Traits with the greatest number of QTL containing genes showing AI.

<p>Traits with the greatest number of QTL containing genes showing AI.</p

SNP categories.

<p>SNP categories.</p

Schematic diagram of the process used for identification of marker SNPs and genes showing allelic imbalance in expression.

<p>Reads were mapped to the reference genome and potential SNPs identified. For each SNP and sample, a genotype was determined and heterozygous loci were then selected. Read counts for each genotype in these samples were then used in a Poisson model to test for AI at heterozygous loci. Genes showing AI were identified by combining multiple SNP results to the gene level via a minimum P-value. In addition, down-sampling and ranking of genes for AI was used to compensate for gene expression level bias in the discovery process. A conservative list of 1,293 genes was then identified that consisted of the top ranked 1,500 genes identified by down-sizing analysis, which was filtered to include genes with a Bonferroni P-value <0.05 for AI.</p

Transcription factor binding site enrichments¹ for the top ranked genes showing AI.

<p>Transcription factor binding site enrichments<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180378#t003fn001" target="_blank">¹</a> for the top ranked genes showing AI.</p

SNPs in highly expressed genes are ranked more highly for significant AI in the absence of down-sampling.

<p>A. The proportion of SNPs that are in highly expressed genes (top 5%) within the top-ranked SNPs showing AI versus size of top ranked SNP list (TopSNPs). Results without down-sampling are shown by blue triangles; results with down-sampling (D = 50, k = 20) are shown by orange circles. SNPs were considered to be highly expressed if they were greater than the 95% quantile of the overall gene expression associated with the filtered SNPs. It is expected that overall approximately 5% of the top-ranking SNPs for AI should be associated with highly expressed transcripts. B. Overall bias and variance for different values of D are shown. Overall bias was estimated by the absolute Spearman rank correlation between AI P-values (negative log-transformed) and gene expression over all samples. Overall variance was measured as the median over SNPs of the variance for k = 10 repetitions of down-sampling. Different values of D are shown on the figure panel. C. The stability of down-sampling compared to a random selection of SNPs is shown. For each number of top ranked SNPs, the number of common SNPs among k = 20 random samples is graphed. The observed SNP rank data are shown as orange solid lines and the random SNP rank data are shown as blue dashed lines. The inset shows an enlargement of the original plot for all 24,355 tested SNP, showing that eventually all SNPs are included in the list using either method.</p

Top ranked 20 genes showing AI after incorporation of the down-sampling approach.

<p>Top ranked 20 genes showing AI after incorporation of the down-sampling approach.</p

Scatterplots of highest ranked genes.

<p>For the 20 top ranked genes for AI, scatterplots of read depths for the reference (abscissa) and alternate (ordinate) alleles are graphed for the highest ranking SNP for that gene. Read depths are transformed into log₂ (1 + read counts). Also shown for each gene is the gene symbol, genome coordinate and the reference and alternative alleles. Data for all animals are shown, including some that are homozygous at the locus. Blue circles denote individuals with heterozygous marker genotypes. Red circles represent those individuals classified as homozygous and thus were not included in the AI testing. Dashed lines represent minimum expression thresholds. The diagonal line represents allelic balance in gene expression.</p

Down-sampling leads to more informative results.

<p>The three panels show the relationship between the chi-squared statistic for AI as a function of the proportion of expression of the reference allele. Black triangles represent SNPs associated with highly expressed genes and red circles represent SNPs that are not associated with highly expressed genes. A. No down-sampling. The plot shows that when using the original analysis framework many highly significant SNPs for AI were also highly expressed, indicating an intrinsic bias towards SNPs with higher expression. Highly expressed SNPs were defined as those with overall expression in the top 5% of the entire set of SNPs and are represented by red triangles. B. One round of down-sampling. C. The panel shows the mean test statistics over twenty repetitions of down-sampling and displays stability associated with the down-sampling method by comparison with panel B.</p

Skeletal muscle Akt1, Akt2, Thr 308 and Thr 473 phosphoAkt protein abundance in four month old lambs of normal weight ewes (CC), normal weight ewes put on a dietary restriction regime for one month before and one week after conception (CR), overnourished, obese ewes (HH) and obese ewes put on a dietary restriction regime for one month before and one week after conception (HR).

<p>Different superscripts denote mean values that are significantly different. <i>n</i>=6 lambs, CC, 2 males, 3 females; CR, HH & HR, 3 males, 3 females. All data are mean ± s.e.m.</p

Skeletal muscle GSK3α, Ser 21 phospho-GSK3α, GSK3β, Ser 9 phospho-GSK3β, GS and Ser 641 phospho-GS protein abundance in four month old lambs of normal weight ewes (CC), normal weight ewes put on a dietary restriction regime for one month before and one week after conception (CR), overnourished, obese ewes (HH) and obese ewes put on a dietary restriction regime for one month before and one week after conception (HR).

<p>Different superscripts denote mean values that are significantly different. <i>n</i>=6 lambs, CC, 2 males, 3 females; CR, HH & HR, 3 males, 3 females. All data are mean ± s.e.m.</p