Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men

Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men Vanessa Hayes, Tingting Gong, Jue Jiang, Riana Bornman, Kazzem Gheybi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4531885/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Prostate cancer (PCa) is highly heritable, with men of African ancestry at greatest risk and associated lethality. Lack of representation in genomic data means germline testing guidelines exclude for African men. Established that structural variations (SVs) are major contributors to human disease and prostate tumourigenesis, their role is under-appreciated in familial and therapeutic testing. Utilising a clinico-methodologically matched African (n = 113) versus European (n = 57) deep-sequenced PCa resource, we interrogated 42,966 high-quality germline SVs using a best-fit pathogenicity prediction workflow. We identified 15 potentially pathogenic SVs representing 12.4% African and 7.0% European patients, of which 72% and 86% met germline testing standard-of-care recommendations, respectively. Notable African-specific loss-of-function gene candidates include DNA damage repair MLH1 and BARD1 and tumour suppressors FOXP1, WASF1 and RB1 . Representing only a fraction of the vast African diaspora, this study raises considerations with respect to the contribution of kilo-to-mega-base rare variants to PCa pathogenicity and African associated disparity. Health sciences/Oncology/Cancer/Urological cancer/Prostate cancer Health sciences/Oncology/Cancer/Cancer screening prostate cancer pathogenic variants structural variants health disparity African ancestry germline testing Figures Figure 1 Figure 2 Introduction Prostate cancer (PCa) is a significant global health burden and a leading cause of male associated cancer deaths 1 . With one of the highest heritability rates (estimated 58%), PCa risk shows a great degree of variability 2 , particularly when considering a man’s ancestral heritage. In the United States, Black men are at greatest risk for aggressive disease presentation 3 and depending on age at diagnosis an over double to triple (< 65 years) the risk for PCa-associated mortality than White Americans 4, 5 . Contributed by a complex interaction of socioeconomic factors and genetics 6 , inherited risk includes a combination of both common (low-risk with combined genetic risk scores) and rare (high-risk or pathogenic) germline variants 7, 8 . Revolutionised through advancement of precision oncology, most notably the approval of the poly-(ADP ribose) polymerase (PARP) inhibitors Olaparib 9 and rucaparib 10 for the treatment of metastatic castrate resistant PCa for patients harbouring rare pathogenic variants in specified DNA repair genes 11 , has increased the value for germline testing. Furthermore, the National Comprehensive Cancer Network (NCCN) recommends germline testing for all men with metastatic, recurrent or high-risk localized PCa, regardless of family history 12 . Although a significant risk factor for aggressive disease, no consensus could be reached for men of African ancestry 13 , while a recent review further highlighted the knowledge gap 14 . The lack of consensus for PCa germline testing in Black men is directly attributed to a lack of available data, compounded by a lack of African-relevant genomic data that captures the true extent of elevated genetic diversity. While consensus has yet to be reached for minority inclusion in the benefits of recent breakthroughs in PCa precision oncology, contradictory studies suggest that Black American patients harbour more 15 and conversely less actionable pathogenic variants than White Americans 16 . The picture is no different for Africa, although more recently PCa genomics has reached the continent with the first whole exome (n = 45 Nigerian) 17 and whole genome sequencing studies (n = 113 Black South Africans) 18 . Although preliminary, notable differences within Africa are emerging. For example, an elevated frequency of BRCA1 germline mutations reported for Nigerian patients, reflecting African American data 17, 19 , is lacking in Southern African cases 20 . Additionally, we have recently reflected on the lack of the West African exclusive and functionally relevant common PCa susceptibility variants CHEK2 p.Ile448Ser (rs17886163) and HOXB13 p.Ter285Lys (rs77179853) in Southern Africa 21, 22 . Reporting a 2.1-fold age-adjusted increase in aggressive PCa presentation in Black South African versus Black American men 23 , through deep sequenced interrogation for the 20 most common genes included in PCa germline testing panels using NCCN inclusion criteria (Gleason score ≥ 8), we observed a prevalence for rare pathogenic variants of 5.6% 20 , comparable with a single East African study (5.7%) 24 and almost half that reported for non-Africans (11.8%) 25 . These studies highlight the need for developing African-relevant PCa germline testing panels through African inclusion in genome profiling. Again, it is well established that Structural Variations (SVs) play a critical role in prostate tumour progression with prognostic and therapeutic potential 26, 27 , including tumours derived from men of African ancestry 18, 28 . Yet, irrespective of patient ancestry, little is known with regards to the contribution of germline potentially pathogenic rare SVs. Typically, greater than 50 bases in length, SVs encompassing large deletions (DEL), duplications (DUP), insertions (INS), inversions (INV) and translocations (TRA), are overlooked and/or difficult to resolve using current germline genetic testing assays. While it is well established that SVs play a critical role in diagnostic screening for inherited genetic diseases 29 , more recently, long-read sequencing has been used to identify potential pathogenic SVs in hereditary cancer syndromes 30 and known breast cancer susceptibility genes 31 , however, the impact of rare pathogenic SVs on PCa predisposition, and in turn targeted treatment, remains unknown. Expanding on our earlier work 18, 20, 28 , including deep sequenced germline genomes for 113 African (Black South African) and 57 European (4 South African, 53 Australian) PCa patients, through high-quality SV calling and genotyping, comprehensive gene annotation and best-fit pathogenicity prediction workflow, we interrogate for rare potentially pathogenic SVs (PP-SVs). While agreeably a small study size, this resource is not only unique for the African continent, importantly it provides clinically and technically matched non-African data for direct comparative analyses, while the whole genome approach increases sensitivity for SV detection. As such, the study aims to limit spurious findings between the ancestries, while providing a foundation for further efforts across the continent. Identifying candidate PP-SVs highlights the value of whole genome interrogation not only to improve the detection rate for rare pathogenic PCa variants, but importantly begin to contribute to the much-needed emphasis on an all-African inclusion model for germline testing and associated clinical care. Results NCCN high-risk characterisation for ancestrally assigned PCa patients Clinically and technically matched whole genome sequenced germline data (mean coverage 45.9X; range 30.2-97.6X) was derived from 170 PCa patients, ancestrally classified previously using 7,472,833 genome-wide SNVs and population substructure analysis 18 . In brief, 113 Black South African patients presented with an African ancestral genetic fraction of > 85%, while the 57 White patients presented with European ancestral genetic fractions of > 90% (4 South African, 52 Australian) and 73.7% European and 26.3% Asian substructure (1 Australian) ( Supplementary Table 1 ). Importantly, although mean age was 5-years younger at presentation or surgery, a greater number of European (86%; 49/57) over African patients (72%; 81/113) met current NCCN guidelines for germline testing based on International Society of Urological Pathology (ISUP) Group Grading defined as high-risk localized PCa (ISUP 4/5 or Gleason score \(\ge\) 8). Notably, we have previously provided evidence for the extension of these criteria for Black South African men to include ISUP 3, which would expand our cohort of high-risk Black men to 82% (93/113) 20 . While Black South Africans present with significantly elevated median and range of prostate specific antigen (PSA) levels (median 244 ng/mL versus 9.4), as previously presented 18, 23 , still the study was biased towards over representation of NCCN guidelines for PSA inclusive high-risk PCa for the European (70.2%; 40/57) over African patients (65/113; 57.5%). Genome-wide gene-disrupting SV discovery In this study, we identified and genotyped 42,966 high-quality germline SVs. We found a median of 9,206 SVs (range: 8,891 to 9,708) per-African genome, which is significantly higher than the median of 7,490 (range: 7,309 to 8,050) per-European genome (p-value = 1.1e-26 by Wilcoxon test). In total, we identified 38,668 African derived SVs (18,674 private) and 24,292 European derived SVs (4,298 private) ( Supplementary Table 2 ). Including only high-quality genotype calls for allele frequency (AF) estimation left a total of 33,243 high-confidence SVs. Excluding for common SVs, defined as minor allele frequency (MAF) > 5%, a total of 20,982 rare (MAF < 1%) and low-frequency (MAF = 1 to 5%) SVs remained across the ancestries for further annotation (Fig. 1 ). Further interrogation for gene regions overlapping, we identified 1,857 gene-disruptive SVs, including 1,752 potential Loss-of-Function (pLoF), 52 Copy Gain (CG) and 53 Intragenic Exon DUP (IED) (detailed in Methods ). Notably, pLoF, CG and IED SVs can have functional impact on genes through either gene inactivation or increased dosage effect 32 . Conversely, there is no clear or direct coding effect by SVs with other gene impact types, which included in our study 109 partial gene DUP, 22 partial exon DUP, 48 whole-gene INV, 343 promoter SVs, 9,431 intronic SVs and 258 enhancer SVs. As such, the latter SVs were not discussed further. In total, we identified 1,857 (MAF \(\le\) 5%) gene-disruptive SVs of which 1,407 are African-relevant, including 93% (1,314) African-private, and 543 European-relevant, including 83% (450) European-private ( Supplementary Table 2 ). There were 93 SVs (5%) shared by both African and European PCa patients. The 1,857 gene-disruptive SVs (1,050 rare in both African and European) underwent further downstream interrogation for potential clinical relevance. Of the 1,857 gene-disruptive SVs, 1,167 were previously reported in dbVar database of SVs, while 690 were absent and as such regarded as novel, of which 513 (74%) are uniquely African (Fig. 1 ). Characterising ClinVar verified candidate potentially pathogenic SVs Of the 1,167 dbVar reported gene-disruptive SVs, 14 (1.2%) were recorded in ClinVar, with three reported as ‘pathogenic’ or ‘likely pathogenic’ based on functional prediction consensus. One 2,958 bp likely pathogenic DEL results in loss of exon 7 in OCA2 ( Supplementary Fig. 1 ), a 5,064 bp pathogenic DEL leads to exon 5–7 loss in PIGN ( Supplementary Fig. 2 ), while a 235 bp likely pathogenic DUP duplicates exon 3 of SLC3A1 ( Supplementary Fig. 3 ). The OCA2 and PIGN DELs were identified in a single African patient each, while the SLC3A1 DUP presented in two African patients (Table 1 ). Although pathogenic in ClinVar, none have been associated with cancer phenotypes and include rather oculocutaneous albinism, multiple congenital anomalies-hypotonia-seizures syndrome and cystinuria, respectively. As such, we searched the literature for plausibility with further ascertainment derived from normal prostate and tumour tissue data sets using GENT2 33 . Reported to be downregulated in numerous cancer types (all-type P < 0.001, GENT2 T-test), although not significant for PCa, pLoF deletion of the pigmentation gene OCA2 has been linked not only to Prader-Willi syndrome, but also Prader-Willi associated malignancies 34 , and melanoma 35 , with recent studies linking melanoma with increased PCa risk 36 . Highly expressed in normal prostate tissue with significant upregulation in tumour tissue (P < 0.001, GENT2 T-test), PIGN functions as a cancer chromosomal instability suppressor gene 37, 38 . Although at lower levels, SLC3A1 is also upregulated in PCa (P < 0.001, GENT2 T-test), with overexpression in breast cancer associated with tumourigenesis 39 . These observations taken together provide the rational for characterising the pLoF OCA2 and PIGN DELs and SLC3A1 IED as potentially pathogenic SVs (PP-SVs). Notably, all three SVs are reported as rare (irrespective of ancestry) in multiple population-wide studies including gnomAD SV 32 , 1000 genomes Project (1KGP) 40, 41 and TOPMed SV 42 ( Supplementary Data 1 ). Table 1 Candidate potentially pathogenic (PP) SVs identified in 170 PCa patients. Genes Gene impact type 1 chrom1 pos1 chrom2 pos2 SV type ClinVar / dbVar concordance MAF African (this study) MAF European (this study) MAF African (dbVar) 2 MAF European (dbVar) 2 Potentially Pathogenic SV (PP-SV) SLC3A1 IED chr2 44281377 chr2 44281612 DUP Likely pathogenic 0.01 3 0 0.0075 1.3e-04 OCA2 pLoF chr15 28017719 chr15 28020677 DEL Likely pathogenic 0.004 0 0.0015 0.001 PIGN pLoF chr18 62152637 chr18 62157701 DEL Pathogenic 0.004 0 0.0013 1.3e-04 SLC7A2 pLoF chr8 17418976 chr8 17544122 DEL In dbVar 0.009 0 0.003 0 DNAJC15 pLoF chr13 43078470 chr13 43079390 DEL In dbVar 0 0.009 0 1.0e-04 BCL2L11 pLoF chr2 111122626 chr2 111125901 DEL novel 0.005 0 NA NA BARD1 pLoF chr2 214768022 chr2 214772899 DEL novel 0.005 0 NA NA COL4A2/ COL4A1 CG chr13 110294204 chr13 110633815 DUP In dbVar 0.005 0 1.3e-04 6.3e-06 SLC2A5 IED chr1 9045605 chr1 9049441 DUP In dbVar 0 0.009 7.3e-04 0.002 FOXP1 pLoF chr3 71097066 chr3 74525618 INV novel 0.009 0 NA NA WASF1 pLoF chr6 108167886 chr6 110172775 INV In dbVar 0.004 0 9.6e-05 0 MLH1 pLoF chr3 37000362 chr3 39352689 INV In dbVar 0.004 0 4e-04 6.4e-06 RB1 pLoF chr13 48466588 chr13 48473911 INV In dbVar 0.004 0 1.8e-04 1.3e-05 CTNNA1 pLoF chr5 138903881 chr19 21614900 TRA novel 0 0.009 NA NA AK8-DST pLoF chr9 132876361 chr6 56896165 TRA novel 0 0.009 NA NA PP-SV candidates classified as ‘cautionary’ LTBP1/ BIRC6 CG chr2 32403832 chr2 33107415 DUP In dbVar 0 0.009 1.0e-04 0.0018 PHC3- PRKACA pLoF chr3 170090742 chr19 14110142 TRA novel 0.004 0 NA NA KCTD3-DST pLoF chr1 215567414 chr6 56652607 TRA novel 0.009 0 NA NA PKHD1 pLoF chr6 51981375 chr15 30874073 TRA novel 0.009 0 NA NA 1 Gene impact type based on gene annotation. pLoF: Potential loss-of-function. CG: Copy gain. IED: Intragenic Exon Duplication. 2 The ancestry related MAF in dbVar were based on gnomAD 32 or TOPMed 42 SV study. The detail of all dbVar studies (dbVar study name and ID) and reported allele frequencies were shown in Supplementary Data 1 . 