Genetics Absolute Mean Difference Cutoff for Continuous Predictors Absolute Proportion Difference Cutoff for Class Predictors Accession Number Variable Add markers dropped out from marker reduction analysis back to the linkage groups Additional Fixed Effects Additional PROC CLUSTER Options Additional PROC TREE Options Additional Random Effects Adjust permutation p-values for multiple testing Affected Offspring Variables Affected Sib-Pair Tests Affected Value of Trait Variable Algorithm All markers are biallelic Allele Characters for A (P1 line) and B (P2 line) Allele Variables Alpha Alpha for Dprime Confidence Limits Alpha for LSMeans Confidence Intervals Alpha for Selecting PCs Alpha Level for Covariates Alpha Level for Empirical LOD Thresholds Alpha value for Beta distribution Annotation Accession Variable Annotation Analysis Group Variable for Collapsing Rare Variants Annotation Analysis Group Variable Annotation By Group Variable Annotation Input SAS Data Set Annotation SAS Data Set Annotation Group Variable Annotation Label Variable Annotation Location Variable Annotation Location Variable Units Annotation MAF Variable Annotation Major Allele Variable Annotation Minor Allele Variable Annotation Plotting Group Variable Annotation Variables to Drop Annotation Variables to Keep Annotation Weight Variable Append markers dropped out from marker reduction analysis to the output data sets Append prefix to current marker name Apply adaptive weights Apply mean-correction to covariates Apply stopping rules for map order optimization Apply VIF for genomic control Association Tests Assume Hardy-Weinberg equilibrium at all loci Asymmetric Loss Fitting Proportion Automated Linkage Group Clustering Method Average Pool Size Backcross Parental Lines Variable Bandwidth Base Input SAS Data Set Baseline Unit Beta value for Beta distribution Binary Trait Variable Binary Trait Variables Block Partition Variable Bootstrap Confidence Interval Alpha Bootstrap Samples Break linkage groups based on: Break linkage groups between markers with large ordered distances Build a Combined Wide Data Set By Variables Calculate allele odds ratios Calculate trend odds ratios Calculate p-values for F statistics Categorical Variables Category Variable Censor Limit (-log10 scale) Censor Limit Censor Values Censor Variable Censor Variables Choose a linkage grouping method Choose a method for RIL simulation Choose the mating type Chromosome Chromosome Label Chromosome Label from Base Input SAS Data Set Chromosome Number Chromosome Variable Class Covariates Class Variables Cluster Center Variable Cluster Variable Cluster Variables Clustering Method Co-factor Variables Color of Bars ({r,g,b}) Color Theme Color Variable Color Variable Type Columns of Q Matrix sum to 1 Columns of Q Matrix sum to 1 in Q1 Model Columns of Q Matrix sum to 1 in Q2 Model Columns of Q Matrix sum to 1 in Q3 Model Compress output data set Compression Method Compression Rate Compute Cholesky Root of Matrix Compute sandwich (empirical) estimator of covariance matrix Compute the root of the matrix by SVD Constrain negative IBD estimates to 0 Continuous Covariates Continuous Trait Variables Control Marker Data Set - Sorted Marker List Control Marker Number Control Marker Selection Method Convergence Criterion Conversion for F Statistics p-Values Conversion for p-Values Correlation Radius for Clustering Correlation Value for Clustering Corresponding Key Chromosome Number Variable from Merge Input SAS Data Set (1-12) Corresponding Key Grid Variable from Merge Input SAS Data Set (1-12) Corresponding Key Marker Label from Merge Input SAS Data Set (1-12) Corresponding Key Testing Location Variable from Merge Input SAS Data Set (1-12) Covariates Create cell plot Create data set containing haplotype frequency estimates Create data set of covariance parameter estimates for every model fit Create data set with numerically coded genotypes Create data sets of CorrCoeff2 in matrix format Create frequency charts Create haplotype frequency charts Create HTML output Create merged PCA output data set Create output data set containing allelic transmissions Create PARMS statement from optimal model covariance parameter estimates Create Phase Assignment Data Set Create SAS data set containing htSNP indicator variable Create Significance Indicator Columns Create subset data set Create tagSNP subset indicator variable Criterion for Evaluating Sets of htSNPs Criterion for Optimal Compression Level Criterion for Stopping Model Selection Cross Type Cumulative Proportion of Variation to Explain with Principal Components Current Value(s) Denoting Missing Genotypes or Alleles Cutoff Level of Tree Axis CV Partitioning Method D’ between Marker and Disease Locus Data Step Statements Define linkage groups based on the: Delete