Additional file 1: of Evidence for sex-specific genetic architectures across a spectrum of human complex traits

Additional methods and results. Figure S1 Phenotypic distributions for both sexes for considered phenotypes. Figure S2 SNP MAFs. Figure S3 Comparison of the results of the main analysis with those obtained on rank normalized phenotypes. Figure S4 Comparison of the results of the main analysis with those of simulated phenotypes. Figure S5 Comparison of the results of the main analysis with those obtained after exclusion of spouses. Figure S6 Comparison of the results of the main analysis with those obtained after adjusting for further socio-economic and health covariates. Table S1 Phenotype-specific sample sizes for the sets of unrelated and related white British individuals. Table S2 Sexual dimorphism indices for all phenotypes. Table S3 Comparison of the parameter estimates for alternative analyses of blood pressure phenotypes. Table S4 Estimates of heritabilities and genetic correlations for models fitted to related individuals with common SNPs and unrelated individuals including both common and common and rare SNPs. Table S5 Estimates of heritabilities, genetic correlations and P values for differences in heritability and genetic correlations differing from one, for categorical phenotypes using subsamples of the unrelated white British individuals with matching phenotypic distributions between the sexes. Table S6 Comparison of the results of the main analysis with those obtained on alternative analyses. Table S7 Estimates of heritabilities, genetic correlations and P values for differences in heritability and genetic correlations differing from one, for rank normalized phenotypes. Table S8 Parameter estimates for simulated phenotypes. Table S9 Estimates of heritabilities, genetic correlations and P values for differences in heritability and genetic correlations differing from one, after exclusion of spouses. Table S10 Estimates of heritabilities, genetic correlations and P values for differences in heritability and genetic correlations differing from one, when adjusting for additional socio-economic and health factors. Table S11 Prediction accuracies for bivariate and univariate genomic prediction models for all traits. (DOCX 2206 kb

Across-population prediction accuracies.

<p>Across-population prediction accuracies.</p

Prediction accuracies from GWAS analyses.

<p>Predictions on self reported White-British obtained using independently estimated SNP effects from a GWAS. We plot the accuracies obtained for subsets of SNPs selected based on a particular p-value threshold against this threshold value. Different colours indicate different traits. Dashed lines indicate maximum accuracies obtained when the effects of all SNP were estimated jointly (SNP-BLUP) using DISSECT.</p

Prediction accuracies on related White-British and self-reported White-British.

<p>Prediction accuracies on related White-British and self-reported White-British.</p

GStream method for SNP genotyping.

<p>This figure shows how GStream genotyping method works on two example markers, the first one representing a typical marker capturing a SNP (A and B) and the second one capturing both a SNP and a CNV (C and D). The leftmost graphs show the effects of the normalization procedure for the two markers, where the dotted blue lines enclose the ranges where candidate homozygotes and heterozygotes are identified in order to compute the scaling factors for each channel (black points over the axes). The rightmost graphs give an overview of the genotyping procedure: Upper subfigures represent the scaled BAF probability density function with the solid vertical lines setting the identified genotype centres, the dotted vertical lines setting the genotype limits and the horizontal lines representing the sequential search of genotype cluster peaks. Medium and lower subfigures represent genotype calls and quality call scores respectively.</p

Power to detect CNP associations.

A<p>CNV dataset provided by custom genotyping microarray-based studies.</p>B<p>CNV datasets provided by CGH-based studies.</p><p>Percentage of −log₁₀<i>P</i> ratios higher than 0.9 and lower than 1.1 over the CNV population-associated regions computed for each study. Platform difference refers to the percentage differences between HumanOmni1-Quad and Human1M-Duo platforms.</p

CNV regions for each dataset and platform used to evaluate the power to detect genome-wide associations.

<p>N_CNVR refers to the number of CNV loci selected from each study. Coverage with at least one marker within the CNV loci of both platforms is very similar although the marker density differs considerably. N_ASSOCS column refers to the total number of associated regions for the three population tests detected over the golden standard calls.</p

Public microarray data used in this study.

<p>The used microarray data comes from four different Illumina BeadChip platforms and the sample data comes from three HapMap populations. The total number of autosomal markers and the number of markers used for SNP genotyping evaluation are shown.</p

CNV loci highly correlated with trait-associated SNPs.

<p>This table shows significant SNP-CNV pairs found in high LD. N stands for the number of CNV microarray markers correlated with the SNP genotypes, and r2CEU, r2YRI and r2CHBJPT stand for the linkage disequilibrium measures between the SNP and the CNV. Reported GWAS <i>P</i>-value is also shown together with a field indicating if the CNV association has been previously reported.</p

Evaluating SNP genotyping performance.

<p>Plots comparing SNP genotyping algorithms for each microarray platform are tested. The vertical axis represents the percentage of SNPs that are excluded from the accuracy calculation by the lowest quality score criteria. GStream performed better at all the drop rate levels in all the platforms. A high decrease in performance is observed for GenCall when drop rate values are lower than its uncall rate (i.e. ∼2% in Human610Quad).</p