Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Experimental Dermatology group, Diamantina Institute, University of Queensland, Brisbane, Australia.
Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
J Invest Dermatol. 2022 Jun;142(6):1607-1616. doi: 10.1016/j.jid.2021.08.449. Epub 2021 Nov 20.
Genome-wide association studies (GWAS) have identified a number of risk loci for cutaneous melanoma. Cutaneous melanoma shares overlapping genetic risk (genetic correlation) with a number of other traits, including its risk factors such as sunburn propensity. This genetic correlation can be exploited to identify additional cutaneous melanoma risk loci by multitrait analysis of GWAS (MTAG). We used bivariate linkage disequilibrium-score regression score regression to identify traits that are genetically correlated with clinically confirmed cutaneous melanoma and then used publicly available GWAS for these traits in a multitrait analysis of GWAS. Multitrait analysis of GWAS allows GWAS to be combined while accounting for sample overlap and incomplete genetic correlation. We identified a total of 74 genome-wide independent loci, 19 of them were not previously reported in the input cutaneous melanoma GWAS meta-analysis. Of these loci, 55 were replicated (P < 0.05/74, Bonferroni-corrected P-value in two independent cutaneous melanoma replication cohorts from Melanoma Institute Australia and 23andMe, Inc. Among the, to our knowledge, previously unreported cutaneous melanoma loci are ones that have also been associated with autoimmune traits including rs715199 near LPP and rs10858023 near AP4B1. Our analysis indicates genetic correlation between traits can be leveraged to identify new risk genes for cutaneous melanoma.
全基因组关联研究(GWAS)已经确定了许多皮肤黑色素瘤的风险位点。皮肤黑色素瘤与许多其他特征(包括其危险因素,如晒伤易感性)共享重叠的遗传风险(遗传相关性)。这种遗传相关性可以通过 GWAS 的多性状分析(MTAG)来识别额外的皮肤黑色素瘤风险基因座。我们使用双变量连锁不平衡评分回归分析来识别与临床确诊的皮肤黑色素瘤具有遗传相关性的特征,然后使用这些特征的公开 GWAS 在 GWAS 的多性状分析中进行分析。GWAS 的多性状分析允许在考虑样本重叠和不完全遗传相关性的情况下合并 GWAS。我们总共确定了 74 个全基因组独立的基因座,其中 19 个在输入的皮肤黑色素瘤 GWAS 荟萃分析中以前没有报道过。在这些基因座中,有 55 个得到了复制(P < 0.05/74,在澳大利亚黑色素瘤研究所和 23andMe,Inc. 的两个独立的皮肤黑色素瘤复制队列中经过 Bonferroni 校正的 P 值)。在我们所知的以前未报道过的皮肤黑色素瘤基因座中,有一些与自身免疫特征有关,包括 LPP 附近的 rs715199 和 AP4B1 附近的 rs10858023。我们的分析表明,性状之间的遗传相关性可以用来识别皮肤黑色素瘤的新风险基因。