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1. Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Asona, Lui, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth R, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Blot, William J, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Wiklund, Fredrik, Penney, Kathryn L, Huff, Chad D, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Ost, Piet, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Abraham, Aswin, Claessens, Frank, Castelao, Jose Esteban, Townsend, Paul A, Crawford, Dana C, Petrovics, Gyorgy, van Schaik, Ron HN, Parent, Marie-Élise, Hu, Jennifer J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Urologic Diseases, Prevention, Genetics, Good Health and Well Being, Male, Humans, Prostate-Specific Antigen, Prostatic Neoplasms, Early Detection of Cancer, Polymorphism, Single Nucleotide, Risk Factors, Risk Assessment, Genetic Predisposition to Disease, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Published
2022

2. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, UKGPCS collaborators, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, APCB BioResource (Australian Prostate Cancer BioResource), Grönberg, Henrik, Walsh, Eleanor I, Turner, Emma L, Lane, Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Wiklund, Fredrik, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Vega, Ana, IMPACT Study Steering Committee and Collaborators, Kogevinas, Manolis, Penney, Kathryn L, Teixeira, Manuel R, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, Razack, Azad, Newcomb, Lisa F, Canary PASS Investigators, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Roobol, Monique J, Zheng, Wei, Profile Study Steering Committee, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and PRACTICAL Consortium

Subjects
UKGPCS collaborators, APCB BioResource, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Prevention, Urologic Diseases, Cancer, Prostate Cancer, Aging, Urology & Nephrology, Oncology and Carcinogenesis
Abstract

BackgroundPolygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.Results166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.ConclusionsIncorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Published
2021

3. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Walsh, Eleanor I, Turner, Emma L, Lane, Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Wiklund, Fredrik, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L, Teixeira, Manuel R, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, Razack, Azad, Newcomb, Lisa F, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Roobol, Monique J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Urologic Diseases, Prostate Cancer, Aging, Cancer, Prevention, Adult, Aged, Biomarkers, Tumor, Follow-Up Studies, Humans, Male, Middle Aged, Models, Statistical, Polymorphism, Single Nucleotide, Predictive Value of Tests, Prognosis, Prostatic Neoplasms, Risk Assessment, Risk Factors, UKGPCS collaborators, APCB BioResource, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundPolygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.Results166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.ConclusionsIncorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Published
2021

4. Polygenic hazard score is associated with prostate cancer in multi-ethnic populations.

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Nielsen, Sune F, Nordestgaard, Børge G, Wiklund, Fredrik, Tangen, Catherine M, Giles, Graham G, Wolk, Alicja, Albanes, Demetrius, Travis, Ruth C, Blot, William J, Zheng, Wei, Sanderson, Maureen, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Khaw, Kay-Tee, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Thibodeau, Stephen N, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L, Huff, Chad, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Cannon-Albright, Lisa, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Pandha, Hardev, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Bush, William S, Roobol, Monique J, Parent, Marie-Élise, Hu, Jennifer J, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, UKGPCS collaborators, APCB (Australian Prostate Cancer BioResource), NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, and PRACTICAL Consortium

Subjects
UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Humans, Prostatic Neoplasms, Neoplasm Invasiveness, Multivariate Analysis, Multifactorial Inheritance, Aged, Middle Aged, Ethnic Groups, Male, Self Report, Aging, Urologic Diseases, Cancer, Prostate Cancer
Abstract

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p

Published
2021

5. Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Nielsen, Sune F, Nordestgaard, Børge G, Wiklund, Fredrik, Tangen, Catherine M, Giles, Graham G, Wolk, Alicja, Albanes, Demetrius, Travis, Ruth C, Blot, William J, Zheng, Wei, Sanderson, Maureen, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Khaw, Kay-Tee, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Thibodeau, Stephen N, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Penney, Kathryn L, Huff, Chad, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Cannon-Albright, Lisa, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Pandha, Hardev, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Bush, William S, Roobol, Monique J, Parent, Marie-Élise, Hu, Jennifer J, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Urologic Diseases, Cancer, Prostate Cancer, Aging, Aged, Ethnicity, Humans, Male, Middle Aged, Multifactorial Inheritance, Multivariate Analysis, Neoplasm Invasiveness, Prostatic Neoplasms, Self Report, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium
Abstract

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p

Published
2021

6. The effect of sample size on polygenic hazard models for prostate cancer

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, ZSofia, Amin Al Olama, Ali, Benlloch Garcia, Sara, Muir, Kenneth, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo LJ, Nordestgaard, Børge G, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Schöttker, Ben, Holleczek, Bernd, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Cancer, Aging, Urologic Diseases, Clinical Trials as Topic, Genome-Wide Association Study, Humans, Male, Models, Genetic, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Proportional Hazards Models, Prostatic Neoplasms, Sample Size, Australian Prostate Cancer BioResource, PRACTICAL Consortium, Genetics, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Published
2020

7. The effect of sample size on polygenic hazard models for prostate cancer.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, ZSofia, Amin Al Olama, Ali, Benlloch Garcia, Sara, Muir, Kenneth, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo LJ, Nordestgaard, Børge G, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Schöttker, Ben, Holleczek, Bernd, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda, Australian Prostate Cancer BioResource (APCB), Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and PRACTICAL Consortium

Subjects
Australian Prostate Cancer BioResource, PRACTICAL Consortium, Genetics & Heredity, Genetics, Clinical Sciences
Abstract

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Published
2020

8. A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Walsh, Eleanor I, Turner, Emma L, Lane, J Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, Zsofia, Al Olama, Ali Amin, Garcia, Sara Benlloch, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo LJ, Nordestgaard, Børge Grønne, Key, Timothy J, Travis, Ruth C, Pharoah, Paul DP, Pashayan, Nora, Khaw, Kay-Tee, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa A, Brenner, Hermann, Schöttker, Ben, Holleczek, Bernd, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar Kroumov, Kaneva, Radka P, Mitev, Vanio I, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda B, BioResource, for the Australian Prostate Cancer, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and Consortium, for the PRACTICAL

Subjects
Biomedical and Clinical Sciences, Health Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Prevention, Urologic Diseases, Good Health and Well Being, Aged, Early Detection of Cancer, Humans, Male, Middle Aged, Neoplasm Grading, Population Control, Prostatic Neoplasms, Australian Prostate Cancer BioResource, PRACTICAL Consortium, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundA polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.MethodsUnited Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.ResultsThe expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.ConclusionsPHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.ImpactPersonalized genetic risk assessments could inform prostate cancer screening decisions.

Published
2020

9. A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Author

Huynh-Le, Minh-Phuong, Fan, Chun Chieh, Karunamuni, Roshan, Walsh, Eleanor I, Turner, Emma L, Lane, J Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, Zsofia, Amin Al Olama, Ali, Benlloch Garcia, Sara, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo Lj, Nordestgaard, Børge Grønne, Key, Timothy J, Travis, Ruth C, Pharoah, Paul DP, Pashayan, Nora, Khaw, Kay-Tee, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa A, Brenner, Hermann, Schöttker, Ben, Holleczek, Bernd, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar Kroumov, Kaneva, Radka P, Mitev, Vanio I, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda B, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, Australian Prostate Cancer BioResource (APCB), and PRACTICAL Consortium

Subjects
Australian Prostate Cancer BioResource, PRACTICAL Consortium, Clinical Research, Cancer, Urologic Diseases, Prostate Cancer, Clinical Trials and Supportive Activities, Aging, Prevention, Epidemiology, Medical and Health Sciences
Abstract

BackgroundA polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.MethodsUnited Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.ResultsThe expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.ConclusionsPHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.ImpactPersonalized genetic risk assessments could inform prostate cancer screening decisions.

Published
2020

10. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers.

Author

Zhang, Yan Dora, Hurson, Amber N, Zhang, Haoyu, Choudhury, Parichoy Pal, Easton, Douglas F, Milne, Roger L, Simard, Jacques, Hall, Per, Michailidou, Kyriaki, Dennis, Joe, Schmidt, Marjanka K, Chang-Claude, Jenny, Gharahkhani, Puya, Whiteman, David, Campbell, Peter T, Hoffmeister, Michael, Jenkins, Mark, Peters, Ulrike, Hsu, Li, Gruber, Stephen B, Casey, Graham, Schmit, Stephanie L, O'Mara, Tracy A, Spurdle, Amanda B, Thompson, Deborah J, Tomlinson, Ian, De Vivo, Immaculata, Landi, Maria Teresa, Law, Matthew H, Iles, Mark M, Demenais, Florence, Kumar, Rajiv, MacGregor, Stuart, Bishop, D Timothy, Ward, Sarah V, Bondy, Melissa L, Houlston, Richard, Wiencke, John K, Melin, Beatrice, Barnholtz-Sloan, Jill, Kinnersley, Ben, Wrensch, Margaret R, Amos, Christopher I, Hung, Rayjean J, Brennan, Paul, McKay, James, Caporaso, Neil E, Berndt, Sonja I, Birmann, Brenda M, Camp, Nicola J, Kraft, Peter, Rothman, Nathaniel, Slager, Susan L, Berchuck, Andrew, Pharoah, Paul DP, Sellers, Thomas A, Gayther, Simon A, Pearce, Celeste L, Goode, Ellen L, Schildkraut, Joellen M, Moysich, Kirsten B, Amundadottir, Laufey T, Jacobs, Eric J, Klein, Alison P, Petersen, Gloria M, Risch, Harvey A, Stolzenberg-Solomon, Rachel Z, Wolpin, Brian M, Li, Donghui, Eeles, Rosalind A, Haiman, Christopher A, Kote-Jarai, Zsofia, Schumacher, Fredrick R, Al Olama, Ali Amin, Purdue, Mark P, Scelo, Ghislaine, Dalgaard, Marlene D, Greene, Mark H, Grotmol, Tom, Kanetsky, Peter A, McGlynn, Katherine A, Nathanson, Katherine L, Turnbull, Clare, Wiklund, Fredrik, Breast Cancer Association Consortium (BCAC), Barrett’s and Esophageal Adenocarcinoma Consortium (BEACON), Colon Cancer Family Registry (CCFR), Transdisciplinary Studies of Genetic Variation in Colorectal Cancer (CORECT), Endometrial Cancer Association Consortium (ECAC), Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), Melanoma Genetics Consortium (GenoMEL), Glioma International Case-Control Study (GICC), International Lung Cancer Consortium (ILCCO), Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium, International Consortium of Investigators Working on Non-Hodgkin’s Lymphoma Epidemiologic Studies (InterLymph), Ovarian Cancer Association Consortium (OCAC), Oral Cancer GWAS, Pancreatic Cancer Case-Control Consortium (PanC4), Pancreatic Cancer Cohort Consortium (PanScan), and Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL)

Subjects
Breast Cancer Association Consortium, Barrett’s and Esophageal Adenocarcinoma Consortium, Colon Cancer Family Registry, Transdisciplinary Studies of Genetic Variation in Colorectal Cancer, Endometrial Cancer Association Consortium, Genetics and Epidemiology of Colorectal Cancer Consortium, Melanoma Genetics Consortium, Glioma International Case-Control Study, International Lung Cancer Consortium, Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium, International Consortium of Investigators Working on Non-Hodgkin’s Lymphoma Epidemiologic Studies, Ovarian Cancer Association Consortium, Oral Cancer GWAS, Pancreatic Cancer Case-Control Consortium, Pancreatic Cancer Cohort Consortium, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome, Renal Cancer GWAS, Testicular Cancer Consortium, Animals, Humans, Neoplasms, Genetic Predisposition to Disease, Incidence, Risk Assessment, Risk Factors, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Models, Genetic, Female, Male, Genome-Wide Association Study, Human Genome, Prevention, Cancer, Prostate Cancer, Genetics, Urologic Diseases, 2.1 Biological and endogenous factors
Abstract

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

Published
2020

11. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Author

Zhang, Yan Dora, Hurson, Amber N, Zhang, Haoyu, Choudhury, Parichoy Pal, Easton, Douglas F, Milne, Roger L, Simard, Jacques, Hall, Per, Michailidou, Kyriaki, Dennis, Joe, Schmidt, Marjanka K, Chang-Claude, Jenny, Gharahkhani, Puya, Whiteman, David, Campbell, Peter T, Hoffmeister, Michael, Jenkins, Mark, Peters, Ulrike, Hsu, Li, Gruber, Stephen B, Casey, Graham, Schmit, Stephanie L, O’Mara, Tracy A, Spurdle, Amanda B, Thompson, Deborah J, Tomlinson, Ian, De Vivo, Immaculata, Landi, Maria Teresa, Law, Matthew H, Iles, Mark M, Demenais, Florence, Kumar, Rajiv, MacGregor, Stuart, Bishop, D Timothy, Ward, Sarah V, Bondy, Melissa L, Houlston, Richard, Wiencke, John K, Melin, Beatrice, Barnholtz-Sloan, Jill, Kinnersley, Ben, Wrensch, Margaret R, Amos, Christopher I, Hung, Rayjean J, Brennan, Paul, McKay, James, Caporaso, Neil E, Berndt, Sonja I, Birmann, Brenda M, Camp, Nicola J, Kraft, Peter, Rothman, Nathaniel, Slager, Susan L, Berchuck, Andrew, Pharoah, Paul DP, Sellers, Thomas A, Gayther, Simon A, Pearce, Celeste L, Goode, Ellen L, Schildkraut, Joellen M, Moysich, Kirsten B, Amundadottir, Laufey T, Jacobs, Eric J, Klein, Alison P, Petersen, Gloria M, Risch, Harvey A, Stolzenberg-Solomon, Rachel Z, Wolpin, Brian M, Li, Donghui, Eeles, Rosalind A, Haiman, Christopher A, Kote-Jarai, Zsofia, Schumacher, Fredrick R, Al Olama, Ali Amin, Purdue, Mark P, Scelo, Ghislaine, Dalgaard, Marlene D, Greene, Mark H, Grotmol, Tom, Kanetsky, Peter A, McGlynn, Katherine A, Nathanson, Katherine L, Turnbull, Clare, Wiklund, Fredrik, Chanock, Stephen J, Chatterjee, Nilanjan, and Garcia-Closas, Montserrat

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Oncology and Carcinogenesis, Prevention, Prostate Cancer, Urologic Diseases, Human Genome, Cancer, Aetiology, 2.1 Biological and endogenous factors, Animals, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Incidence, Male, Models, Genetic, Multifactorial Inheritance, Neoplasms, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Breast Cancer Association Consortium, Barrett’s and Esophageal Adenocarcinoma Consortium, Colon Cancer Family Registry, Transdisciplinary Studies of Genetic Variation in Colorectal Cancer, Endometrial Cancer Association Consortium, Genetics and Epidemiology of Colorectal Cancer Consortium, Melanoma Genetics Consortium, Glioma International Case-Control Study, International Lung Cancer Consortium, Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium, International Consortium of Investigators Working on Non-Hodgkin’s Lymphoma Epidemiologic Studies, Ovarian Cancer Association Consortium, Oral Cancer GWAS, Pancreatic Cancer Case-Control Consortium, Pancreatic Cancer Cohort Consortium, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome, Renal Cancer GWAS, Testicular Cancer Consortium
Abstract

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

Published
2020

12. Author Correction: Germline variation at 8q24 and prostate cancer risk in men of European ancestry.

Author

Matejcic, Marco, Saunders, Edward J, Dadaev, Tokhir, Brook, Mark N, Wang, Kan, Sheng, Xin, Olama, Ali Amin Al, Schumacher, Fredrick R, Ingles, Sue A, Govindasami, Koveela, Benlloch, Sara, Berndt, Sonja I, Albanes, Demetrius, Koutros, Stella, Muir, Kenneth, Stevens, Victoria L, Gapstur, Susan M, Tangen, Catherine M, Batra, Jyotsna, Clements, Judith, Gronberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Wolk, Alicja, West, Catharine, Mucci, Lorelei, Kraft, Peter, Cancel-Tassin, Géraldine, Sorensen, Karina D, Maehle, Lovise, Grindedal, Eli M, Strom, Sara S, Neal, David E, Hamdy, Freddie C, Donovan, Jenny L, Travis, Ruth C, Hamilton, Robert J, Rosenstein, Barry, Lu, Yong-Jie, Giles, Graham G, Kibel, Adam S, Vega, Ana, Bensen, Jeanette T, Kogevinas, Manolis, Penney, Kathryn L, Park, Jong Y, Stanford, Janet L, Cybulski, Cezary, Nordestgaard, Børge G, Brenner, Hermann, Maier, Christiane, Kim, Jeri, Teixeira, Manuel R, Neuhausen, Susan L, De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F, Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A, Gago-Dominguez, Manuela, Roobol, Monique J, Menegaux, Florence, Khaw, Kay-Tee, Cannon-Albright, Lisa A, Pandha, Hardev, Thibodeau, Stephen N, Schaid, Daniel J, PRACTICAL Consortium, Wiklund, Fredrik, Chanock, Stephen J, Easton, Douglas F, Eeles, Rosalind A, Kote-Jarai, Zsofia, Conti, David V, and Haiman, Christopher A

Subjects
PRACTICAL Consortium
Abstract

The original version of this Article contained an error in the spelling of the author Manuela Gago-Dominguez, which was incorrectly given as Manuela G. Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.

Published
2019

13. Author Correction: Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Author

Matejcic, Marco, Saunders, Edward J, Dadaev, Tokhir, Brook, Mark N, Wang, Kan, Sheng, Xin, Olama, Ali Amin Al, Schumacher, Fredrick R, Ingles, Sue A, Govindasami, Koveela, Benlloch, Sara, Berndt, Sonja I, Albanes, Demetrius, Koutros, Stella, Muir, Kenneth, Stevens, Victoria L, Gapstur, Susan M, Tangen, Catherine M, Batra, Jyotsna, Clements, Judith, Gronberg, Henrik, Pashayan, Nora, Schleutker, Johanna, Wolk, Alicja, West, Catharine, Mucci, Lorelei, Kraft, Peter, Cancel-Tassin, Géraldine, Sorensen, Karina D, Maehle, Lovise, Grindedal, Eli M, Strom, Sara S, Neal, David E, Hamdy, Freddie C, Donovan, Jenny L, Travis, Ruth C, Hamilton, Robert J, Rosenstein, Barry, Lu, Yong-Jie, Giles, Graham G, Kibel, Adam S, Vega, Ana, Bensen, Jeanette T, Kogevinas, Manolis, Penney, Kathryn L, Park, Jong Y, Stanford, Janet L, Cybulski, Cezary, Nordestgaard, Børge G, Brenner, Hermann, Maier, Christiane, Kim, Jeri, Teixeira, Manuel R, Neuhausen, Susan L, De Ruyck, Kim, Razack, Azad, Newcomb, Lisa F, Lessel, Davor, Kaneva, Radka, Usmani, Nawaid, Claessens, Frank, Townsend, Paul A, Gago-Dominguez, Manuela, Roobol, Monique J, Menegaux, Florence, Khaw, Kay-Tee, Cannon-Albright, Lisa A, Pandha, Hardev, Thibodeau, Stephen N, Schaid, Daniel J, Wiklund, Fredrik, Chanock, Stephen J, Easton, Douglas F, Eeles, Rosalind A, Kote-Jarai, Zsofia, Conti, David V, and Haiman, Christopher A

Subjects
Prostate Cancer, Cancer, Urologic Diseases, PRACTICAL Consortium
Abstract

The original version of this Article contained an error in the spelling of the author Manuela Gago-Dominguez, which was incorrectly given as Manuela G. Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.

Published
2019

14. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts.

Author

Seibert, Tyler M, Fan, Chun Chieh, Wang, Yunpeng, Zuber, Verena, Karunamuni, Roshan, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, ZSofia, Al Olama, Ali Amin, Garcia, Sara Benlloch, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo Lj, Nordestgaard, Børge G, Nielsen, Sune F, Weischer, Maren, Bisbjerg, Rasmus, Røder, M Andreas, Iversen, Peter, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Cuk, Katarina, Saum, Kai-Uwe, Park, Jong Y, Sellers, Thomas A, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej, Karow, David S, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and PRACTICAL Consortium*

Subjects
PRACTICAL Consortium*, Humans, Prostatic Neoplasms, Kallikreins, Prostate-Specific Antigen, Disease-Free Survival, Risk Assessment, Survival Analysis, Cohort Studies, Predictive Value of Tests, Age of Onset, Genotype, Polymorphism, Single Nucleotide, Aged, Middle Aged, European Continental Ancestry Group, Male, Early Detection of Cancer, Outcome Assessment, Health Care, Polymorphism, Single Nucleotide, Outcome Assessment, Health Care, Aging, Urologic Diseases, Cancer, Genetic Testing, Prevention, Prostate Cancer, Genetics, 2.1 Biological and endogenous factors, General & Internal Medicine, Public Health and Health Services, Clinical Sciences
Abstract

ObjectivesTo develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.DesignAnalysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.SettingMultiple institutions that were members of international PRACTICAL consortium.ParticipantsAll consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.Main outcome measuresPrediction with hazard score of age of onset of aggressive cancer in validation set.ResultsIn the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.ConclusionsPolygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Published
2018

15. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts

Author

Seibert, Tyler M, Fan, Chun Chieh, Wang, Yunpeng, Zuber, Verena, Karunamuni, Roshan, Parsons, J Kellogg, Eeles, Rosalind A, Easton, Douglas F, Kote-Jarai, ZSofia, Olama, Ali Amin Al, Garcia, Sara Benlloch, Muir, Kenneth, Grönberg, Henrik, Wiklund, Fredrik, Aly, Markus, Schleutker, Johanna, Sipeky, Csilla, Tammela, Teuvo LJ, Nordestgaard, Børge G, Nielsen, Sune F, Weischer, Maren, Bisbjerg, Rasmus, Røder, M Andreas, Iversen, Peter, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Brenner, Hermann, Cuk, Katarina, Saum, Kai-Uwe, Park, Jong Y, Sellers, Thomas A, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Clements, Judith A, Spurdle, Amanda, Teixeira, Manuel R, Paulo, Paula, Maia, Sofia, Pandha, Hardev, Michael, Agnieszka, Kierzek, Andrzej, Karow, David S, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Consortium*, The PRACTICAL

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Genetics, Aging, Prevention, Prostate Cancer, Urologic Diseases, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Age of Onset, Aged, Cohort Studies, Disease-Free Survival, Early Detection of Cancer, Genotype, Humans, Kallikreins, Male, Middle Aged, Outcome Assessment, Health Care, Polymorphism, Single Nucleotide, Predictive Value of Tests, Prostate-Specific Antigen, Prostatic Neoplasms, Risk Assessment, Survival Analysis, White People, PRACTICAL Consortium*, