described earlier [65]. Nonlinear models usually require sophisticated computational procedures and stochastic computations known as Markov Chain Monte Carlo methods [67]. These methods are very powerful and are commonly used in genetics as they are the engine of very accurate procedures for haplotype reconstruction from unphased data [68] and different imputation methods [69,70]. A Bayesian method has also been proposed to discover the most likely set of functional SNPs in fine mapping or sequence data. This method, known as Bayesian quantitative trait nucleotide (BQTN) analysis, uses Bayesian model selection to test the associations of all possible sets of SNPs with a trait. Recently, it was used to identify four to seven variants that explain the total variability of plasma levels of clotting factor VII (FVII), a risk factor for cardiovascular disease [71].
