Prostate cancer genetic risk and associated aggressive disease in men of African ancestry

Supplementary Table 1. Summary of clinical information for 113 South African men with prostate cancer where deep sequence data was available (Jaratlerdsiri et al., 2022). Supplementary Table 2. Summary of clinical information for the exome study population. PSA, prostate-specific antigen; ISUP, International Society of Urological Pathology. Supplementary Table 3. Number of SNPs and proportion (out of 247,780 assayed variants) per minor allele frequency (MAF) interval for 780 samples genotyped on the Illumina HumanExome BeadChip v1.0 array, prior to and post-processing of rare variants (MAF£0.01) with zCall v3.4 (Goldstein et al., 2012). Supplementary Table 4. Risk allele frequency (RAF) in our South African population (sequence data and exome array data where available) compared to previously reported RAF in African ancestry controls (N=61,620) (Chen et al., 2023). Supplementary Table 5. Risk allele frequency (RAF) in our South African population (N=113) for the top 136 associated variants from the Uganda prostate cancer GWAS study (UGPCS) (Du et al., 2018) and for the African Ancestry prostate cancer study (AAPC) which were reported by Du et al., 2018. None of these variants were genotyped in the exome array. Supplementary Table 6. Risk allele frequencies (RAF) for the top 30 associated variants from the Ghana GWAS study (Cook et al., 2014) in South African PCa sequenced cases, and for the samples genotyped on the exomic array, where available. Supplementary Table 7. Risk allele frequencies, odds ratios (OR), and P-values for 397 known cancer variants out out of 2477 previously summarised (Harlemon et al., 2020) that were available on the exomic array. Supplementary Table 8. Genes significantly associated to PCa in the rare variant gene-based analysis, and the frequencies of each set of genotypes in cases compared to controls, as well as predicted consequences of each variant. Supplementary Table 9. Genes significantly associated to PCa in the gene-based analysis including common and rare variants, and the frequencies of each set of genotypes in cases compared to controls, as well as predicted consequences of each variant. Supplementary Table 10. Genes significantly associated to HRPCa in the gene-based analysis including common and rare variants, and the frequencies of each set of genotypes in cases compared to controls, as well as predicted consequences of each variant.补充表1. 113名携带深度测序数据的南非前列腺癌男性患者的临床信息汇总（Jaratlerdsiri等，2022）。 补充表2. 外显子组研究队列的临床信息汇总。前列腺特异性抗原（prostate-specific antigen, PSA）；国际泌尿病理学会（International Society of Urological Pathology, ISUP）。 补充表3. 针对780例经Illumina HumanExome BeadChip v1.0芯片分型的样本，在使用zCall v3.4（Goldstein等，2012）对次要等位基因频率（minor allele frequency, MAF≤0.01）的罕见变异进行预处理与后处理前后，各MAF区间内的单核苷酸多态性（single nucleotide polymorphism, SNP）数量及其占总检测变异（共247780个）的比例。 补充表4. 本研究南非人群（含可获取的测序数据与外显子组芯片数据）的风险等位基因频率（risk allele frequency, RAF）与既往报道的非洲血统对照人群（N=61620）的RAF对比（Chen等，2023）。 补充表5. 本研究南非人群（N=113）中，来自乌干达前列腺癌全基因组关联研究（Genome-Wide Association Study, GWAS, UGPCS）筛选出的前136个关联变异的RAF，以及Du等2018年报道的非洲血统前列腺癌研究（African Ancestry prostate cancer study, AAPC）相关变异的RAF；上述变异均未在外显子组芯片中进行分型。 补充表6. 加纳全基因组关联研究（Genome-Wide Association Study, GWAS, Cook等，2014）筛选出的前30个关联变异的RAF，以及可获取的南非前列腺癌测序病例与外显子组芯片分型样本的对应RAF。 补充表7. 外显子组芯片中可检测的、既往汇总的2477个癌症相关变异中的397个已知致癌变异的RAF、比值比（odds ratio, OR）与P值（Harlemon等，2020）。 补充表8. 基于罕见变异的基因水平分析中与前列腺癌（prostate cancer, PCa）显著相关的基因，以及病例组与对照组各基因型组合的频率，同时包含各变异的预测功能后果。 补充表9. 纳入常见与罕见变异的基因水平分析中与PCa显著相关的基因，以及病例组与对照组各基因型组合的频率，同时包含各变异的预测功能后果。 补充表10. 纳入常见与罕见变异的基因水平分析中与去势抵抗性前列腺癌（hormone-refractory prostate cancer, HRPCa）显著相关的基因，以及病例组与对照组各基因型组合的频率，同时包含各变异的预测功能后果。