Abstract
Modulations in melanin synthesis and distribution caused by underlying genetic variants are considered to be majorly responsible for the inter-personal human pigmentation variation. In the publicly available Color Genes dataset, 171 cloned murine loci are documented to be involved with alterations in mice coat color. We hypothesize that the human orthologues of these 171 loci may also be implicated towards human pigmentation variation through their polymorphic variants. We used several freely available bioinformatic tools and designed a predictive pipeline to prioritize the Single Nucleotide Variants (SNVs) within and in the vicinity of the 171 human orthologues, according to their functional potential. The genes associated with the prioritized SNVs were annotated a potential function in the pigmentation pathway, based on extensive literature review and assessment of protein–protein interaction networks. Our analyses could prioritize 77 candidate SNVs including 10 non-synonymous SNVs, 45 synonymous SNVs and 22 regulatory SNVs associated with 46 genes that can potentially contribute towards human pigmentation variation. Our study, thus outlines a comprehensive bioinformatic pipeline using freely available web-tools that can be utilized in similar kind of studies dealing with other complex human traits and diseases where individual nucleotide variant imparts subtle functional roles in regulating the phenotype.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Sharing Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Abbreviations
- SNV:
-
Single Nucleotide Variant
- ENCODE:
-
Encyclopedia of DNA elements
- GTEx:
-
The Genotype-Tissue Expression
- rSNV:
-
Regulatory single nucleotide variants
- eQTL:
-
Expression quantitative trait locus
- DHS:
-
DNAse hypersensitive sites
- GRCh37:
-
Genome Reference Consortium Human Build 37
- MAF:
-
Minor Allele Frequency
- ESPCR:
-
European Society for Pigment Cell Research
- IFPCS:
-
International Federation of Pigment Cell Societies
- Non-syn- SNV:
-
Non-synonymous single nucleotide variant
- Syn-SNV:
-
Synonymous single nucleotide variant
References
Adhikari, K., et al. 2019. A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia. Nature Communications 10 (1): 1–16. https://doi.org/10.1038/s41467-018-08147-0.
Adzhubei, I.A., et al. 2010. A method and server for predicting damaging missense mutations. Nature Methods 7 (4): 248–249. https://doi.org/10.1038/nmeth0410-248.
Aguet, F., et al. 2017. Genetic effects on gene expression across human tissues. Nature 550 (7675): 204–213. https://doi.org/10.1038/nature24277.
Ainger, S.A., et al. 2017. Skin pigmentation genetics for the clinic. Dermatology 233 (1): 1–15. https://doi.org/10.1159/000468538.
Arshad, M., A. Bhatti, and P. John. 2018. Identification and in silico analysis of functional SNPs of human TAGAP protein: a comprehensive study. PLoS ONE 13 (1): e0188143. https://doi.org/10.1371/journal.pone.0188143 (Edited by Y. Zhang).
Boyle, A.P., et al. 2012. Annotation of functional variation in personal genomes using RegulomeDB. Genome Research 22 (9): 1790–1797. https://doi.org/10.1101/gr.137323.112.
Branicki, W., U. Brudnik, and A. Wojas-Pelc. 2009. Interactions between HERC2, OCA2 and MC1R may influence human pigmentation phenotype. Annals of Human Genetics 73 (2): 160–170. https://doi.org/10.1111/j.1469-1809.2009.00504.x.
Calabrese, R., et al. 2009. Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation 30 (8): 1237–1244. https://doi.org/10.1002/humu.21047.
Candille, S.I., et al. 2012. ‘Genome-wide association studies of quantitatively measured skin, hair, and eye pigmentation in four European populations. PLoS ONE 7 (10): e48294. https://doi.org/10.1371/journal.pone.0048294 (Edited by n. J. Timpson).
Chaki, M., et al. 2011. Molecular and functional studies of tyrosinase variants among indian oculocutaneous albinism type 1 patients. Journal of Investigative Dermatology. 131 (1): 260–262. https://doi.org/10.1038/jid.2010.274 (Nature Publishing Group).
