Integrative genetic analysis suggests that skin color modifies the genetic architecture of melanoma

<div><p>Melanoma is the deadliest form of skin cancer and presents a significant health care burden in many countries. In addition to ultraviolet radiation in sunlight, the main causal factor for melanoma, genetic factors also play an important role in melanoma susceptibility. Although genome-wide association studies have identified many single nucleotide polymorphisms associated with melanoma, little is known about the proportion of disease risk attributable to these loci and their distribution throughout the genome. Here, we investigated the genetic architecture of melanoma in 1,888 cases and 990 controls of European non-Hispanic ancestry. We estimated the overall narrow-sense heritability of melanoma to be 0.18 (<i>P</i> < 0.03), indicating that genetics contributes significantly to the risk of sporadically-occurring melanoma. We then demonstrated that only a small proportion of this risk is attributable to known risk variants, suggesting that much remains unknown of the role of genetics in melanoma. To investigate further the genetic architecture of melanoma, we partitioned the heritability by chromosome, minor allele frequency, and functional annotations. We showed that common genetic variation contributes significantly to melanoma risk, with a risk model defined by a handful of genomic regions rather than many risk loci distributed throughout the genome. We also demonstrated that variants affecting gene expression in skin account for a significant proportion of the heritability, and are enriched among melanoma risk loci. Finally, by incorporating skin color into our analyses, we observed both a shift in significance for melanoma-associated loci and an enrichment of expression quantitative trait loci among melanoma susceptibility variants. These findings suggest that skin color may be an important modifier of melanoma risk. We speculate that incorporating skin color and other non-genetic factors into genetic studies may allow for an improved understanding of melanoma susceptibility and guide future investigations to identify melanoma risk genes.</p></div

The <i>CDKN2A</i> region surpasses the genome-wide significance threshold whereas the <i>MC1R</i> and <i>HERC2/OCA2</i> loci do not upon conditioning on skin color in the melanoma etiology GWAS.

<p>Manhattan plot of the p-values for the association between imputed SNPs and melanoma using skin color as a covariate. The x-axis shows the chromosomal positions whereas the y-axis shows the–log₁₀ p-values of the SNPs. The p-values were obtained by logistic regression analysis including age, sex, skin color and the first two PCs from the PCA of GWAS (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185730#pone.0185730.s003" target="_blank">S3 Fig</a>) as covariates. The red horizontal line is the widely used genome-wide significance threshold (p = 5 x 10⁻⁸) that was estimated by correcting independent common variants, which is roughly 1,000,000. The blue horizontal line is the suggestive significance threshold (p = 1 x 10⁻⁵). The <i>CDKN2A</i> region on chromosome 9 (black) reaches genome-wide significance upon the inclusion of skin color as a covariate in the analysis while the pigmentation loci <i>MC1R</i> (chromosome 16) and <i>HERC2/OCA2</i> (chromosome 15) are no longer significant.</p

Melanoma heritability stratified by chromosome.

<p>Heritability estimates for each chromosome are plotted against chromosome length. The solid line is the regression of heritability on chromosome length and the dashed lines represent the 95% confidence intervals for the slope of the regression line. Chromosomes 6, 9, 11 and 16 are outside the 95% confidence interval and have higher heritability estimates than that expected based on chromosome length.</p

Heritability of melanoma partitioned based on genic annotation.

<p>Heritability of melanoma partitioned based on genic annotation.</p

eQTL enrichment in the melanoma GWAS dataset becomes more significant upon conditioning on skin color.

<p>The distributions of the number of skin cis-eQTLs (at two different p-value thresholds: p < 0.01 and p < 0.001) in the permuted 1,000 GWAS datasets, generated by shuffling case-control status each time, are shown in the histograms. In each permuted dataset, the number of eQTLs are determined among the top 2,000 (top panel) or top 1,000 (bottom panel) associations. The solid black circle is the actual eQTL count among the melanoma-associated SNPs. The p-values correspond to empirical p-values, calculated as the proportion of permuted GWAS datasets in which the eQTL count exceeds the actual observed count among the top associations. <b>Panel (A)</b> displays results of the analysis without conditioning on skin color. Enrichment of eQTLs among the top 1,000 melanoma associated SNPs is statistically significant (<i>P</i> < 0.05), while enrichment among the top 2,000 SNPs fails to reach statistical significance but is highly suggestive. <b>Panel (B)</b> displays results of the analysis in which skin color is included as a covariate in the analysis. Upon conditioning on skin color, enrichment of eQTLs among the top melanoma-associated SNPs becomes more pronounced and is statistically significant at all thresholds tested.</p

Polymerase activity of avian-human viral ribonucleoprotein (RNP) complexes.

<p>(A) A549 cells were transfected in duplicate with pPol1-NS-Renilla and pSV40-Luc reporter plasmids, together with plasmids expressing PB2, PB1, PA and NP from either WY03 (human symbol) or TH04 (chicken symbol) viruses. Cells were incubated at 33°C (hatched bars) or 37°C (solid bars) for 24 hours and cell lysates were analyzed to measure Renilla and firefly luciferase activities. The latter was used to normalize transfection efficiency. Values shown represent the activities of each RNP relative to that of WY03 measured at 37°C (100%). (B) Viral RNP activities derived from WY03 (human symbol) or VN04 (chicken symbol) viruses are shown as described in panel A.</p

Replication kinetics of avian-human reassortant viruses in differentiated human tracheobronchial epithelial (HTBE) cells.

<p>HTBE cells were infected in duplicate with parental TH04 and WY03 (A) or rH5N1 viruses (B, C, D) at an moi of 0.02; progeny viruses were collected and titrated on MDCK cells.</p

Replication of avian-human reassortant viruses in mice.

<p>Symbols and virus nomenclature are as in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1000072#ppat-1000072-g001" target="_blank">Figure 1</a>. The mouse infectious dose (MID₅₀) and lethal dose (LD₅₀) are expressed as the log₁₀ pfu required to give one MID₅₀ or one LD_50. Maximum mean weight loss was determined from five mice per group (percent weight loss relative to dpi 0) following intranasal infection with 10⁴ pfu. MST denotes the mean survival time in days following infection with 10⁴ pfu. Virus titer in lung, spleen, brain or nasal turbinate are geometric means of the log₁₀ pfu at 4 dpi of three mice infected with 10⁴ pfu. LD₅₀ values of rH5N1 in group A1 were significantly different from A2 and those from A1 and A2 were significantly different from TH04 WT (<i>P<</i>0.001) by analysis of variance. The — indicate that tissue titers were below limit of detection of the assay (0.7 log₁₀ pfu/ml). Viruses are listed in ascending LD₅₀ values. Viruses with identical LD₅₀ are listed by descending weight loss.</p

Characteristics of moderate to low yield avian-human reassortant viruses in cell culture.

<p>Symbols and virus nomenclatures are the same as described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1000072#ppat-1000072-g001" target="_blank">Figure 1</a>. Rescue efficiency represents virus titer (log₁₀ pfu/ml) from cell cultures at 72 hours after transfection; geometric mean from 3 independent experiments. Plaque formation by reassortant viruses with <100 pfu/ml rescue efficiency was not determined (ND).</p

Mixed Effects Modeling of Proliferation Rates in Cell-Based Models: Consequence for Pharmacogenomics and Cancer

<div><p>The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.</p> </div