Additional file 1: of Genome-wide meta-analysis identifies novel loci associated with parathyroid hormone level

Supporting Information. (XLSX 54 kb

Plot of mean improvement in imputation accuracy (r²) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.

<p>Plot of mean improvement in imputation accuracy (r²) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.</p

Frequency plot of imputation accuracy (r²) using 1000 Genomes data alone against 1000 Genomes plus a local reference panel for SNPs with Minor Allele Frequencies (MAF) of 1–3.2%.

<p>Frequency plot of imputation accuracy (r²) using 1000 Genomes data alone against 1000 Genomes plus a local reference panel for SNPs with Minor Allele Frequencies (MAF) of 1–3.2%.</p

Mean accuracy of imputation (r² of allelic dosage across all samples for a SNP) averaged across SNPs split by Minor Allele Frequency (MAF).

<p>MAF bins increase by factors of √10, to create four exponentially increasing bins.</p><p>N SNPs: number of SNPs in MAF bin.</p><p>1kG: 1000 Genomes used as reference panel.</p><p>1kG+LRP: 1000 Genomes plus local reference panel.</p><p>Increase r²: Average across all SNPs in MAF bin increase in r².</p><p>Std dev: The standard deviation (across SNPs) of the increase in r² at each SNP.</p><p>Inc. Sample: Increase in effective sample size for GWAS.</p><p>The standard errors of mean increases are less than 0.003. All improvements in r² are significantly different from zero and significantly different between MAF bands (P<0.001, two-sided t tests).</p

Preparation of array data and local reference panel for imputation.

<p>The genotype data were quality controlled and phased. These data were then used in further downstream analysis.</p

Illustration of the procedure to estimate imputation accuracy.

<p>We used a drop one-out crossvalidation approach. For the imputation step each subject was removed from the reference panel in turn, and this subject’s exome sequence SNPs were then imputed using either the 1000 Genomes reference panel alone or in conjunction with a second local reference panel. All subjects’ imputed allelic dosages were then compared with the exome sequence genotype data (“gold standard”).</p

An analysis of pleiotropy between loci associated with IgG glycans and previously reported disease/trait susceptibility loci, with linkage disequilibrium computed between the most significantly associated SNPs.

<p>Associations are those found in the GWAS Catalog track of USCS Genome browser (accessed 04/07/2012) and LD has been calculated using SNAP (<a href="http://www.broadinstitute.org/mpg/snap/Johnson" target="_blank">http://www.broadinstitute.org/mpg/snap/Johnson</a>, A. D., Handsaker, R. E., Pulit, S., Nizzari, M. M., O'Donnell, C. J., de Bakker, P. I. W. SNAP: A web-based tool for identification and annotation of proxy SNPs using HapMap <i>Bioinformatics, 2008 24(24):2938–2939</i>).</p

Groups of IgG N-glycans from Table 3 that showed statistically significant difference in observed values (corrected by sex, age, and African admixture) between 101 Afro-Caribbean cases with SLE and 183 controls.

*<p>t-test for equality of means (2-tailed).</p

Twelve groups of IgG N-glycans (of 77 measured) that showed nominally significant difference (p<0.05) in observed values between 5 mice that were heterozygous <i>Ikzf1</i> knock-outs (Neo) and 5 wild-type controls (wt).

<p>The global difference test was significant (p = 0.03). ^*t-test for equality of means (2-tailed).</p

Validation of biomarker potential of IGP48 IgG N-glycan percentage in prediction of Systemic Lupus Erythematosus (SLE) in 101 Afro-Caribbean cases and 183 matched controls.

<p>As shown in the graph, age and sex do not have any predictive power for this disease, but addition of IGP48 substantially increases sensitivity and specificity of prediction, with area under receiver-operator curve increased to 0.828.</p