Nonmatching Rows Delete Rows Not in Base Input SAS Data Set Denominator Degrees of Freedom Method Dependent Class Variable Display cell plot of Mendelian errors Display column attributes Display marker genotype cell color plots Display markers Display principal components plots Display QTL location Distance to Include Downstream and Upstream of Gene Distance Unit Distance Unit for Defining Maximum Range of LD Blocks Dprime Lower Confidence Limit greater than: Dprime Upper Confidence Limit greater than: Dprime Upper Confidence Limit less than: Drop Alleles with frequency below: Drop Last Allele for Each Marker Effect Estimate or Direction Variable EigenCore Multiple Testing Method Environment Variables Estimate Rho and K Rho Estimated Frequency Cutoff for Combining Rare Haplotypes Estimation Method Event Trait Value Exclude single-SNP genes Expanded Genotype Recoding Expected Segregation Ratios for AA AB BB Extreme Sampling Percentile Family Association Tests Family Test Options Feature Selection Criterion File Filter Expression Filter Defining First SNP in Interaction Filter Defining Second SNP in Interaction Filter to Include Annotation Rows Filter to Include Linkage Groups Filter to Include Markers Filter to Include Markers (applies to both data sets) Filter to Include Observations Filter to Include Observations for HWE Test Filter to Include Predictor Class Variables Filter to Include Predictor Continuous Variables Filter to Include Rows in Annotation SAS Data Set Filter to Include Windows Filter to Select Null SNPs Filter Variables First Column to Display First Row to Display Fit Square Root of Recombination Fractions in MDS Algorithm Fix covariance parameters Fixed Effects Fixed Threshold Flip alleles for A/T or C/G SNPs with major/minor alleles reversed from reference Folder Containing Scoring Code Files Folder of Input SAS Data Sets Folder of Linkage Map Files Force all markers into linkage groups Format of Marker Variables Format of SNP Variables Framework Linkage Group Variable Framework Map Data Set Framework Marker Name Variable Framework Order Variable Frequency Cutoff Frequency Cutoff for Combining Haplotypes Frequency Initialization Frequency Variable Function of Covariates Function of Trait Variable Gene By Variables Gene Input SAS Data Set Gene Label Gene Start Variable Gene Stop Variable Genetic Distance Break Value Genetic Distance Unit for Results Plots Genotype Data Set Genotype Delimiter Genotype Probability Data Set Genotype Probability Variables Genotype Recoding Genotype Recoding for Breeding: Additive (1 0 -1) and Dominant (0 1 0) Genotype to code as 2 for the Trend Test Genotype to code as 2 for the Trend Test ORs Genotype Variable Genotyping Generation (n) Grouping LOD Threshold Grouping Method Grouping Recombination Fraction Threshold Grouping Variable Haplotype Estimation Method Haplotype Frequency Data Set High-risk Allele Frequency Hold L at starting value Hold M at starting value Hold-Out Method Hold-Out Size, Specify as: Hotelling’s T-squared Test IBD Data Set IBS sharing counted for: ID Variables Identity By Descent Threshold Identity By State Threshold Impute zeros for missing continuous predictor values Impute zeros for missing values Inbreeding Coefficient Include 3D plots Include direction of variant effect in test Include framework markers in the K-Means clustering analysis Include self crosses Increase R Software memory limit Increment Individual ID Individual ID Variable Individuals to Include in Matrix Initial Number of Linkage Groups Input data contains means of genotypes on each environment Input SAS Data Set Input Genotype SAS Data Set Input K Matrix Data Set Input Linkage Map SAS Data Set Interaction Block Variables Interaction Effects Interactive Clustering Method Interactive Linkage Group Clustering Method Italics Value Italics Variable JMP Script Output File Name K for K-Fold CV K for K-Fold or 1/K Hold-Out K Matrix is compressed K Matrix is compressed in K1 Model K Matrix is compressed in K2 Model K Matrix Square Root Variables K Matrix Square Root Variables in K1 Model K Matrix Square Root Variables in K2 Model K-Means Clustering Method Keep original marker variables Kernel Function Key Chromosome Number Variable from Base Input SAS Data Set Key Chromosome Variable from Base Input SAS Data Set Key Grid Variable from Base Input SAS Data Set Key Marker Label from Base Input SAS Data Set Key Testing Location Variable from Base Input SAS Data Set Kinship Matrix Diagonal Krho L for Leave-L-Out Label Variable Linear-Bilinear Model Linkage Group Hierarchical Clustering Method Linkage Group Variable Linkage Group Variable 1 Linkage Group Variable 2 Linkage Map Files Linkage Map Weight List Linkage Phase Data Set Linkage Phase of Adjacent Markers List every model fit Listing for