Differential expression of the genes encoding outer dynein arms, inner dynein arms, radial spokes and intraflagellar transport proteins in the group of PCD cases compared to the non-PCD controls.

<p># In case of genes represented by multiple probes, mean values were used for calculations</p

Gene annotation analysis of the genes up-regulated in PCD (fold change >2 and p<0.05).

#<p>Count is non-exclusive number of genes in each annotation category.</p>*<p>False discovery rate.</p

Gene annotation analysis of the genes down-regulated in PCD (fold change >2.0, and p<0.05).

#<p>Count is a non-exclusive number of genes in each annotation category.</p>*<p>False discovery rate.</p

Additional file 2: of An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Detected cis -eQTLs and trans -eQTLs in the complete dataset. (XLSX 90 kb

Loci previously reported to colocalise with liver eQTL, but not supported by our analysis.

<p>Gene/eQTL associations previously reported as having a probable shared variant but not supported by our method based on PP3 (posterior probability for distinct signal values) >75%. *Secondary signals are reported only when there is a secondary eQTL at a p-value greater than . Colocalisation tests are computed using the expression data conditioned on the listed SNP. Other genes in the same region as the gene listed that colocalise using our method are reported.</p

Additional file 1: of An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Differentially expressed genes between the different obesity levels. (XLSX 59 kb

Simulation analysis with a shared causal variant between two studies.

<p>The two datasets used are one eQTL (sample size 966 samples, 10% of the variance explained by the variant) and one biomarker (such as LDL). The variance explained by the biomarker is colour coded and the x-axis shows the sample size of the biomarker study. The y axis shows the median, 10% and 90% quantile of the distribution of PP4 values (which supports a shared common variant).</p

Differential expression of the genes mutated in PCD and two genes mutated in syndromic disorders associated with PCD symptoms (<i>RPGR</i> and <i>ODF1</i>).

*<p>In case of genes represented by multiple probes, the most significant probe is shown.</p>#<p><i>TXNDC3</i> was not stably expressed, <i>CCDC39, CCDC164</i> and <i>SPAG1</i> were not represented on the array.</p

Novel loci not previously reported to colocalise with liver eQTL, but colocalising based on our analysis.

<p>Signals previously not reported as having a probable shared variant but supported by our method based on PP4 (posterior probability for a shared signal) >75% for colocalisation between the liver eQTL dataset and the Teslovich et al. meta-analysis of LDL, HDL, TG, TC, using the strict prior . For 11 genes with strong candidate status for lipid metabolism, we list a key reference that describes their function (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004383#pgen.1004383.s016" target="_blank">Text S2</a> for more details of gene functions).</p

LDL association and eQTL association plots at the <i>SYPL2</i> locus.

<p>The x-axis shows the physical position on the chromosome (Mb) <b>A</b>: -log10(p) association p-values for LDL. The p-values are from the Teslovich et al published meta-analysis of >100,000 individuals. <b>B</b>: −log10(p) association p-values for <i>SYPL2</i> expression in 966 liver samples. <b>C</b>: −log10(p) association p-values for <i>SYPL2</i> expression conditional on the top eQTL associated SNP at this locus (rs2359653).</p