3 Presenting at low-frequency rather than rare variants within the ancestrally-defined patient cohort. Characterising candidate potentially pathogenic SVs absent from ClinVar Among 1,843 SVs with unknown classification in ClinVar or absent from dbVar, we predicted their potential pathogenicity based on four SV impact prediction tools, including StrVCTVRE 43 , CADD-SV 44 , POSTRE 45 and PhenoSV 46 . The number of scored SVs by four tools and their types were shown in Supplementary Fig. 4 and Supplementary Table 3 . Candidate SVs were required to meet two of the following criteria: StrVCTVRE score \(\ge\) 0.37, CADD-SV score \(\ge\) 10, POSTRE score \(\ge\) 0.8 and/or PhenoSV score \(\ge\) 0.5 ( Supplementary Table 4 and Methods ). Based on this criterion, all three ClinVar identified pathogenic or likely pathogenic SVs and the single SV of uncertain significance were successfully annotated as pathogenic candidates, while conversely our workflow excluded for all 10 ClinVar characterised benign SVs ( Supplementary Table 5 ). Using our criteria, 291 SVs were defined as PP-SV candidates (107 DELs, 16 DUPs, 11 INVs and 157 TRAs) disrupting 419 genes. In total 190 candidate SVs were private to African and 88 to European patients, with 13 shared between the ancestries ( Supplementary Table 4 ). To further define cancer-related pathogenic potential, we assessed for the presence of disrupted genes by PP-SV candidates in gene sets derived from the Human Molecular Signature Database (MSigDB) oncogenic signature and hallmark gene sets 47 and COSMIC Cancer Gene Census (COSMIC CGC) cancer driver genes 48 . Requiring disrupted genes in two of the three cancer gene sets, 58 SVs were defined as cancer-related PP-SV candidates, including 20 DELs, 3 DUPs, 6 INVs and 29 TRAs, disrupting 56 genes. Of the 58 candidates, 23 of them were identified with MAF between 1–5% in either African or European patients, leaving 35 rare PP-SV candidates for further consideration, of which 16 have been reported in dbVar. Two dbVar SVs including TRA disrupting gene NBEA and POLR2C DEL were reported at low-frequencies (AF = 0.03 and 0.01, respectively) ( Supplementary Data 1 ) and were therefore excluded from further analysis. Using our criteria, 33 rare cancer-related PP-SV candidates were identified ( Supplementary Data 2 and Fig. 1 ), including 15 DELs, 3 DUPs (1 IED and 2 CGs), 5 INVs and 10 TRAs. Of the 15 pLoF DELs, 11 were excluded as PP-SVs, with impacting genes showing oncogenic behaviour in multiple cancer types or no strong evidence for their tumour suppressor effects ( Supplementary Table 7 ). Conversely, four pLoF DELs were defined as PP-SVs, impacting known tumour suppressors or established DNA damage repair gene ( Supplementary Table 7 ). Two of them are known to dbVar, including a SLC7A2 125,146 bp DEL identified in two African ( Supplementary Figs. 5 ) and a DNAJC15 920 bp DEL in a European patient ( Supplementary Figs. 6 ). Another two identified PP-SVs are novel pLoF DELs, which identified in a single African patient each, including a BCL2L11 3,275 bp ( Supplementary Fig. 7 ) and DNA damage repair gene BARD1 4,877 bp DEL (Fig. 2 A, Supplementary Fig. 8 ). Of the two dbVar whole-gene DUPs, the COL4A2 339,611 bp CG, with breakpoints disrupting COL4A1 and NAXD , observed in a single African patient is defined as a PP-SV ( Supplementary Fig. 9 ), as COL4A2 indicating oncogenic behaviour in gastric and breast cancers ( Supplementary Table 7 ). In contrast, the TTC27 703,583 bp DUP observed in a single European patient is afforded ‘cautionary’ PP-SV status ( Supplementary Fig. 10 ). Although TTC27 is absent in three cancer gene databases, the breakpoints disrupt MSigDB and COSMIC CGC genes BIRC6 and LTBP1 , resulting in a LTBP1 - BIRC6 gene fusion of unclear effect. Observed in a single European patient, a 3,836 base DUP directly impacts exon 4 of SLC2A5 ( Supplementary Fig. 11 ), which downregulated in PCa (P < 0.001, GENT2 T-test) and has been identified an oncogenic behaviour ( Supplementary Table 7 ), therefore allocated PP-SV status. Of the five pLoF INVs, those impacting MLH1 , RB1 and WASF1 are in dbVar, while FOXP1 and NSD3 INVs are novel. As NSD3 has been identified as oncogenic in multiple cancers, the associated INV is classified here as unlikely pathogenic, with all remaining pLoF INVs classified as PP-SVs, as they disrupting known to PCa and Lynch Syndrome predisposing DNA mismatch repair gene MLH1 and PCa tumour suppressor genes RB1 , WASF1 , and FOXP1 ( Supplementary Table 7 ). Identified in a single African patient each ( Supplementary Fig. 12–14) , the three dbVar INVs were reported as rare by the recent TOPMed SV study 42 , in which WASF1 INV was also identified as African-specific (Table 1 and Supplementary Data 1) . The novel INV impacting FOXP1 was identified in two African patients (Fig. 2 E, Supplementary Fig. 15 ). Of the 10 pLoF TRAs, five impacting genes of GRM8 , WDR43 , NPM1 , NUSAP1 and MECOM with oncogenic properties ( Supplementary Table 7 ), therefore are classified as unlikely pathogenic. PKHD1 TRA identified in two African patients received a ‘cautionary’ PP-SV classification, as identified potential oncogenic in colon cancer, while potential tumour suppressor in colorectal cancer ( Supplementary Table 7 ). As CTNNA1 was known to have tumour suppressor behaviour across multiple tumour types ( Supplementary Table 7 ), here we classify the European-specific pLoF CTNNA1 TRA as a PP-SV ( Supplementary Fig. 16 ). The remaining pLoF TRAs result in PHC3-PRKACA (1 African patient), KCTD3-DST (2 African patients) and AK8-DST (1 European patient, Supplementary Fig. 17 ) novel gene fusions. PHC3-PRKACA was classified as ‘cautionary’ PP-SV, as PHC3 showed potential cancer suppressor effect in PCa, while PRKACA appears to portray oncogenic behaviour ( Supplementary Table 7 ). Although unknown to PCa, both DST and AK8 have demonstrated tumour suppressor behaviour, conversely, KCTD3 with an unclear role in cancer ( Supplementary Table 7 ). Here we classify AK8-DST as a PP-SV, while KCTD3-DST is assigned ‘cautionary’ PP-SV status. Correlating PP-SVs and ‘cautionary’ PP-SVs with clinical features The clinicopathological features of the study cohort has been previously described 18, 28 . In brief, African patients show a 5-year greater mean age and 25-fold greater PSA level at diagnosis compared to European patients ( Supplementary Table 1 ). Based on our previous observations 20 , high-risk or aggressive PCa were defined as ISUP GG \(\ge\) 3 and conversely, low-risk disease presentation as ISUP GG \(<\) 3. Biased towards aggressive disease presentation (82% African, 86.0% European), it was notable that all four patients with a pathogenic or likely pathogenic SV presented with aggressive disease at diagnosis, 92.9% (13/14) of PP-SV and 83.3% (5/6) cautionary PP-SV presenting patients (Table 2 ). Table 2 Clinicopathological features of patients by ethnicity presenting with potentially pathogenic (PP) SVs and cautionary PP-SVs as defined by this study criteria. Gene name Pathogenicity SV type Patient ID Ethnicity Age PSA ISUP GG Family history SLC3A1 PP-SV Likely Pathogenic DUP N0001 African 75 22.9 4 SMU094 African 64 15 4 OCA2 PP-SV Likely Pathogenic DEL N0059 African 79 153 5 PIGN PP-SV Pathogenic DEL SMU083 African 86 40.5 3 SLC7A2 PP-SV DEL UP2035 African 70 680 5 KAL0054 African 64 42.9 5 DNAJC15 PP-SV DEL 17135 European 63 7.8 5 BCL2L11 PP-SV DEL KAL0101 African 71 32.3 5 BARD1 PP-SV DEL N0073 African 62 unknown unknown COL4A2/COL4A1 PP-SV DUP UP2039 African 71 319 4 SLC2A5 PP-SV DUP 11099 European 70 9.9 5 FOXP1 PP-SV INV UP2101 African 57 75 5 N0084 African 65 591 4 WASF1 PP-SV INV N0048 African 70 83.3 5 MLH1 PP-SV INV SMU080 African 64 23.3 4 Sister with cervical cancer RB1 PP-SV INV SMU064 African 70 13.7 3 CTNNA1 PP-SV TRA 13179 European 59 8.4 5 AK8-DST PP-SV TRA 11452 European 67 11 1 LTBP1/BIRC6 Cautionary PP-SV DUP 5287 European 54 4.3 5 PHC3-PRKACA Cautionary PP-SV TRA SMU061 African 65 12.1 3 Mother with stomach cancer KCTD3-DST Cautionary PP-SV TRA UP2039 African 71 319 4 SMU101 African 70 4.3 3 PKHD1 Cautionary PP-SV TRA N0056 African 70 153 5 SMU196 African 47 9.5 1 Discussion ClinVar defined pathogenic (or likely pathogenic) SVs disrupting SLC3A1 , OCA2 or PIGN were observed in 3.5% (4/113) of African patients. Specifically, the SLC3A1 intragenic exon DUP was identified in two patients presenting with ISUP GG4, while the OCA2 and PIGN pLoF DELs presented in a single patient each with ISUP GG5 and ISUP GG3 PCa, respectively (Table 2 ). Visually inspecting the three PP-SVs using Integrative Genomic Viewer 49 , SLC3A1 DUP was found with three supporting read-pairs in sample N0001 ( Supplementary Fig. 3 ), and split-reads and more than 40% increase in read depth comparing to \(\pm\) 10 kb of the SV region in both samples ( Supplementary Table 6 ), while OCA2 and PIGN DELs were found with 16 and 6 supporting read-pairs respectively ( Supplementary Fig. 1–2 ), and have 44–51% reduction in read depth ( Supplementary Table 6 ). Solute carrier family 3 member 1 ( SLC3A1 ) is an amino acid transporter, which through heterodimerisation with SLC7A9 is responsible for cystine reabsorption through cationic and neutral amino acid exchange 50 . Mutations, including SVs, in SCL3A1 are associated with cystinuria, an inherited disease that results in the formation of cystine stones in the kidney, with disease presentation suggested to require biallelic loss 51 . SCL3A1 over-expression has been associated with enhanced tumourigenesis in breast cancer, while blocking SCL3A1 has suggestive therapeutic potential 39 . OCA2 is a pigmentation gene with inherited mutations associated with oculocutaneous albinism 52 . Polymorphisms have been associated with skin cancers 53 , as well as clinical response and survival in breast cancer patients having received neoadjuvant chemotherapy 54 . Inherited PIGN mutations have been associated with multiple congenital anomalies-hypotonia-seizures syndrome and Fryns syndrome, with some mutations related to milder forms of clinical presentation 55, 56 . Coding for phosphatidylinositol glycan anchor biosynthesis class N, PIGN is involved in the biosynthesis of glycosylphosphatidylinositol, which has been shown to suppress cancer chromosomal instability 37 through PIGN complexed spindle assembly checkpoint regulation 38 , a common phenomenon in solid tumours 57 . Notably, no previous associations have been made between SCL3A1, OCA2 or PIGN mutation and PCa. As our study is biased towards under-represented African patients, it is highly plausible that the majority of SVs detected are unlikely to be represented in ClinVar. As such, it is critical that we developed a best-fit workflow for PP-SV prediction. The four SV impact prediction tools used in this study were chosen based on the criteria of easy-to-use (either web-based or packed as software), providing pathogenicity scores or labels, accepting multiple SVs and covering all SV types. However, there are multiple factors to be taken into consideration when using SV impact prediction tools to establish potential pathogenicity, as different tools have limitations in applicable SV types, regions or diseases, as well as different scoring systems. While all tools can predict the impact of DELs and DUPs, StrVCTVRE is limited to DELs and DUPs in exonic regions. Besides predicting the simpler SVs, CADD-SV is capable of annotating INSs and POSTRE annotates INVs and TRAs, while PhenoSV is able to predict the impact of all these three types. POSTRE doesn’t work for all diseases or phenotypes. Therefore, combining multiple tools is necessary to cover all SV types and increase the confidence level. Another factor is the choice of threshold to establish pathogenicity. POSTRE and PhenoSV defines the threshold of pathogenicity, but StrVCTVRE and CADD-SV are limited to scores and calling for thresholds to be established depending on individual study aims. In this study, we have decided the thresholds based on tools’ validated results from database (90% sensitivity in ClinVar by StrVCTVRE 43 and top 10% in gnomAD by CADD-SV 44 ). When combining results from multiple tools, we found the requirement of passing thresholds of all four tools identified two PP-SV candidates (out of 1,843 SVs) ( Supplementary Table 4 ), with notable failure to identify the three ClinVar pathogenic/likely pathogenic SVs ( Supplementary Table 5 ). As such, PP-SV candidate classification in this study required an SV to pass thresholds of at least two impact prediction tools, with disrupted genes requiring further clarification as hallmark or drivers in cancer gene databases (MSigDB and COSMIC CGC). Using our described workflow, 12 SVs were predicted as PP-SVs, identified in 7.0% (4/57) of European and 8.8% (10/113) of African patients, bringing the total of African patients presenting with a potential pathogenic SV to 12.4% (14/113). Remarkably, five of our African-specific PP-SVs included well-known pathogenic cancer genes and/or PCa tumour suppressor genes, including DNA damage response genes. Most notably, the DNA mismatch repair tumour suppressor gene MLH1 commonly mutated in Lynch Syndrome, including cases with PCa 58 , is a known candidate gene in PCa germline testing panels 20 . While PCa patients presenting with pathogenic MLH1 mutations were reported to have significantly higher disease burden for African Americans 24 , here we found a dbVar known MLH1 pLoF INV with around 11 supporting short read-pairs ( Supplementary Fig. 12 ) in a 64 year old African male presenting with ISUP GG4 at diagnosis. Not recognised as a PCa germline testing panel gene, FOXP1 is an established PCa tumour suppressor driver gene, with CN loss increasing cell proliferation and migration, and poor prognosis 59 . Recently, we showed FOXP1 to be equally impacted by predominantly CN loss in African compared with European derived tumours (20% of 183 tumours) 18 . Here we found a germline inverted duplication impacting FOXP1 with around 18 supporting read-pairs in two African patients ( Supplementary Fig. 15 ). Notably, one African patient (UP2101) presented 10 years earlier than the cohort average receiving an ISUP GG5 diagnosis. Loss of the BRAC1 associated RING domain-1 ( BARD1 ) DNA damage repair gene has been found to induce homologous recombination deficiency and increase the sensitivity to PARP inhibitor in PCa cell lines 60 . Here the novel BARD1 exon 5 DEL, supported by 10 read-pairs and with around 50% reduction in read depth comparing to \(\pm\) 10 kb of the DEL region ( Supplementary Fig. 8, Supplementary Table 6 ), was identified in a 62-year-old African PCa patient with unknown pathology. While a paediatric cancer predisposing tumour suppressor gene commonly mutated in retinoblastoma and to a lesser extent osteosarcoma 61 , and less common as an adult cancer predisposing gene 62 , RB1 is recognised as one of five most prevalent somatically mutated genes in metastatic cancers 63 , with RB1 loss in prostate tumours associated with poor patient outcomes 64 . To the best of our knowledge, this is the first report of a germline potentially pathogenic RB1 PCa variant, which includes a pLoF INV of exon 