PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sparse Instrumental Variables (SPIV) for Genome-Wide Studies
Felix Agakov, Paul McKeigue, Jon Krohn and Amos Storkey
in Neural Information Processing Systems (NIPS 2010) Volume 23, pp. 28-36, 2011.


This paper describes a probabilistic framework for studying associations between multiple genotypes, biomarkers, and phenotypic traits in the presence of noise and unobserved confounders for large genetic studies. The framework builds on sparse linear methods developed for regression and modified here for inferring causal structures of richer networks with latent variables. The method is motivated by the use of genotypes as “instruments” to infer causal associations between phenotypic biomarkers and outcomes, without making the common restrictive assumptions of instrumental variable methods. The method may be used for an effective screening of potentially interesting genotype-phenotype and biomarker-phenotype associations in genome-wide studies, which may have important implications for validating biomarkers as possible proxy endpoints for early-stage clinical trials. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs) detected in genetic linkage studies. The method is applied for examining effects of gene transcript levels in the liver on plasma HDL cholesterol levels for a sample of sequenced mice from a heterogeneous stock, with ∼ 105 genetic instruments and ∼ 47 × 103 gene transcripts.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7530
Deposited By:Amos Storkey
Deposited On:17 March 2011