Analysis of the genetics of boar taint reveals both single SNPs and regional effects

BACKGROUND: Boar taint is an offensive urine or faecal-like odour, affecting the smell and taste of cooked pork from some mature non-castrated male pigs. Androstenone and skatole in fat are the molecules responsible. In most pig production systems, males, which are not required for breeding, are castrated shortly after birth to reduce the risk of boar taint. There is evidence for genetic variation in the predisposition to boar taint. A genome-wide association study (GWAS) was performed to identify loci with effects on boar taint. Five hundred Danish Landrace boars with high levels of skatole in fat (>0.3 μg/g), were each matched with a litter mate with low levels of skatole and measured for androstenone. DNA from these 1,000 non-castrated boars was genotyped using the Illumina PorcineSNP60 Beadchip. After quality control, tests for SNPs associated with boar taint were performed on 938 phenotyped individuals and 44,648 SNPs. Empirical significance thresholds were set by permutation (100,000). For androstenone, a ‘regional heritability approach’ combining information from multiple SNPs was used to estimate the genetic variation attributable to individual autosomes. RESULTS: A highly significant association was found between variation in skatole levels and SNPs within the CYP2E1 gene on chromosome 14 (SSC14), which encodes an enzyme involved in degradation of skatole. Nominal significance was found for effects on skatole associated with 4 other SNPs including a region of SSC6 reported previously. Genome-wide significance was found for an association between SNPs on SSC5 and androstenone levels and nominal significance for associations with SNPs on SSC13 and SSC17. The regional analyses confirmed large effects on SSC5 for androstenone and suggest that SSC5 explains 23% of the genetic variation in androstenone. The autosomal heritability analyses also suggest that there is a large effect associated with androstenone on SSC2, not detected using GWAS. CONCLUSIONS: Significant SNP associations were found for skatole on SSC14 and for androstenone on SSC5 in Landrace pigs. The study agrees with evidence that the CYP2E1 gene has effects on skatole breakdown in the liver. Autosomal heritability estimates can uncover clusters of smaller genetic effects that individually do not exceed the threshold for GWAS significance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-424) contains supplementary material, which is available to authorized users

Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies

<p>Abstract</p> <p>Background</p> <p>It has been shown that if genetic relationships among individuals are not taken into account for genome wide association studies, this may lead to false positives. To address this problem, we used Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification analyses. To account for linkage disequilibrium among the significant markers, principal components loadings obtained from top markers can be included as covariates. Estimation of Bayesian networks may also be useful to investigate linkage disequilibrium among SNPs and their relation with environmental variables.</p> <p>For the quantitative trait we first estimated residuals while taking polygenic effects into account. We then used a single SNP approach to detect the most significant SNPs based on the residuals and applied principal component regression to take linkage disequilibrium among these SNPs into account. For the categorical trait we used principal component stratification methodology to account for background effects. For correction of linkage disequilibrium we used principal component logit regression. Bayesian networks were estimated to investigate relationship among SNPs.</p> <p>Results</p> <p>Using the Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification approach we detected around 100 significant SNPs for the quantitative trait (p<0.05 with 1000 permutations) and 109 significant (p<0.0006 with local FDR correction) SNPs for the categorical trait. With additional principal component regression we reduced the list to 16 and 50 SNPs for the quantitative and categorical trait, respectively.</p> <p>Conclusions</p> <p>GRAMMAR could efficiently incorporate the information regarding random genetic effects. Principal component stratification should be cautiously used with stringent multiple hypothesis testing correction to correct for ancestral stratification and association analyses for binary traits when there are systematic genetic effects such as half sib family structures. Bayesian networks are useful to investigate relationships among SNPs and environmental variables.</p