24 with three supporting read-pairs ( Supplementary Fig. 13 ) in a single ISUP GG3 diagnosed African patient. Lastly, the tumour suppressor gene WASF1 with loss associated with aggressive or metastatic lethal PCa 65 . Identifying a potentially pathogenic INV previously reported at MAF of 9.6e-05 in Africans and resulting in NR2E1-WASF1 fusion was identified in a single African patient presenting at 70 years of age with ISUP GG5 PCa, showed 14 supporting read-pairs ( Supplementary Fig. 14 ). Other notable PP-SV DELs impacting tumour suppressor genes unknown to PCa, includes SLC7A2 and DNAJC15 . Knockdown of SLC7A2 has been shown to promote viability, invasion and migration of ovarian cancer 66 and enhance proliferation of non-small-cell lung cancer cells 67 , while DNAJC15 has tumour suppressor behaviour in breast cancer 68 . Identified in two African patients presenting with ISUP GG5 disease, loss of SLC7A2 exons 1 and 2, supported by 10 read-pairs and with around 50% reduction in read depth ( Supplementary Fig. 5 and Supplementary Table 6 ) has previously been reported in African populations at MAF of 0.03 ( Supplementary Data 1 ). Specific to Europeans (MAF = 1.0e-04), loss of DNAJC5 exon 4 supported by 13 read-pairs and with around 50% reduction in read depth ( Supplementary Fig. 6 and Supplementary Table 6 ), was identified in a single European patient presenting for surgery at age 63 years with ISUP GG5 disease. While not associated with PCa, the loss of BCL2L11 and CTNNA1 has been identified to leading tumourigenesis and promoting invasion and metastasis of multiple cancers 69, 70 . Here the novel BCL2L11 pLoF DEL on exon 2 with more than 20 supporting read-pairs and with around 50% reduction in read depth ( Supplementary Fig. 7 and Supplementary Table 6 ) was identified in a single African patient presenting at age 71 years with ISUP GG5 PCa, while the novel pLoF TRA interrupting CTNNA1 with more than 20 supporting read-pairs ( Supplementary Fig. 16 ) in a single European patient presenting at age 59 years with ISUP GG5 PCa. Another identified novel potentially pathogenic inter-chromosomal TRA with around 18 supporting read-pairs ( Supplementary Fig. 17 ) leading to a AK8-DST fusion in a single European patient (ISUP GG1, 67 years). Although no associations have been made between PCa, higher expression of DST has been identified to promote pathogenesis and development of breast cancer, while AK8 downregulation has been found to promote migration and invasion of uterine carcinosarcoma 71 . Two known PP-SVs identified to potentially increase gene dosage of well-known oncogenes COL4A2 and SLC2A5 , through whole-gene duplication and intra-genic exon duplication respectively. Although not associated with PCa, COL4A2 loss has been identified to inhibit triple-negative breast cancer cell proliferation and migration 72 and its mutations as risk factor for familial cerebrovascular disease 73 , while inactivation of SLC2A5 has been found to inhibit cell proliferation and migration in multiple cancer cell lines 74 . The whole COL4A2 DUP with more than 20 supporting read-pairs and more than 50% gain in read depth ( Supplementary Fig. 9 and Supplementary Table 6 ) was identified in a single African patient (ISUP GG4, 71 years) and the exon 4 DUP in SLC2A5 with more than 20 supporting read-pairs and more than 50% increase in read depth ( Supplementary Fig. 11 and Supplementary Table 6 ) was identified in a single European patient (ISUP GG5, 70 years). Using short-read sequencing data for SV calling and genotyping remains a potential limitation, appreciating that SVs in difficult-to-sequence regions may have been overlooked 75 . To ensure the highest possible accuracy of SV detection and population allele frequency estimation, we required high-confidence calls from two SV callers and high-quality genotype calls at both the population- and individual-level, while all PP-SVs were visually inspected. Due to lack of available expression data, we were unable to validate the direct impact of identified PP-SVs and cautionary PP-SVs. Further guidelines related to criteria for pathogenic SV identification using short read sequencing technologies and/or long read sequencing approaches are required, making these methods accessible for routine germline testing. Conclusion Here we have described a first-of-its-kind pathogenicity investigation of SVs in PCa patients with ancestry disparity. We observed three ClinVar-defined pathogenic or likely pathogenic PP-SVs ( SLC3A1 , OCA2 and PIGN ) and 12 predicted PP-SVs, including seven known SVs ( SLC7A2, DNAJC15 , COL4A2 , SLC2A5 , WASF1 , MLH1 and RB1 ), and five novel SVs ( BCL2L11, BARD1, FOXP1 , CTNNA1 and AK8-DST ), suggesting that inherited SVs may constitute an under-appreciated contribution to PCa pathogenicity. Furthermore, the identification of African-private (eight known and three novel) and European-private (two known and two novel) PP-SVs allows for further speculation with regards to associated racial disparities, while improving the detection rate for PCa germline testing with SV inclusivity, and in turn raising limitations for African inclusion and associated clinical care. Methods WGS data generation To avoid technical and analytical biases, all samples (whole blood) were processed (beginning at DNA extraction), data generated and analysed within a single laboratory using a single computational pipeline, as previously described 18, 28 . In brief, whole-genome sequencing data were generated using Illumina HiSeq X Ten (21 cases) or NovoSeq (149 cases) instruments with 2×150 cycle paired-end mode at the Kinghorn Centre for Clinical Genomics (Garvan Institute of Medical Research, Australia). Following the BROAD’s best practice recommendations for “data pre-processing for variant discovery”, sequencing reads were aligned to GRCh38 reference genome with alternative contigs using scalable FASTQ-to-BAM (v2.0) workflow with default settings 76 . The mean depth of coverage for all samples were 45.9X (range 30.2-97.6X). Structural variant calling and high-confidence SV filtering Germline SVs were called using Manta (v1.6.0) 77 and GRIDSS (v2.13.3) 78, 79 . SV types reported by Manta included DEL, tandem DUP, INS and adjacent breakends (BNDs) for a fusion junction with inverted sequence or in an inter-chromosomal rearranged genome. Pairs of BND in inverted junction were annotated as inversions (INV). Pairs of BND in different chromosomes were annotated as inter-chromosomal translocations (TRA). Conversely, GRIDSS reports BND for all fusion junctions resulting from any SV event. Simple SV types, defined as DEL, DUP, INS, INV and TRA, were assigned based on the strands and ALT field in VCF (modified from GRIDSS accompanied R script: simple-event-annotation.R).To obtain high-confidence SV call set, we integrated call sets from Manta and GRIDSS and generated concordant call set for each genome. Two SV calls were considered as concordant if they were reported as “PASS” by one of the two callers and have matching SV type and reported breakpoint positions within 200bp of each other. Bedtools pairtopair 80 was used to compare two call sets. Population-level genotyping and high-confidence genotype call filtering We used Graphtyper2 (v2.7.5) 81 to re-genotype SVs for all samples. Following published guidelines, we merged all high-confidence SV set per-sample (individual VCFs) using svimmer ( https://github.com/DecodeGenetics/svimmer ) with default parameters. The individual VCFs were in format of Manta VCFs, as Manta provides detailed information on the exact breakpoint sequence, which is the essential information required by Graphtyper2. We extracted all SVs with “aggregate” model as suggested, and obtained 57,096 SVs with “PASS” in FILTER field in VCF. To further filtering SV genotype calls on a per-sample basis, we required more than 50% genotype calls as “PASS” (PASS_ratio \(\ge\) 0.5 in INFO field), resulting in 42,966 SVs. To further filtering SV genotype calls on a per-sample basis, we set SV genotype as missing if genotype filter tag (FT) is not “PASS” for all SVs, except BND. For BND, as FT tag is not available, we set BND genotype with genotype quality (GQ) 20% in either African or European genomes, resulting in 33,340 SVs. We further removed 97 SVs with allele frequency of 100%, indicating the difference of sample genomes to reference genome. The allele frequency of each SV was then calculated based on the high-quality genotype calls only. Gene annotation and functional impact of SVs All SVs were annotated against gene regions from the Ensembl human gene annotation file (GRCh38 assembly, release 108). As multiple transcripts can be available for a single gene, the Ensembl Canonical transcript was used ( http://www.ensembl.org/info/genome/genebuild/canonical.html ). By comparing the position of SV breakpoint with gene regions using bedtools 80 , we examined nine gene overlapping categories with gnomAD 32 , including potential Loss of Function (pLoF), Copy Gain (CG), Intragenic Exon DUP (IED), partial gene DUP, whole-gene INV, UTR SVs, promoter SVs, intronic SVs and intergenic SVs. In addition, we defined partial-exon DUP as both breakpoints contained within the same gene, while neither both within exons (pLoF) nor fully overlapped at least one exon (IED). Promoters were defined as 1kb window before each transcription start site on the transcribed strand. We labelled SVs as enhancer-disruptive if at least one breakpoint was contained within a gene’s enhancer, by comparing to GeneHancer 82 regulatory elements regions. GeneHancer regulatory elements and gene interactions “double elite” subset was downloaded from UCSC Table geneHancerInteractionsDoubleElite [last updated 15/01/2019] from GeneHancer track for GRCh38. The transcript structure plots were generated based on Ensembl human gene annotation (GRCh38 assembly, release 108) using R package ggtranscript (v0.99.3) 83 . The sequencing depth of DEL or DUP regions and their \(\pm\) 10 kb regions were calculated using samtools (v1.6) depth command 84 . Short-read data detect the SV signatures from aligned reads around the SV breakpoints and is hard to capture the whole large SVs 85 . Therefore, we restricted the disrupted genes of SVs greater than 1Mbp to be genes overlapped by SV breakpoints for downstream analysis. Identification of dbVar concordance and novel SVs The NCBI’s database of human genomic structural variation (dbVar) [last updated 30/10/2023] 86 were used to identify dbVar concordance and novel SVs. The dbVar database included a total of 6,476,337 unique SVs, including 86,686 SVs with interpretations of their significance to disease in ClinVar database 87 . Structural variants concordant to dbVar SVs were defined as having both breakpoints within 200 bases of dbVar defined SV breakpoints. The ancestry related variant allele frequency of SVs ( Supplementary Data 1 ) were derived from dbVar pages of SVs or VCFs uploaded by different dbVar studies to dbVar’s FTP site. Pathogenicity prediction The pathogenicity of SVs were predicted through prediction tools StrVCTVRE 43 , CADD-SV 44 , POSTRE 45 and PhenoSV 46 . StrVCTVRE only scores the deleteriousness of DEL and DUP overlapping one or more exons, CADD-SV scores DEL, DUP and INS, POSTRE predicts the impact of DEL, DUP, INV and TRA, and PhenoSV works for all five SV types. As POSTRE only accepts genome coordinates on reference genome Hg19, the liftOver function from rtracklayer package in R was used to lift SV coordinates from Hg38 to Hg19. As suggested by StrVCTVRE, the ClinVar 90% sensitivity threshold (0.37) was used to define potentially pathogenic SVs. The scaled CADD-SV scores range from 0 (potentially benign) to 48 (potentially pathogenic), indicating the position of the input SV within the gnomAD-SV score distribution. The threshold of 10 for CADD-SV score was used to establish potential pathogenicity, corresponding to top 10% score observed in gnomAD-SV. The threshold of 0.8 and 0.5 for POSTRE and PhenoSV score respectively was used in this study, which is the threshold of pathogenicity labelling defined by POSTRE and PhenoSV. The hallmark gene sets and oncogenic signature gene sets were downloaded from the Human Molecular Signature Database (MSigDB v2023.1) 47 . The MSigDB oncogenic signature gene sets included genes representing signatures of cellular pathways which are often dis-regulated in cancer. Cancer-driver genes were downloaded from COSMIC Cancer Gene Census (GRCh38 COSMIC v98, downloaded 26/09/2023). Abbreviations PCa Prostate cancer SV Structural variation DEL Deletion INS Insertion DUP Duplication INV Inversion TRA Translocation WGS Whole genome sequencing PSA Prostate Specific Antigen BND Breakend ISUP GG International Society of Urological pathology Group Grading pLoF potential Loss of Function CG Copy Gain IED Intragenic Exon Duplication PPSV potentially pathogenic SV NCCN National Comprehensive Cancer Network 1KGP 1000 genomes Project Phase3 COSMIC CGC COSMIC Cancer Gene Census. Declarations Ethics approval and consent to participate Irrespective of country of origin, all individuals provided informed consent to participate in the study. Conforming to the principles of the Helsinki Declaration, South African patients were recruited as part of the Southern African Prostate Cancer Study (SAPCS) with approval granted by the University of Pretoria Faculty of Health Sciences Research Ethics Committee (HREC, with US Federal wide assurance FWA00002567 and IRB00002235 IORG0001762; #43/2010). In Australia, participant recruitment was approved by the St Vincent’s HREC (#SVH/12/231). Samples were shipped to the Garvan Institute of Medical Research in accordance with institutional Material Transfer Agreements (MTAs), as well as additional Republic of South Africa Department of Health Export Permit (National Health Act 2003; J1/2/4/2 #1/12). This study was approved by the St. Vincent’s HREC (#SVH/15/227) for genomic interrogation. Additional IRB review and approval for genomic interrogation was granted by the Human Research Protection Office of the US Army Medical Research and Development Command E02371 (TARGET Africa) and E03280 (HEROIC PCaPH Africa1K). Consent for publication Not applicable Availability of data and materials The sequence data were obtained and accessible through Jaratlerdsiri et al 18 , in the European Genome‐Phenome Archive (EGA; https://ega‐archive.org) under overarching accession EGAS00001006425 and including the Southern African Prostate Cancer Study (SAPCS) Dataset (EGAD00001009067) and Garvan/St Vincent’s Prostate Cancer Database (EGAD00001009066). The dbVar SV sites and their variant allele frequencies were downloaded from https://www.ncbi.nlm.nih.gov/dbvar. The ENSEMBL gene set was downloaded from https://www.ensembl.org. The MSigDB gene sets are available at https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#H. The gene set in COCMIC CGC was downloaded from https://cancer.sanger.ac.uk/census. The scripts for sequence read alignment and quality control are available at GitHub (https://github.com/Sydney-Informatics-Hub/Bioinformatics). All computational code for SV callset comparison and integration are available at GitHub (https://github.com/tgong1/StructuralVariantUtil) 88 . Competing Interests The authors declare that they have no competing interests. Funding Genomic sequencing was supported by the National Health and Medical Research Council (NHMRC) of Australia through a Project Grant (APP1165762 to V.M.H.) and Ideas Grants (APP2001098 to V.M.H. and M.S.R.B.; APP2APP2010551 to V.M.H.). Further analytics was supported by a U.S.A. Congressionally Directed Medical Research Programs (CDMRP) Prostate Cancer Research Program (PCRP) Idea Development Award (PC200390, TARGET Africa to V.M.H.) and HEROIC Consortium Award (PC210168, HEROIC PCaPH Africa1K to V.M.H. and M.S.R.B., as well as co-leads Professors Gail Prins, University of Illinois at Chicago, U.S.A. and Mungai Peter Ngugi, University of Nairobi, Kenya), a U.S.A. National Institute of Health (NIH) National Cancer Institute (NCI) Award (1R01CA285772-01 to V.M.H.) and a U.S.A. Prostate Cancer Foundation (PCF) Challenge Award (2023CHAL4150to V.M.H.). V.M.H. was further supported by the Petre Foundation via the University of Sydney Foundation, Australia. Author contributions Conception and design: T.G. and V.M.H.; Financial support: V.M.H.; Methodology: T.G. and J.J.; Formal analysis: T.G., J.J. and V.M.H.; Data curation: W.J., K.G. and J.J.; Participant recruitment, clinical data and sample collection: P.D.S., S.B.A.M. and M.S.R.B.; Supervision: V.M.H.; Writing-review, figures and editing: T.G. and V.M.H. Critical technical review: J.W. All authors have read and approved the final manuscript. Acknowledgements We are forever grateful to the patients and their families who have contributed to this study; without their contribution, this research would not be possible. We acknowledge the contributions of the many clinical staff across the SAPCS (South Africa) and the St Vincent’s Hospital Sydney (Australia), who over many years have recruited patients and provided samples to these critical bioresources, as well as the additional authors who contributed to the initial published genome profiling project (Jaratlerdsiri et al ., 2022) 18 . 