A Statistical Framework for Expression Quantitative Trait Loci Mapping

In 2001, Sen and Churchill reported a general Bayesian framework for quantitative trait loci (QTL) mapping in inbred line crosses. The framework is a powerful one, as many QTL mapping methods can be represented as special cases and many important considerations are accommodated. These considerations include accounting for covariates, nonstandard crosses, missing genotypes, genotyping errors, multiple interacting QTL, and nonnormal as well as multivariate phenotypes. The dimension of a multivariate phenotype easily handled within the framework is bounded by the number of subjects, as a full-rank covariance matrix describing correlations across the phenotypes is required. We address this limitation and extend the Sen–Churchill framework to accommodate expression quantitative trait loci (eQTL) mapping studies, where high-dimensional gene-expression phenotypes are obtained via microarrays. Doing so allows for the precise comparison of existing eQTL mapping approaches and facilitates the development of an eQTL interval-mapping approach that shares information across transcripts and improves localization of eQTL. Evaluations are based on simulation studies and a study of diabetes in mice.

[1]  R. Jansen.  A general mixture model for mapping quantitative trait loci by using molecular markers , 1992, Theoretical and Applied Genetics.

[2]  Robert Gentleman,et al.  Using Go for Statistical Analyses , 2004 .

[3]  B. Mangin,et al.  Constructing confidence intervals for QTL location. , 1994, Genetics.

[4]  C. Kendziorski,et al.  Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping , 2006, Biometrics.

[5]  Deepayan Sarkar,et al.  Detecting differential gene expression with a semiparametric hierarchical mixture method. , 2004, Biostatistics.

[6]  Rachel B. Brem,et al.  Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors , 2003, Nature Genetics.

[7]  R. Jansen,et al.  University of Groningen High Resolution of Quantitative Traits Into Multiple Loci via Interval Mapping , 2022 .

[8]  P M Visscher,et al.  Confidence intervals in QTL mapping by bootstrapping. , 1996, Genetics.

[9]  Ping Wang,et al.  A review of statistical methods for expression quantitative trait loci mapping , 2006, Mammalian Genome.

[10]  J. Ibrahim,et al.  Proximity Model for Expression Quantitative Trait Loci (eQTL) Detection , 2007, Biometrics.

[11]  B. Yandell,et al.  Genetic obesity unmasks nonlinear interactions between murine type 2 diabetes susceptibility loci. , 2000, Diabetes.

[12]  N. Yi,et al.  Bayesian mapping of quantitative trait loci for complex binary traits. , 2000, Genetics.

[13]  Z. Zeng.  Precision mapping of quantitative trait loci. , 1994, Genetics.

[14]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[15]  Dennis B. Troup,et al.  NCBI GEO: mining millions of expression profiles—database and tools , 2004, Nucleic Acids Res..

[16]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Sean Davis,et al.  Genome-Wide Analysis of Menin Binding Provides Insights into MEN1 Tumorigenesis , 2006, PLoS genetics.

[18]  Nengjun Yi,et al.  A Unified Markov Chain Monte Carlo Framework for Mapping Multiple Quantitative Trait Loci , 2004, Genetics.

[19]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[20]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[21]  David BotsteinS’B.  Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps , 2002 .

[22]  Hao Wu,et al.  R/qtl: QTL Mapping in Experimental Crosses , 2003, Bioinform..

[23]  Z B Zeng,et al.  Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[24]  M A Newton,et al.  A bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. , 1996, Genetics.

[25]  C M Kendziorski,et al.  On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles , 2003, Statistics in medicine.

[26]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.

[27]  C. Kendziorski,et al.  PGC-1α at the crossroads of type 2 diabetes , 2003, Nature Genetics.

[28]  N. Cox.  Human genetics: An expression of interest , 2004, Nature.

[29]  G. Churchill,et al.  A statistical framework for quantitative trait mapping. , 2001, Genetics.

[30]  S. Keleş,et al.  Statistical Applications in Genetics and Molecular Biology Asymptotic Optimality of Likelihood-Based Cross-Validation , 2011 .

[31]  D Siegmund,et al.  Statistical methods for mapping quantitative trait loci from a dense set of markers. , 1999, Genetics.

[32]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[33]  X. Cui,et al.  Improved statistical tests for differential gene expression by shrinking variance components estimates. , 2005, Biostatistics.

[34]  Expression of interest , 2005, Nature.

[35]  Christina Kendziorski,et al.  Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping , 2006, PLoS genetics.

[36]  Dennis B. Troup,et al.  NCBI GEO: mining tens of millions of expression profiles—database and tools update , 2006, Nucleic Acids Res..

[37]  Chunfang Jin,et al.  Selective Phenotyping for Increased Efficiency in Genetic Mapping Studies , 2004, Genetics.