Each Model Fit List of optimization sense for progeny selection List-Style Specification of K Matrix Square Root Variables List-Style Specification of K Matrix Square Root Variables in K1 Model List-Style Specification of K Matrix Square Root Variables in K2 Model List-Style Specification of Marker Variables List-Style Specification of Matrix Variables List-Style Specification of Predictor Class Variables List-Style Specification of Predictor Continuous Variables List-Style Specification of Q Matrix Variables List-Style Specification of Q Matrix Variables in Q1 Model List-Style Specification of Q Matrix Variables in Q2 Model List-Style Specification of Q Matrix Variables in Q3 Model List-Style Specification of Recombination Rate Variables List-Style Specification of SNP Variables List-Style Specification of Numeric SNP Variables List-Style Specification of SNP Variables to Keep in Output Data Set List-Style Specification of SNP Variables to Retain in Output Data Set List-Style Specification of Trait Variables List-Style Specification of Variables to Be Standardized List-Style Specification of Variables to Keep in Output Data Set List-Style Specification of Variables to Retain in Output Data Set Location Variable LOD Threshold [1,10] LOD Threshold for Entry into the MIM Model LOD Threshold for Staying in the MIM Model -log10(p-Value) Cutoff log10 Regularization Parameter MAF Threshold for Rare Variants Main QTL Test Step in cM Major Allele Variable MANOVA Statistic Map 1 Label Map 2 Label Map Function Marker 1 Label Marker 2 Label Marker 1 Location Marker 2 Location Marker Genotype Variables Marker ID Variable Marker ID Merge Variable Marker Label Marker Location Unit Marker locus is disease locus Marker Name Variable Marker Name Variable 1 Marker Name Variable 2 Marker Names Variable Marker Order Variable Marker Physical Position Marker Position Marker Type Marker Units Marker Variables Numeric Marker Variables Maternal Value of the Sex Variable Matrix Variables Max Number of Categories Allowed in a Predictor Max Number of Effects in the Model Max Number of Variables to Consider for Splitting a Node Maximum Maximum Dimension of K Matrix Maximum Depth of Tree Maximum Distance Maximum Interval Window List Maximum Iterations Maximum Number of Chromosomes Per Row in 3D Display Maximum Number of Clusters Maximum Number of Filtered Predictors Maximum Number of Intervals to Fit Together as a Region Maximum Number of K-Means Clusters / Predictors Maximum Number of Principal Components Maximum Number of Steps Maximum Number of Trees Maximum Number of Variables to Select with Model Averaging Maximum Number of Variables to Select with Pooling Maximum Order of Interactions Maximum Range of LD Blocks Maximum Recombination Fraction Threshold Maximum Subset Size Maximum Time per Algorithm Iteration (in minutes) Maximum X Chromosome Heterozygosity for Males Mean Square Blocks or Replicates within Environments Mean Square Environment Mean Square Error Mean Square Error Degrees of Freedom Measure for LD Contour Plot Measure for LD Decay Plot Measure of Genetic Distance Merge Individual ID Variable Merge Input SAS Data Set (1-12) Merge Key Variables Method to Use Minimum Minimum Dimension of K Matrix Minimum LOD Threshold Minimum Number of Linkage Groups Minimum Number of Observations Required for a Branch Minimum Number of Offspring Sharing for Computing Runs Minimum Proportion of Nonmissing Genotypes Minimum Run Length for Including in Output Minimum X Chromosome Heterozygosity for Females Minor Allele Frequency at Marker Locus Minor Allele Frequency Threshold Minor Allele Frequency Threshold for Including SNPs Model Selection Method Multi-Marker Model Selection Multiple-Locus Regression Model Multiple Testing Correction Multiple Testing Method Multiple Testing Method for Segregation Tests Nearby Marker Recombination Constraint Nominalize Continuous Dependent Variables Null SNP Variables Number of Backcross Generations Number of Blocks or Replicates Number of Clusters Number of Clusters for Automated Compression Number of Columns Number of Contours Number of Distinct Genotypes Number of Intervals to Overlap in Consecutive Regions Number of Legend Decimals Number of Legend Levels Number of Linkage Groups Number of Markers in Each Group Number of model averaging samples Number of Nearest Neighbor Markers Number of Nearest Neighbor Samples Number of Permutations Number of Permutations or Simulations to Perform Number of Permutations to Compute Number of Permutations to Perform Number of Principal Components Number of Random Hold-Out Iterations Number of Reconfigurations Number of Representative Markers Number of Rounds of Selection Number of Rows Number of Selections to Display Number of Selfing Generations Number of Shuffles Number of Simulated