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Supplementary Files Supplementarydata1.xlsx Supplementary Data 1 Supplementarydata2.xlsx Supplementary Data 2 PCaSVpathogenicNatCommsSUPP.docx Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4531885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":313743425,"identity":"e8b2b074-3b6e-41e1-8fe1-63473f07ab88","order_by":0,"name":"Vanessa Hayes","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4524-7280","institution":"University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Hayes","suffix":""},{"id":313743426,"identity":"9d0ef169-e0aa-459b-b700-cb812e12583d","order_by":1,"name":"Tingting Gong","email":"","orcid":"https://orcid.org/0000-0001-5907-2445","institution":"Human Phenome Institute, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Gong","suffix":""},{"id":313743427,"identity":"66c30d43-6ec9-4f2f-b6ed-385656db728b","order_by":2,"name":"Jue Jiang","email":"","orcid":"https://orcid.org/0000-0003-0920-8310","institution":"Garvan Institute of Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Jiang","suffix":""},{"id":313743428,"identity":"b05e8c7f-5eb5-414c-8aaa-cc561e0cb0b5","order_by":3,"name":"Riana Bornman","email":"","orcid":"https://orcid.org/0000-0003-3975-2333","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Riana","middleName":"","lastName":"Bornman","suffix":""},{"id":313743429,"identity":"2315d48f-4f60-4e49-9b21-996e1fba1cd3","order_by":4,"name":"Kazzem Gheybi","email":"","orcid":"","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Kazzem","middleName":"","lastName":"Gheybi","suffix":""},{"id":313743430,"identity":"a5438db5-6c0a-4edd-a483-ff7e78fa1ed6","order_by":5,"name":"Phillip Stricker","email":"","orcid":"https://orcid.org/0000-0002-0934-0656","institution":"St. Vincent's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Phillip","middleName":"","lastName":"Stricker","suffix":""},{"id":313743431,"identity":"fa0fc815-4096-4859-b7a7-e3ce7431ff26","order_by":6,"name":"Joachim Weischenfeldt","email":"","orcid":"https://orcid.org/0000-0002-3917-5524","institution":"BRIC, University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Joachim","middleName":"","lastName":"Weischenfeldt","suffix":""},{"id":313743432,"identity":"07e49d20-fba3-49a1-80a4-3bd788b44253","order_by":7,"name":"Shingai Mutambirwa","email":"","orcid":"","institution":"Sefako Makgatho Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Shingai","middleName":"","lastName":"Mutambirwa","suffix":""}],"badges":[],"createdAt":"2024-06-05 06:40:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4531885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4531885/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-57312-9","type":"published","date":"2025-03-10T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58314250,"identity":"9a373253-05e7-4ce1-b4a8-e418bec4dd57","added_by":"auto","created_at":"2024-06-13 20:41:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of PCa potentially pathogenic SV (PP-SV) identification. \u003c/strong\u003eThe detailed criteria to predict the potential pathogenicity were shown in Supplementary Table 3. The identification of tumour suppressor or oncogenic effect for disrupted genes by pathogenic candidates and the related literatures were shown in Supplementary Table 7.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/9e76225e6d2afed832ce11dc.png"},{"id":58314251,"identity":"f9af8f8f-a081-49d8-9bda-58b43a35bf25","added_by":"auto","created_at":"2024-06-13 20:41:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAfrican-specific PP-SVs disrupting well-known pathogenic cancer genes and/or PCa tumour suppressor genes, including DNA damage response genes.\u003c/strong\u003e \u0026nbsp;(\u003cstrong\u003eA\u003c/strong\u003e) 4,877 base pLoF deletion on DNA damage repair gene \u003cem\u003eBARD1\u003c/em\u003e. (\u003cstrong\u003eB\u003c/strong\u003e) pLoF INV impacting PCa DNA mismatch repair gene \u003cem\u003eMLH1\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) pLoF INV impacting PCa tumour suppressor \u003cem\u003eRB1\u003c/em\u003e. (\u003cstrong\u003eD\u003c/strong\u003e) pLoF INV impacting PCa tumour suppressor gene \u003cem\u003eWASF\u003c/em\u003e1. (\u003cstrong\u003eE\u003c/strong\u003e) pLoF INV impacting PCa tumour suppressor gene \u003cem\u003eFOXP1\u003c/em\u003e. More details of SV region and/or breakpoints on impacted genes and visual inspection of sequencing reads using Integrative Genomic Viewer\u003csup\u003e49\u003c/sup\u003e are shown in Supplementary Fig. 8, 12, 13, 14 and 15, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/6a5f56aa08da452030ac754f.png"},{"id":78228016,"identity":"414f714d-f119-4f26-9222-e9736201931c","added_by":"auto","created_at":"2025-03-11 07:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2497556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/c9069f6f-38b3-4344-89b3-d034d041833a.pdf"},{"id":58314249,"identity":"a969c53e-31c0-4373-95d2-f8779364c713","added_by":"auto","created_at":"2024-06-13 20:41:59","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19578,"visible":true,"origin":"","legend":"Supplementary Data 1","description":"","filename":"Supplementarydata1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/70bf91601295bf8e80658efa.xlsx"},{"id":58314648,"identity":"4af6ad5e-9e74-4058-a00d-1afd0d60a454","added_by":"auto","created_at":"2024-06-13 20:49:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20259,"visible":true,"origin":"","legend":"Supplementary Data 2","description":"","filename":"Supplementarydata2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/b4d31d421956acc4d90372ef.xlsx"},{"id":58314253,"identity":"ad8c21cb-7049-44d2-bd5d-ee12fe404dc2","added_by":"auto","created_at":"2024-06-13 20:41:59","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3290514,"visible":true,"origin":"","legend":"","description":"","filename":"PCaSVpathogenicNatCommsSUPP.docx","url":"https://assets-eu.researchsquare.com/files/rs-4531885/v1/4e17fcac2eda5ab4f646e6f0.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is a significant global health burden and a leading cause of male associated cancer deaths\u003csup\u003e1\u003c/sup\u003e. With one of the highest heritability rates (estimated 58%), PCa risk shows a great degree of variability\u003csup\u003e2\u003c/sup\u003e, particularly when considering a man\u0026rsquo;s ancestral heritage. In the United States, Black men are at greatest risk for aggressive disease presentation\u003csup\u003e3\u003c/sup\u003e and depending on age at diagnosis an over double to triple (\u0026lt;\u0026thinsp;65 years) the risk for PCa-associated mortality than White Americans\u003csup\u003e4, 5\u003c/sup\u003e. Contributed by a complex interaction of socioeconomic factors and genetics\u003csup\u003e6\u003c/sup\u003e, inherited risk includes a combination of both common (low-risk with combined genetic risk scores) and rare (high-risk or pathogenic) germline variants\u003csup\u003e7, 8\u003c/sup\u003e. Revolutionised through advancement of precision oncology, most notably the approval of the poly-(ADP ribose) polymerase (PARP) inhibitors Olaparib\u003csup\u003e9\u003c/sup\u003e and rucaparib\u003csup\u003e10\u003c/sup\u003e for the treatment of metastatic castrate resistant PCa for patients harbouring rare pathogenic variants in specified DNA repair genes\u003csup\u003e11\u003c/sup\u003e, has increased the value for germline testing. Furthermore, the National Comprehensive Cancer Network (NCCN) recommends germline testing for all men with metastatic, recurrent or high-risk localized PCa, regardless of family history\u003csup\u003e12\u003c/sup\u003e. Although a significant risk factor for aggressive disease, no consensus could be reached for men of African ancestry\u003csup\u003e13\u003c/sup\u003e, while a recent review further highlighted the knowledge gap\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe lack of consensus for PCa germline testing in Black men is directly attributed to a lack of available data, compounded by a lack of African-relevant genomic data that captures the true extent of elevated genetic diversity. While consensus has yet to be reached for minority inclusion in the benefits of recent breakthroughs in PCa precision oncology, contradictory studies suggest that Black American patients harbour more\u003csup\u003e15\u003c/sup\u003e and conversely less actionable pathogenic variants than White Americans\u003csup\u003e16\u003c/sup\u003e. The picture is no different for Africa, although more recently PCa genomics has reached the continent with the first whole exome (n\u0026thinsp;=\u0026thinsp;45 Nigerian)\u003csup\u003e17\u003c/sup\u003e and whole genome sequencing studies (n\u0026thinsp;=\u0026thinsp;113 Black South Africans)\u003csup\u003e18\u003c/sup\u003e. Although preliminary, notable differences within Africa are emerging. For example, an elevated frequency of \u003cem\u003eBRCA1\u003c/em\u003e germline mutations reported for Nigerian patients, reflecting African American data\u003csup\u003e17, 19\u003c/sup\u003e, is lacking in Southern African cases\u003csup\u003e20\u003c/sup\u003e. Additionally, we have recently reflected on the lack of the West African exclusive and functionally relevant common PCa susceptibility variants \u003cem\u003eCHEK2\u003c/em\u003e p.Ile448Ser (rs17886163) and \u003cem\u003eHOXB13\u003c/em\u003e p.Ter285Lys (rs77179853) in Southern Africa\u003csup\u003e21, 22\u003c/sup\u003e. Reporting a 2.1-fold age-adjusted increase in aggressive PCa presentation in Black South African \u003cem\u003eversus\u003c/em\u003e Black American men\u003csup\u003e23\u003c/sup\u003e, through deep sequenced interrogation for the 20 most common genes included in PCa germline testing panels using NCCN inclusion criteria (Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;8), we observed a prevalence for rare pathogenic variants of 5.6%\u003csup\u003e20\u003c/sup\u003e, comparable with a single East African study (5.7%)\u003csup\u003e24\u003c/sup\u003e and almost half that reported for non-Africans (11.8%)\u003csup\u003e25\u003c/sup\u003e. These studies highlight the need for developing African-relevant PCa germline testing panels through African inclusion in genome profiling.\u003c/p\u003e \u003cp\u003eAgain, it is well established that Structural Variations (SVs) play a critical role in prostate tumour progression with prognostic and therapeutic potential\u003csup\u003e26, 27\u003c/sup\u003e, including tumours derived from men of African ancestry\u003csup\u003e18, 28\u003c/sup\u003e. Yet, irrespective of patient ancestry, little is known with regards to the contribution of germline potentially pathogenic rare SVs. Typically, greater than 50 bases in length, SVs encompassing large deletions (DEL), duplications (DUP), insertions (INS), inversions (INV) and translocations (TRA), are overlooked and/or difficult to resolve using current germline genetic testing assays. While it is well established that SVs play a critical role in diagnostic screening for inherited genetic diseases\u003csup\u003e29\u003c/sup\u003e, more recently, long-read sequencing has been used to identify potential pathogenic SVs in hereditary cancer syndromes \u003csup\u003e30\u003c/sup\u003e and known breast cancer susceptibility genes\u003csup\u003e31\u003c/sup\u003e, however, the impact of rare pathogenic SVs on PCa predisposition, and in turn targeted treatment, remains unknown.\u003c/p\u003e \u003cp\u003eExpanding on our earlier work\u003csup\u003e18, 20, 28\u003c/sup\u003e, including deep sequenced germline genomes for 113 African (Black South African) and 57 European (4 South African, 53 Australian) PCa patients, through high-quality SV calling and genotyping, comprehensive gene annotation and best-fit pathogenicity prediction workflow, we interrogate for rare potentially pathogenic SVs (PP-SVs). While agreeably a small study size, this resource is not only unique for the African continent, importantly it provides clinically and technically matched non-African data for direct comparative analyses, while the whole genome approach increases sensitivity for SV detection. As such, the study aims to limit spurious findings between the ancestries, while providing a foundation for further efforts across the continent. Identifying candidate PP-SVs highlights the value of whole genome interrogation not only to improve the detection rate for rare pathogenic PCa variants, but importantly begin to contribute to the much-needed emphasis on an all-African inclusion model for germline testing and associated clinical care.