Additional file 1 of Associations of plasma neurofilament light chain with cognition and neuroimaging measures in community-dwelling early old age men

Supplementary Material

Gene expression data of the Allen Human Brain Atlas were mapped onto the 12 genetically based cortical regions in the MR space.

<p>A) Resulting volume registration between FreeSurfer surface (fsaverage) and Allen brain MNI coordinates displayed as a point cloud, with a slice of the MRI imaging at the bottom (colin27). B) After the volume registration, gene expression data points are mapped to FreeSurfer surface vertices by assigning each surface vertex the gene expression of the closest (Euclidean distance) Allen brain data point using nearest neighbor interpolation. If two vertices have the same closest Allen brain data point, they belong to the same patch and the patch id is displayed as color. Thus, the color patches illustrate the local density of data points. The color patches with similar sizes across the cortex represent an even distribution of Allen brain data points and their surface correspondences. Colors of the dots in both (A) and (B) panels represent cortical regions to which they were assigned, corresponding to the color schemes in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g001" target="_blank">Fig 1B</a>.</p

Region-specific gene expression profiles in each lobe.

<p>A) The majority of genes were ubiquitously expressed in the cortical surface areas of all four lobes of the brain. A small percentage of the genes were either distinctively expressed in one lobe or co-expressed in multiple but not all four lobes of the brain. The frontal lobe exhibits the most distinctively expressed genes. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s006" target="_blank">S5 Table</a> for the lists of genes and locations. B) The distribution of functional annotations of the transcripts distinctively expressed in the frontal lobe. “All” indicates the distribution of all transcripts included in our analysis, irrespective of their expression levels and anatomical locations. There are a higher proportion of intergenic transcripts in the frontal lobe (22% compared to 14%). C) A gene network analysis for the frontal lobe (excluding intergenic transcripts). The yellow-colored genes belong to the most significantly associated pathway: interferon-gamma-mediated signaling pathway, related to immunity (FDR = 3.2 x10^-4). Half of the genes were originally from the transcripts distinctively expressed in the frontal lobe. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s008" target="_blank">S7 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s009" target="_blank">S8 Table</a> for the complete list of associated pathways.</p

Applying the genetically based cortical parcellations to independent data.

<p>A) The phenotypic correlation matrix of VETSA twin cohort versus the phenotypic correlation matrix of combined-5-cohort (C5C). The Mantel test confirmed that the similarity between them was highly significant (<i>p</i> = 0.0001). B) Cortical brain phenotypes—surface area measures of 12 cortical regions after controlling for total surface area. The cortex was parceled into 12 genetically based regions of maximal shared genetic influence derived from the VETSA sample [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.ref016" target="_blank">16</a>]. 1. motor & premotor; 2. dorsolateral prefrontal; 3. dorsomedial frontal; 4. orbitofrontal; 5. pars opercularis & subcentral; 6. superior temporal; 7. posterolateral temporal; 8. anteromedial temporal; 9. inferior parietal; 10. superior parietal; 11. precuneus; 12. occipital. C) The phenotypic correlation versus the genetic correlations (<i>r</i>_<i>g</i>) matrices of VETSA. The correlation of the two matrices was also highly significant (<i>p</i> < 0.0001), suggesting high genetic contributions to the cortical patterning. Correlation coefficients are listed in Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s002" target="_blank">S1 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s003" target="_blank">S2 Table</a>.</p

Significant associations among correlation matrices.

<p>Correlation matrices among cortical surface areas derived from a variety of measures are highly consistent with each other as quantified by the Mantel test coefficients with 95% confidence intervals. Each pair of bars represents two correlation methods used in the Mantel test: linear regression with errors-in-both-variables (correlation with EIV) in gray color on the left versus Pearson’s correlation in light-gray color on the right. Variables were standardized in regression analysis. *<i>p</i>≤0.05, **<i>p</i>≤0.01, ***<i>p</i>≤0.001, ****<i>p</i>≤0.0001. Twin refers to VETSA cohort, and twin-based method was used to derive genetic correlations. Genotype-based method was used to derive genetic correlations for ombined-5-cohort. The corresponding matrices are visualized in Figs <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g001" target="_blank">1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.g002" target="_blank">2</a>. See also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s002" target="_blank">S1 Fig</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s003" target="_blank">S2 Table</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s004" target="_blank">S3 Table</a>, and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006143#pgen.1006143.s005" target="_blank">S4 Table</a>.</p

Associations (expressed by regression coefficients with 95% CIs, BMI units (kg/m²)) of former smoking with BMI compared to current smokers (reference) in twin individuals (n = 156,593) and same-sex twin pairs (DZ or MZ pairs) discordant for their smoking status (m = 10,551 pairwise measurements) by sex and time period from the CODATwins database, 1960–2012.

<p>BMI = body mass index; CI = confidence interval; DZ = dizygotic; MZ = monozygotic.</p

Associations (expressed by regression coefficients with 95% CIs, BMI units (kg/m²)) of former smoking with BMI compared to never smokers (reference) in twin individuals (n = 156,593) and same-sex twin pairs (DZ or MZ pairs) discordant for their smoking status (m = 9,336 pairwise measurements) by sex and time period from the CODATwins database, 1960–2012.

<p>BMI = body mass index; CI = confidence interval; DZ = dizygotic; MZ = monozygotic.</p

Associations (expressed by regression coefficients with 95% CIs, BMI units (kg/m²)) of current smoking with BMI compared to never smokers (reference) in twin individuals (n = 156,593) and same-sex twin pairs (DZ or MZ pairs) discordant for their smoking status (m = 10,128 pairwise measurements) by sex and time period from the CODATwins database, 1960–2012.

<p>BMI = body mass index; CI = confidence interval; DZ = dizygotic; MZ = monozygotic.</p

Flow chart of the CODATwins dataset (n = 156,593 twin individuals and 30,014 pairwise comparisons in smoking discordant same-sexed twin pairs) included in the study.

<p>BMI = body mass index; MZ = monozygotic.</p