Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait

Background. Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on
simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were
estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. The probability of a SNP being modelled in the second distribution (pi) was estimated.

Results. Estimated heritability of the simulated quantitative trait was 0.31. Mean posterior probability of SNP modelled in the second distribution, i.e. SNP with large effect, was 0.0075 (95% highest posterior density region: 0.0014-0.0139). The genome-wide association analysis resulted in 18 significant SNP, comprising 6 QTL on chromosome 1, 2 and 3. In total 23 putative SNP, comprising 8 putative QTL were detected.

Conclusions. Bayesian Variable Selection using thousands of SNP was success-fully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Strong and putative QTL were detected and breeding values were estimated for individuals with and without phenotypes.