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNCCN high-risk characterisation for ancestrally assigned PCa patients\u003c/h2\u003e \u003cp\u003eClinically and technically matched whole genome sequenced germline data (mean coverage 45.9X; range 30.2-97.6X) was derived from 170 PCa patients, ancestrally classified previously using 7,472,833 genome-wide SNVs and population substructure analysis\u003csup\u003e18\u003c/sup\u003e. In brief, 113 Black South African patients presented with an African ancestral genetic fraction of \u0026gt;\u0026thinsp;85%, while the 57 White patients presented with European ancestral genetic fractions of \u0026gt;\u0026thinsp;90% (4 South African, 52 Australian) and 73.7% European and 26.3% Asian substructure (1 Australian) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Importantly, although mean age was 5-years younger at presentation or surgery, a greater number of European (86%; 49/57) over African patients (72%; 81/113) met current NCCN guidelines for germline testing based on International Society of Urological Pathology (ISUP) Group Grading defined as high-risk localized PCa (ISUP 4/5 or Gleason score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 8). Notably, we have previously provided evidence for the extension of these criteria for Black South African men to include ISUP 3, which would expand our cohort of high-risk Black men to 82% (93/113)\u003csup\u003e20\u003c/sup\u003e. While Black South Africans present with significantly elevated median and range of prostate specific antigen (PSA) levels (median 244 ng/mL \u003cem\u003eversus\u003c/em\u003e 9.4), as previously presented\u003csup\u003e18, 23\u003c/sup\u003e, still the study was biased towards over representation of NCCN guidelines for PSA inclusive high-risk PCa for the European (70.2%; 40/57) over African patients (65/113; 57.5%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide gene-disrupting SV discovery\u003c/h2\u003e \u003cp\u003eIn this study, we identified and genotyped 42,966 high-quality germline SVs. We found a median of 9,206 SVs (range: 8,891 to 9,708) per-African genome, which is significantly higher than the median of 7,490 (range: 7,309 to 8,050) per-European genome (p-value\u0026thinsp;=\u0026thinsp;1.1e-26 by Wilcoxon test). In total, we identified 38,668 African derived SVs (18,674 private) and 24,292 European derived SVs (4,298 private) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Including only high-quality genotype calls for allele frequency (AF) estimation left a total of 33,243 high-confidence SVs. Excluding for common SVs, defined as minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;5%, a total of 20,982 rare (MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%) and low-frequency (MAF\u0026thinsp;=\u0026thinsp;1 to 5%) SVs remained across the ancestries for further annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther interrogation for gene regions overlapping, we identified 1,857 gene-disruptive SVs, including 1,752 potential Loss-of-Function (pLoF), 52 Copy Gain (CG) and 53 Intragenic Exon DUP (IED) (detailed in \u003cb\u003eMethods\u003c/b\u003e). Notably, pLoF, CG and IED SVs can have functional impact on genes through either gene inactivation or increased dosage effect\u003csup\u003e32\u003c/sup\u003e. Conversely, there is no clear or direct coding effect by SVs with other gene impact types, which included in our study 109 partial gene DUP, 22 partial exon DUP, 48 whole-gene INV, 343 promoter SVs, 9,431 intronic SVs and 258 enhancer SVs. As such, the latter SVs were not discussed further. In total, we identified 1,857 (MAF \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e5%) gene-disruptive SVs of which 1,407 are African-relevant, including 93% (1,314) African-private, and 543 European-relevant, including 83% (450) European-private (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). There were 93 SVs (5%) shared by both African and European PCa patients. The 1,857 gene-disruptive SVs (1,050 rare in both African and European) underwent further downstream interrogation for potential clinical relevance. Of the 1,857 gene-disruptive SVs, 1,167 were previously reported in dbVar database of SVs, while 690 were absent and as such regarded as novel, of which 513 (74%) are uniquely African (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCharacterising ClinVar verified candidate potentially pathogenic SVs\u003c/h2\u003e \u003cp\u003eOf the 1,167 dbVar reported gene-disruptive SVs, 14 (1.2%) were recorded in ClinVar, with three reported as \u0026lsquo;pathogenic\u0026rsquo; or \u0026lsquo;likely pathogenic\u0026rsquo; based on functional prediction consensus. One 2,958 bp likely pathogenic DEL results in loss of exon 7 in \u003cem\u003eOCA2\u003c/em\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e), a 5,064 bp pathogenic DEL leads to exon 5\u0026ndash;7 loss in \u003cem\u003ePIGN\u003c/em\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), while a 235 bp likely pathogenic DUP duplicates exon 3 of \u003cem\u003eSLC3A1\u003c/em\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). The \u003cem\u003eOCA2\u003c/em\u003e and \u003cem\u003ePIGN\u003c/em\u003e DELs were identified in a single African patient each, while the \u003cem\u003eSLC3A1\u003c/em\u003e DUP presented in two African patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough pathogenic in ClinVar, none have been associated with cancer phenotypes and include rather oculocutaneous albinism, multiple congenital anomalies-hypotonia-seizures syndrome and cystinuria, respectively. As such, we searched the literature for plausibility with further ascertainment derived from normal prostate and tumour tissue data sets using GENT2\u003csup\u003e33\u003c/sup\u003e. Reported to be downregulated in numerous cancer types (all-type P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GENT2 T-test), although not significant for PCa, pLoF deletion of the pigmentation gene \u003cem\u003eOCA2\u003c/em\u003e has been linked not only to Prader-Willi syndrome, but also Prader-Willi associated malignancies\u003csup\u003e34\u003c/sup\u003e, and melanoma\u003csup\u003e35\u003c/sup\u003e, with recent studies linking melanoma with increased PCa risk\u003csup\u003e36\u003c/sup\u003e. Highly expressed in normal prostate tissue with significant upregulation in tumour tissue (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GENT2 T-test), \u003cem\u003ePIGN\u003c/em\u003e functions as a cancer chromosomal instability suppressor gene\u003csup\u003e37, 38\u003c/sup\u003e. Although at lower levels, \u003cem\u003eSLC3A1\u003c/em\u003e is also upregulated in PCa (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GENT2 T-test), with overexpression in breast cancer associated with tumourigenesis\u003csup\u003e39\u003c/sup\u003e. These observations taken together provide the rational for characterising the pLoF \u003cem\u003eOCA2\u003c/em\u003e and \u003cem\u003ePIGN\u003c/em\u003e DELs and \u003cem\u003eSLC3A1\u003c/em\u003e IED as potentially pathogenic SVs (PP-SVs). Notably, all three SVs are reported as rare (irrespective of ancestry) in multiple population-wide studies including gnomAD SV\u003csup\u003e32\u003c/sup\u003e, 1000 genomes Project (1KGP)\u003csup\u003e40, 41\u003c/sup\u003e and TOPMed SV\u003csup\u003e42\u003c/sup\u003e (\u003cb\u003eSupplementary Data 1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate potentially pathogenic (PP) SVs identified in 170 PCa patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene impact type\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003echrom1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epos1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003echrom2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epos2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSV type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClinVar /\u003c/p\u003e \u003cp\u003edbVar concordance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMAF African\u003c/p\u003e \u003cp\u003e(this study)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMAF European\u003c/p\u003e \u003cp\u003e(this study)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMAF African (dbVar)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMAF European (dbVar)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotentially Pathogenic SV (PP-SV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLC3A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44281377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44281612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLikely pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01 \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.3e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28017719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28020677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLikely pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePIGN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62152637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62157701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.3e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLC7A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17418976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17544122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDNAJC15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43078470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43079390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.0e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCL2L11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111122626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111125901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBARD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214768022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e214772899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOL4A2/ COL4A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110294204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110633815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.3e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.3e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLC2A5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9045605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9049441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.3e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFOXP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71097066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74525618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWASF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108167886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110172775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.6e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37000362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39352689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.4e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48466588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48473911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.8e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.3e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCTNNA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138903881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21614900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAK8-DST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132876361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56896165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePP-SV candidates classified as \u0026lsquo;cautionary\u0026rsquo;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLTBP1/\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBIRC6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32403832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33107415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIn dbVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePHC3-\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePRKACA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170090742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14110142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKCTD3-DST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215567414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56652607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePKHD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epLoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51981375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003echr15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30874073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enovel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Gene impact type based on gene annotation. pLoF: Potential loss-of-function. CG: Copy gain. IED: Intragenic Exon Duplication.\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003e The ancestry related MAF in dbVar were based on gnomAD\u003csup\u003e32\u003c/sup\u003e or TOPMed\u003csup\u003e42\u003c/sup\u003e SV study. The detail of all dbVar studies (dbVar study name and ID) and reported allele frequencies were shown in \u003cb\u003eSupplementary Data 1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003e Presenting at low-frequency rather than rare variants within the ancestrally-defined patient cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCharacterising candidate potentially pathogenic SVs absent from ClinVar\u003c/h2\u003e \u003cp\u003eAmong 1,843 SVs with unknown classification in ClinVar or absent from dbVar, we predicted their potential pathogenicity based on four SV impact prediction tools, including StrVCTVRE\u003csup\u003e43\u003c/sup\u003e, CADD-SV\u003csup\u003e44\u003c/sup\u003e, POSTRE\u003csup\u003e45\u003c/sup\u003e and PhenoSV\u003csup\u003e46\u003c/sup\u003e. The number of scored SVs by four tools and their types were shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e. Candidate SVs were required to meet two of the following criteria: StrVCTVRE score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e0.37, CADD-SV score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 10, POSTRE score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e0.8 and/or PhenoSV score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e0.5 (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e and \u003cb\u003eMethods\u003c/b\u003e). Based on this criterion, all three ClinVar identified pathogenic or likely pathogenic SVs and the single SV of uncertain significance were successfully annotated as pathogenic candidates, while conversely our workflow excluded for all 10 ClinVar characterised benign SVs (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Using our criteria, 291 SVs were defined as PP-SV candidates (107 DELs, 16 DUPs, 11 INVs and 157 TRAs) disrupting 419 genes. In total 190 candidate SVs were private to African and 88 to European patients, with 13 shared between the ancestries (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo further define cancer-related pathogenic potential, we assessed for the presence of disrupted genes by PP-SV candidates in gene sets derived from the Human Molecular Signature Database (MSigDB) oncogenic signature and hallmark gene sets\u003csup\u003e47\u003c/sup\u003e and COSMIC Cancer Gene Census (COSMIC CGC) cancer driver genes\u003csup\u003e48\u003c/sup\u003e. Requiring disrupted genes in two of the three cancer gene sets, 58 SVs were defined as cancer-related PP-SV candidates, including 20 DELs, 3 DUPs, 6 INVs and 29 TRAs, disrupting 56 genes. Of the 58 candidates, 23 of them were identified with MAF between 1\u0026ndash;5% in either African or European patients, leaving 35 rare PP-SV candidates for further consideration, of which 16 have been reported in dbVar. Two dbVar SVs including TRA disrupting gene \u003cem\u003eNBEA\u003c/em\u003e and \u003cem\u003ePOLR2C\u003c/em\u003e DEL were reported at low-frequencies (AF\u0026thinsp;=\u0026thinsp;0.03 and 0.01, respectively) (\u003cb\u003eSupplementary Data 1\u003c/b\u003e) and were therefore excluded from further analysis. Using our criteria, 33 rare cancer-related PP-SV candidates were identified (\u003cb\u003eSupplementary Data 2\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including 15 DELs, 3 DUPs (1 IED and 2 CGs), 5 INVs and 10 TRAs.