Choi, Y., and A.P. Chan. 2015. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31 (16): 2745–2747. https://doi.org/10.1093/bioinformatics/btv195.
Clewes, O., et al. 2011. Human epidermal neural crest stem cells (hEPI-NCSC)-- characterization and directed differentiation into osteocytes and melanocytes. Stem Cell Reviews and Reports 7 (4): 799–814. https://doi.org/10.1007/s12015-011-9255-5.
Crawford, N.G., et al. 2017. Loci associated with skin pigmentation identified in African populations. Science. https://doi.org/10.1126/science.aan8433.
Davis, C.A., et al. 2018. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Research 46 (D1): D794–D801. https://doi.org/10.1093/nar/gkx1081.
Duffy, D.L., et al. 2007. A Three–single-nucleotide polymorphism haplotype in intron 1 of OCA2 explains most human eye-color variation. The American Journal of Human Genetics 80 (2): 241–252. https://doi.org/10.1086/510885.
Dunham, I., et al. 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489 (7414): 57–74. https://doi.org/10.1038/nature11247.
Frändberg, P.A., et al. 1998. Human pigmentation phenotype: a point mutation generates nonfunctional MSH receptor. Biochemical and Biophysical Research Communications 245 (2): 490–492. https://doi.org/10.1006/bbrc.1998.8459.
Frudakis, T., et al. 2003. Sequences associated with human iris pigmentation. Genetics 165 (4): 2071–2083.
Galván-Femenía, I., et al. 2018. Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort. Journal of Medical Genetics 55 (11): 765–778. https://doi.org/10.1136/jmedgenet-2018-105437.
Ganguly, K., et al. 2019. Meta-analysis and prioritization of human skin pigmentation- associated GWAS-SNPs using ENCODE data-based web-tools. Archives of Dermatological Research 311 (3): 163–171. https://doi.org/10.1007/s00403-019-01891-3.
Gautam, R., et al. 2004. The Hermansky-Pudlak syndrome 3 (cocoa) protein is a component of the biogenesis of lysosome-related organelles complex-2 (BLOC-2). The Journal of Biological Chemistry 279 (13): 12935–12942. https://doi.org/10.1074/jbc.M311311200.
Ginger, R.S., et al. 2008. SLC24A5 encodes a trans-Golgi network protein with potassium- dependent sodium-calcium exchange activity that regulates human epidermal melanogenesis. Journal of Biological Chemistry 283 (9): 5486–5495. https://doi.org/10.1074/jbc.M707521200.
Guo, L., et al. 2014. rSNPBase: A database for curated regulatory SNPs. Nucleic Acids Research 42 (D1): D1033–D1039. https://doi.org/10.1093/nar/gkt1167.
Hysi, P.G., et al. 2018. Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability. Nature Genetics 50 (5): 652–656. https://doi.org/10.1038/s41588-018-0100-5.
Ito, S., and K. Wakamatsu. 2011. Human hair melanins: What we have learned and have not learned from mouse coat color pigmentation. Pigment Cell & Melanoma Research 24 (1): 63–74. https://doi.org/10.1111/j.1755-148X.2010.00755.x.
Karolchik, D. 2004. The UCSC Table Browser data retrieval tool. Nucleic Acids Research 32 (90001): 493D – 496. https://doi.org/10.1093/nar/gkh103.
Kirmizis, A., et al. 2004. Silencing of human polycomb target genes is associated with methylation of histone H3 Lys 27. Genes and Development 18 (13): 1592–1605. https://doi.org/10.1101/gad.1200204.
Larribère, L., and J. Utikal. 2016. Multiple roles of NF1 in the melanocyte lineage. Pigment Cell & Melanoma Research 29 (4): 417–425. https://doi.org/10.1111/pcmr.12488.
Lee, S.T., et al. 1995. Organization and sequence of the human P gene and identification of a new family of transport proteins. Genomics 26 (2): 354–363. https://doi.org/10.1016/0888-7543(95)80220-G.
Lee, J.S., et al. 2000. hCds1-mediated phosphorylation of BRCA1 regulates the DNA damage response. Nature 404 (6774): 201–204. https://doi.org/10.1038/35004614.
