneously correct for population structure and relatedness with Q-K mixed model analysis.
With support for intensity, aligned read, and count data formats, JMP Genomics lets biologists and biostatisticians normalize and analyze both array data and summaries from next-gen studies, providing several scaling and normalization methods tailored for count data sets. Point-and-click workflows simplify gene and exon expression, and RNA-seq analysis for new users.
JMP Genomics also supports more complex expression analyses, such as screening for allele-specific expression, filtering intensities or counts, performing batch normalization, and applying sample and gene filters to quickly reanalyze subsets of your data.
JMP Genomics provides exceptional tools for statistical geneticists, from simple case-control association and linkage disequilibrium analyses to complex linear models supporting covariates, interactions and random effects. The software seamlessly imports variant data sets from VCF files, CLCbio SNP and indel reports, summary files from Complete Genomics, PLINK text and binary files, and common output formats from SNP arrays.
With comprehensive GWAS capabilities, JMP Genomics lets you examine associations between SNPs and multiple continuous traits, correct for population structure, and explore SNP-SNP interactions. Select from an extensive set of rare variant association methods to group rare SNP variants within genes, pathways or positional groups, and find identical by state genomic regions between related or unrelated individuals.
Point-and-click Q-K mixed model analysis simplifies the creation, compression and integration of relationship matrices into association tests, making it easy to simultaneously correct for population structure and relatedness.
Next-generation sequencing experiments yield a vast amount of information, from variant genotypes to RNA expression levels. JMP Genomics provides downstream statistical analysis capabilities for aligned reads from state-of-the-art sequence analysis pipelines.
Import counts from text formats, or summarize counts from SAM and BAM files to take advantage of normalization and generalized linear modeling methods tailored for count data. Basic workflows for RNA-seq and miRNA-seq streamline steps in statistical analysis.
Load genotypes directly from a variety of text formats or VCF files, or call variants from a set of BAM files using a reference genome. JMP Genomics supports a wide range of methods for association analysis of rare and common SNP variants and can identify regions identical by state (IBS) between related or unrelated individuals.
JMP Genomics lets researchers take advantage of the rich information within sequencing experiments by screening for significant correlations between different data types. The JMP Genomics Browser provides comprehensive views of next-gen data, showing counts or statistical analysis results, and overlaying histogram and heat plot tracks with individual- or group-level summaries to complement known SNP and gene track.
JMP Genomics provides a suite of interactive processes for the construction, optimization, and visualization of genetic marker linkage maps used to improve agronomic crops and guide animal breeding efforts.
Geneticists can identify linkage groups automatically or interactively using genetic distance, recombination fraction, or genotypes from experimental inbred crosses. JMP Genomics includes advanced optimization algorithms to order markers within linkage groups, with options to designate consensus groups and framework markers. Intuitive graphics let you visualize and filter newly created or imported marker maps, or create high quality multichromosome views.
JMP Genomics provides algorithms that allow extremely large marker data sets in linkage mapping analysis. Additional support for outcrossing populations is also available, both in linkage mapping and downstream QTL analysis.
Marker maps in JMP Genomics can be readily used to explore genotype-environment interactions in multienvironment trials, summarize phenotype information with interactive graphics, and perform QTL analysis. Haley-Knott regression and permutation options expand capabilities for interval and composite interval mapping of QTLs. Multiple interval mapping methods in JMP Genomics allow more extensive QTL analyses.
- Construct and visualize genetic linkage maps from molecular markers for both inbred and outcrossed populations to support crop research.
- Uncover genomic loci, including epistatic relationships, driving phenotypes with QTL Multiple Interval mapping methods.
JMP Genomics excels at predictive modeling, offering a broad and robust array of methods, as well as options for predictor filtering, predictor lock-in, and cross-validation. The software guides biologists and biostatisticians through comprehensive exploratory analyses of separate and paired data types and allows combining multiple predictor types to build, test and cross-validate biomarker signatures with a choice of hold-out methods.
JMP Genomics provides improvements in computational speed with high performance logistic regression. Predictive modeling capabilities include Ridge regression, elastic net regularization, and additional methods for predictor optimization and variable selection – a significant advantage for high-dimensional genomics data. The predictive modeling review tool allows users to easily build and compare multiple models for multiple traits.
Extensive participation of JMP developers in the MicroArray Quality Control consortium has influenced the development of predictive modeling functions in JMP Genomics. Replication and iteration strategies implemented in the software seek to reduce bias, with honest cross-validation approaches that can accurately assess the relative performance of hundreds of different models at a time.
Key Features of JMP® Genomics
JMP Genomics imports data from a variety of formats, including:
- Read counts, intensities, or genotypes in single or multiple text files.
- Aligned reads in SAM or BAM format and variants in VCF files.
- Complete Genomics pipeline and testvariant output files.
- CLC Bio SNP and indel summary files.
- Illumina BeadStudio or GenomeStudio expression, SNP, copy number and other data types.
- A variety of Affymetrix CEL and CHP files, as well as BAR, LOHCHP, CNCHP, CNAT, and Cytogenetics CEL and CHP files.
- GenePix, QuantArray, one-color and two-color Agilent files.
- Excel and comma-separated files, including data formats from multiple NimbleGen platforms.