\u003c/p\u003e \u003cp\u003eOf the 15 pLoF DELs, 11 were excluded as PP-SVs, with impacting genes showing oncogenic behaviour in multiple cancer types or no strong evidence for their tumour suppressor effects (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Conversely, four pLoF DELs were defined as PP-SVs, impacting known tumour suppressors or established DNA damage repair gene (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Two of them are known to dbVar, including a \u003cem\u003eSLC7A2\u003c/em\u003e 125,146 bp DEL identified in two African (\u003cb\u003eSupplementary Figs.\u0026nbsp;5\u003c/b\u003e) and a \u003cem\u003eDNAJC15\u003c/em\u003e 920 bp DEL in a European patient (\u003cb\u003eSupplementary Figs.\u0026nbsp;6\u003c/b\u003e). Another two identified PP-SVs are novel pLoF DELs, which identified in a single African patient each, including a \u003cem\u003eBCL2L11\u003c/em\u003e 3,275 bp (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e) and DNA damage repair gene \u003cem\u003eBARD1\u003c/em\u003e 4,877 bp DEL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eOf the two dbVar whole-gene DUPs, the \u003cem\u003eCOL4A2\u003c/em\u003e 339,611 bp CG, with breakpoints disrupting \u003cem\u003eCOL4A1\u003c/em\u003e and \u003cem\u003eNAXD\u003c/em\u003e, observed in a single African patient is defined as a PP-SV (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e), as \u003cem\u003eCOL4A2\u003c/em\u003e indicating oncogenic behaviour in gastric and breast cancers (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). In contrast, the \u003cem\u003eTTC27\u003c/em\u003e 703,583 bp DUP observed in a single European patient is afforded \u0026lsquo;cautionary\u0026rsquo; PP-SV status (\u003cb\u003eSupplementary Fig.\u0026nbsp;10\u003c/b\u003e). Although \u003cem\u003eTTC27\u003c/em\u003e is absent in three cancer gene databases, the breakpoints disrupt MSigDB and COSMIC CGC genes \u003cem\u003eBIRC6\u003c/em\u003e and \u003cem\u003eLTBP1\u003c/em\u003e, resulting in a \u003cem\u003eLTBP1\u003c/em\u003e-\u003cem\u003eBIRC6\u003c/em\u003e gene fusion of unclear effect. Observed in a single European patient, a 3,836 base DUP directly impacts exon 4 of \u003cem\u003eSLC2A5\u003c/em\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;11\u003c/b\u003e), which downregulated in PCa (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GENT2 T-test) and has been identified an oncogenic behaviour (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e), therefore allocated PP-SV status.\u003c/p\u003e \u003cp\u003eOf the five pLoF INVs, those impacting \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e and \u003cem\u003eWASF1\u003c/em\u003e are in dbVar, while \u003cem\u003eFOXP1\u003c/em\u003e and \u003cem\u003eNSD3\u003c/em\u003e INVs are novel. As \u003cem\u003eNSD3\u003c/em\u003e has been identified as oncogenic in multiple cancers, the associated INV is classified here as unlikely pathogenic, with all remaining pLoF INVs classified as PP-SVs, as they disrupting known to PCa and Lynch Syndrome predisposing DNA mismatch repair gene \u003cem\u003eMLH1\u003c/em\u003e and PCa tumour suppressor genes \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003eWASF1\u003c/em\u003e, and \u003cem\u003eFOXP1\u003c/em\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Identified in a single African patient each (\u003cb\u003eSupplementary Fig.\u0026nbsp;12\u0026ndash;14)\u003c/b\u003e, the three dbVar INVs were reported as rare by the recent TOPMed SV study\u003csup\u003e42\u003c/sup\u003e, in which \u003cem\u003eWASF1\u003c/em\u003e INV was also identified as African-specific (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplementary Data 1)\u003c/b\u003e. The novel INV impacting \u003cem\u003eFOXP1\u003c/em\u003e was identified in two African patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cb\u003eSupplementary Fig.\u0026nbsp;15\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eOf the 10 pLoF TRAs, five impacting genes of \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eWDR43\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003e and \u003cem\u003eMECOM\u003c/em\u003e with oncogenic properties (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e), therefore are classified as unlikely pathogenic. \u003cem\u003ePKHD1\u003c/em\u003e TRA identified in two African patients received a \u0026lsquo;cautionary\u0026rsquo; PP-SV classification, as identified potential oncogenic in colon cancer, while potential tumour suppressor in colorectal cancer (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). As \u003cem\u003eCTNNA1\u003c/em\u003e was known to have tumour suppressor behaviour across multiple tumour types (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e), here we classify the European-specific pLoF \u003cem\u003eCTNNA1\u003c/em\u003e TRA as a PP-SV (\u003cb\u003eSupplementary Fig.\u0026nbsp;16\u003c/b\u003e). The remaining pLoF TRAs result in \u003cem\u003ePHC3-PRKACA\u003c/em\u003e (1 African patient), \u003cem\u003eKCTD3-DST\u003c/em\u003e (2 African patients) and \u003cem\u003eAK8-DST\u003c/em\u003e (1 European patient, \u003cb\u003eSupplementary Fig.\u0026nbsp;17\u003c/b\u003e) novel gene fusions. \u003cem\u003ePHC3-PRKACA\u003c/em\u003e was classified as \u0026lsquo;cautionary\u0026rsquo; PP-SV, as \u003cem\u003ePHC3\u003c/em\u003e showed potential cancer suppressor effect in PCa, while \u003cem\u003ePRKACA\u003c/em\u003e appears to portray oncogenic behaviour (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Although unknown to PCa, both \u003cem\u003eDST\u003c/em\u003e and \u003cem\u003eAK8\u003c/em\u003e have demonstrated tumour suppressor behaviour, conversely, \u003cem\u003eKCTD3\u003c/em\u003e with an unclear role in cancer (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Here we classify \u003cem\u003eAK8-DST\u003c/em\u003e as a PP-SV, while \u003cem\u003eKCTD3-DST\u003c/em\u003e is assigned \u0026lsquo;cautionary\u0026rsquo; PP-SV status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelating PP-SVs and \u0026lsquo;cautionary\u0026rsquo; PP-SVs with clinical features\u003c/h2\u003e \u003cp\u003eThe clinicopathological features of the study cohort has been previously described\u003csup\u003e18, 28\u003c/sup\u003e. In brief, African patients show a 5-year greater mean age and 25-fold greater PSA level at diagnosis compared to European patients (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Based on our previous observations\u003csup\u003e20\u003c/sup\u003e, high-risk or aggressive PCa were defined as ISUP GG\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 and conversely, low-risk disease presentation as ISUP GG\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e3. Biased towards aggressive disease presentation (82% African, 86.0% European), it was notable that all four patients with a pathogenic or likely pathogenic SV presented with aggressive disease at diagnosis, 92.9% (13/14) of PP-SV and 83.3% (5/6) cautionary PP-SV presenting patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological features of patients by ethnicity presenting with potentially pathogenic (PP) SVs and cautionary PP-SVs as defined by this study criteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathogenicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSV type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatient ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eISUP GG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSLC3A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003cp\u003eLikely Pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003cp\u003eLikely Pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePIGN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003cp\u003ePathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSLC7A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP2035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKAL0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDNAJC15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCL2L11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKAL0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBARD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOL4A2/COL4A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP2039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLC2A5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eFOXP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP2101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWASF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSister with cervical cancer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCTNNA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAK8-DST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLTBP1/BIRC6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCautionary PP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePHC3-PRKACA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCautionary PP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMother with stomach cancer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eKCTD3-DST\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCautionary PP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP2039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePKHD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCautionary PP-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMU196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eClinVar defined pathogenic (or likely pathogenic) SVs disrupting \u003cem\u003eSLC3A1\u003c/em\u003e, \u003cem\u003eOCA2\u003c/em\u003e or \u003cem\u003ePIGN\u003c/em\u003e were observed in 3.5% (4/113) of African patients. Specifically, the \u003cem\u003eSLC3A1\u003c/em\u003e intragenic exon DUP was identified in two patients presenting with ISUP GG4, while the \u003cem\u003eOCA2\u003c/em\u003e and \u003cem\u003ePIGN\u003c/em\u003e pLoF DELs presented in a single patient each with ISUP GG5 and ISUP GG3 PCa, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Visually inspecting the three PP-SVs using Integrative Genomic Viewer\u003csup\u003e49\u003c/sup\u003e, \u003cem\u003eSLC3A1\u003c/em\u003e DUP was found with three supporting read-pairs in sample N0001 (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e), and split-reads and more than 40% increase in read depth comparing to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e10 kb of the SV region in both samples (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e), while \u003cem\u003eOCA2\u003c/em\u003e and \u003cem\u003ePIGN\u003c/em\u003e DELs were found with 16 and 6 supporting read-pairs respectively (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u0026ndash;2\u003c/b\u003e), and have 44\u0026ndash;51% reduction in read depth (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). Solute carrier family 3 member 1 (\u003cem\u003eSLC3A1\u003c/em\u003e) is an amino acid transporter, which through heterodimerisation with \u003cem\u003eSLC7A9\u003c/em\u003e is responsible for cystine reabsorption through cationic and neutral amino acid exchange\u003csup\u003e50\u003c/sup\u003e. Mutations, including SVs, in \u003cem\u003eSCL3A1\u003c/em\u003e are associated with cystinuria, an inherited disease that results in the formation of cystine stones in the kidney, with disease presentation suggested to require biallelic loss\u003csup\u003e51\u003c/sup\u003e. \u003cem\u003eSCL3A1\u003c/em\u003e over-expression has been associated with enhanced tumourigenesis in breast cancer, while blocking \u003cem\u003eSCL3A1\u003c/em\u003e has suggestive therapeutic potential\u003csup\u003e39\u003c/sup\u003e. \u003cem\u003eOCA2\u003c/em\u003e is a pigmentation gene with inherited mutations associated with oculocutaneous albinism\u003csup\u003e52\u003c/sup\u003e. Polymorphisms have been associated with skin cancers\u003csup\u003e53\u003c/sup\u003e, as well as clinical response and survival in breast cancer patients having received neoadjuvant chemotherapy\u003csup\u003e54\u003c/sup\u003e. Inherited \u003cem\u003ePIGN\u003c/em\u003e mutations have been associated with multiple congenital anomalies-hypotonia-seizures syndrome and Fryns syndrome, with some mutations related to milder forms of clinical presentation\u003csup\u003e55, 56\u003c/sup\u003e. Coding for phosphatidylinositol glycan anchor biosynthesis class N, PIGN is involved in the biosynthesis of glycosylphosphatidylinositol, which has been shown to suppress cancer chromosomal instability\u003csup\u003e37\u003c/sup\u003e through PIGN complexed spindle assembly checkpoint regulation\u003csup\u003e38\u003c/sup\u003e, a common phenomenon in solid tumours\u003csup\u003e57\u003c/sup\u003e. Notably, no previous associations have been made between \u003cem\u003eSCL3A1, OCA2\u003c/em\u003e or \u003cem\u003ePIGN\u003c/em\u003e mutation and PCa.