Lonsdale, J., et al. 2013. The Genotype-Tissue Expression (GTEx) project. Nature Genetics 45 (6): 580–585. https://doi.org/10.1038/ng.2653.
Maric, G., et al. 2013. Glycoprotein non-metastatic b (GPNMB): a metastatic mediator and emerging therapeutic target in cancer. OncoTargets and Therapy 6: 839–852. https://doi.org/10.2147/OTT.S44906.
Martin, A.R., et al. 2017. An unexpectedly complex architecture for skin pigmentation in Africans. Cell 171 (6): 1340-1353.e14. https://doi.org/10.1016/j.cell.2017.11.015.
Milenković, T., et al. 2010. Systems-level cancer gene identi.cation from protein interaction network topology applied to melanogenesis-related functional genomics data. Journal of the Royal Society Interface 7 (44): 423–437. https://doi.org/10.1098/rsif.2009.0192.
Mondal, M., M. Sengupta, and K. Ray. 2016. Functional assessment of tyrosinase variants identified in individuals with albinism is essential for unequivocal determination of genotype- to-phenotype correlation. British Journal of Dermatology 175 (6): 1232–1242. https://doi.org/10.1111/bjd.14977.
Montoliu L, Oetting WS, Bennett DC. Color Genes. (October, 2011) (no date) Color Genes - ESPCR & IFPCS, European Society for Pigment Cell Research. Available at: http://www.espcr.org/micemut/ (Accessed: 12 January 2020).
Morgan, M.D., et al. 2018. Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability. Nature Communications. https://doi.org/10.1038/s41467-018-07691-z.
Ohta, Y., et al. 2002. Effect of the transcriptional repressor Mad1 on proliferation of human melanoma cells. Experimental Dermatology 11 (5): 439–447. https://doi.org/10.1034/j.1600-0625.2002.110507.x.
Rouillard, A.D., et al. 2016. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016: baw00. https://doi.org/10.1093/database/baw100.
Rousseau, K., et al. 2007. Proopiomelanocortin (POMC), the ACTH/melanocortin precursor, is secreted by human epidermal keratinocytes and melanocytes and stimulates melanogenesis. FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology 21 (8): 1844–1856. https://doi.org/10.1096/fj.06-7398com.
Sengupta, M., et al. 2015. In silico analyses of missense mutations in coagulation factor VIII: Identification of severity determinants of haemophilia A. Haemophilia 21 (5): 662–669. https://doi.org/10.1111/hae.12662.
Sermadiras, S. et al. (1997) ‘Expression of Bcl-2 and Bax in cultured normal human keratinocytes and melanocytes: relationship to differentiation and melanogenesis.’, The British jJournal of Dermatology, 137(6), pp. 883–9. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9470903 (Accessed: 26 January 2020).
Sharan, R., I. Ulitsky, and R. Shamir. 2007. Network-based prediction of protein function. Molecular Systems Biology 3 (1): 88. https://doi.org/10.1038/msb4100129.
Sheffield, N.C., et al. 2013. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Research 23 (5): 777–788. https://doi.org/10.1101/gr.152140.112.
Shihab, H.A., et al. 2013. Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Human Mutation 34 (1): 57–65. https://doi.org/10.1002/humu.22225.
Shihab, H.A., et al. 2014. Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Human Genomics 8 (1): 11. https://doi.org/10.1186/1479-7364-8-11.
Shriver, M.D., et al. 2003. Skin pigmentation, biogeographical ancestry and admixture mapping. Human Genetics 112 (4): 387–399. https://doi.org/10.1007/s00439-002-0896-y.
Singh, S.K., W.A. Abbas, and D.J. Tobin. 2012. Bone morphogenetic proteins differentially regulate pigmentation in human skin cells. Journal of Cell Science 125 (Pt 18): 4306–4319. https://doi.org/10.1242/jcs.102038.
Sitek, A., et al. 2016. Selected gene polymorphisms effect on skin and hair pigmentation in Polish children at the prepubertal age. Anthropologischer Anzeiger 73 (4): 283–293. https://doi.org/10.1127/anthranz/2016/0632.
