Functional characterization of a multi-cancer risk locus on chromosome band 2q33.1 near CASP8

30 Genome-wide association studies (GWAS) of melanoma have identified numerous susceptibility 31 loci. However, causal genes and variants underlying risk have yet to be established for most. It 32 is becoming apparent that many functional variants underlying complex traits act via cis-33 regulation that may be context-specific, dependent on availability of specific transcription 34 factors/complexes in specific cell types and cell-states. To characterize a risk locus on 35 chromosome band 2q33.1 associated with melanoma, breast cancer, and keratinocyte cancers, 36 we integrated fine-mapping, cell-type specific expression quantitative trait locus (eQTL) analysis, 37 a massively parallel reporter assay, individual luciferase assays, and SNP-based proteomics. 38 Integrated analysis implicates the presence of multiple functional variants lying primarily within a 39 promoter for CASP8. A haplotype containing rs3769823 appeared have the largest effect on 40 expression. Strikingly, both tumor/normal context and this risk-associated haplotype play critical 41 roles in mediating allelic cis-regulatory activity. Quantitative mass spectrometry for rs3769823 42 identified both E4F1, a transcriptional repressor, and IRF2, a transcriptional activator, as binding 43 preferentially to risk-associated rs3969823-A. The binding of these transcription factors was 44 validated via EMSA, supershift, and chromatin immunoprecipitation (ChIP) assays. The relative 45 levels of E4F1 and IRF2 differ by cell-type and play a role in mediating transcriptional activity in 46 a cell-type specific manner. Our results indicate that the top credible causal set variant 47 rs3769823 likely influences expression of CASP8 and FLACC1 in a cell-type specific manner 48 and may be a relevant functional variant for multiple cancers associated with this locus. 49 50

51 Melanoma; genome-wide association study; GWAS; expression quantitative trait locus; eQTL; 52 fine-mapping; caspase 8; CASP8 53 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint

54 Over the past decade, genome-wide association studies (GWAS) have identified 54 genome-55 wide significant susceptibility loci for cutaneous malignant melanoma (CMM) in populations of 56 European ancestry 1-10. Of these, Barrett and colleagues initially identified a genome-wide 57 significant locus for melanoma on chromosome band 2q33.1 adjacent to the Caspase 8 gene 58 (CASP8) 5. Multiple subsequent melanoma GWAS meta-analyses since have replicated this 59 finding, including the most recent meta-analysis of 36,760 melanoma cases (rs10931936-T, P = 60 2.12 × 10-12, OR = 1.08)10. This region is a multi-cancer risk locus, with highly-correlated risk 61 alleles for melanoma and other cancers sharing a common haplotype, including signals for 62 breast cancer (rs3769821, P = 3.97 x 10-18, r2 with rs10931936 = 0.63) 11, keratinocyte cancers 63 including both cutaneous basal cell carcinoma (BCC, rs6714430, P = 5.42 x 10-55, r2 with 64 rs10931936 = 0.99), squamous cell carcinoma (SCC; rs10931936, P = 1.03 x 10-7), or both 65 combined (rs6743068, P = 1.50 x 10-48, r2 with 10931936 = 0.99)12,13, and non-small cell lung 66 cancer (rs3769821, P = 4.45 x 10-8, r2 with rs10931936 = 0.63)14. Additionally, the region also 67 harbors a highly-correlated signal for prostate cancer (rs59308963, P = 2.41 x 10-8, r2 with 68 rs10931936 = 0.93)15, where the protective allele is highly correlated with risk alleles for 69 melanoma and the other cancers. However, whether causal variants and genes are shared 70 across multiple cancers remains under-explored. 71 The topologically-associated domain (TAD) at 2q33.1 harbors multiple strong a priori candidate 72 genes with well-established roles in apoptosis, including Caspase-8 (CASP8), Caspase 10 73 (CASP10), and the CASP8 and FADD-like apoptosis regulator (CFLAR). CASP8 is an apical 74 caspase that initiates apoptosis through the extrinsic pathway via a subset of proteins in the 75 tumor necrosis factor superfamily (TNFRSF) that includes TNFR1 (tumor necrosis factor 76 receptor-1), CD95/Fas, and TRAIL-R (TNF-related apoptosis-inducing ligand receptor) 16,17. 77 CASP8 is composed of a catalytic domain, a protease domain and two death domains 78 comprising the prodomain, which is important for forming the death-inducing signaling complex 79 (DISC), death receptor-FADD-CASP8 18,19. Like all caspases, CASP8 is present as a 80 monomeric procaspase zymogen20. Binding of CASP8 to the DISC facilitates zymogen 81 maturation by dimerization and proteolytic cleavage of the caspase itself 21,22. Fully processed 82 active CASP8 is released from DISC and activates downstream effector caspases. Notably, two 83 proteins encoded by adjacent genes at this locus, CASP10 and CFLAR, are highly similar to the 84 N-terminus of CASP8 and play a different role in the apoptotic pathway by interacting with 85 FADD and CASP8 23,24. While CASP10 is an apical caspase like CASP8, CFLAR splice variants 86 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint inhibit different processing steps of CASP8 activation through heterodimerization 24-26. Given 87 that most common trait loci are thought to function via cis-gene regulation, often at long 88 distances, other genes within this TAD cannot be ruled out as candidate causal genes. 89 In this study, we fine-mapped multiple cancer GWAS datasets and integrate tissue-based and 90 cell-type specific quantitative trait locus (QTL) data in order to identify likely causal variants and 91 genes. We identified multiple highly-correlated potentially cis-regulatory variants, and establish 92 rs3769823 as a key functional variant, likely acting via altered binding of the IRF2 and E4F1 93 transcription factors. We establish cis-regulation of CASP8 as a likely causal mechanism 94 underlying melanoma and breast cancer risk at this locus, and given the strong LD between 95 signals, this mechanism may potentially play a role in risk of other associated cancers. 96 97

98 Comparison of the 2q33.1 melanoma risk signal with GWAS of susceptibility to other 99 cancers 100 The most recent GWAS meta-analyses of melanoma identified rs10931936 as the sentinel 101 variant at the 2q33.1 risk locus (rs10931936-T, P = 2.12 × 10-12, OR = 1.08)10. Conditional and 102 joint analysis of summary GWAS meta-analysis data27,28 identified no additional genome-wide 103 significant association signals at this locus10. The next most significant signal within 1 Mb in 104 either direction of rs10931936 is rs563855920 (Pconditional = 3.05 x 10-4; Figure S1; Table S1) 105 more than 300 kb away, suggesting that there are no additional major signals. 106 This region has been identified as a genome-wide significant risk locus via GWAS of multiple 107 other cancer types including breast cancer (122,977 cases, rs3769821-C, P = 3.97 × 10-18, 1000 108 Genomes EUR r2 with rs10931936 = 0.63)11 and keratinocyte cancers (47,742 cases, 109 rs6743068-A, PMTAG = 1.50 × 10-48, r2 rs10931936 = 0.99)13, including cutaneous BCC (31,787 cases, 110 rs6714430-C, P = 5.42 × 10-55, r2 rs10931936 = 0.99)13 and SCC (9,674 cases, rs10931936-T, P = 111 1.93 × 10-7)13 where the genotypes of these risk alleles are correlated. To formally assess 112 whether these traits may share common causal variants with melanoma risk, we compared 113 melanoma fine-mapping data to other cancer datasets using both HyPrColoc29 and eCAVIAR30 114 and summary statistic data for the set of SNPs +/- 100 kb centered over the melanoma GWAS 115 lead SNP. Colocalization analyses suggest a likely shared causal between melanoma risk and 116 breast cancer (HyPrColoc PP = 0.93; maximum eCAVIAR CLPP = 0.04). Across the larger 117 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint region, HyPrColoc finds the strongest evidence for shared causals for rs3769821 (HyPrColoc 118 SNP score = 0.52, r2 = 0.63 to rs10931936) and rs3769823 (SNP score = 0.11, r2 = 0.77 to 119 rs10931936) (Figure S2, Tables S2 and S3). Colocalization analyses of keratinocyte cancers 120 (both cutaneous BCC and SCC combined; PP = 0.99, maximum CLPP = 0.09), cutaneous BCC 121 (PP = 0.98; maximum CLPP = 0.02), and SCC (PP = 0.96; maximum CLPP = 0.02) all show 122 evidence for colocalization with melanoma risk, primarily showing the strongest evidence for 123 melanoma lead SNP rs10931936 (SNP score is 0.42, 0.22 and 0.29, respectively for all 124 keratinocyte cancers, BCC, and SCC) (Tables S2 and S3). LocusCompare31 plots of regional 125 summary data for melanoma compared to keratinocyte cancers or BCC suggest that the signals 126 for these non-melanoma skin cancers may be complex and involve multiple potential signals 127 (Figures S3-S5). Overall, these results suggest that a risk signal tagged by rs10931936 is 128 common to all tested cancers and nominate several potential shared causal variants. 129 Fine-mapping of melanoma risk-associated variants at 2q33.1 130 To comprehensively identify candidate causal variants within the 2q33.1 melanoma risk locus, 131 we performed fine-mapping using multiple complementary approaches. Firstly, using GWAS 132 summary data from Landi and colleagues10, we identified a set of 24 variants with a log-133 likelihood ratio (LLR) of up to 1:1000 relative to the lead SNP at the locus (rs10931936; Figure 134 1, Table S4). We also performed Bayesian fine-mapping using DAP-G32,33, identifying two 135 clusters of potential candidate variants. The first cluster consisted of a 95% credible set of 15 136 variants (set 1; marked by rs10931936, PIP = 0.16; Figure 1, Tables S6 and S7). A second 137 cluster consisted of a set of six variants with cumulatively low posterior inclusion probabilities 138 (PIP; set 2; led by rs77292590, PIP = 0.01; Tables S5 and S6) consistent with conditional 139 analysis of the melanoma GWAS that suggested no major second signal at this locus. 140 Considering the possibility of a cis-regulatory function for this locus, we also fine-mapped using 141 PAINTOR34,35, weighting the priors using melanocyte-specific epigenomic annotations from the 142 RoadMap Epigenome Project and locations of a set of genes specifically expressed in 143 melanocytes36 (Figure 1, Table S7), identifying a 95% credible set of seven SNPs. Of these, 144 rs3769823, which was also fine-mapped by the other methods, had by far the highest posterior 145 probability (0.789) of any variant. PAINTOR identified only one variant not in the DAP-G credible 146 set 1 (rs74574949, r2 = 0.02 with rs10931936, DAP-G credible set 3, PIP = 0.0002; Table S5). 147 Lastly, we assessed whether other variants not successfully imputed in the meta-analysis, and 148 thus not directly fine-mapped, could also be credible causal variants. Here, we chose an LD-149 based cutoff of r2 = 0.625 to the lead variant, identifying an additional nine potential candidate 150 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint causal variants. Combining variants from these analyses, we identified a total set of 27 credible 151 causal variants for the melanoma risk signal (Figure 1, Table S8). 