\u003c/p\u003e \u003cp\u003eAs our study is biased towards under-represented African patients, it is highly plausible that the majority of SVs detected are unlikely to be represented in ClinVar. As such, it is critical that we developed a best-fit workflow for PP-SV prediction. The four SV impact prediction tools used in this study were chosen based on the criteria of easy-to-use (either web-based or packed as software), providing pathogenicity scores or labels, accepting multiple SVs and covering all SV types. However, there are multiple factors to be taken into consideration when using SV impact prediction tools to establish potential pathogenicity, as different tools have limitations in applicable SV types, regions or diseases, as well as different scoring systems. While all tools can predict the impact of DELs and DUPs, StrVCTVRE is limited to DELs and DUPs in exonic regions. Besides predicting the simpler SVs, CADD-SV is capable of annotating INSs and POSTRE annotates INVs and TRAs, while PhenoSV is able to predict the impact of all these three types. POSTRE doesn\u0026rsquo;t work for all diseases or phenotypes. Therefore, combining multiple tools is necessary to cover all SV types and increase the confidence level. Another factor is the choice of threshold to establish pathogenicity. POSTRE and PhenoSV defines the threshold of pathogenicity, but StrVCTVRE and CADD-SV are limited to scores and calling for thresholds to be established depending on individual study aims. In this study, we have decided the thresholds based on tools\u0026rsquo; validated results from database (90% sensitivity in ClinVar by StrVCTVRE\u003csup\u003e43\u003c/sup\u003e and top 10% in gnomAD by CADD-SV\u003csup\u003e44\u003c/sup\u003e). When combining results from multiple tools, we found the requirement of passing thresholds of all four tools identified two PP-SV candidates (out of 1,843 SVs) (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e), with notable failure to identify the three ClinVar pathogenic/likely pathogenic SVs (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). As such, PP-SV candidate classification in this study required an SV to pass thresholds of at least two impact prediction tools, with disrupted genes requiring further clarification as hallmark or drivers in cancer gene databases (MSigDB and COSMIC CGC).\u003c/p\u003e \u003cp\u003eUsing our described workflow, 12 SVs were predicted as PP-SVs, identified in 7.0% (4/57) of European and 8.8% (10/113) of African patients, bringing the total of African patients presenting with a potential pathogenic SV to 12.4% (14/113). Remarkably, five of our African-specific PP-SVs included well-known pathogenic cancer genes and/or PCa tumour suppressor genes, including DNA damage response genes. Most notably, the DNA mismatch repair tumour suppressor gene \u003cem\u003eMLH1\u003c/em\u003e commonly mutated in Lynch Syndrome, including cases with PCa\u003csup\u003e58\u003c/sup\u003e, is a known candidate gene in PCa germline testing panels\u003csup\u003e20\u003c/sup\u003e. While PCa patients presenting with pathogenic \u003cem\u003eMLH1\u003c/em\u003e mutations were reported to have significantly higher disease burden for African Americans\u003csup\u003e24\u003c/sup\u003e, here we found a dbVar known \u003cem\u003eMLH1\u003c/em\u003e pLoF INV with around 11 supporting short read-pairs (\u003cb\u003eSupplementary Fig.\u0026nbsp;12\u003c/b\u003e) in a 64 year old African male presenting with ISUP GG4 at diagnosis. Not recognised as a PCa germline testing panel gene, \u003cem\u003eFOXP1\u003c/em\u003e is an established PCa tumour suppressor driver gene, with CN loss increasing cell proliferation and migration, and poor prognosis\u003csup\u003e59\u003c/sup\u003e. Recently, we showed \u003cem\u003eFOXP1\u003c/em\u003e to be equally impacted by predominantly CN loss in African compared with European derived tumours (20% of 183 tumours)\u003csup\u003e18\u003c/sup\u003e. Here we found a germline inverted duplication impacting \u003cem\u003eFOXP1\u003c/em\u003e with around 18 supporting read-pairs in two African patients (\u003cb\u003eSupplementary Fig.\u0026nbsp;15\u003c/b\u003e). Notably, one African patient (UP2101) presented 10 years earlier than the cohort average receiving an ISUP GG5 diagnosis. Loss of the \u003cem\u003eBRAC1\u003c/em\u003e associated RING domain-1 (\u003cem\u003eBARD1\u003c/em\u003e) DNA damage repair gene has been found to induce homologous recombination deficiency and increase the sensitivity to PARP inhibitor in PCa cell lines\u003csup\u003e60\u003c/sup\u003e. Here the novel \u003cem\u003eBARD1\u003c/em\u003e exon 5 DEL, supported by 10 read-pairs and with around 50% reduction in read depth comparing to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e10 kb of the DEL region (\u003cb\u003eSupplementary Fig.\u0026nbsp;8, Supplementary Table\u0026nbsp;6\u003c/b\u003e), was identified in a 62-year-old African PCa patient with unknown pathology. While a paediatric cancer predisposing tumour suppressor gene commonly mutated in retinoblastoma and to a lesser extent osteosarcoma\u003csup\u003e61\u003c/sup\u003e, and less common as an adult cancer predisposing gene\u003csup\u003e62\u003c/sup\u003e, \u003cem\u003eRB1\u003c/em\u003e is recognised as one of five most prevalent somatically mutated genes in metastatic cancers\u003csup\u003e63\u003c/sup\u003e, with \u003cem\u003eRB1\u003c/em\u003e loss in prostate tumours associated with poor patient outcomes\u003csup\u003e64\u003c/sup\u003e. To the best of our knowledge, this is the first report of a germline potentially pathogenic \u003cem\u003eRB1\u003c/em\u003e PCa variant, which includes a pLoF INV of exon 24 with three supporting read-pairs (\u003cb\u003eSupplementary Fig.\u0026nbsp;13\u003c/b\u003e) in a single ISUP GG3 diagnosed African patient. Lastly, the tumour suppressor gene \u003cem\u003eWASF1\u003c/em\u003e with loss associated with aggressive or metastatic lethal PCa\u003csup\u003e65\u003c/sup\u003e. Identifying a potentially pathogenic INV previously reported at MAF of 9.6e-05 in Africans and resulting in \u003cem\u003eNR2E1-WASF1\u003c/em\u003e fusion was identified in a single African patient presenting at 70 years of age with ISUP GG5 PCa, showed 14 supporting read-pairs (\u003cb\u003eSupplementary Fig.\u0026nbsp;14\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eOther notable PP-SV DELs impacting tumour suppressor genes unknown to PCa, includes \u003cem\u003eSLC7A2\u003c/em\u003e and \u003cem\u003eDNAJC15\u003c/em\u003e. Knockdown of \u003cem\u003eSLC7A2\u003c/em\u003e has been shown to promote viability, invasion and migration of ovarian cancer\u003csup\u003e66\u003c/sup\u003e and enhance proliferation of non-small-cell lung cancer cells\u003csup\u003e67\u003c/sup\u003e, while \u003cem\u003eDNAJC15\u003c/em\u003e has tumour suppressor behaviour in breast cancer\u003csup\u003e68\u003c/sup\u003e. Identified in two African patients presenting with ISUP GG5 disease, loss of \u003cem\u003eSLC7A2\u003c/em\u003e exons 1 and 2, supported by 10 read-pairs and with around 50% reduction in read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;5 and Supplementary Table\u0026nbsp;6\u003c/b\u003e) has previously been reported in African populations at MAF of 0.03 (\u003cb\u003eSupplementary Data 1\u003c/b\u003e). Specific to Europeans (MAF\u0026thinsp;=\u0026thinsp;1.0e-04), loss of \u003cem\u003eDNAJC5\u003c/em\u003e exon 4 supported by 13 read-pairs and with around 50% reduction in read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;6 and Supplementary Table\u0026nbsp;6\u003c/b\u003e), was identified in a single European patient presenting for surgery at age 63 years with ISUP GG5 disease. While not associated with PCa, the loss of \u003cem\u003eBCL2L11\u003c/em\u003e and \u003cem\u003eCTNNA1\u003c/em\u003e has been identified to leading tumourigenesis and promoting invasion and metastasis of multiple cancers\u003csup\u003e69, 70\u003c/sup\u003e. Here the novel \u003cem\u003eBCL2L11\u003c/em\u003e pLoF DEL on exon 2 with more than 20 supporting read-pairs and with around 50% reduction in read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;7 and Supplementary Table\u0026nbsp;6\u003c/b\u003e) was identified in a single African patient presenting at age 71 years with ISUP GG5 PCa, while the novel pLoF TRA interrupting \u003cem\u003eCTNNA1\u003c/em\u003e with more than 20 supporting read-pairs (\u003cb\u003eSupplementary Fig.\u0026nbsp;16\u003c/b\u003e) in a single European patient presenting at age 59 years with ISUP GG5 PCa. Another identified novel potentially pathogenic inter-chromosomal TRA with around 18 supporting read-pairs (\u003cb\u003eSupplementary Fig.\u0026nbsp;17\u003c/b\u003e) leading to a \u003cem\u003eAK8-DST\u003c/em\u003e fusion in a single European patient (ISUP GG1, 67 years). Although no associations have been made between PCa, higher expression of \u003cem\u003eDST\u003c/em\u003e has been identified to promote pathogenesis and development of breast cancer, while \u003cem\u003eAK8\u003c/em\u003e downregulation has been found to promote migration and invasion of uterine carcinosarcoma\u003csup\u003e71\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTwo known PP-SVs identified to potentially increase gene dosage of well-known oncogenes \u003cem\u003eCOL4A2\u003c/em\u003e and \u003cem\u003eSLC2A5\u003c/em\u003e, through whole-gene duplication and intra-genic exon duplication respectively. Although not associated with PCa, \u003cem\u003eCOL4A2\u003c/em\u003e loss has been identified to inhibit triple-negative breast cancer cell proliferation and migration\u003csup\u003e72\u003c/sup\u003e and its mutations as risk factor for familial cerebrovascular disease\u003csup\u003e73\u003c/sup\u003e, while inactivation of \u003cem\u003eSLC2A5\u003c/em\u003e has been found to inhibit cell proliferation and migration in multiple cancer cell lines\u003csup\u003e74\u003c/sup\u003e. The whole \u003cem\u003eCOL4A2\u003c/em\u003e DUP with more than 20 supporting read-pairs and more than 50% gain in read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e) was identified in a single African patient (ISUP GG4, 71 years) and the exon 4 DUP in \u003cem\u003eSLC2A5\u003c/em\u003e with more than 20 supporting read-pairs and more than 50% increase in read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;11\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e) was identified in a single European patient (ISUP GG5, 70 years).\u003c/p\u003e \u003cp\u003eUsing short-read sequencing data for SV calling and genotyping remains a potential limitation, appreciating that SVs in difficult-to-sequence regions may have been overlooked\u003csup\u003e75\u003c/sup\u003e. To ensure the highest possible accuracy of SV detection and population allele frequency estimation, we required high-confidence calls from two SV callers and high-quality genotype calls at both the population- and individual-level, while all PP-SVs were visually inspected. Due to lack of available expression data, we were unable to validate the direct impact of identified PP-SVs and cautionary PP-SVs. Further guidelines related to criteria for pathogenic SV identification using short read sequencing technologies and/or long read sequencing approaches are required, making these methods accessible for routine germline testing.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere we have described a first-of-its-kind pathogenicity investigation of SVs in PCa patients with ancestry disparity. We observed three ClinVar-defined pathogenic or likely pathogenic PP-SVs (\u003cem\u003eSLC3A1\u003c/em\u003e, \u003cem\u003eOCA2\u003c/em\u003e and \u003cem\u003ePIGN\u003c/em\u003e) and 12 predicted PP-SVs, including seven known SVs (\u003cem\u003eSLC7A2, DNAJC15\u003c/em\u003e, \u003cem\u003eCOL4A2\u003c/em\u003e, \u003cem\u003eSLC2A5\u003c/em\u003e, \u003cem\u003eWASF1\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eRB1\u003c/em\u003e), and five novel SVs (\u003cem\u003eBCL2L11, BARD1, FOXP1\u003c/em\u003e, \u003cem\u003eCTNNA1\u003c/em\u003e and \u003cem\u003eAK8-DST\u003c/em\u003e), suggesting that inherited SVs may constitute an under-appreciated contribution to PCa pathogenicity. Furthermore, the identification of African-private (eight known and three novel) and European-private (two known and two novel) PP-SVs allows for further speculation with regards to associated racial disparities, while improving the detection rate for PCa germline testing with SV inclusivity, and in turn raising limitations for African inclusion and associated clinical care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWGS data generation\u003c/h2\u003e \u003cp\u003eTo avoid technical and analytical biases, all samples (whole blood) were processed (beginning at DNA extraction), data generated and analysed within a single laboratory using a single computational pipeline, as previously described\u003csup\u003e18, 28\u003c/sup\u003e. In brief, whole-genome sequencing data were generated using Illumina HiSeq X Ten (21 cases) or NovoSeq (149 cases) instruments with 2\u0026times;150 cycle paired-end mode at the Kinghorn Centre for Clinical Genomics (Garvan Institute of Medical Research, Australia). Following the BROAD\u0026rsquo;s best practice recommendations for \u0026ldquo;data pre-processing for variant discovery\u0026rdquo;, sequencing reads were aligned to GRCh38 reference genome with alternative contigs using scalable FASTQ-to-BAM (v2.0) workflow with default settings\u003csup\u003e76\u003c/sup\u003e. The mean depth of coverage for all samples were 45.9X (range 30.2-97.6X).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStructural variant calling and high-confidence SV filtering\u003c/h2\u003e \u003cp\u003eGermline SVs were called using Manta (v1.6.0)\u003csup\u003e77\u003c/sup\u003e and GRIDSS (v2.13.3)\u003csup\u003e78, 79\u003c/sup\u003e. SV types reported by Manta included DEL, tandem DUP, INS and adjacent breakends (BNDs) for a fusion junction with inverted sequence or in an inter-chromosomal rearranged genome. Pairs of BND in inverted junction were annotated as inversions (INV). Pairs of BND in different chromosomes were annotated as inter-chromosomal translocations (TRA). Conversely, GRIDSS reports BND for all fusion junctions resulting from any SV event. Simple SV types, defined as DEL, DUP, INS, INV and TRA, were assigned based on the strands and ALT field in VCF (modified from GRIDSS accompanied R script: simple-event-annotation.R).To obtain high-confidence SV call set, we integrated call sets from Manta and GRIDSS and generated concordant call set for each genome. Two SV calls were considered as concordant if they were reported as \u0026ldquo;PASS\u0026rdquo; by one of the two callers and have matching SV type and reported breakpoint positions within 200bp of each other. \u003cem\u003eBedtools pairtopair\u003c/em\u003e\u003csup\u003e80\u003c/sup\u003e was used to compare two call sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation-level genotyping and high-confidence genotype call filtering\u003c/h2\u003e \u003cp\u003eWe used Graphtyper2 (v2.7.5)\u003csup\u003e81\u003c/sup\u003e to re-genotype SVs for all samples. Following published guidelines, we merged all high-confidence SV set per-sample (individual VCFs) using svimmer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/DecodeGenetics/svimmer\u003c/span\u003e\u003cspan address=\"https://github.com/DecodeGenetics/svimmer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters. The individual VCFs were in format of Manta VCFs, as Manta provides detailed information on the exact breakpoint sequence, which is the essential information required by Graphtyper2. We extracted all SVs with \u0026ldquo;aggregate\u0026rdquo; model as suggested, and obtained 57,096 SVs with \u0026ldquo;PASS\u0026rdquo; in FILTER field in VCF. To further filtering SV genotype calls on a per-sample basis, we required more than 50% genotype calls as \u0026ldquo;PASS\u0026rdquo; (PASS_ratio \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 0.5 in INFO field), resulting in 42,966 SVs.