Quantitative trait nucleotide analysis using Bayesian model selection

In this era of genomic science our current approach to understanding the genetic architecture of a complex phenotype usually follows a specific trajectory. First, the underlying quantitative trait locus (QTL) [...]
Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis. KEY WORDS: STATISTICAL GENOMICS, MODEL AVERAGING, SEQUENCE DATA, SINGLE NUCLEOTIDE POLYMORPHISMS, BAYESIAN QUANTITATIVE TRAIT NUCLEOTIDE (BQTN) ANALYSIS.