152 153 154 Figure 1. Fine-mapping of multiple melanoma GWAS signals on chromosome band 155 2q33.1. (Top) A view of the 2q33.1 locus (hg19) including all fine-mapped candidate causal 156 variants nominated by log-likelihood ratio (LLR; red), Bayesian fine-mapping either unweighted 157 (DAP-G, green) or weighted using melanocyte-specific epigenomic annotations (PAINTOR, 158 brown), or LD to the melanoma lead SNP (rs10931936, r2 ≥ 0.625, orange). Also shown are 159 tracks representing NCBI RefSeq genes and imputed chromatin state (ChromHMM) from two 160 primary melanocyte cultures, two primary keratinocyte cultures, and two breast mammary 161 cultures generated by the Roadmap Epigenome Project (Red: Active TSS; Orange-Red: 162 Flanking Active TSS; Yellow: Enhancers; Green-Yellow: Genic enhancers; Green: Strong 163 transcription, Dark Green: Weak transcription). (Bottom) A zoomed-in view of the same 164 annotations for a region encompassing select candidate causal variants. Data and rendering 165 was performed using the UCSC Genome Browser. 166 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 167 Potential molecular mechanisms underlying the 2q33.1 locus 168 Of these fine-mapped variants, only one, rs3769823, is protein-coding, being a missense variant 169 of codon 14 (K14R) of a single CASP8 isoform (isoform G; ENST00000358485.4; RefSeq 170 NM_001080125.2; Figures 1 and S6), where the allele encoding K14 is associated with 171 increased risk. We assessed potential functions via Jpred4, which suggested that CASP8K14R 172 may be located within a helical structure (Figure S7). To assess potential impacts on protein 173 function, we used five different in silico prediction tools via PredictSNP2 37. Of these, the 174 majority (4/5) predicted rs3769823 to be benign; FunSeq2 however suggested that this variant 175 may be deleterious (Table S9). While we cannot rule out a protein-coding function for 176 rs3769823, these data suggest rs3769823 is benign. 177 We also assessed splicing QTLs (sQTL) from primary human melanocytes38. Notably, a 178 significant risk-associated SNP, rs10804111 (PGWAS = 6.79 × 10-10, LD to rs10931936 r2 = 0.62 179 and D’ = 1; Table S1), is a significant sQTL for alternative splicing of the CASP8 exon 8 to 9 180 junction (PsQTL = 7.33 × 10-12; Figure S8). Specifically, this generates an alternative CASP8 181 transcript (isoform H; ENST00000339403.6, RefSeq NR_111983.2; Figures 1 and S6), 182 retaining part of intron 8 and leading to frameshift and premature termination of the CASP8 183 transcript, which may act as a dominant-negative inhibitor of the caspase cascade39. Here, 184 rs10804111-C is associated with higher melanoma risk and lower levels of the alternative 185 isoform H (Figure S8B). To test the potential of rs10804111 to mediate alternative splicing, we 186 generated C- or T- allele-specific mini-genes (Figure S9A, see material and methods). Using 187 qRT-PCR in melanoma cells, we confirmed the association between rs10804111-C and lower 188 usage of the exon junction for isoform H (exon 8-9 junction, P = 7.23 x 10-5; exon 8L-9 junction, 189 P = 1.78 x 10-3; Figure S9B). However, we note fine-mapping of the melanoma GWAS signal 190 does not identify rs10804111 as a member of any credible causal set at this locus, and formal 191 colocalization between the melanoma risk signal and melanocyte sQTL signal did not provide 192 significant evidence of colocalization. 193 Given the lack of strong support for protein-coding or splice variants explaining the 2q33.1 194 signal, as well as a clustering of fine-mapped variants within regions marked as promoter or 195 enhancer in melanocytes (Figure 1, Table S8), we turned to eQTL analysis to explore the 196 potential of this signal being explained by cis-gene regulation. We first investigated previously-197 published cell-type specific eQTL data generated from primary cultures of human melanocytes 198 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint (n=106; Table S10)36. While multiple genes within the TAD harboring the association signal had 199 nominally significant eQTL, CASP8 is the only significant eQTL gene for the lead melanoma 200 GWAS SNP (rs10931936; PCASP8 eQTL = 1.2 x 10-9, slope = 0.67, where lower CASP8 expression 201 associated with the T-risk allele; Figure 2A, Table S10)36. Conditional analysis of melanocyte 202 CASP8 eQTL identified multiple variants comprising only a single eQTL signal within this locus 203 (Table S11). Colocalization analysis of the CASP8 eQTL and melanoma GWAS summary data 204 using both HyPrColoc29 and eCAVIAR30 suggest CASP8 as a strong candidate gene 205 (HyPrColoc PP = 0.97, eCAVIAR colocalization posterior probability/CLPP = 0.09 for rs3769823) 206 with the strongest evidence of shared causal variants for rs3769823 (HyPrColoc SNP score = 207 0.86, eCAVIAR CLPP = 0.09, r2 to rs10931936 = 0.77) and rs3769821 (HyPrColoc SNP score = 208 0.14, eCAVIAR CLPP = 0.03, r2 to rs10931936 = 0.63) (Figure 3, Table S12 and S13). 209 Collectively these results suggest a shared common causal variant between melanoma risk and 210 melanocyte-specific cis-regulation of CASP8. While no other genes had nominally significant 211 melanocyte eQTLs for the sentinel melanoma risk SNP rs10931936, other strongly risk-212 associated SNPs at this locus were, notably for the CASP8-adjacent gene FLACC1 (rs3769821, 213 P = 2.42 x 10-5, r2 to rs10931936 = 0.63; lead FLACC1 eQTL SNP rs796181752, P = 9.01 x 10-6; 214 Table S10), where the direction of effect relative to risk allele is opposite to that for CASP8. 215 Colocalization using HyPrColoc29 and eCAVIAR30 give discordant results, with eCAVIAR 216 suggesting potential eQTL colocalization (CLPP = 0.04 for rs3769821; Table S13) while 217 HyPrColoc does not (HyPrColoc PP < 0.5; Table S12). A LocusCompare31 plot suggests cis-218 regulation of FLACC1 in melanocytes may be complex and involve multiple potential signals 219 potentially explaining the discordant results (Figure S10). Collectively, these data suggest that 220 the melanoma GWAS signal may be explained by cis-regulation of CASP8 and FLACC1, with 221 opposite directions of effect for the two genes. 222 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 223 Figure 2. The melanoma risk-associated T allele of rs10931936 is correlated with lower 224 CASP8 expression. eQTL analyses were performed for rs10931936 combining genotype and 225 expression level data derived from (A) 106 human primary melanocyte cultures (TT, n=7; TC, 226 n=39; CC, n=60), (B) 349 melanoma tumors from TCGA-SKCM (TT, n=42; TC, n=126; CC, 227 n=181), and (C) a panel of 59 early-passage melanoma cell lines (TT, n=8; TC, n=23; CC, 228 n=25), where the risk-T allele is labeled in red. A significant eQTL effect with higher expression 229 driven from the C-protective allele was observed for CASP8 in both melanocytes and melanoma 230 tumors (P = 1.2 x 10-9 and P = 6.8 x 10-3, respectively), while the result was marginal but in the 231 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint same direction in melanoma cell lines (P = 0.07). Significance determined by linear regression; 232 mean with SEM are plotted along with individual data values. 233 234 235 Figure 3. CASP8 eQTL in human primary melanocyte cultures is colocalized with the 236 melanoma GWAS. (A) LocusZoom plots present –log10 P-values for melanoma GWAS (upper) 237 and melanocyte CASP8 eQTL (lower) for a 1 Mb region encompassing rs10931936. The 238 melanoma risk lead SNP rs10931936 is labeled and highlighted in purple in both panels, and LD 239 (r2 based on 1000G EUR) of all other SNPs to the melanoma GWAS lead SNP is color-coded. 240 (B) A LocusCompare plot compares P-values between melanoma GWAS (x-axis) and 241 melanocyte CASP8 eQTL (y axis) for the same region. Genomic coordinates are based on hg19. 242 243 These data are consistent with results from a transcriptome wide association study (TWAS) 244 study we previously performed using the same GWAS summary statistics and melanocyte 245 eQTL data10, where only CASP8 is significant and FLACC1 is marginal (PCASP8 = 5.57 x 10-12; 246 PFLACC1 = 3.83 x 10-5; Table S14). Assessing models trained on each of 47 tissue types in the 247 GTEx v.7; GTEx portal (https://gtexportal.org)40 identified CASP8 as a significant TWAS gene in 248 30 out of 47 GTEx tissues, strikingly with the direction of effect for six tissues being opposite to 249 that observed for melanocytes, including multiple brain tissues and pituitary (Table S15)10. 250 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint TWAS across GTEx tissues likewise found a significant positive association between imputed 251 FLACC1 levels and risk in 37 tissues (Table S16). We also assessed eQTLs in both 349 252 melanoma tumors from TCGA-SKCM36,41,42 as well as 59 early-passage melanoma cell lines 253 using expression microarray data43. In TCGA melanomas, we observed an FDR-significant 254 eQTL for FLACC1 (P = 6.57 x 10-4), as well marginal eQTLs for CASP8 (P = 6.82 x 10-3; Figure 255 2B) and TRAK2 (P = 0.03) (Table S17). In melanoma cultures, the rs10931936 risk-T allele is 256 marginally associated with multiple transcripts, including CASP8 (P = 0.07; Figure 2C, Table 257 S18). Both CASP8 eQTLs were in the same direction as observed in melanocytes. 258 Finally, given potential colocalization of melanoma risk with signals for breast cancer and 259 cutaneous SCC, we also assessed candidate genes specifically in mammary and skin tissues 260 profiled by GTEx v.740. Colocalization analyses with the melanoma risk signal suggests 261 melanoma risk may share common causal variants with eQTLs for CASP8 (eCAVIAR CLPP = 262 0.012 for rs3769821) and FLACC1 (eCAVIAR CLPP = 0.05 for rs3769823) in mammary tissues, 263 and FLACC1 in skin-not-sun-exposed (HyPrColoc PP = 0.97; SNP score = 0.62 and CLPP = 264 0.013 for rs3769823) and skin-sun-exposed tissues (HyPrColoc PP = 0.79; SNP score = 0.15 265 and CLPP = 0.012 for rs3769823) (Tables S19 and S20). TWAS of melanoma risk using 266 mammary and skin tissues found both CASP8 and FLACC1 to be significant TWAS genes with 267 the same direction of effect observed in melanocytes (Tables S15 and S16). These results 268 suggest that the 2q33.1 melanoma risk locus may not only share common causal variants with 269 breast and other cancers, but also with cis-regulatory variants influencing CASP8 and FLACC1 270 levels in breast and skin tissues, suggesting a potential shared cis-regulatory etiology for 271 multiple cancers. 272 Prioritization of potential cis-regulatory variants using epigenomic data 273 Given the strong evidence for the 2q33.1 GWAS signals for melanoma as well as perhaps other 274 cancers being explained by cis-regulation of CASP8 or potentially FLACC1, we next prioritized 275 credible set variants for assessment of allelic cis-regulatory activity. We prioritized for further 276 study eight variants with active imputed ChromHMM chromatin states consistent with 277 melanocyte gene regulatory regions as annotated by Regulome DB and HaploReg (Figure 1, 278 Table S8; individual melanocyte regulatory marks are shown in Figure S11)44. Most of these 279 eight variants also overlapped H3K27ac histone marks and or open chromatin (FAIRE-seq) 280 from a panel 11 melanoma cultures45 (Figure S12). Given the shared GWAS signal between 281 melanoma, breast cancer, and keratinocyte cancers, we additionally prioritized fine-mapped 282 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint variants that overlap regulatory imputed ChromHMM states in primary keratinocytes and 283 mammary epithelial tissues (Table S8); all such variants were already prioritized from 284 melanocyte ChromHMM data. Thus, we proceeded to move forward with eight fine-mapped 285 variants for functional assessment. Of these eight, three variants in close proximity (rs3769823, 286 rs3769821, rs59308963, within 641 bp), are located in a single continuous region of active 287 transcription start site (TSS)-proximal promoter state (TssA, Table S8)44 in melanocytes, 288 keratinocytes, and mammary epithelial cells, overlapping the first exon and first intron of two 289 annotated CASP8 isoforms, isoform G and H (Figures S11 and S12), potentially representing a 290 cis-regulatory haplotype. 