\u003c/p\u003e \u003cp\u003eTo further filtering SV genotype calls on a per-sample basis, we set SV genotype as missing if genotype filter tag (FT) is not \u0026ldquo;PASS\u0026rdquo; for all SVs, except BND. For BND, as FT tag is not available, we set BND genotype with genotype quality (GQ)\u0026thinsp;\u0026lt;\u0026thinsp;20 as missing. We then excluded SVs with genotype missingness rate\u0026thinsp;\u0026gt;\u0026thinsp;20% in either African or European genomes, resulting in 33,340 SVs. We further removed 97 SVs with allele frequency of 100%, indicating the difference of sample genomes to reference genome. The allele frequency of each SV was then calculated based on the high-quality genotype calls only.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGene annotation and functional impact of SVs\u003c/h2\u003e \u003cp\u003eAll SVs were annotated against gene regions from the Ensembl human gene annotation file (GRCh38 assembly, release 108). As multiple transcripts can be available for a single gene, the Ensembl Canonical transcript was used (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ensembl.org/info/genome/genebuild/canonical.html\u003c/span\u003e\u003cspan address=\"http://www.ensembl.org/info/genome/genebuild/canonical.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). By comparing the position of SV breakpoint with gene regions using bedtools\u003csup\u003e80\u003c/sup\u003e, we examined nine gene overlapping categories with gnomAD\u003csup\u003e32\u003c/sup\u003e, including potential Loss of Function (pLoF), Copy Gain (CG), Intragenic Exon DUP (IED), partial gene DUP, whole-gene INV, UTR SVs, promoter SVs, intronic SVs and intergenic SVs. In addition, we defined partial-exon DUP as both breakpoints contained within the same gene, while neither both within exons (pLoF) nor fully overlapped at least one exon (IED). Promoters were defined as 1kb window before each transcription start site on the transcribed strand. We labelled SVs as enhancer-disruptive if at least one breakpoint was contained within a gene\u0026rsquo;s enhancer, by comparing to GeneHancer\u003csup\u003e82\u003c/sup\u003e regulatory elements regions. GeneHancer regulatory elements and gene interactions \u0026ldquo;double elite\u0026rdquo; subset was downloaded from UCSC Table \u003cem\u003egeneHancerInteractionsDoubleElite\u003c/em\u003e [last updated 15/01/2019] from GeneHancer track for GRCh38. The transcript structure plots were generated based on Ensembl human gene annotation (GRCh38 assembly, release 108) using R package ggtranscript (v0.99.3)\u003csup\u003e83\u003c/sup\u003e. The sequencing depth of DEL or DUP regions and their \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e10 kb regions were calculated using samtools (v1.6) \u003cem\u003edepth\u003c/em\u003e command\u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eShort-read data detect the SV signatures from aligned reads around the SV breakpoints and is hard to capture the whole large SVs\u003csup\u003e85\u003c/sup\u003e. Therefore, we restricted the disrupted genes of SVs greater than 1Mbp to be genes overlapped by SV breakpoints for downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of dbVar concordance and novel SVs\u003c/h2\u003e \u003cp\u003eThe NCBI\u0026rsquo;s database of human genomic structural variation (dbVar) [last updated 30/10/2023]\u003csup\u003e86\u003c/sup\u003e were used to identify dbVar concordance and novel SVs. The dbVar database included a total of 6,476,337 unique SVs, including 86,686 SVs with interpretations of their significance to disease in ClinVar database\u003csup\u003e87\u003c/sup\u003e. Structural variants concordant to dbVar SVs were defined as having both breakpoints within 200 bases of dbVar defined SV breakpoints. The ancestry related variant allele frequency of SVs (\u003cb\u003eSupplementary Data 1\u003c/b\u003e) were derived from dbVar pages of SVs or VCFs uploaded by different dbVar studies to dbVar\u0026rsquo;s FTP site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePathogenicity prediction\u003c/h2\u003e \u003cp\u003eThe pathogenicity of SVs were predicted through prediction tools StrVCTVRE\u003csup\u003e43\u003c/sup\u003e, CADD-SV\u003csup\u003e44\u003c/sup\u003e, POSTRE\u003csup\u003e45\u003c/sup\u003e and PhenoSV\u003csup\u003e46\u003c/sup\u003e. StrVCTVRE only scores the deleteriousness of DEL and DUP overlapping one or more exons, CADD-SV scores DEL, DUP and INS, POSTRE predicts the impact of DEL, DUP, INV and TRA, and PhenoSV works for all five SV types. As POSTRE only accepts genome coordinates on reference genome Hg19, the \u003cem\u003eliftOver\u003c/em\u003e function from \u003cem\u003ertracklayer\u003c/em\u003e package in R was used to lift SV coordinates from Hg38 to Hg19. As suggested by StrVCTVRE, the ClinVar 90% sensitivity threshold (0.37) was used to define potentially pathogenic SVs. The scaled CADD-SV scores range from 0 (potentially benign) to 48 (potentially pathogenic), indicating the position of the input SV within the gnomAD-SV score distribution. The threshold of 10 for CADD-SV score was used to establish potential pathogenicity, corresponding to top 10% score observed in gnomAD-SV. The threshold of 0.8 and 0.5 for POSTRE and PhenoSV score respectively was used in this study, which is the threshold of pathogenicity labelling defined by POSTRE and PhenoSV.\u003c/p\u003e \u003cp\u003eThe hallmark gene sets and oncogenic signature gene sets were downloaded from the Human Molecular Signature Database (MSigDB v2023.1)\u003csup\u003e47\u003c/sup\u003e. The MSigDB oncogenic signature gene sets included genes representing signatures of cellular pathways which are often dis-regulated in cancer. Cancer-driver genes were downloaded from COSMIC Cancer Gene Census (GRCh38 COSMIC v98, downloaded 26/09/2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsertion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDuplication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInversion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranslocation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole genome sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate Specific Antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreakend\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISUP GG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society of Urological pathology Group Grading\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epLoF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epotential Loss of Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy Gain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntragenic Exon Duplication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPSV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epotentially pathogenic SV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Comprehensive Cancer Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e1KGP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1000 genomes Project Phase3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOSMIC CGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCOSMIC Cancer Gene Census.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIrrespective of country of origin, all individuals provided informed consent to participate in the study. Conforming to the principles of the Helsinki Declaration, South African patients were recruited as part of the Southern African Prostate Cancer Study (SAPCS) with approval granted by the University of Pretoria Faculty of Health Sciences Research Ethics Committee (HREC, with US Federal wide assurance FWA00002567 and IRB00002235 IORG0001762; #43/2010). In Australia, participant recruitment was approved by the St Vincent\u0026rsquo;s HREC (#SVH/12/231). Samples were shipped to the Garvan Institute of Medical Research in accordance with institutional Material Transfer Agreements (MTAs), as well as additional Republic of South Africa Department of Health Export Permit (National Health Act 2003; J1/2/4/2 #1/12). This study was approved by the St. Vincent\u0026rsquo;s HREC (#SVH/15/227) for genomic interrogation. Additional IRB review and approval for genomic interrogation was granted by the Human Research Protection Office of the US Army Medical Research and Development Command E02371 (TARGET Africa) and E03280 (HEROIC PCaPH Africa1K).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence data were obtained and accessible through Jaratlerdsiri et al\u003csup\u003e18\u003c/sup\u003e, in the European Genome‐Phenome Archive (EGA; https://ega‐archive.org) under overarching accession EGAS00001006425 and including the Southern African Prostate Cancer Study (SAPCS) Dataset (EGAD00001009067) and Garvan/St Vincent\u0026rsquo;s Prostate Cancer Database (EGAD00001009066). The dbVar SV sites and their variant allele frequencies were downloaded from https://www.ncbi.nlm.nih.gov/dbvar. The ENSEMBL gene set was downloaded from https://www.ensembl.org. The MSigDB gene sets are available at https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#H. The gene set in COCMIC CGC was downloaded from https://cancer.sanger.ac.uk/census. The scripts for sequence read alignment and quality control are available at GitHub (https://github.com/Sydney-Informatics-Hub/Bioinformatics). All computational code for SV callset comparison and integration are available at GitHub (https://github.com/tgong1/StructuralVariantUtil) \u003csup\u003e88\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic sequencing was supported by the National Health and Medical Research Council (NHMRC) of Australia through a Project Grant (APP1165762 to V.M.H.) and Ideas Grants (APP2001098 to V.M.H. and M.S.R.B.; APP2APP2010551 to V.M.H.). Further analytics was supported by a U.S.A.\u0026nbsp;Congressionally Directed Medical Research Programs (CDMRP) Prostate Cancer Research Program (PCRP)\u0026nbsp;Idea Development Award (PC200390, TARGET Africa to V.M.H.) and HEROIC Consortium Award (PC210168, HEROIC PCaPH Africa1K to V.M.H. and M.S.R.B., as well as co-leads Professors Gail Prins, University of Illinois at Chicago, U.S.A. and Mungai Peter Ngugi, University of Nairobi, Kenya), a U.S.A. National Institute of Health (NIH) National Cancer Institute (NCI) Award (1R01CA285772-01 to V.M.H.) and\u0026nbsp;a U.S.A. Prostate Cancer Foundation (PCF) Challenge Award (2023CHAL4150to V.M.H.). V.M.H. was further supported by the Petre Foundation via the University of Sydney Foundation, Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: T.G. and V.M.H.; Financial support: V.M.H.; Methodology: T.G. and J.J.; Formal analysis: T.G., J.J. and V.M.H.; Data curation: W.J., K.G. and J.J.; Participant recruitment, clinical data and sample collection: P.D.S., S.B.A.M. and M.S.R.B.; Supervision: V.M.H.; Writing-review, figures and editing: T.G. and V.M.H. Critical technical review: J.W. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are forever grateful to the patients and their families who have contributed to this study; without their contribution, this research would not be possible. We acknowledge the contributions of the many clinical staff across the SAPCS (South Africa) and the St Vincent\u0026rsquo;s Hospital Sydney (Australia), who over many years have recruited patients and provided samples to these critical bioresources, as well as the additional authors who contributed to the initial published genome profiling project (Jaratlerdsiri \u003cem\u003eet al\u003c/em\u003e., 2022)\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F\u003cem\u003e, et al.\u003c/em\u003e Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e \u003cstrong\u003en/a\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHjelmborg JB\u003cem\u003e, et al.\u003c/em\u003e The Heritability of Prostate Cancer in the Nordic Twin Study of Cancer. \u003cem\u003eCancer Epidemiology, Biomarkers \u0026amp; Prevention\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 2303-2310 (2014).\u003c/li\u003e\n\u003cli\u003eSmith ZL, Eggener SE, Murphy AB. 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StructuralVariantUtil. \u003cem\u003eGitHub\u003c/em\u003e, (https://github.com/tgong1/StructuralVariantUtil (2022)).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"prostate cancer, pathogenic variants, structural variants, health disparity, African ancestry, germline testing","lastPublishedDoi":"10.21203/rs.3.rs-4531885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4531885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProstate cancer (PCa) is highly heritable, with men of African ancestry at greatest risk and associated lethality. Lack of representation in genomic data means germline testing guidelines exclude for African men. Established that structural variations (SVs) are major contributors to human disease and prostate tumourigenesis, their role is under-appreciated in familial and therapeutic testing. Utilising a clinico-methodologically matched African (n\u0026thinsp;=\u0026thinsp;113) \u003cem\u003eversus\u003c/em\u003e European (n\u0026thinsp;=\u0026thinsp;57) deep-sequenced PCa resource, we interrogated 42,966 high-quality germline SVs using a best-fit pathogenicity prediction workflow. We identified 15 potentially pathogenic SVs representing 12.4% African and 7.0% European patients, of which 72% and 86% met germline testing standard-of-care recommendations, respectively. Notable African-specific loss-of-function gene candidates include DNA damage repair \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eBARD1\u003c/em\u003e and tumour suppressors \u003cem\u003eFOXP1, WASF1\u003c/em\u003e and \u003cem\u003eRB1\u003c/em\u003e. Representing only a fraction of the vast African diaspora, this study raises considerations with respect to the contribution of kilo-to-mega-base rare variants to PCa pathogenicity and African associated disparity.\u003c/p\u003e","manuscriptTitle":"Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 20:41:55","doi":"10.21203/rs.3.rs-4531885/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fe595faa-375f-4b5d-9f42-dbc105fdcc72","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33177204,"name":"Health sciences/Oncology/Cancer/Urological cancer/Prostate cancer"},{"id":33177205,"name":"Health sciences/Oncology/Cancer/Cancer screening"}],"tags":[],"updatedAt":"2025-03-11T07:09:02+00:00","versionOfRecord":{"articleIdentity":"rs-4531885","link":"https://doi.org/10.1038/s41467-025-57312-9","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-03-10 04:00:00","publishedOnDateReadable":"March 10th, 2025"},"versionCreatedAt":"2024-06-13 20:41:55","video":"","vorDoi":"10.1038/s41467-025-57312-9","vorDoiUrl":"https://doi.org/10.1038/s41467-025-57312-9","workflowStages":[]},"version":"v1","identity":"rs-4531885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4531885","identity":"rs-4531885","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}