Progeny for Each Cross Number of Simulations for MAX Test Number of Starts Number of Steps Number of Variables to Process at a Time Numerical Parameter for Advanced Standardization Methods Observed Frequency Cutoff for Dropping Rare Haplotypes Output most probable haplotype pair only Optimize the continuous predictor set Optimized Automated Clustering Method Options to Define Cluster Membership Order Algorithm Order Data Set Other Accession Number Variables Other Chromosome Variables Other Effect Estimate Variables Other Location Variables Other Major Allele Variables Other p-Value Variables Other Sample Size Variables Other SNP ID Variables Other Standard Error Variables Other Variables to Keep in Output Data Set Other Variables to Retain in Output Data Set Output Annotation Data Set Output covariance parameter estimates from every model Output Data Set Output Data Set Prefix Output File Name Output File Prefix Output fitness statistics for every model Output Folder Output Genotype Data Set Output genotype LS means Output genotype LS means and diffs Output M and L Data Set Output Map Data Set Output markers with significant p-values only Output parameter estimates from every model Output predicted values for the AMMI models Output predicted values from every model Output predicted values from every model from global test Output R-Square from the logistic regression of binary, nominal, and ordinal traits Output residuals from every model Output residuals from every model from global test Output SAS Data Set from the Genomic BLUP Process Output SAS data set of between and within family component variables for the O-QTDT Output survival function estimates for viewing survival curves P for P-Percent-Hold-Out p-Value Adjustment p-Value Combination Method p-Value Cutoff for Segregation Test Plots p-Value Variable Parent 1 ID Parent 2 ID Parent Variables PARMS Statement Values and/or Options PC Regression Model PCA Data Set Pedigree Data Set Pedigree ID Pedigree ID Variable Pedigree ID Variable in K1 Model Pedigree ID Variable in K2 Model Perform 2-sided Test Perform association tests on selected markers Perform association tests on unselected markers conditional on selected markers Perform EigenCorr to select PCs Perform F statistics calculations Perform genetic distance matrix calculations Perform LD calculations for all pairs within annotation groups Perform model averaging Perform poolwise selection Perform Principal Components Analysis Perform recoding with respect to the: Perform SAS-based clustering on the genetic distance matrix Permutations Phase Assignment ID Variables Phase Assignment Probability Cutoff Phased Haplotypes Data Set Phenotyping Generation (m) Plot haplotypes with significant p-values only Plot heatmaps for linkage groups Plot Markers with significant p-values only Plot Markers with significant F ST p-values only Plot Relationship Matrix heat map Population Prevalence Population Variable Position Variable Position Variable 1 Position Variable 2 Power Values Predictor Class Variables Predictor Continuous Variables Prefix Prefix for Column Names of Expanded Genotypes Prefix for Column Names of Recoded Genotypes Prefix for Naming Distance Output Variables Prefix for Naming Output Variables Preliminary Delimiter Separating Annotation Categories (Enclose in Quotes) Prior Probabilities / Prevalences PROC GENESELECT Statement Options PROC GLIMMIX Estimation Method PROC MIXED Estimation Method Proportion of Alleles Identical by State Threshold Proportion of Informative Pairs in Strong LD greater than: Proximity to Optimal Mapping Order Q and K Data Set Q Matrix Variables Q Matrix Variables in Q1 Model Q Matrix Variables in Q2 Model Q Matrix Variables in Q3 Model QTDT Tests QTL Effect Size Variables from Base Input SAS Data Set QTL Effect Size Variables from Merge Input SAS Data Set (1-12) QTL Indicator Variable from Base Input SAS Data Set QTL Indicator Variable from Merge Input SAS Data Set (1-12) QTL Mapping Method QTL Mapping Model Algorithm QTL Test Size Variables from Base Input SAS Data Set QTL Test Size Variables from Merge Input SAS Data Set (1-12) QTL Test Step in cM QTL Test Step in cM (1-D Genomewide Scan) QTL Test Step in cM (2-D Genomewide Scan) Quantile Level Quantitative Trait Variables Quantitative Variables R Software Memory Size (Mb) R-squared Threshold Radial Basis Machine (Kernel Method) Random Effects Random Mating Generation (t) Prior to Inbreeding Random Number Seed Random Number Seed for Forest Random Number Seed for Testing F Statistics Random Statement Options Range of Markers Variable Rare Variant Recoding RATE= Option Recombination Fraction Break Value Recombination Fraction Cutoff Recombination Rate Variables Reference Annotation SAS Data Set Reference Annotation Label Variable Reference