Flexible workflows for new and experienced users:
- JMP Genomics Wizard guides import of new data sets.
- Basic workflows for gene and exon expression, RNA-seq, copy number and linkage map construction.
- Intermediate options for expression QC and analysis.
- Q-K and rare variant association workflows.
- Workflow Builder for creation of custom workflows.
Integrate statistics into next-gen sequencing workflows to:
- Normalize and analyze sequence counts generated at the exon, transcript, or gene level by various pipelines.
- Assess RNA-seq data using point-and-click workflows.
- Test for association between traits and rare or common variants.
- Perform cross-correlation analysis to relate sequence counts to other numeric genomic measures.
Assess genome-wide variant data sets to:
- Examine individual and marker missing data patterns.
- Summarize marker properties including allele and genotype frequencies, HWE, heterozygosity and diversity.
- Filter data sets by marker or sample attributes.
- Visualize and adjust for population structure using PCA or MDS.
- Explore associations with one or more binary or quantitative traits.
- Calculate interactions and adjust for covariates.
- Test for associations using imputed SNP data.
- Calculate and visualize linkage disequilibrium and LD blocks.
- Perform GWAS meta-analysis using p-values or effects.
Expand analysis options for marker data to incorporate:
- Association test corrections for relatedness and structure.
- Identification of genomic regions that are identical by state.
- Calculation of IBD, IBS and allele-sharing relationship matrices.
- Genetic distance matrices for individuals or populations.
- Testing SNP variants grouped within a locus or pathway.
- Rare variant association tests with permutation options.
- Compression of K matrices to save computational time.
- Estimation of haplotypes and haplotype-trait association.
- Selection of haplotype tagSNPs or LD tagSNPs.
- Reconciliation of strand differences between studies.
Improve crop and livestock breeding strategies by:
- Visualizing categorical and continuous phenotype distributions among individuals, genotypes, or lines.
- Assessing genotypes in multi-environment trials.
- Identifying linkage groups from experimental cross data.
- Ordering markers within linkage groups using advanced optimization algorithms.
- Visualizing linkage maps created in JMP Genomics and other mapping software packages.
- Performing single-marker, interval and composite-interval QTL mapping with permutation option.
- Simulating crosses and their progeny to identify breeding strategies for optimal future performance.
Transition large-scale genomics studies to clinical practice by:
- Performing large GWAS with various clinical outcomes.
- Identifying molecular markers that predict survival outcomes.
- Using cross-validated predictive modeling and learning curves to optimize biomarker signatures.
- Integrating analysis of molecular marker and clinical efficacy and safety outcomes to develop targeted therapies.
- Maximizing treatment performance using subgroup analysis.
- Uncovering variants in tumor cells for potential targeted therapies.
Assess the quality of large expression data sets to:
- Identify data quality issues and remove outliers.
- Pinpoint factors that explain variance in your data.
- Visualize distributions, PCA plots, and sample clusters.
- Normalize across samples.
- Remove batch effects due to technical variables.
- Adjust count distributions with TMM and KDMM.
- Perform loess, quantile, RMA, GCRMA, and ANOVA normalization or standardize to a variety of statistics.
Apply trusted statistical modeling methods to:
- Discover significant differences using ANOVA and generalized linear models.
- Adjust for covariates and random effects.
- Apply a variety of multiple test adjustments.
- Analyze censored survival data.
- Screen for allele-specific expression.
- Plot expression profiles by sample or group with dynamic selection and filtering.
- Perform hierarchical and K-means clustering.
Use advanced predictive modeling analysis tools to allow:
- Identification of biomarkers from high-dimensional data sets.
- Selection of predictors from multiple data types.
- Customized predictor filtering during model construction.
- Lock-in of key class or continuous predictors.
- Performance comparison across several different methods.
- Cross-validation with many hold-out and iteration options.
- Learning Curve analysis to assess sample size impact.
Use JMP Genomics annotation tools to:
- Merge functional information with statistical results.
- Upload results to Ingenuity Pathways Analysis to seek points of interaction between SNP, gene and protein lists and color pathways.
- Perform enrichment analysis or gene set scoring using functional information from Ingenuity Pathways Analysis.
- Visualize sets of co-regulated genes in KEGG pathways.
- Download annotation and library files from Affymetrix NetAffx.
- Create Venn diagrams to assess overlap of significant results for up to five groups.
Create genome-level views that allow you to:
- Visualize chromosomes with customizable color themes.
- Compare multiple experiments to find regions of shared significance.
- Use a variety of continuous measures for p-value summarization.
- Drill down on interesting regions to plot p-values and view gene, SNP, bar chart, and color map tracks.
The JMP software platform provides:
- Graph Builder for visual exploration of data patterns.
- Point-and-click creation of a variety of custom graphics.
- Easy copy-and-paste into Word and PowerPoint.
- Built-in scripts for capturing and sharing findings.
- Add-in capabilities for external analytics (e.g., R, SAS).
Interactive graphics generated automatically during analysis:
- Are organized into dynamic reports linked to underlying data.
- Offer point-and-click selection and easy subset creation.
- Can be queried dynamically using the JMP Data Filter.
- Can be converted to static reports.