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Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Gusev A, Shi H, Kichaev G, Pomerantz M, Li F, Long HW, Ingles SA, Kittles RA, Strom SS, Rybicki BA, Nemesure B, Isaacs WB, Zheng W, Pettaway CA, Yeboah ED, Tettey Y, Biritwum RB, Adjei AA, Tay E, Truelove A, Niwa S, Chokkalingam AP, John EM, Murphy AB, Signorello LB, Carpten J, Leske MC, Wu SY, Hennis AJ, Neslund-Dudas C, Hsing AW, Chu L, Goodman PJ, Klein EA, Witte JS, Casey G, Kaggwa S, Cook MB, Stram DO, Blot WJ, Eeles RA, Easton D, Kote-Jarai Z, Al Olama AA, Benlloch S, Muir K, Giles GG, Southey MC, Fitzgerald LM, Gronberg H, Wiklund F, Aly M, Henderson BE, Schleutker J, Wahlfors T, Tammela TL, Nordestgaard BG, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Stanford JL, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Teerlink C, Brenner H, Dieffenbach AK, Arndt V, Park JY, Sellers TA, Lin HY, Slavov C, Kaneva R, Mitev V, Batra J, Spurdle A, Clements JA, Teixeira MR, Pandha H, Michael A, Paulo P, Maia S, Kierzek A, PRACTICAL consortiumConti DV, Albanes D, Berg C, Berndt SI, Campa D, Crawford ED, Diver WR, Gapstur SM, Gaziano JM, Giovannucci E, Hoover R, Hunter DJ, Johansson M, Kraft P, Le Marchand L, Lindström S, Navarro C, Overvad K, Riboli E, Siddiq A, Stevens VL, Trichopoulos D, Vineis P, Yeager M, Trynka G, Raychaudhuri S, Schumacher FR, Price AL, Freedman ML, Haiman CA, Pasaniuc B - Nat Commun (2016)

Bottom Line: Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown.We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines.Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

View Article: PubMed Central - PubMed

Affiliation: Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.

ABSTRACT
Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

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Related in: MedlinePlus

Partitioning of heritability across functional classes in prostate cancer.Visual representation of heritability enrichment in three studies a,b: iCOGS; c: AAPC; d: BPC3 (shown numerically in Table 1). Each subplot corresponds to an analysis of the listed joint model, with coloured slices representing the functional annotations evaluated. Volume of each interior (light coloured) pie-chart slice represents the %SNP for the functional annotation, which is equal to the expected  under the  of no enrichment. Volume of each shaded pie-chart slice represents the actual  inferred by the model. Slices extending outside/inside the middle pie correspond to enrichment/depletion in SNP heritability, as indicated by the dotted lines. Colour coding is consistent across all subpanels. * (**) denotes significant deviation at P<0.05 (P<0.05/15) of fraction of SNP heritability ( from  model of  by Z-test; see Supplementary Table 6 for P values).
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f5: Partitioning of heritability across functional classes in prostate cancer.Visual representation of heritability enrichment in three studies a,b: iCOGS; c: AAPC; d: BPC3 (shown numerically in Table 1). Each subplot corresponds to an analysis of the listed joint model, with coloured slices representing the functional annotations evaluated. Volume of each interior (light coloured) pie-chart slice represents the %SNP for the functional annotation, which is equal to the expected under the of no enrichment. Volume of each shaded pie-chart slice represents the actual inferred by the model. Slices extending outside/inside the middle pie correspond to enrichment/depletion in SNP heritability, as indicated by the dotted lines. Colour coding is consistent across all subpanels. * (**) denotes significant deviation at P<0.05 (P<0.05/15) of fraction of SNP heritability ( from model of by Z-test; see Supplementary Table 6 for P values).

Mentions: Although the results above showcase the power of the variance component approach in finding epigenetic marks relevant for PrCa, such marks often overlap making the causal mark difficult to identify (Supplementary Fig. 1). To account for the correlation among marks we grouped the 82 marginally significant annotations into 15 biologically relevant, non-overlapping groups organized by mark and cell line, and partitioned across all groups in a joint model (see Methods, Table 1, Fig. 5 and Supplementary Table 6). Five components were nominally significant in the joint model at P<0.05; out of the five components three remained significant after accounting for 15 tests: H3k27ac marks in LNCaP (P=2.5 × 10−20 by Z-test); DHS marks in other cancer cell types (P=3.9 × 10−5 by Z-test); and repressed segmentations (P=2.1 × 10−20 by Z-test). To further refine our model, we restricted to the significant annotations (and the background components accounting for LD to coding regions) and re-evaluated them jointly, referred to as the ‘selected' model. This selected model localized 51.0% of the within 12.1% of SNPs (LNCaP: H3K27ac+ARBS+DHS cancer), whereas coding regions only explained 3.3% (s.e. 1.4%) of within 1.8% of SNPs (Supplementary Table 7). The localization was even stronger with imputed data, where 86% of the was localized to 8.6% of SNPs (Table 1 and Supplementary Tables 8 and 9). Estimates from imputed markers were more representative of underlying enrichment in our simulations (see Methods, Supplementary Table 2) but may include the effects of nearby markers12 and so we consider them as an upper bound. None of the estimates changed significantly after adjusting for known GWAS associations2 (79 of which were typed in this data), underscoring the polygenic nature of this effect.


Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Gusev A, Shi H, Kichaev G, Pomerantz M, Li F, Long HW, Ingles SA, Kittles RA, Strom SS, Rybicki BA, Nemesure B, Isaacs WB, Zheng W, Pettaway CA, Yeboah ED, Tettey Y, Biritwum RB, Adjei AA, Tay E, Truelove A, Niwa S, Chokkalingam AP, John EM, Murphy AB, Signorello LB, Carpten J, Leske MC, Wu SY, Hennis AJ, Neslund-Dudas C, Hsing AW, Chu L, Goodman PJ, Klein EA, Witte JS, Casey G, Kaggwa S, Cook MB, Stram DO, Blot WJ, Eeles RA, Easton D, Kote-Jarai Z, Al Olama AA, Benlloch S, Muir K, Giles GG, Southey MC, Fitzgerald LM, Gronberg H, Wiklund F, Aly M, Henderson BE, Schleutker J, Wahlfors T, Tammela TL, Nordestgaard BG, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Stanford JL, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Teerlink C, Brenner H, Dieffenbach AK, Arndt V, Park JY, Sellers TA, Lin HY, Slavov C, Kaneva R, Mitev V, Batra J, Spurdle A, Clements JA, Teixeira MR, Pandha H, Michael A, Paulo P, Maia S, Kierzek A, PRACTICAL consortiumConti DV, Albanes D, Berg C, Berndt SI, Campa D, Crawford ED, Diver WR, Gapstur SM, Gaziano JM, Giovannucci E, Hoover R, Hunter DJ, Johansson M, Kraft P, Le Marchand L, Lindström S, Navarro C, Overvad K, Riboli E, Siddiq A, Stevens VL, Trichopoulos D, Vineis P, Yeager M, Trynka G, Raychaudhuri S, Schumacher FR, Price AL, Freedman ML, Haiman CA, Pasaniuc B - Nat Commun (2016)

Partitioning of heritability across functional classes in prostate cancer.Visual representation of heritability enrichment in three studies a,b: iCOGS; c: AAPC; d: BPC3 (shown numerically in Table 1). Each subplot corresponds to an analysis of the listed joint model, with coloured slices representing the functional annotations evaluated. Volume of each interior (light coloured) pie-chart slice represents the %SNP for the functional annotation, which is equal to the expected  under the  of no enrichment. Volume of each shaded pie-chart slice represents the actual  inferred by the model. Slices extending outside/inside the middle pie correspond to enrichment/depletion in SNP heritability, as indicated by the dotted lines. Colour coding is consistent across all subpanels. * (**) denotes significant deviation at P<0.05 (P<0.05/15) of fraction of SNP heritability ( from  model of  by Z-test; see Supplementary Table 6 for P values).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4829663&req=5

f5: Partitioning of heritability across functional classes in prostate cancer.Visual representation of heritability enrichment in three studies a,b: iCOGS; c: AAPC; d: BPC3 (shown numerically in Table 1). Each subplot corresponds to an analysis of the listed joint model, with coloured slices representing the functional annotations evaluated. Volume of each interior (light coloured) pie-chart slice represents the %SNP for the functional annotation, which is equal to the expected under the of no enrichment. Volume of each shaded pie-chart slice represents the actual inferred by the model. Slices extending outside/inside the middle pie correspond to enrichment/depletion in SNP heritability, as indicated by the dotted lines. Colour coding is consistent across all subpanels. * (**) denotes significant deviation at P<0.05 (P<0.05/15) of fraction of SNP heritability ( from model of by Z-test; see Supplementary Table 6 for P values).
Mentions: Although the results above showcase the power of the variance component approach in finding epigenetic marks relevant for PrCa, such marks often overlap making the causal mark difficult to identify (Supplementary Fig. 1). To account for the correlation among marks we grouped the 82 marginally significant annotations into 15 biologically relevant, non-overlapping groups organized by mark and cell line, and partitioned across all groups in a joint model (see Methods, Table 1, Fig. 5 and Supplementary Table 6). Five components were nominally significant in the joint model at P<0.05; out of the five components three remained significant after accounting for 15 tests: H3k27ac marks in LNCaP (P=2.5 × 10−20 by Z-test); DHS marks in other cancer cell types (P=3.9 × 10−5 by Z-test); and repressed segmentations (P=2.1 × 10−20 by Z-test). To further refine our model, we restricted to the significant annotations (and the background components accounting for LD to coding regions) and re-evaluated them jointly, referred to as the ‘selected' model. This selected model localized 51.0% of the within 12.1% of SNPs (LNCaP: H3K27ac+ARBS+DHS cancer), whereas coding regions only explained 3.3% (s.e. 1.4%) of within 1.8% of SNPs (Supplementary Table 7). The localization was even stronger with imputed data, where 86% of the was localized to 8.6% of SNPs (Table 1 and Supplementary Tables 8 and 9). Estimates from imputed markers were more representative of underlying enrichment in our simulations (see Methods, Supplementary Table 2) but may include the effects of nearby markers12 and so we consider them as an upper bound. None of the estimates changed significantly after adjusting for known GWAS associations2 (79 of which were typed in this data), underscoring the polygenic nature of this effect.

Bottom Line: Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown.We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines.Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

View Article: PubMed Central - PubMed

Affiliation: Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.

ABSTRACT
Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

Show MeSH
Related in: MedlinePlus