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Multiple Imputation of Missing Phenotype Data for QTL Mapping

  • Jennifer F Bobb , Daniel O. Scharfstein , Michael J. Daniels , Francis S Collins and Samir Kelada
Published/Copyright: June 23, 2011
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 10 Issue 1

Missing phenotype data can be a major hurdle to mapping quantitative trait loci (QTL). Though in many cases experiments may be designed to minimize the occurrence of missing data, it is often unavoidable in practice; thus, statistical methods to account for missing data are needed. In this paper we describe an approach for conjoining multiple imputation and QTL mapping. Methods are applied to map genes associated with increased breathing effort in mice after lung inflammation due to allergen challenge in developing lines of the Collaborative Cross, a new mouse genetics resource. Missing data poses a particular challenge in this study because the desired phenotype summary to be mapped is a function of incompletely observed dose-response curves. Comparison of the multiple imputation approach to two naive approaches for handling missing data suggest that these simpler methods may yield poor results: ignoring missing data through a complete case analysis may lead to incorrect conclusions, while using a last observation carried forward procedure, which does not account for uncertainty in the imputed values, may lead to anti-conservative inference. The proposed approach is widely applicable to other studies with missing phenotype data.

Keywords: multiple imputation; missing data; quantitative trait loci
Published Online: 2011-6-23

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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  1. Invited Editorial
  2. Measurement of Evidence and Evidence of Measurement
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  44. Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power
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  46. Multiple Imputation of Missing Phenotype Data for QTL Mapping
  47. Sparse Canonical Covariance Analysis for High-throughput Data
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  49. Random Forests for Genetic Association Studies
  50. Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
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https://doi.org/10.2202/1544-6115.1676
Keywords for this article
multiple imputation; missing data; quantitative trait loci

Articles in the same Issue

  1. Invited Editorial
  2. Measurement of Evidence and Evidence of Measurement
  3. Article
  4. Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays
  5. Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains
  6. Assessing Modularity Using a Random Matrix Theory Approach
  7. Choice of Summary Statistic Weights in Approximate Bayesian Computation
  8. Genetic Linkage Analysis in the Presence of Germline Mosaicism
  9. Fitting Boolean Networks from Steady State Perturbation Data
  10. Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing
  11. Bayesian Learning from Marginal Data in Bionetwork Models
  12. Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome
  13. Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures
  14. Modeling Read Counts for CNV Detection in Exome Sequencing Data
  15. Multiscale Characterization of Signaling Network Dynamics through Features
  16. A Calibrated Multiclass Extension of AdaBoost
  17. False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies
  18. A Markov-Chain Model for the Analysis of High-Resolution Enzymatically 18O-Labeled Mass Spectra
  19. Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery
  20. Learning Monotonic Genotype-Phenotype Maps
  21. A Comparison of Multifactor Dimensionality Reduction and L1-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies
  22. Accuracy and Computational Efficiency of a Graphical Modeling Approach to Linkage Disequilibrium Estimation
  23. Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy
  24. A Three Component Latent Class Model for Robust Semiparametric Gene Discovery
  25. Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions
  26. A Robust Statistical Method to Detect Null Alleles in Microsatellite and SNP Datasets in Both Panmictic and Inbred Populations
  27. Large Sample Approximations of Probabilities of Correct Evolutionary Tree Estimation and Biases of Maximum Likelihood Estimation
  28. Interval Estimation of Familial Correlations from Pedigrees
  29. Information Metrics in Genetic Epidemiology
  30. Linear Combination Test for Hierarchical Gene Set Analysis
  31. Exploratory Analysis of Multiple Omics Datasets Using the Adjusted RV Coefficient
  32. Application of the Lasso to Expression Quantitative Trait Loci Mapping
  33. A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction
  34. Imputation Estimators Partially Correct for Model Misspecification
  35. On the Statistical Properties of SGoF Multitesting Method
  36. Meta-Analysis of Family-Based and Case-Control Genetic Association Studies that Use the Same Cases
  37. A Non-Parametric Method for Detecting Specificity Determining Sites in Protein Sequence Alignments
  38. Performance of Matrix Representation with Parsimony for Inferring Species from Gene Trees
  39. Disequilibrium Coefficient: A Bayesian Perspective
  40. Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review
  41. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq
  42. Inferring Gene Networks using Robust Statistical Techniques
  43. A Two-Stage Poisson Model for Testing RNA-Seq Data
  44. Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power
  45. The Joint Null Criterion for Multiple Hypothesis Tests
  46. Multiple Imputation of Missing Phenotype Data for QTL Mapping
  47. Sparse Canonical Covariance Analysis for High-throughput Data
  48. Comparison of Clinical Subgroup aCGH Profiles through Pseudolikelihood Ratio Tests
  49. Random Forests for Genetic Association Studies
  50. Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
  51. High-Dimensional Regression and Variable Selection Using CAR Scores
  52. Surveying the Manifold Divergence of an Entire Protein Class for Statistical Clues to Underlying Biochemical Mechanisms
  53. Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes
  54. Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests
  55. Weighted Lasso with Data Integration
  56. MA-SNP -- A New Genotype Calling Method for Oligonucleotide SNP Arrays Modeling the Batch Effect with a Normal Mixture Model
  57. A Modified Maximum Contrast Method for Unequal Sample Sizes in Pharmacogenomic Studies
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