Spichenok, O., et al. 2011. Prediction of eye and skin color in diverse populations using seven SNPs. Forensic Science International: Genetics 5 (5): 472–478. https://doi.org/10.1016/j.fsigen.2010.10.005.
Stokowski, R.P., et al. 2007. A genomewide association study of skin pigmentation in a South Asian population. American Journal of Human Genetics 81 (6): 1119–1132. https://doi.org/10.1086/522235.
Sturm, R.A. 2009. Molecular genetics of human pigmentation diversity. Human Molecular Genetics 18 (R1): R9–R17. https://doi.org/10.1093/hmg/ddp003.
Szklarczyk, D., et al. 2017. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Research 45 (D1): D362–D368. https://doi.org/10.1093/nar/gkw937.
Szklarczyk, D., et al. 2019. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research 47 (D1): D607–D613. https://doi.org/10.1093/nar/gky1131.
The GTEx Consortium. 2020. ‘The GTEx Consortium atlas of genetic regulatory effects across human tissues’. Science New York NY, 369 (6509): 1318–1330. https://doi.org/10.1126/science.aaz1776.
Thul, P.J., et al. 2017. A subcellular map of the human proteome. Science. https://doi.org/10.1126/science.aal3321.
Uhlen, M., et al. 2015. Tissue-based map of the human proteome. Science 347 (6220): 1260419–1260419. https://doi.org/10.1126/science.1260419.
Uhlen, M., et al. 2017. A pathology atlas of the human cancer transcriptome. Science. https://doi.org/10.1126/science.aan2507.
Visconti, A., et al. 2018. Genome-wide association study in 176,678 Europeans reveals genetic loci for tanning response to sun exposure. Nature Communications. https://doi.org/10.1038/s41467-018-04086-y.
Ward, L.D., and M. Kellis. 2012. HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Research 40 (D1): D930–D934. https://doi.org/10.1093/nar/gkr917.
Yaar, M., et al. 2006. Bone morphogenetic protein-4, a novel modulator of melanogenesis. The Journal of Biological Chemistry 281 (35): 25307–25314. https://doi.org/10.1074/jbc.M600580200.
Yadegari, F., and K. Majidzadeh. 2019. In silico analysis for determining the deleterious nonsynonymous single nucleotide polymorphisms of BRCA genes. Molecular Biology Research Communications 8 (4): 141–150. https://doi.org/10.22099/mbrc.2019.34198.1420.
Yang, R., et al. 2011. Generation of melanocytes from induced pluripotent stem cells. The Journal of Investigative Dermatology 131 (12): 2458–2466. https://doi.org/10.1038/jid.2011.242.
Yang, X., et al. 2019. Trafficking and secretion of keratin 75 by ameloblasts in vivo. Journal of Biological Chemistry 294 (48): 18475–18487. https://doi.org/10.1074/jbc.RA119.010037.
Zuker, M. 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31 (13): 3406–3415. https://doi.org/10.1093/nar/gkg595.
Acknowledgements
The study was supported by departmental infra-structural support by Department of Science and Technology-Promotion of University Research and Scientific Excellence (DST PURSE), Government of India. K Ganguly is currently supported by Senior Research Fellowship (SRF) from DST-PURSE Programme Phase II (DST-PURSE-II SRF) at University of Calcutta and during study period he was earlier supported by Senior Research Fellowship (CSIR-SRF) from the Council of Scientific & Industrial Research (CSIR), Government of India. D Sengupta and T Saha were supported by Senior Research Fellowship (UGC-SRF) from University Grants Commission (UGC), Government of India. T Dutta and A Saha are supported by Junior Research Fellowship (UGC-JRF) from University Grants Commission (UGC), Government of India.