291 Identification of functional cis-regulatory fine-mapped variants 292 To identify potentially functional cis-regulatory variants from these eight candidates, we 293 performed individual reporter assays for each in the context of ~140 bp of DNA sequence 294 surrounding the variant cloned in both forward and reverse orientations and tested in two 295 different melanoma cell lines (summarized in Table S21). Only one variant, rs3769823, showed 296 a significant allelic difference in both forward and reverse orientations in both cell lines (Figures 297 4A and S13, Table S21), albeit with the opposite direction of effect relative to the melanocyte 298 CASP8 eQTL. rs3769821 also had a significant albeit weaker allelic effect in the reverse 299 direction in both cell lines with the direction of effect matching the CASP8 eQTL, while in one 300 cell line in the forward direction there we noted a reproducible allelic difference in the opposite 301 direction (Figure S14, Table S21). Lastly, two other variants, rs59308963 and rs1861270 302 (Table S21), showed significant but weak allelic difference across cell lines only in the reverse 303 orientation (Figure S15, Table S21), with the effect direction for only rs59308963 matching the 304 CASP8 eQTL. We also examined two previously published melanoma MPRA studies which 305 assayed five of these variants in human melanoma cells as well as HEK293T kidney cells 306 (MPRA v.146; Figure S16), or six of these variants in both melanoma cells and human primary 307 melanocytes (MPRA v.2)47 with both studies testing a similar insert size (Table S21). Of the 308 eight variants tested in either study, only rs3769823 showed an FDR-significant (FDR < 0.01) 309 allelic effect in melanoma cells (PMPRA v.1 = 1.86 x 10-12, PMPRA v.2 = 1.09 x 10-52; Table S21), 310 where the direction of effect was consistent with data from individual reporter assays and where 311 the allelic effect is opposite to the CASP8 eQTL. Likewise, in the second MPRA study, of the six 312 variants tested, only rs3769823 was FDR-significant in primary melanocytes (PMPRA v.2 = 3.96 x 313 10-9), albeit with a smaller effect size (beta = -0.11) as compared to that observed in melanoma 314 cells (betaMPRA v.1 = -0.65; betaMPRA v.2 = -0.45) (Table S21). Of the remaining variants, 315 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint rs3769821 was significant only in a joint analysis of data from both melanoma and HEK293T 316 kidney cells (PMPRA v.1 = 3.11 x 10-4; Table S21) with direction of effect matching that observed in 317 individual luciferase assays (reverse orientation) and consistent with the CASP8 eQTL. Given 318 the potential of shared signal in this region with breast cancer, we also tested three variants, 319 rs3769823, rs3769821, rs59308963, in multiple breast cancer cell lines replicating the 320 significant allelic effect and direction observed in melanoma for rs3769823, but with inconsistent 321

for the other two variants (Figures S13-S15). Collectively, these data suggest the 322 potential for multiple cis-regulatory variants, with risk-associated allelic effects in differing 323 directions and in some cases possibly dependent on cellular context, with the strongest 324 evidence for rs3769823. 325 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 326 Figure 4. The melanoma-associated rs3769823 is a functional cis-regulatory variant and 327 displays allele-preferential binding by E4F1 and IRF2. (A) Individual luciferase reporter 328 activity assays for rs3769823 were conducted using the melanoma cell line UACC903. 138 bp 329 sequences encompassing rs3769823 construct were cloned 5’ of the pGL4.23 minimal TATA 330 promoter and transfected. Luciferase activity was measured 24 h after transfection and was 331 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint normalized against Renilla luciferase activity. One representative set from three biological 332 replicate experiments is shown. Mean with SEM are plotted. Individual P values are shown for 333 A-risk allele versus G-protective allele for the replicate shown (two-tailed, unpaired t-test 334 assuming unequal variances). TATA, minimal promoter control; A, risk allele construct marked 335 in red; G, protective allele construct. (B) EMSAs were performed using 21 bp biotin-labeled 336 double-stranded oligonucleotides for the A-risk (red) or G-protective (black) alleles of rs3769823 337 and nuclear extract from UACC903 melanoma cells. 2x, 5x, or 10x molar excess of unlabeled 338 competitor was added in specified lanes. One representative set from three replicate 339 experiments is shown. (C) Allele-specific rs3769823 binding proteins were identified by 340 quantitative mass spectrometry using UACC903 melanoma nuclear extracts and 21 bp 341 biotinylated double-stranded DNA probed with A-risk or G-protective alleles. The dimethyl-342 labeling ratios of proteins bound to A/G or G/A probes are plotted on the x and y axes for label-343 swapping experiments. Red-filled circles highlight proteins enriched above the background in 344 both experiments. (D) Antibody super-shift assay of rs3769823 EMSA in UACC903 melanoma 345 cells using anti-E4F1 or anti-IRF2 antibody is shown, where the A-risk specific shifted band 346 (arrows) is diminished. One representative set from three independent experiments is shown. (E) 347 ChIP was performed using antibody to anti-E4F1, anti-IRF2 or anti-IgG and chromatin prepared 348 from UACC903 melanoma cells, followed by qPCR. DNA quantity was normalized to input DNA 349 for each immunoprecipitation. Mean of qPCR triplicates with SEM are plotted and P-values 350 shown for one representative experiment from three biological replicates. 351 352 Three of these variants are in close proximity and located within a common melanocyte 353 enhancer element, and individual luciferase assays for all three reflect enhancer activity well 354 above empty vector controls. We speculated that these may influence enhancer function in the 355 context of a haplotype and thus performed reporter assays for a larger 641 bp region harboring 356 all three variants in the context of the four most common haplotypes observed in the 1000 357 Genomes EUR sample set. Here, the haplotype harboring risk alleles of all three represent 27% 358 of alleles, which that containing protective alleles for each is 64% (Table S22). Comparing the 359 all-risk (A/C/ATTCTGTC for rs3769823, rs3769821, and rs59308963) to all-protective (G/T/-) 360 haplotypes in melanocytes, we observed significantly lower reporter activity for the risk 361 haplotype in the forward direction (cloned relative to the direction of CASP8 transcription 362 (Figure S17A). This direction matches the eQTL direction of effect for rs3769823 and CASP8 in 363 melanocytes, melanomas, skin, and mammary tissues (Figure S18A-F). In contrast, reporter 364 assays in melanoma cells and breast cancer cells showed the opposite direction of effect, with 365 higher reporter expression driven from the all-risk haplotype (Figures S17B, C). These data 366 suggest a complex cis-regulatory architecture overlapping annotated melanocyte promoter for 367 multiple CASP8 isoforms, that allelic patterns of regulation may be highly dependent on both 368 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint adjacent functional variants as well as cellular context, and where the observed direction of 369 effect in melanocytes in vitro is consistent with CASP8 as plausible causal gene. 370 The genomic region containing rs3769823, rs3769821 and rs59308963 physically 371 interacts with FLACC1 372 Given that rs3769823, rs3769821, rs59308963 are all correlated with not only CASP8 373 expression but also FLACC1 in melanocytes (rs3769823, P = 7.14 x 10-4, slope = -0.23, Figure 374 S18A; rs3769821, P = 2.42 x 10-5, slope = -0.28; rs59308963, P = 0.01, slope = -0.19; Table 375 S10), we investigated whether these or other credible causal variants physically interact with 376 FLACC1. We first assessed interactions using a region-specific Capture-C assay performed in 377 five independent human primary melanocyte cultures, where capture baits were tiled across the 378 entire region of association 48,49 at 2q33.1. Here, we observed a direct physical contact between 379 the restriction fragments containing these three variants and the FLACC1 promoter (Figure 380 S19A, Table S23 and S24). We did not observe physical interaction between these variants and 381 any other gene within the TAD containing the association signal. We further assessed this 382 interaction using chromatin conformation capture (3C) in two independent melanocyte cultures 383 as well as the UACC903 melanoma cell line, using the region harboring these three variants as 384 bait and using primers spanning CASP8 and FLACC1. Here, we confirmed a physical 385 interaction between these variants and the FLACC1 promoter in both melanocyte cultures, with 386 little evidence of a strong association in melanoma cells (Figure S19B). These data suggest 387 that the transcriptional regulatory region harboring rs3769823, rs3769821, and rs59308963 388 could potentially play a role in regulation not only of CASP8, but also FLACC1. 389 Multiple transcription factors bind to rs3769823 in an allele-preferential manner 390 Given potential cis-regulatory roles for rs3769823, rs3769821, rs59308963, we further assessed 391 whether these variants influence the binding of nuclear proteins to the sequences 392 encompassing them. We first performed electromobility shift assays (EMSAs) using nuclear 393 extracts from melanoma cell lines, primary melanocytes, or breast cancer cell lines. We 394 observed preferential binding of nuclear protein to the A-risk allele compared to G-protective 395 allele of rs3769823 across all cell lines tested, where competition with unlabeled probe 396 containing the A-risk allele is stronger than that containing the G allele, demonstrating specificity 397 (Figures 4B and S20). For rs3769821, we observed suggestive evidence of preferential binding 398 of nuclear protein to the T-protective allele compared to C-risk allele in melanocytes, melanoma, 399 and breast cancer cell lines, albeit with weak specificity (Figure S21A). Finally, for rs59308963, 400 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint EMSA results show preferential binding to the insertion-risk allele compared to deletion allele 401 with weak specificity in melanocytes, melanoma, and breast cancer cells (Figure S22A). These 402 data suggest a strong and consistent pattern of allelic protein binding to rs3769823, consistent 403 with the large effect observed in reporter assays, and weaker evidence for allelic binding for the 404 other two variants. 405 We also performed quantitative mass spectrometry with oligonucleotides corresponding to the 406 risk or the protective allele of each of these variants incubated with nuclear extracts from 407 melanoma or breast cancer cell lines to directly identify allele preferential binding proteins. For 408 rs3769823, we noted shared allele-specific binding proteins between both cell lines; specifically, 409 IRF2 and E4F1 were observed to preferentially bind the A-risk allele, while REST and POU2F1 410 bound the G-protective allele (Figures 4C and S23A). The data for A-specific binding proteins 411 are consistent with the patterns observed from the EMSA data, suggesting E4F1 or IRF2 as 412 potential risk-allele binding transcription factors. Further, sequence-based motif prediction was 413 also consistent with these mass spectrometry data, indicating that the sequence around 414 rs3769823 forms a consensus binding site for both E4F1 and IRF2, favoring binding to the A-415 risk allele (Figure S24). For rs3769821, most of the potential preferential binding was found to 416 be the T-protective allele via EMSA, however mass spectrometry found few T-specific binders 417 and no overlapping T-binding proteins between cells; PRDM14 and ZBTB9 were both found to 418 preferentially bind the C-allele (Figure S21B). We also identified BNC2 as a strong risk-419 insertion binding protein to the risk-associated allele of rs59308963 in melanoma cells (Figure 420 S22B). 