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Genome-Wide Association Studies of Quantitatively Measured Skin, Hair, and Eye Pigmentation in Four European Populations

<div><p>Pigmentation of the skin, hair, and eyes varies both within and between human populations. Identifying the genes and alleles underlying this variation has been the goal of many candidate gene and several genome-wide association studies (GWAS). Most GWAS for pigmentary traits to date have been based on subjective phenotypes using categorical scales. But skin, hair, and eye pigmentation vary continuously. Here, we seek to characterize quantitative variation in these traits objectively and accurately and to determine their genetic basis. Objective and quantitative measures of skin, hair, and eye color were made using reflectance or digital spectroscopy in Europeans from Ireland, Poland, Italy, and Portugal. A GWAS was conducted for the three quantitative pigmentation phenotypes in 176 women across 313,763 SNP loci, and replication of the most significant associations was attempted in a sample of 294 European men and women from the same countries. We find that the pigmentation phenotypes are highly stratified along axes of European genetic differentiation. The country of sampling explains approximately 35% of the variation in skin pigmentation, 31% of the variation in hair pigmentation, and 40% of the variation in eye pigmentation. All three quantitative phenotypes are correlated with each other. In our two-stage association study, we reproduce the association of rs1667394 at the <em>OCA2/HERC2</em> locus with eye color but we do not identify new genetic determinants of skin and hair pigmentation supporting the lack of major genes affecting skin and hair color variation within Europe and suggesting that not only careful phenotyping but also larger cohorts are required to understand the genetic architecture of these complex quantitative traits. Interestingly, we also see that in each of these four populations, men are more lightly pigmented in the unexposed skin of the inner arm than women, a fact that is underappreciated and may vary across the world.</p> </div

Population structure of the GWAS samples.

<p>(<b>A</b>) Plots of GWAS individuals on genetic PC1 and PC2 show that individuals largely cluster by country of sampling. PC1 divides the samples according to a North/South geographical axis, while PC2 divides the samples along an East/West geographical axis. Individuals from Ireland, Poland, Italy, and Portugal are colored in yellow, red, blue, and gray, respectively. (<b>B</b>) The plot of individuals on PC1 and PC3 shows that individuals from Portugal tend to have lower values on PC3 than individuals from Italy and Ireland, while individuals from Poland have intermediate values on PC3.</p

Distribution of skin, hair, and eye pigmentation.

<p>Skin pigmentation histogram (<b>A</b>) and boxplot by country of sampling and sex (<b>B</b>) in 469 individuals showing the normality of the phenotype distribution and the differences between sexes and among countries. Males (M) have consistently lighter pigmentation (lower scored) than females (F) in all four countries. Among countries, the largest pigmentation difference is with Ireland, where, in our sample, individuals have lighter pigmentation or lower M index on average than in Poland, Italy, or Portugal. Hair pigmentation histogram (<b>C</b>) and boxplot by country (<b>D</b>) in 341 individuals showing the distribution of hair pigmentation and the differences among countries. In our sample, individuals from Northern European countries (Ireland, Poland) have on average lighter hair pigmentation than individuals from Southern European countries (Italy, Portugal). The distributions in males are similar to those in females in all countries except Ireland, where, in our sample, males have darker hair color than females (not shown). Eye pigmentation histogram (<b>E</b>) and boxplot by country (<b>F</b>) in 468 individuals showing the bimodal distribution of eye pigmentation and the differences among countries. Comparison with self-reported phenotypes shows that the two modes of the distribution correspond to blue and brown eye color, while individuals reporting green and hazel eye color have intermediate C’ values. As with hair pigmentation, in our sample, individuals from Northern European countries have on average lighter eye pigmentation than individuals from Southern European countries.</p

GWAS results.

<p>Manhattan plots for the GWAS results for the skin (<b>A</b>), hair (<b>B</b>), and eye (<b>C</b>) pigmentation. The log-transformed p-values from the test of association are plotted as a function of the chromosomal position. Genome-wide significance is defined as the Bonferroni corrected 5% significance threshold (p-value<1.6×10⁻⁷) and is indicated as a red line. For skin pigmentation, one SNP on chromosome 3 in the <i>FLNB</i> gene almost reaches genome-wide significance (p-value = 1.8×10⁻⁷). No SNP achieves genome-wide significance in the hair pigmentation GWAS. For eye pigmentation, two SNPs, one near the pigmentation gene <i>OCA2</i> on chromosome 15 and one in the <i>SCIN</i> gene on chromosome 7 achieve genome-wide significance.</p

Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis

<div><p>Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type content for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package <i>BrainInABlender</i> (validated and publicly-released on github). Using this method, we found that the principal components of variation in the datasets strongly correlated with the predicted neuronal/glial content of the samples. This variability was not simply due to dissection–the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.</p></div

GWAS, replication, and combined association results for all signals with p-value<10⁻⁵ in the GWAS.

*<p>Minor/Major allele in the GWAS.</p>†<p>Allele frequencies (AF) and ^‡regression coefficients (beta) are given with respect to the number of copies of the minor allele in the GWAS.</p

Thousands of transcripts have been identified as specifically-enriched in particular cortical cell types within published single-cell or purified cell type transcriptomic experiments.

<p>The experimental and statistical methods for determining whether a transcript was enriched in a cell type varied by publication, and included both RNA-Seq and microarray datasets.</p

Cell content predictions derived from microarray data match known relationships between subject variables and brain tissue cell content.

<p>Boxplots represent the median and interquartile range, with whiskers illustrating either the full range of the data or 1.5x the interquartile range. <b>A.</b> Within the CMC dataset, cortical tissue samples that were dissected to only contain gray matter (PITT) show lower predicted oligodendrocyte and microglia content and more neurons and vasculature (bars: β+/- SE, red/blue: p<0.05). <b>B.</b> Subjects who died in a manner that involved exsanguination had a notably low red blood cell index in both the Pritzker (p = 0.00056) and Narayan et al. datasets (p = 0.052*trend). <b>C.</b> The presence of prolonged hypoxia around the time of death, as indicated by high agonal factor score, was associated with a large increase in the endothelial cell index (p = 2.85e-07) matching previous demonstrations of cerebral angiogenesis, activation, and proliferation in low oxygen environments [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.ref045" target="_blank">45</a>]. <b>D.</b> High agonal factor was also associated with a clear decrease in neuronal indices (p = 3.58e-09) mirroring the vulnerability of neurons to low oxygen [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.ref046" target="_blank">46</a>]. <b>E.</b> Age was associated with a decrease in the neuronal indices (p = 0.000956) which fits known decreases in gray matter density in the frontal cortex in aging humans [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.ref047" target="_blank">47</a>]. <b>F</b>. Major Depressive Disorder was associated with a moderate decrease in astrocyte index (p = 0.0118), which fits what has been observed morphometrically [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.ref048" target="_blank">48</a>]. <b>G.</b> The most highly-replicated relationships between subject variables and predicted cortical cell content across all five of the post-mortem human datasets. Provided in the table are the T-stats for the effects (red = upregulation, blue = downregulation), derived from a larger linear model controlling for confounds <b>(Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.e001" target="_blank">1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.e002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.e003" target="_blank">3</a></b>), as well as the nominal p-values from the meta-analysis of the results across the four microarray studies, and p-values following multiple-comparisons correction (q-value). Only effects that had a q<0.05 in either our meta-analysis or the large CMC RNA-Seq dataset are included in the table. Asterisks denote effects that had consistent directionality in the meta-analysis and CMC dataset (*) or consistent directionality and q<0.05 in both datasets (**). Please note that lower pH and higher agonal factor are both indicators of greater hypoxia prior to death, but have an inverted relationship and therefore show opposing relationships with the cell type indices.</p

Including cell content predictions in the analysis of microarray data improves model fit and enhances the detection of previously-identified diagnosis-related genes in some datasets.

<p><b>A.</b> Diagnosis effects were likely to be partially confounded by dissection variability within the Pritzker and CMC datasets. <b>B:</b> We examined a series of differential expression models of increasing complexity, including a base model (M1), a standard model (M2), and three models that included cell type co-variates (M3-M5). <b>C-D.</b> Model fit improved with the addition of cell type (M1/M2 vs. M3-M5) when examining either <b>C</b>. all expressed genes in the dataset (example from CMC: points = AVE +/-SE). <b>D.</b> genes with previously-documented relationships with psychiatric illness in particular cell types (example from Pritzker: BIC values for all models for each gene were centered prior to analysis. Boxes represent the median and interquartile range of the data). <b>E.</b> Evaluating the replication of previously-observed psychiatric effects (<b>Figure L in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200003#pone.0200003.s001" target="_blank">S1 Text</a></b>) in three datasets (Pritzker, CMC, and Barnes) using a standard differential expression model (M2) vs. models that include cell type co-variates (M3-5). Letters (a vs. b, c vs. d) denote significant model comparisons (Fisher’s exact test: p<0.05). Top graphs: The percentage of genes (y-axis: 0–1) replicating the direction of previously-documented psychiatric effects on cortical gene expression sometimes increases with the addition of cell type to the model (p<0.05: Barnes (effects of Schiz): M2 vs. M5, CMC (effects of Bipolar Disorder): M2 vs. M3). Middle graphs: The detection of previously-identified psychiatric effects on gene expression (p<0.05 & replicated direction of effect) increases with the addition of cell type to the model in some datasets (p<0.05, Barnes: M2 vs. M5, Pritzker: M2 vs. M5) but decreases in others (p<0.05, CMC: M2 vs. M5, M3 vs. M5). Bottom graphs: In some datasets we see an enrichment of psychiatric effects (*p<0.05) in previously-identified psychiatric gene sets only after controlling for cell type (Barnes: M3, M4, Pritzker: M5, M3). For the CMC dataset, we see an enrichment using all models (*p<0.05).</p