Funding
The study was supported by departmental infra-structural support by Department of Science and Technology-Promotion of University Research and Scientific Excellence (DST PURSE), Government of India. K Ganguly is currently supported by Senior Research Fellowship (SRF) from DST-PURSE Program Phase II (DST-PURSE-II SRF) at University of Calcutta and during study period he was earlier supported by Senior Research Fellowship (CSIR-SRF) from the Council of Scientific & Industrial Research (CSIR), Government of India. D Sengupta and T Saha were supported by Senior Research Fellowship (UGC-SRF) from University Grants Commission (UGC), Government of India. T Dutta and A Saha are supported by Junior Research Fellowship (UGC-JRF) from University Grants Commission (UGC), Government of India.
Author information
Authors and Affiliations
Contributions
Contribution of authors for the current study can be summarized as follows: Conceptualization and manuscript editing: KG, MS; Experimentation, data curation and analysis, manuscript writing: KG; Conceptualization, experimentation and data curation: DS; Experimentation and data curation: NS, NM, TD, AS, TS, BG, SC, PB, AK.
Corresponding author
Ethics declarations
Conflict of Interest
The authors have no conflict of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
12595_2022_449_MOESM1_ESM.pdf
All the variants were found to have statistically significant differences between African and European population (1000 genome data, GRCh37) clusters with respect to allele counts. (PDF 218 kb)
12595_2022_449_MOESM2_ESM.pdf
All the variants were found to have statistically significant differences between African and European population (1000 genome data, GRCh37) clusters with respect to allele counts (PDF 349 kb)
12595_2022_449_MOESM3_ESM.pdf
The file contains all results of non-synonymous single nucleotide variant analyses done with SIFT, PROVEAN, SPNs&GO, PolyPhen2.0 and fathmm. It is to be noted here, for each non-syn- SNV, we checked results of all possible allelic substitutions and for all possible isoforms of concerned protein (PDF 542 kb)
12595_2022_449_MOESM4_ESM.pdf
Fold changes in the codon usage fraction values for different alleles of each synonymous SNV, were checked from standard format of Codon usage table of Kazusa (https://www.kazusa.or.jp/codon/). Codon usage changes of more than 2 folds, either increasing or decreasing, were considered significant in the current study (PDF 604 kb)
12595_2022_449_MOESM5_ESM.pdf
mFold analyses were carried out for those syn-SNVs for which Codon Usage fraction values were found to alter by 2 folds or more. Any change in circular plots for secondary structure of mRNA, was noted and marked with red circles (PDF 2015 kb)
12595_2022_449_MOESM6_ESM.pdf
Syn-SNVs having 2 folds or more increase or decrease in codon usage biasness were checked for change in secondary structure of mRNA using mfold. SNVs having significant changes in Codon usage fractions values and secondary mRNA folding (as evident from comparative circular plots of Supplementary file 5), were considered as prioritized syn- SNVs for which cell specific expression checking should be followed (see Supplementary file 6a) (PDF 643 kb)
12595_2022_449_MOESM7_ESM.pdf
2 syn-SNVs (rs1042503, rs1126758) of PAH gene and 1 syn-SNV (rs62006815) of VSX2 gene, were excluded after the cell specific expression checking (see the table) because no evidence was found in support of either PAH or VSX2 to express in any skin cell included in The Human Protein Atlas dataset (PDF 398 kb)
12595_2022_449_MOESM8_ESM.pdf
Regulatory SNVs (rSNVs) as determined by rSNPBase, were searched for their extent of being regulatory, from RegulomeDB and results of those SNVs are shown in the table which procured score 1 and score 2 from RegulomeDB. Column I containing the SNP-Gene association data as procured from GTEx eQTL calculator is noteworthy (p-value of less than 0.05 as calculated by eQTL calculator, define significant interaction between the SNV-Gene pair). For our analyses we procured “SNP-Gene association” data of non-sun-exposed-skin tissue (PDF 780 kb)
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ganguly, K., Sengupta, D., Sarkar, N. et al. Comprehensive in Silico Analyses of Single Nucleotide Variants of the Human Orthologues of 171 Murine Loci to Seek Novel Insights into the Genetics of Human Pigmentation. Proc Zool Soc 75, 361–380 (2022). https://doi.org/10.1007/s12595-022-00449-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12595-022-00449-y