421 Based on the clear results and strong allelic pattern of cis-regulatory activity for rs3769823, we 422 sought to verify whether E4F1 or IRF2 proteins bound the A-allele by using antibodies against 423 these proteins in conjunction with EMSAs. Antibodies against either E4F1 or IRF2 consistently 424 resulted in loss of A allele-specific protein binding in both melanoma and breast cancer cell lines 425 (Figures 4D and S23B). To further establish the binding of E4F1 or IRF2 to rs3769823, we 426 performed chromatin immunoprecipitation (ChIP) for E4F1 or IRF2 followed by quantitative PCR, 427 noting an enrichment of binding at rs3769823 in melanoma cell lines with homozygous for 428 rs3769823-A as well as primary melanocytes heterozygous for this variant (Figures 4E and 429 S25B). We assessed enrichment at other locations across the CASP8 gene that had previously 430 been shown via ChIP-seq to be bound by IRF2 or E4F1, and DHS data from ENCODE (Figure 431 S25A), however ChIP only showed enrichment of both in the region over rs3769823 432 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint (UACC903(A/A), PE4F1 = 1.46 x 10-3, PIRF2 = 0.016; C87(A/G), PE4F1 = 3.25 x 10-3, PIRF2 = 9.47 x 433 10-3; Figure S25B). Using the heterozygous melanocyte culture, we also assessed allelic 434 enrichment in the immunoprecipitants using quantitative real-time PCR genotyping assay and 435 noted a significant enrichment of the A allele as compared to the G allele at rs3769823 in 436 primary melanocytes for both transcription factors (Figure S25C). Together, these data clearly 437 suggest that E4F1 and IRF2 may act through rs3769823 at this locus. 438 E4F1 and IRF2 as potential repressor and activator of CASP8 transcription, respectively 439 Given the allelic binding observed for E4F1 and IRF2 to the A-risk allele of cis-regulatory SNP 440 rs3769823, we next explored relationships between levels of these transcription factors and 441 expression of CASP8 and FLACC1 in various cell and tissue types. In primary melanocytes, we 442 observe clear negative correlation between both E4F1 mRNA levels with CASP8 (E4F1, P = 443 3.80 x 10-6, Pearson r = -0.43; Figure S26, Table S25) while IRF2 is not significant (IRF2, P = 444 0.39, r = 0.08; Figure S26, Table S25); multiple linear regression shows both rs3769823 and 445 E4F1 to be significant (E4F1 P = 1.70 x 10-15; rs3769823 P = 5.39 x 10-7; IRF2 P = 0.43; Table 446 S26). These data are consistent with a known role for E4F1 as a transcriptional repressor50 and 447 suggests a role in regulation of CASP8. This direction of effect is consistent with transcriptional 448 activity observed in reporter assays in melanocytes potentially being attributable in part of a 449 repressive effect for E4F1 when considering the longer three-variant risk haplotype, but not the 450 short (138 bp) fragment assays considering rs3769823 alone. In contrast, in TCGA melanoma 451 tumors, while significant negative correlation with E4F1 was observed (E4F1, P = 2.26 x 10-4, r 452 = -0.20; Figure S26, Table S25), a much stronger positive with CASP8 was observed for IRF2 453 (IRF2, P = 1.69 x 10-27, r = 0.54; Figure S26, Table S25); both transcription factors and 454 rs3769823 are significant via multiple linear regression (E4F1 P = 0.0018; IRF2 P = 2 x 10-16; 455 rs3769823 P = 0.035; Table S27), again with the strongest effect for IRF2. We did not find 456 significant evidence for an interaction between rs3769823 and either of the transcription factors 457 (Table S27). We were able to replicate this positive correlation specifically between IRF2 and 458 CASP8 in independent panels of melanoma cell lines (P = 4.23 x 10-4, r = 0.38; Table S25) and 459 the Leeds Melanoma Cohort (P = 8.74 x 10-4, r = 0.30; Table S25). This direction of effect is 460 consistent with a potential role for IRF2 as a potential activator in both individual and haplotype-461 based reporter assays, but inconsistent with the direction of the relatively weak eQTL for 462 CASP8 in melanoma tumors. We formally tested for potential interactions between E4F1 and 463 rs3769823 in melanocyte expression data as well as between E4F1 and/or IRF2 and rs3769823 464 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint in TCGA melanoma tumors but did not find significant supporting evidence (Tables S26 and 465 S27). 466 We also assessed transcripts encoding two T-protective allele binding proteins for correlations 467 with potential target genes, observing a positive correlation between the transcriptional 468 repressor REST, as well as POU2F1, and CASP8 in melanocytes (REST, P = 7.21 x 10-11; r = 469 0.58; POU2F1, P = 8.40 x 10-3, r = 0.25), a weak negative correlation for POU2F1 in TCGA 470 melanomas (P = 0.04, r = -0.11), and a positive correlation for REST in the Leeds Melanoma 471 Cohort (P = 1.75 x 10-14, r = 0.30) (Table S25). This positive correlation with CASP8 in 472 melanocytes and some melanoma tumors is inconsistent with the established role for REST as 473 a transcriptional repressor. Still, this direction of effect is consistent with a potential role for 474 REST repressing reporter activity in both individual (short fragment) reporter assays for 475 rs3769823 as well as both previously-reported MPRA assays. Multiple linear regression 476 considering all three individually correlated risk-A and protective-G binding alleles and genotype 477 for rs3769823 in melanocytes suggests all four are significant (REST P = 6.71 x 10-7; rs3769823 478 P = 4.29 x 10-6; POU2F1 P = 0.0019; E4F1 P = 0.010; Table S28). We note similarly complex 479 patterns of correlation between both risk- and protective- allele rs3769823 binding proteins in 480 non-melanocytic tissues (GTEx skin, GTEx breast mammary, and TCGA breast invasive 481 carcinoma; Table S25) and expression of CASP8 as well as FLACC1 (Table S25). These data 482 are consistent with the activity of the regulatory region encompassing rs3769823 being 483 modulated by a complex interplay of multiple allele-specific transcriptional regulators interacting 484 with risk and protective alleles in a tissue-specific manner. 485 Finally, we also investigated correlations between CASP8 and allele-specific binding proteins for 486 rs3769821 (Table S29) and rs59308963 (Table S30), observing negative correlations with the 487 transcripts for rs3769821 risk-allele binding protein ZBTB9 and the rs59308963 risk-allele 488 binding protein BNC2 (ZBTB9, TCGA P = 2.15 x 10-11, TCGA r = -0.35, Leeds P = 3.40 x 10-3, 489 Leeds r = -0.12; BNC2, TCGA P = 2.70 x 10-4, TCGA r = -0.19, Leeds P = 1.43 x 10-13, Leeds r 490 = -0.29; Tables S29 and S30). For FLACC1, we note similar correlations in melanocytes to 491 those observed for CASP8, with negative correlation observed for E4F1 (P = 3.40 x 10-6, r = -492 0.43), and positive correlations observed for REST and POU2F1 (REST, P = 1.72 x 10-10, r = 493 0.57; POU2F1, P = 4.71 x 10-12, r = 0.61), but do not observe consistent correlations for IRF2 or 494 REST across melanoma datasets (Table S25). Similar to what is observed for rs3769823, these 495 correlations vary considerably between melanocytic and non-melanocytic tissues (Tables S29 496 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint and S30) observing similarly complex patterns of correlation. Collectively, these data suggest a 497 potentially complex gene regulatory interplay, prominently of multiple allele-specific rs3769823 498 binding proteins, as well as other transcriptional regulators, some of which may bind this region 499 differentially in risk and protective haplotypes. 500 To determine the effects of depleting the two rs3769823 A-risk allele-specific binding proteins 501 on CASP8 transcription in melanoma, primary melanocyte cultures, and breast cancer cell lines, 502 we knocked these genes down using siRNAs. Knockdown of E4F1 resulted in an increase in 503 CASP8 levels while IRF2 knockdown results in reduction of CASP8 RNA levels in melanoma 504 (Figure 5A and 5B, S27A and S27B), breast cancer (Figure S28A and S28B), and 505 melanocytes (Figure S29A), consistent with respective roles for E4F1 and IRF2 as a potential 506 repressor and activator in cells of melanocytic lineage. Short fragment (138 bp) reporter assays 507 for rs3769823 in conjunction with knockdown of A-risk allele binding protein IRF2 resulted in a 508 significant decrease of expression driven primarily from the A-risk allele compared to the 509 protective G-allele in both forward and reverse directions in melanoma (UACC903 cells, Pforward 510 = 1.22 x 10-7, Preverse = 2.84 x 10-8, Figure 5C; UACC1113, Pforward = 1.65 x 10-5, Preverse = 2.20 x 511 10-3, Figure S27C) and breast cancer cell lines (T47D, Pforward = 2.92 x 10-7, Preverse =3.18 x 10-7, 512 Figure S28C), consistent with a potentially prominent role for IRF2 in CASP8 regulation in 513 melanoma cells. Consistent with the weaker correlations between E4F1 and CASP8 in 514 melanoma cells, E4F1 knockdown only modestly increased reporter activity for the A-risk allele, 515 and only in the forward direction, suggesting a potentially weak repressor role for E4F1 in the 516 context of melanoma cells (UACC903, Pforward = 8.05 x 10-4, Preverse = 0.74, Figure 5C; 517 UACC1113, Pforward = 3.47 x 10-3, Preverse = 0.02, Figure S27C). In primary melanocytes, reporter 518 assays for rs3769823 with knockdown of A-risk-allele binding E4F1 resulted in a significant 519 increase of promoter activity in the A-risk allele compared to the protective G-allele in both 520 direction (C23 melanocytes, Pforward = 1.27 x 10-9, Preverse = 0.013, Figure S29B), suggesting a 521 potentially repressor role for E4F1 in regulation of CASP8 in melanocytes. 522 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 523 524 Figure 5. E4F1 or IRF2 and rs3769823 regulate CASP8 expression in UACC903 melanoma 525 cells. (A) E4F1 or IRF2 were knocked down using respective pools of four different siRNAs in 526 UACC903 melanoma cells, and E4F1, IRF2 and CASP8 levels were measured. GAPDH-527 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint normalized E4F1, IRF2 or CASP8 mRNA levels are shown as fold change over those from non-528 targeting siRNA. A representative experiment from three biological replicates is shown 529 (individual datapoints, mean, and SEM are plotted). P-values are shown from one 530 representative set. (B) Protein levels were examined using anti-E4F1, anti-IRF2, or anti-GAPDH 531 antibody with UACC903 cell lysates of siRNAs transfected with either non-targeting control, 532 E4F1, or IRF2 siRNAs. GAPDH was used as a loading control. One representative set of three 533 replicate experiments is shown. (C) Individual luciferase assays were performed by co-534 transfecting rs3769823 luciferase constructs with E4F1, IRF2, or non-targeting control siRNAs 535 into UACC903 melanoma cells. Renilla-normalized relative luciferase activities are shown 536 relative to an empty construct containing only a minimal promoter (TATA). One representative 537 set is shown from three biological replicates. Mean with SEM is plotted, n = 6 technical 538 replicates. A two-tailed t-test assuming unequal variances was used to calculate all P values 539 shown against control siRNA. 540 541 542

543 In this study, we focused on a melanoma susceptibility locus over the CASP8 gene on 2q33.1 544 that was originally identified by Barrett and colleagues5 and has since been replicated in a large 545 melanoma GWAS meta-analysis (rs10931936-T, Pfixed = 2.12 x 10-12, Prandom = 2.17 x 10-8, OR = 546 1.08)10. In order to comprehensively identify additional independent signals as well as nominate 547 one or more sets of credible causal variants associated with such signals, we used multiple 548 complementary approaches. Conditional and joint analyses previously found no evidence for a 549 second genome-wide significant signal at 2q33.110; here, we observe no evidence for a 550 prominent independent melanoma risk signal of marginal significance. Fine-mapping using 551 Bayesian approaches similarly identified one prominent signal at this locus with little evidence 552 for additional signals. 553 Germline variants is this region have been identified as associated with multiple cancers, 554 including breast cancer (rs3769821) 11, keratinocyte cancers (cutaneous BCC and SCC 555 combined, rs6743068) 13, cutaneous BCC (rs6714430) 13, cutaneous SCC (rs10931936) 13, and 556 non-small cell lung cancer (rs3769821) 51 where the direction of effect is the same and the risk 557 alleles share a common haplotype with the melanoma risk allele of rs10931936. In addition, a 558 highly correlated risk signal was previously identified for prostate cancer (rs59308963) 52 where 559 the direction of effect is opposite to that of melanoma and the other cancers. This locus was 560 also identified via GWAS for chronic lymphocytic leukemia (CLL; rs3769825) 53, however this 561 signal appears to be considerably less correlated with the melanoma risk signal (r2 with 562 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint rs10931936 = 0.26). For several of these cancers with summary data readily available, we 563 evaluated evidence for colocalization, with the results suggesting sharing of one or more causal 564 variants between melanoma and signals for breast cancer, cutaneous BCC, and cutaneous 565 SCC and further suggesting that our work to fine-map identify functional, potentially causal 566 variants for melanoma risk may be relevant for these other cancers. 567 Using multiple complementary fine-mapping approaches, we identified a set of 27 potential 568 causal variants. Collectively, these variants present multiple potential alternative or 569 complementary functional mechanisms for influencing gene function and cancer risk. Notably, 570 rs3769823 is a missense protein-coding variant in codon 14 of an alternative first exon of 571 CASP8 utilized by multiple isoforms and expressed in human primary melanocytes (isoforms G 572 and H; Figure S6). However, a majority of algorithms designed to predict variant function 573 suggest this variant is likely benign and K14R is not located within conserved domains or known 574 or predicted sites of post-translational modification. Despite this, we did not directly test for an 575 effect of the two alleles of this variant on CASP8 protein function and cannot rule out a role for 576 altered protein sequence as a mechanism underlying risk at this locus. Zhang and colleagues 577 studied the functionality of rs3769823 by expressing the two alleles in lung cancer cell lines and 578 noted differences in proliferation and cell migration in vitro and in vivo 54. Notably, expression 579 from the K14R (protective) allele generated more CASP8 protein, which the authors posited 580 may be due to altered protein stability. Downstream phenotypic assays did not distinguish 581 between the effects of protein abundance and protein sequence-specific effects, and potential 582 transcriptional differences for the two constructs were not controlled for, so a protein-coding 583 function for this variant on CASP8 remains possible but still unclear. Further work is needed to 584 assess potential effects of this protein-coding change on CASP8 protein function. 585 We also observed a significant CASP8 sQTL in melanocyte and other GTEx eQTL datasets 586 relevant to cancers with associations for this locus (marked by rs10804111; r2 to rs10931936 = 587 0.62; D’ = 1) reflecting alternative splicing events at the exon 8 to exon 9 junction. The 588 alternative splicing event is more strongly associated with the protective allele generates an 589 alternative CASP8 transcript, isoform H. This transcript retains only part of canonical intron 8 590 and introduces a frameshift in the transcript, leading to premature termination. The resultant 591 protein retains the CASP8 death effector domain but lacks the catalytic domain, and notably, 592 Himeji and colleagues demonstrated that this protein could inhibit apoptosis mediated by wild-593 type CASP8 in a dominant-negative manner39. Thus, the transcript preferentially driven by the 594 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint protective allele could potentially lead to loss of CASP8 function; this stands in functional 595 contrast to the melanocyte eQTL observed where the protective allele of rs10931936 is 596 associated with higher rather than lower levels of CASP8 transcript. Nonetheless, while LD of 597 rs10804111 with rs10931936 is comparable to that of other fine-mapped credible causal 598 variants for melanoma at this locus, none of the fine-mapping approaches we applied nominates 599 rs10804111 as a credible causal variant itself. Still, much as for the protein-coding change 600 caused by rs3769823, we cannot rule out a role for altered CASP8 splicing contributing to 601 melanoma risk. 602 In contrast, eQTL and TWAS results in human primary melanocytes10,36 provide strong evidence 603 suggesting cis-gene regulation as a likely mechanism underlying melanoma risk attributable to 604 this locus, where melanoma risk-associated alleles were significantly correlated with lower 605 levels of CASP8. Both TWAS and colocalization approaches suggest that melanoma risk and 606 melanocyte eQTL for CASP8 likely share one or more common causal variant, with 607 colocalization strongly nominating rs3769823. Both also provide some support for FLACC1, 608 where in contrast to the CASP8 eQTL, higher FLACC1 levels are associated with the melanoma 609 risk allele. Several fine-mapped melanoma risk variants at this locus are clearly located within 610 melanocyte gene-regulatory regions, including regions annotated as active promoters, 611 consistent with a potential role for this signal regulating CASP8 levels. Consistent with observed 612 FLACC1 QTLs, melanocyte-specific capture-C and 3C assays showed a physical interaction 613 between fine-mapped melanoma risk variants and the FLACC1 promoter region (Figure S19) 614 suggesting risk variants indeed may play a role in regulating both genes. Notably, none of these 615 analyses suggest a prominent role for other nearby genes, including the apoptosis regulatory 616 genes CASP10 and CFLAR. 617 Based on these data, we comprehensively assessed fine-mapped risk variants for potential cis-618 regulatory function, first identifying eight variants overlapping with cis-regulatory histone marks 619 in disease relevant tissue (melanocytes, keratinocytes, human mammary epithelial cells, and 620 breast myoepithelial primary cells). We assessed each for allelic cis-regulatory potential both in 621 two previously published massively parallel reporter assays performed in melanocytes and/or 622 melanoma cells, identifying rs3769823 as the only significant variant, with allelic effects in both 623 melanoma cells and to a lesser degree melanocytes. We likewise tested each in individual 624 luciferase reporter assays in two melanoma cell lines, again identifying rs3769823 as the only 625 variant with consistently significant allelic cis-regulatory activities in both cell lines and in both 626 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint forward and reverse orientations where the direction of reporter effect was consistent with the 627 melanocyte FLACC1 eQTL but opposite that of CASP8 (e.g., the risk allele is associated with 628 higher reporter expression). Nonetheless, in individual assays, several variants displayed allelic 629 activity in one orientation, including two additional variants rs3769821 and rs59308963, located 630 nearby rs3769823 in the same annotated gene promoter region, suggesting potential functional 631 contributions from more than one variant. Testing commonly occurring 632 rs3769823/rs3769821/rs59308963 haplotypes in a reporter assay revealed cell-type dependent 633 effects; the risk-associated A/C/ATTCTGTC haplotype was associated with lower expression 634 relative to the protective G/T/- haplotype in melanocytes but higher expression in melanoma 635 cells in the forward orientation relative to CASP8 transcription (Figure S17A, B). This stands in 636 contrast to the results from Camp and colleagues, who tested risk and protective haplotypes of 637 rs3769823/rs3769821 and observed lower reporter gene expression driven from the risk 638 haplotype in both normal breast and breast cancer cell lines 55. The differing direction of effect 639 observed in melanocytes for rs3769823 in short-fragment versus longer-fragment haplotype 640 analysis suggests that while rs3769823 is strongly functional, the allelic effects are influenced 641 by other nearby and potentially functional variants. 642 All three variants showed allelic patterns of protein binding via EMSA. Previously, in the context 643 of lung cancer susceptibility, Long and colleagues observed a cis-regulatory element shared 644 across many lung cell types overlapping rs3769823 and noted that this variant is predicted to 645 alter IRF8 DNA binding 56. Here, we applied a quantitative mass spectrometry workflow and 646 identified multiple allele-specific nuclear binding proteins from both melanoma and breast 647 cancer cells that bound these variants in an allele specific manner. In particular, we identified 648 proteins that typically act either as a transcriptional repressor (E4F1) or activator (IRF2) as both 649 preferentially binding the A-risk allele of rs3769823, and verified binding of both factors via ChIP 650 in melanoma cells and primary melanocytes. While we did not observe significantly different 651 IRF8 binding in this context, the DNA-binding motifs of IRF2 and IRF8 are quite similar. We 652 observed a similar situation for G-protective allele specific binding proteins, where both an 653 activator (POU2F1) and repressor (REST) were found to bind to this allele in an allele 654 preferential manner. Thus, allelic transcriptional activity may be highly dependent on relative 655 availability of these factors as well as competition for binding under different cellular contexts. 656 Indeed, gene expression correlation analyses suggest, for example, that E4F1 may play a 657 prominent role for CASP8 regulation in melanocytes, while the activator IRF2 may play a larger 658 role in regulation of CASP8 in melanomas. Indeed, reporter assay data for rs3769823 in 659 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint conjunction with E4F1 or IRF2 knockdown are consistent with this, where in melanoma cells 660 knockdown of IRF2 resulted in a significant decrease of expression driven from the A-risk allele, 661 while knockdown of E4F1 resulted in a weaker increase of reporter activity driven from the A-662 allele. Given the similarity of DNA-binding motifs for IRF proteins, it may be possible that in 663 addition, tissue-specific availability of other IRF family members could also drive or repress 664 gene expression via rs3769823 and possibly explain the pleiotropy at this signal for prostate 665 cancer where the direction of effect is opposite that of melanoma and the other cancers 15. In 666 contrast knockdown of E4F1 in melanocytes resulted in a significant increase of activity driven 667 from the A-risk allele. We further observed additional allele specific binding proteins for 668 potentially functional adjacent variants rs3769821 and rs59308963, which could well further 669 modulate transcriptional output themselves as well as potentially contribute to preference of 670 transcription factor binding to rs3769823. 671 Our work provides strong evidence for two potential causal genes at this locus, CASP8 and 672 FLACC1. Of these, CASP8 represents a very strong a priori candidate cancer risk gene, given 673 its long-established role as an apoptotic initiator caspase and the fact that lower CASP8 levels 674 are associated with cancer risk. Little is known about the function of FLACC1 (formerly 675 ALS2CR12). It has been found to be component of sperm tail flagellum 57, and functional 676 studies of ALS2CR12-like genes in C. elegans suggest that similar genes play a role in gene 677 and or repetitive DNA silencing, a function appearing to be dependent on the biogenesis of 678 small RNAs 58,59. Notably this region harbors multiple genes involved in apoptosis, including 679 CASP10, encoding another apoptotic initiator caspase 60-62 sharing similar substrate specificity 680 to CASP8 63 but possibly acting as a negative regulator of CASP8-mediated cell death 64. Also 681 located within this region is CFLAR (also known as c-FLIP), encoding a protein structurally-682 similar to CASP8 but lacking caspase activity and acting to prevent CASP8 dimerization and 683 inhibiting apoptosis 19,65-69. Despite the interacting roles of these genes, we observe little 684 evidence that the risk signal drives differences in expression of these antiapoptotic genes. 685 Nonetheless, we cannot rule out potential regulation of these genes from this signal under 686 specific contexts. We do note, however, that at least in human melanocytes we observe no 687 evidence of physical interaction between fine-mapped cancer risk variants (Figure S19) and the 688 promoter regions of either gene. 689 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint In summary, our study functionally interrogated the multi-cancer risk locus on chromosome 690 band 2q33.1, providing strong evidence for cis-regulation of CASP8 via multiple functional 691 variants as a primary causal mechanism influencing cancer risk. 692 693

694 Melanoma GWAS summary data 695 For all analyses using melanoma GWAS data, we used summary statistics from both 696 histologically confirmed, as well as self-reported melanoma cases (from 23andMe and UK 697 Biobank), and controls as previously described10. In total, summary data are derived from 698 36,760 melanoma cases and 375,188 controls; all participants provided informed consent and 699 participation was IRB approved. Participants from 23andMe provided informed consent and 700 participated in the research online under a protocol approved by the external AAHRPP-701 accredited IRB, Ethical and Independent Review Services (E and I Review). 702 Conditional and joint analysis of melanoma GWAS 703 Conditional and joint analysis was performed with summary melanoma GWAS meta-analysis 704 data by using Genome-wide Complex Trait Analysis (GCTA, v.1.26.0)28 to identify independent 705 associated variants. We set the collinearity threshold (-cojo-collinear) to R2 = 0.05 to detect only 706 completely independent SNPs. Linkage disequilibrium (LD) between SNPs was calculated using 707 5,000 randomly selected individuals of European ancestry from UK Biobank. 708 Colocalization of the 2q33.1 melanoma association signal with other GWAS and QTL data 709 For colocalization analyses, we used GWAS summary data from breast cancer (122,977 cases 710 and 105,974 controls; dbGaP accession number phs001265.v1.p1; downloaded from 711 https://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-712 result/gwas-summary-results-breast-cancer-risk-2017/)11 and keratinocyte cancers (47,742 713 cases and 634,414 controls; dbGaP accession number phs000360.v3.p1)13, including 714 cutaneous BCC (31,787 cases and 619,351 controls)13 and cutaneous squamous cell 715 carcinoma (SCC; 9,674 cases and 625,657 controls)13. Colocalization analyses for the 2q33.1 716 locus were performed using both Hypothesis Prioritization in multi-trait Colocalization 717 (HyPrColoc)29 and eQTL and GWAS Causal Variant Identification in Associated Regions 718 (eCAVIAR)30 as implemented in the ezQTL web-based tool 719 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint (https://analysistools.cancer.gov/ezqtl/)70. For both, we used the set of SNPs in +/- 100 kb 720 window size from the melanoma GWAS lead SNP rs1093193636,38. For HyPrColoc, a posterior 721 probability (PP) above 50% (0.5) was considered to display a positive colocalization and SNP 722 score above 5% (0.05) was considered to share a common causal variant. For eCAVIAR, the 723 colocalization posterior probability (CLPP) score was calculated with a maximum number of two 724 causal SNPs in the locus; a CLPP score above 1% (0.01) was considered to display a positive 725 colocalization. 726 GWAS fine-mapping 727 Fine-mapping was performed using several approaches. Firstly, in order to inclusively nominate 728 candidate causal variants for assessment, we used the log-likelihood ratio (LLR) of each SNP in 729 the region relative to lead variant rs10931936, and considered all variants within a ratio of 730 >1:1000. Variants not successfully imputed from the GWAS that thus could not be fine-mapped 731 in this manner nor via other methods requiring summary data, hence we used an LD-based 732 threshold. Specifically, we chose this threshold to be consistent with the observed LD (r2) 733 between LLR-mapped credible set variants with, where the minimum LD from this set with 734 rs10931936 was r2 = 0.625 (1000 Genomes Projects Phase 3 EUR data; 1000G EUR). We thus 735 considered variants not present in the summary dataset where 1000G EUR r2 ≥ 0.625. We also 736 performed Bayesian fine-mapping of melanoma GWAS summary data10 for the 2q33.1 risk 737 region using both Deterministic Approximation of Posteriors (DAP-G)32,33 and Probabilistic 738 Annotation INtegraTOR (PAINTOR)34,35. DAP-G (version 1.0) analysis used a 500 Kb window 739 size centered over the association signal (median position of chr2:202,123,966) and allowing for 740 a maximum number of five causals. A UK Biobank (UKBB) LD reference panel (from 337K 741 UKBB participants, downloaded from https://alkesgroup.broadinstitute.org/UKBB_LD/)71 was 742 aligned with the summary statistics from the melanoma GWAS meta-analysis10 using R (version 743 4.1.0) for input into DAP-G. PAINTOR analyses were performed as described previously46 using 744 PAINTOR 3.0 with a window size of 100 Kb, maximum causals set to 4, 4 melanocyte-specific 745 epigenomic annotations and pairwise LD derived from 1000 Genomes phase 3 EUR (1000G 746 EUR) data computed using PLINK version 1.9 and R version 4.1.0. Functional annotations 747 include a set of 2,000 melanocyte-specific expressed genes from our melanocyte dataset36, 748 melanocyte enhancers, transcribed regions, and a histone mark (H3T11ph) from ENCODE and 749 Roadmap. 750 Prediction of variant effect on protein function and structure 751 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint We predicted the functional impact of missense variant rs3769823 on function of CASP8 protein 752 through the PredictSNP2 37 web-based tool (https://loschmidt.chemi.muni.cz/predictsnp2/), 753 which integrates five functional prediction methods including CADD (Combined Annotation 754 Dependent Depletion) 72, DANN (Deleterious Annotation of genetic variants using Neural 755 Network) 73, FATHMM (Functional Analysis Through Hidden Markov Model) 74, FunSeq2 75, and 756 GWAVA (Genome-Wide Annotation of VAriants)76. We also predicted potential effects of 757 CASP8K14R on secondary structure of the protein through JPred4 77 web server 758 (https://www.compbio.dundee.ac.uk/jpred4/), which also predicts solvent accessibility and 759 coiled-coil regions. Predictions from AlphaMissense78 for rs3769823 are not available. 760 Quantitative trait locus (QTL) data and other expression data 761 QTL data from 106 primary human melanocyte cultures primarily of European ancestry were 762 generated as described previously for both gene expression (eQTL)36 as well as for splicing 763 (sQTL) and CpG methylation (meQTL)38 (dbGaP accession: phs001500.v1.p1). Conditional 764 analysis of the CASP8 melanocyte eQTL was performed using QTLTools 765 (https://qtltools.github.io/qtltools/)79, combining genotype and expression level data, along with a 766 matrix of covariates including the top three genotype principal components and the top 15 767 PEER factors. For other tissues, we used pre-analyzed cis-eQTL data from the Genotype-768 Tissue Expression (GTEx) project (v.7), which were downloaded from the GTEx portal 769 (https://gtexportal.org/home/index.html)40. eQTL analysis was performed previously36 using 770 relative linear copy-number values as a covariate for 349 melanoma tumors from The Cancer 771 Genome Atlas Project (TCGA) skin cutaneous melanoma (SKCM) dataset41,42. eQTL data for 59 772 early passage melanoma cell lines was performed as described previously43 using expression 773 and genotyping data from NCBI GEO (accessions GSE78995 for expression data; GSE99193 774 for genotyping data). We specifically assessed genes within the TAD harboring the lead SNP at 775 2q33.1 (rs10931936; chr2:201,760,000-203,080,000; hg19) based on Hi-C results from 776 SKMEL5 melanoma cells as visualized by the 3D Genome Browser 777 (http://3dgenome.fsm.northwestern.edu/view.php). 778 CASP8 isoform analysis 779 To assess splicing QTLs (sQTLs), we used the same genotype data, population structure 780 covariates, and statistical approaches as used for melanocyte eQTL analysis36, replacing 781 normalized gene expression levels with normalized splice junction events. STAR80 was used to 782 map the RNA-Seq reads onto the genome (hg19) and then LeafCutter81 was applied to quantify 783 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint the splice junctions following the procedures described by the authors 784 (http://davidaknowles.github.io/leafcutter/articles/sQTL.html). Taqman real-time PCR assays 785 targeting unique junctions of CASP8 transcript isoforms were obtained from Thermo Fisher 786 Scientific (Waltham, MA; all isoform transcripts: Hs01018151_m1; exon 8 to 9 junction: 787 Hs01018160_m1; exon 8L (alternative spliced) to 9 junction: Hs04405665_m1). RNA was 788 isolated from primary melanocyte cultures derived from 106 individuals mainly of European 789 decent36, and cDNA was synthesized using iScriptTM Advanced cDNA Synthesis Kit (Bio-Rad, 790 Hercules, CA). Taqman assays were performed in triplicates (technical replicates) and 791 normalized to TBP or PPIA levels. 792 Exon-trap analysis of alternatively spliced exons of CASP8 genes 793 rs10804111 is an intronic variant; no variants strongly linked to the CASP8 melanocyte sQTL 794 were located in consensus splice or branch point sequences, and none could create a cryptic 795 splice site responsible for the observed allelic splicing pattern. We therefore assessed potential 796 effects of the lead sQTL SNP rs10804111 in a minigene assay. The intron from the exon 8L to 9 797 junction is >5,500 bp and not amenable to cloning in a simple minigene vector. We therefore 798 constructed minigene vectors to contain CASP8 exons 8/8L and surrounding intron (100 bp 799 upstream of exon 8 and 100 bp downstream intronic sequence from the exon 8L junction) and 800 CASP8 exon 9 and surrounding intronic sequence (100 bp upstream and 100 bp downstream 801 intronic sequence), linked together with 201 bp of intronic sequence surrounding each allele of 802 rs10804111 (Figure S8). CASP8 exons 8 to 9 (including intron 8; 1,181 bp), along with the 5’ 803 flanking of exon 8 and 3’ flanking of 9 sequences (100 bp for each) were custom-synthesized 804 from Thermo Fisher Scientific and cloned in sense orientation using XhoI and BamHI restriction 805 sites of the Exontrap vector pET01 (MoBiTec, Germany) to generate the pET01-806 rs10804111(C/T) mini-gene constructs containing each allele of rs10804111. Sequence-verified 807 pET01 constructs were transfected into UACC903 melanoma cells using LipofectamineTM 2000 808 (Invitrogen, Waltham, MA) in 6-well format. Cells were harvested 48 h after transfection, and 809 total RNA was extracted with the QIAGEN QIAcube using RNeasy kit with on-column DNase I 810 treatment (Qiagen, Germany). 1 μg of total RNA was converted into cDNA with SuperScriptTM III 811 reverse transcriptase (Invitrogen) and a vector-specific cDNA primer listed in Table S31. 812 Taqman assays targeting unique junctions of pET01-rs10804111 were obtained from Thermo 813 Fisher Scientific (vector exon 2 (VE2) as a control: APH6CGV; VE1 to VE2 junction: APPRMTJ; 814 CASP8 exon 8 to exon 9 junction: APKA42T; CASP8 exon 8L to exon 9 junction: APNKT7M; 815 listed in Table S1). Taqman assays were performed in triplicate and normalized to VE2 levels. 816 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint Capture-C data 817 Capture-C data were generated and analyzed as previously described 48,49. Briefly, regions for 818 bait design were determined for each locus via identification of the set of SNPs with a LLR < 819 100 relative to the leading SNP of a given GWAS signal. For the 2q33.1 locus, capture baits 820 were tiled across the entire region of association (chr2:202,114,359 to 202,205,025); baited 821 fragments are listed in Table S32. Capture-C libraries were made using the Arima HiC Kit 822 (Arima Genomics, Carlsbad, CA) and the KAPA HyperPrep Kit (KAPA Biosystems, Wilmington, 823 MA) following the manufacturer’s protocol. Data are presented for region-specific Capture-C 824 runs with five independent human primary melanocyte cultures (C56, C140, C205, C24, and 825 C27) with three biological replicates for each. Paired-end sequencing reads from biological 826 replicates were pre-processed with the HiCUP pipeline82 and aligned to human genome version 827 19 via bowtie283,84. Chromatin interaction loops were detected at one- and four-fragment 828 resolutions via Capture Hi-C Analysis of Genomic Organization (CHiCAGO) pipeline v.1.16.085. 829 Because we observed no distant interactions between fine-mapped variants and gene promoter 830 at one-fragment resolution, data presented are from four-fragment analysis. Visualization was 831 performed using the WashU Epigenome Browser86. 832 Chromatin conformation capture (3C) 833 Chromatin conformation capture (3C) assays were done based on the protocol from the Dekker 834 Lab87. RP11-1078D1 bacterial artificial chromosome (BAC) clones were purchased from 835 BACPAC Resources Center and purified with the QIAGEN large-construct Maxi Kit to cover the 836 2q33.1 genomic region (chr2:202,045,328 to 202,245,875). BAC libraries were made by HindIII 837 digestion of BAC plasmids, followed by ligation and DNA purification. To generate melanoma 838 cell and melanocyte 3C libraries, we fixed and lysed 1 x 108 cells, followed by HindIII digestion 839 and ligation. Both the BAC libraries, and melanoma and melanocytes 3C libraries were 840 amplified with a Taqman assay containing a primer localized to various regions of 841 chr2:202,045,328 to 202,245,875 and a fixed primer harboring rs3769823, rs3769821, and 842 rs59308963 plus a FAM-labeled probe annealing to 3’ of the fixed primer. Primers are listed in 843 Table S31. PCR cycle conditions were as follows: 95°C, 5min; [95°C, 30s; 62°C, 30s; 72°C, 844 30s]x34; 72°C, 10min. Amplification for different primer pairs from the melanoma and melanocyte 845 3C libraries was normalized to that of BAC libraries. 846 Epigenomic annotations 847 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint Chromatin Primary Core Marks Segmentation by HMM (ChromHMM), DNase Hypersensitivity 848 (DHS), and histone modification data for primary human melanocyte cultures, primary human 849 keratinocyte culture, and human breast mammary cultures “breast variant human mammary 850 epithelial cells” and “breast myoepithelial primary cells” (shown as vHMEC and BMYO, 851 respectively, in Figure 1) were obtained from the Roadmap Epigenomics Project (downloaded 852 through the UCSC Genome Browser; http://genome.ucsc.edu/). Peaks from FAIRE-seq and 853 H3K27ac ChIP for 11 melanoma cultures samples were obtained from the Gene Expression 854 Omnibus (GEO; accession GSE60666)45. 855 Cell culture 856 We originally obtained melanoma cell lines (UACC903 and UACC1113) from the University of 857 Arizona Cancer Center (UACC), which were maintained in RPMI-1640 medium with L-glutamine 858 (Quality Biological, 112-025-101) supplemented with 10% FBS (GenClone, 25-514H), 20 mM 859 HEPES (pH 7.9; Gibco, 15630), and 100 U/ml penicillin-streptomycin (Gibco, 15140). Primary 860 melanocytes from foreskin healthy newborn males, mainly of European descent, were grown in 861 M254 media (Invitrogen, M254500) with HMGS-2 supplement (Invitrogen, S0165). MCF7 and 862 T47D breast cancer cell lines were purchased from ATCC. MCF7 cells were grown in DMEM 863 with L-glutamine (Quality Biological, 112-014-101) supplemented with 10% FBS, and 100 U/ml 864 penicillin-streptomycin. T47D cells were maintained in RPMI 1640 supplemented with 10% FBS, 865 and 100 U/ml penicillin-streptomycin. All cell lines were grown in a 5% CO2 humidified incubator 866 at 37 °C and were tested for mycoplasma contamination every 3-6 months. 867 Luciferase reporter assays 868 Sequences encompassing each variant were PCR-amplified (primers listed in Table S31) from 869 genomic DNA of HapMap CEU panel samples with the appropriate genotypes to obtain clones 870 with each genotype, and cloned into the HindIII and XhoI sites of the pGL4.23[luc2/minP] 871 (Promega, Madison, MI) luciferase vector in the 5’-to-3’ or 3’-to’5’ orientation using In-Fusion HD 872 Cloning Kit (Takara Bio). Sequence-verified pGL4.23 constructs were co-transfected with the 873 pGL4.73[hRluc/SV40] Renilla luciferase control vector (Promega) into melanoma cell lines 874 (UACC903 and UACC1113) using LipofectamineTM 2000 (Invitrogen) in 24-well format. 875 Luciferase activity was measured 24 h after transfection with the Dual Luciferase Reporter 876 Assay System (Promega). Luciferase activity was normalized to the Renilla luciferase activity. 877 All the experiments were performed in six replicate wells and repeated for at least three 878 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint biological replicates. Significance between alleles was assessed using a two-tailed, unpaired t-879 test assuming unequal variance. 880 Massively parallel reporter assay data 881 We used data from two massively parallel reporter assay (MPRA) datasets assessing allelic cis-882 regulatory potential for candidate causal variants from this locus in melanoma cells and 883 melanocytes46,47. Firstly, in a prior MPRA analysis (MPRA v.1)46, 823 variants from 20 genome-884 wide significant loci from a prior melanoma meta-analysis8 were tested in a melanoma cell line, 885 UACC903, as well as HEK293FT cells. For this study, an LD-based selection criteria was used 886 (r2 > 0.4 with the sentinel SNP using 1,000 Genomes phase 3 EUR data), further filtering 887 candidate causal variants to test only those located within annotated open chromatin regions 888 and promoter/enhancer histone marks found in primary melanocytes and/or short-term cultures. 889 A second MPRA study (MPRA v.2)47 investigated 54 loci from a more recent melanoma GWAS 890 meta-analysis10 in both melanoma cells and melanocytes. Here, 1,992 candidate causal 891 variants were tested from 54 genome-wide significant loci. Candidate causal variants were fine-892 mapped using log-likelihood ratios (LLR 0.8 with the 894 lead SNP, 1,000 Genomes EUR). 895 EMSAs and supershift assays 896 Nuclear extracts from melanoma cell lines (UACC903 and UACC1113) or human melanocytes 897 were prepared using the NE-PER Nuclear and Cytoplasmic Extraction Kit (Thermo Fisher 898 Scientific). Nuclear extract lysates for MCF7 and T47D breast cancer cell lines were purchased 899 from Abcam (ab14860 and ab14896, respectively). Oligonucleotides for each SNP were 900 synthesized with or without biotin labeling at the 5’ end and HPLC purified (21-28nt, Life 901 Technologies, listed in Table S31). Forward and reverse strands were then annealed to 902 generate double stranded 5’-end labelled probes or unlabeled competitors. 50 nM probes were 903 added to 2 µg of nuclear extract preincubated with 1 µg of poly d(I-C) (Roche) in LightShiftTM 1 × 904 binding buffer (Thermo Fisher Scientific) for 30 min on ice. Competition experiments were 905 performed by adding 1-100 fold more unlabeled competitor oligonucleotides to the reaction 906 mixture 5 min before the addition of labelled probes. Anti-E4F1 (Abcam, ab70615) or anti-IRF2 907 (Abcam, ab245658) antibodies for supershift assays were incubated with nuclear extract for 1 h 908 at 4 °C prior to adding poly d(I-C). The reactions were run on 5 – 10% TBE gels (Bio-Rad 909 Criterion) on ice at 120 V, transferred to Biodyne B membranes (VWR), transferred blots were 910 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint crosslinked (Stratagene UV Stratalinker 1800) and detected using the LightShift 911 Chemiluminescent EMSA Kit (Thermo Fisher Scientific) and imaged on Chemidoc Touch (Bio-912 Rad). One representative data is shown from three biological replicates. 913 Quantitative mass spectrometry 914 MCF7 breast cancer cells were grown in RPMI 1640 (Gibco) supplemented with 10% FBS, 100 915 U/ml penicillin and 100 μg/ml streptomycin (Gibco). The same culture conditions were used for 916 UACC903 melanoma cells with additional 20 mM HEPES (pH 7.9). Nuclear lysates for DNA 917 affinity purifications were collected as described previously88. Oligonucleotide probes with 5’-918 biotinylation of the forward strand were ordered from Integrated DNA Technologies (Table S31). 919 DNA pulldowns and on-bead trypsin digestion were performed in Eppendorf tubes as described 920 previously89. Briefly, forward and reverse oligos were annealed with a 1.5X molar excess of the 921 reverse strand. For each pulldown, annealed DNA oligos (500 pmol) were immobilized on 10 μl 922 (20 μl slurry) Streptavidin-Sepharose beads (GE Healthcare, Chicago, IL). Immobilized DNA 923 oligos were incubated with nuclear extracts (500 μg) from UACC903 and MCF7, and 10 μg of 924 non-specific competitor DNA (5 μg polydIdC, 5 μg polydAdT). After washing away unbound 925 proteins, beads were resuspended in elution buffer (2 M Urea, 100 mM TRIS (pH 8), 10 mM 926 DTT), peptides were alkylated with 50 mM iodoacetamide, and on-bead digested with 0.25 μg 927 trypsin. Tryptic peptides were labeled by stable isotope dimethyl labeling on StageTips, as 928 described previously89. Samples were eluted from the StageTips and matching light and 929 medium labeled samples were combined. Samples were loaded onto a 30 cm column (heated 930 at 40 °C) packed in-house with 1.8 μm Reprosil-Pur C18-AQ (Dr Maisch) and eluted using a 931 gradient from 9 to 32% Buffer B (80% acetonitrile, 0.1% formic acid) over 114 min at a flow rate 932 of 250 nL/min using an Easy-nLC 1000 (Thermo Fisher Scientific). Peptides were sprayed 933 directly into either a Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) or a Q 934 Exactive mass spectrometer (Thermo Fisher Scientific). The mass spectrometers were operated 935 as described previously46,89. Thermo RAW files were analyzed with MaxQuant 1.6.0.1 by 936 searching against the UniProt curated human proteome (released June 2017) with standard 937 settings90. Protein ratios were normalized by median ratio shifting and used for outlier calling. An 938 outlier cutoff of 1.5 inter-quartile ranges in two out of two biological replicates was used. 939 rs3769823 and rs3769821 were assayed in both UACC903 and MCF7 cells, while rs59308963 940 was assayed only in UACC903. 941 Chromatin immunoprecipitation and genotyping 942 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint Following the manufacturer’s instructions of the Active Motif ChIP-IT High-Sensitivity kit, actively 943 growing melanoma cells (UACC903 and UACC1649) or C87 primary melanocytes were fixed 944 with 1% formaldehyde when 80-90% confluent. Nuclei of 1.0 – 1.5 × 107 cells were prepared 945 and sheared with ME220 Focused-ultrasonicator (Covaris, Woburn, MA) for 20 min following the 946 instructions. 10 – 30 µg sheared chromatin from 1.5 – 4.5 × 106 cells were used for each 947 immunoprecipitation reaction with anti-E4F1, anti-IRF2 or anti-IgG (Abcam, ab37415) following 948 the instructions. Purified immunoprecipitated DNA or input DNA was analyzed by SYBR Green 949 qPCR for enrichment of target sites using the primers listed in Table S31. ChIP DNA or input 950 DNA from UACC1649 or C87 melanocyte cell lines (heterozygous for rs3769823) was assayed 951 for genotyping rs3769823 by Taqman genotyping assay (Assay ID: C__25808407_20). All 952 experiments were performed in triplicate (technical replicates) and repeated for at least three 953 biological replicates. 954 siRNA-mediated knockdown of E4F1 and IRF2 955 SMARTpool ON-TARGETplus Human siRNAs to E4F1 (L-011847-00-0005), IRF2 (L-011705-956 02-0005) and ON-TARGETplus Non-targeting controls (D-001810-01-05) were purchased from 957 Dharmacon (Lafayette, CO). Transfections were carried out using LipofectamineTM RNAiMAX 958 (Invitrogen) according to the manufacturer’s instructions. Cells were collected after 48h for RNA 959 and protein extractions. 960 qPCR 961 RNA was isolated using RNeasy Mini Column (Qiagen), which was always complemented by 962 DNase treatment. cDNA was synthesized from the total RNA using iScriptTM Advanced cDNA 963 Synthesis Kit (Bio-Rad). Gene expression levels were quantified by qPCR using Taqman 964 assays for E4F1 (Hs00231773_m1), IRF2 (Hs01082884_m1), CASP8 (Hs01018151_m1, 965 Hs01018149, Hs01018160_m1, Hs04405665_m1 and Hs01022432_m1), and GAPDH 966 (Hs99999905_m1) from Thermo Fisher Scientific. Gene expression levels of E4F1, IRF2 and 967 CASP8 were normalized to GAPDH. Each experiment was performed in triplicate and replicated 968 three times. Significance was assessed using a Student’s two-tailed T-test assuming unequal 969 variances. 970 Western blotting 971 Whole-cell extracts prepared in RIPA buffer with protease inhibitors (Complete Protease 972 Inhibitor, Roche) or phosphatase inhibitors (PhosSTOP, Roche). Samples were quantified using 973 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint DC Protein Assay (Bio-Rad) and electrophoresed on 4–12% Bis Tris Plus Bolt gels in MES 974 buffer (Invitrogen). Proteins were transferred to nitrocellulose using an iBlot2 (Invitrogen). Blots 975 were blocked with 5% non-fat dry milk in Tris-buffered saline with 0.1% Tween-20 (TBST). 976 Primary and secondary antibodies were diluted in 1-2% milk in TBST, and all washes were 977 performed with TBST. Blots were rinsed briefly with PBS before the addition of ECL Prime 978 Western Blotting Detection Reagent (Amersham, UK). Images were captured on ChemiDoc™ 979 Gel Imaging System (Bio-Rad). The antibodies of anti-E4F1 (Santa Cruz, sc-514718), anti-IRF2 980 (Santa Cruz, sc-101069,) and anti-β-actin (Santa Cruz, sc-47778) were used as primary 981 antibodies, and goat anti-mouse HRP (Santa Cruz, sc-2005) for secondary antibodies. 982 Analysis of melanocyte and melanoma expression data 983 Transcriptomic data were generated as described previously from human primary 984 melanocytes36, TCGA-SKCM36,41,42, early passage melanoma cultures43, the Leeds Melanoma 985 Cohort91, and GTEx tissues (v.7; https://gtexportal.org/home/index.html)40. In brief, RNA 986 Sequencing by Expectation maximization (RSEM, version 1.2.31, 987 http://deweylab.github.io/RSEM/) was used to quantify the gene expression followed by the 988 quantile normalization to obtain the final RSEM for primary melanocytes, TCGA-SKCM, and 989 GTEx tissues as previously described36. For background correction and quantile normalization, 990 Robust Multi-array Average (RMA) algorithm with default settings (Affymetrix) was performed for 991 melanoma cell lines43. A primary melanoma transcriptomic dataset from the Leeds Melanoma 992 Cohort study on 703 patients was generated using the Illumina DASL Human HT12 v4 array 993 platform, as previously described91 (European Genome-phenome Archive accession 994 EGAS00001002922; https://ega-archive/org/studies/EGAS00001002922). 16 samples were 995 removed in quality control (expression data from only one extraction per patient) leaving a 996 cohort of 687 patients91. LUMI92 was used for background correction and quantile normalization 997 as previously reported92. For GTEx (v.7) transcriptomic data, genes were selected based on 998 expression thresholds of > 0.5 RSEM and ≥ 6 reads in at least 10 samples, and expression 999 values for each gene were inverse quantile normalized to a standard normal distribution across 1000 samples and described previously36. We used R (version 4.1.0) for Pearson correlation to 1001 analyze the correlations between candidate target genes and allelic-specific binding 1002 transcription factors. The significance level was set at P 0.4 for all these 1003 tests. Using transcriptomic and genotype datasets from multiple tissues, we also ran multiple 1004 linear regression on R (version 4.1.0) to evaluate the influence of allelic-specific binding 1005 transcription factor or genetic variable of rs3769823 on the CASP8. 1006 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 1007

1008 This research was supported in part by the Intramural Research Program of the National 1009 Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the 1010 United States Government. The findings and conclusions presented in this paper are those of 1011 the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of 1012 Health and Human Services. We would like to thank members at the National Cancer Institute’s 1013 Cancer Genomics Research Laboratory (CGR) for help with sequencing efforts and Rouf 1014 Banday from the National Cancer Institute for assistance with minigene assay design and data 1015 interpretation. This work has been supported by the Intramural Research Program (IRP) of the 1016 National Cancer Institute, US National Institutes of Health. We thank Jeremy Bravo Narula for 1017 assistance with the manuscript. We thank all the cohorts, funders, and investigators who 1018 contributed to the melanoma GWAS, as originally acknowledged by Landi, 23andMe, and 1019 colleagues10; data from this GWAS were used toward fine-mapping. JN receives funding from 1020 Horizon Europe 101136622. The Leeds Melanoma Cohort recruitment, follow up and data 1021 collection was supported by CRUK C588/A19167, C8216/A6129, C588/A10721 and NIH 1022 CA83115. Mark Iles is supported in part by the National Institute for Health and Care Research 1023 (NIHR) Leeds Biomedical Reearch Centre (BRC) (NIHR203331). The views expressed are 1024 those of the author(s) and not necessarily those of the NHS, the NIHR or the department of 1025 Health and Social Care. 1026 1027 1028 Declaration of interest 1029 The authors declare no competing interests. 1030 1031 Data and code availability 1032 Conditional fine-mapping data are available in Tables S1 and S11; Bayesian fine-mapping 1033

are available in Tables S5-S7. Colocalization analyses are available are in Tables S2, 1034 S3, S12, S13, S19, and S20. Data from the 2020 melanoma GWAS meta-analysis performed by 1035 Landi and colleagues was obtained from dbGap (dbGap: phs001868.v1.p1), with the exclusion 1036 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint of self-reported data from 23andMe and UK Biobank. The full GWAS summary statistics for the 1037 23andMe discovery dataset will be made available through 23andMe to qualified researchers 1038 under an agreement with 23andMe that protects the privacy of the 23andMe participants. 1039 Please visit the 23andMe website for more information and to apply to access the data. 1040 Summary data from the remaining self-reported cases are available from the corresponding 1041 authors of that manuscript (Matthew H Law, [email protected]; Mark M Iles, 1042 [email protected]; and Maria Teresa Landi, [email protected]). Melanocyte eQTL data 1043 and RNA-seq expression data from 106 individuals are available from dbGap (dbGap: 1044 phs001500.v1.p1), and pre-analyzed cis-eQTL data from GTEx (v.7) were downloaded from the 1045 GTEx portal. eQTL data from 59 early melanoma cell lines was available from NCBI Gene 1046 Expression Omnibus (accessions GSE78995 for expression data; GSE99193 for genotyping 1047 data). Leeds Melanoma Cohort data from 703 patients was available in the European Genome-1048 phenome Archive accession EGAS00001002922. MPRA data are available in the NCBI Gene 1049 Expression Omnibus as a SuperSeries under the accession number GEO: GSE129250 and 1050 GSE210356. Capture-C data have been previously published48,49; interaction data including 1051 both called interactions as well as raw sequencing data are available through ArrayExpress 1052 (accession: E-MTAB-15079). Specifically, for the 2q33.1 locus restriction fragments baited for 1053 region-specific Capture-C assays are provided in Table S32, and loops called by ChICAGO are 1054 in Tables S23 and S24. Pearson correlations are available in Table S25, S29 and S30. Multiple 1055 linear regression results are available in Tables S26-S28. Luciferase assay fragments, other 1056 primers and Taqman assays are listed in Table S31. 1057 1058 Web resources 1059 23andMe, https://research.23andme.com/collaborate/#dataset-access/ 1060 GTEx portal, https://www.gtexportal.org/home/ 1061 https://ldlink.nci.nih.gov/?tab=home 1062 NCBI GEO, https://www.ncbi.nlm.nih.gov/geo/ 1063 RegulomeDB, https://regulomedb.org/regulome-search/ 1064 The Cancer Genome Atlas (TCGA) Research Network, 1065 https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga 1066 105 and is also made available for use under a CC0 license. (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC The copyright holder for this preprintthis version posted January 6, 2026. ; https://doi.org/10.64898/2026.01.06.697866doi: bioRxiv preprint 1067

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