Annotation Major Allele Variable Reference Annotation Minor Allele Variable Reference Population for FST Reference Trait Value Relationship Matrix to Compute Risk of Observing the Trait in the Heterozygous Genotype (A/a) Relative to the Homozygous Recessive Genotype (a/a) Risk of Observing the Trait in the Homozygous Dominant Genotype (A/A) Relative to the Homozygous Recessive Genotype (a/a) Remove markers not found in Annotation SAS Data Set Reorder Variables Replicate Variable Report SNP x Interaction Effect tests only Reverse Color Theme Rho Sample Proportion of Cases Sample Size Variable Sample Sizes SAS Data Set Indicating Which Crosses to Simulate SAS Data Sets Save Data Set for Each Window Scoring Code Files Search Method Select best simulated progenies to cross Select Neighbor Markers with strongest LD within Baseline Unit Selection Criterion for QTL Main Effect Search and Drop Selection Criterion for Two-way QTL Interaction Search and Drop Sequence Kernel Association Test (SKAT) Server Output Directory Sex Variable Shuffle Markers Shuffling LOD Threshold Shuffling Window Size Significance Level for Entry into the MIM Model Significance Level for Staying in the MIM Model Significance Level for Entry into the Model Significance Level for Staying in the Model Similarity Measure Simulate data from all crosses Simulate multiple generations Simulate only markers present in the score files Simulate progenies Simulate RIL progenies SL for Adding Variables SL for Keeping Variables SL for Keeping Variables in the Selected Model Sliding Window Size Sliding Window Variable SNP Data Set SNP ID Variable SNP Variables SNP Variables (Coded Numerically) Numeric SNP Variables SNP Variables to Keep in Output Data Set SNP Variables to Retain in Output Data Set Specification of Regularization Parameter (Lambda) Standard Error Estimate Standard Error Variable Standardization Method for Predictor Continuous Variables Standardize genotypes Standardize Predictors Row-Wise Starting Value for E, the Exponential Decline of Rho with Physical Distance Starting Value for L, the Bias at Large Distance Starting Value for M, the Proportion of the Youngest Haplotype that is Monophyletic Statistical Testing Method for Continuous Predictors Stepwise EM Cutoff Strata Variable Strata Variables Study Subset Size Suppress all graphical and HTML output Tau Value for TPM Temperature Temperature Reduction Factor Test allelic association (LD) Test Data Set Test each marker individually Test for HWE Test Individual Haplotypes Test Statistic Test Window Size in cM Tests Top Cross Tester Lines Indicator Variable TOTAL= Option Trait Variable Trait Variables Transformation for Predictor Continuous Variables Transformation of Input p-Values Transformation of Output p-Values Transpose data for cell plot Treat missing genotypes as: Type I Error Rate Type of Coefficients to Calculate Type of Kernel to Use Type of Model Type of Tests to Perform Type of Trait Type of Weight to Use Usage of K-Means Clusters Use a variable threshold for including variants Use Annotation Label Variable for Variable Prefixes Use Automated Hierarchical Clustering to Assign Linkage Groups Use bias corrected recombination formula for RIL Use dominant coding for trend test Use Forest to create interaction indicators Use Forest to filter predictors Use genetic map to simulate linkage Use grid computing Use K-Means clustering to reduce marker number Use K-Means clustering to reduce number of markers Use K-Means clustering to reduce predictors Use lower boundary constraint of 0 for K matrix covariance parameter Use Monte Carlo simulations for KBAC p-values Use QTL data numeric coding from JMP Genomics versions prior to 5.1 Use rank-based statistic for tests Use reported value of sex variable for genetic sex when ambiguous Use statistical testing to filter predictors Value of the Backcross Parental Lines Variable Indicating the Parental Line 1 Value of the Backcross Parental Lines Variable Indicating the Parental Line 2 Value Ordering for Nominal Color Variable Value Ordering for Nominal Variables Value Representing Cases Value to Impute for Missing Genotypes Variable By Which to Merge Annotation Data Variable Containing Names of Marker Variables Variable to Define Tree Axis Variables to Be Standardized Variables to Drop Variables to Keep in PCA Data Set Variables to Keep in Linkage Map Data Set Variables to Retain in Linkage Map Data Set Variables to Keep in Output Variables to Keep in Output Data Set Variables to Retain in Output Data Set Variant Weights Weight the MDS Fit Based on Recombination Fractions Weight Variable Weighting Method for Sibships Width of Positional Group Window Overlap Window Size in cM Window Size Unit X-linked Marker Clause X-linked Markers Clause X-linked Markers Clause