Sparse Inverse of Covariance Matrix of QTL Effects with Incomplete Marker Data
-
R. Mark Thallman
Gametic models for fitting breeding values at QTL as random effects in outbred populations have become popular because they require few assumptions about the number and distribution of QTL alleles segregating. The covariance matrix of the gametic effects has an inverse that is sparse and can be constructed rapidly by a simple algorithm, provided that all individuals have marker data, but not otherwise. An equivalent model, in which the joint distribution of QTL breeding values and marker genotypes is considered, was shown to generate a covariance matrix with a sparse inverse that can be constructed rapidly with a simple algorithm. This result makes more feasible including QTL as random effects in analyses of large pedigrees for QTL detection and marker assisted selection. Such analyses often use algorithms that rely upon sparseness of the mixed model equations and require the inverse of the covariance matrix, but not the covariance matrix itself. With the proposed model, each individual has two random effects for each possible unordered marker genotype for that individual. Therefore, individuals with marker data have two random effects, just as with the gametic model. To keep the notation and the derivation simple, the method is derived under the assumptions of a single linked marker and that the pedigree does not contain loops. The algorithm could be applied, as an approximate method, to pedigrees that contain loops.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Article
- Using Alpha Wisely: Improving Power to Detect Multiple QTL
- Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
- Asymptotic Optimality of Likelihood-Based Cross-Validation
- Using Importance Sampling to Improve Simulation in Linkage Analysis
- Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
- Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?
- Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma
- Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
- Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
- Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates
- Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate
- Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
- Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
- A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects
- Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics
- Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
- Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
- A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments
- Mammalian Genomes Ease Location of Human DNA Functional Segments but Not Their Description
- On the Dependence Structure of Sequence Alignment Scores Calculated with Multiple Scoring Matrices
- Increasing Power for Tests of Genetic Association in the Presence of Phenotype and/or Genotype Error by Use of Double-Sampling
- A Method for Evaluating the Impact of Individual Haplotypes on Disease Incidence in Molecular Epidemiology Studies
- Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment
- MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data
- Sparse Inverse of Covariance Matrix of QTL Effects with Incomplete Marker Data
- Maximum Likelihood for Genome Phylogeny on Gene Content
- Confidence Levels for the Comparison of Microarray Experiments
- PLS Dimension Reduction for Classification with Microarray Data
- Statistical Analysis of Genomic Tag Data
- Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays
- Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
- A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks
- Validation and Discovery in Markov Models of Genetics Data
- Making Sense of High-Throughput Protein-Protein Interaction Data
- Reader's Reaction
- Reader Reaction
- Response to Foulkes and De Gruttola
- Software Communication
- BayesMendel: an R Environment for Mendelian Risk Prediction
- Letter to the Editor
- Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors
Articles in the same Issue
- Article
- Using Alpha Wisely: Improving Power to Detect Multiple QTL
- Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
- Asymptotic Optimality of Likelihood-Based Cross-Validation
- Using Importance Sampling to Improve Simulation in Linkage Analysis
- Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
- Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?
- Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma
- Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
- Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
- Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates
- Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate
- Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
- Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
- A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects
- Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics
- Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
- Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
- A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments
- Mammalian Genomes Ease Location of Human DNA Functional Segments but Not Their Description
- On the Dependence Structure of Sequence Alignment Scores Calculated with Multiple Scoring Matrices
- Increasing Power for Tests of Genetic Association in the Presence of Phenotype and/or Genotype Error by Use of Double-Sampling
- A Method for Evaluating the Impact of Individual Haplotypes on Disease Incidence in Molecular Epidemiology Studies
- Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment
- MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data
- Sparse Inverse of Covariance Matrix of QTL Effects with Incomplete Marker Data
- Maximum Likelihood for Genome Phylogeny on Gene Content
- Confidence Levels for the Comparison of Microarray Experiments
- PLS Dimension Reduction for Classification with Microarray Data
- Statistical Analysis of Genomic Tag Data
- Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays
- Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
- A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks
- Validation and Discovery in Markov Models of Genetics Data
- Making Sense of High-Throughput Protein-Protein Interaction Data
- Reader's Reaction
- Reader Reaction
- Response to Foulkes and De Gruttola
- Software Communication
- BayesMendel: an R Environment for Mendelian Risk Prediction
- Letter to the Editor
- Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors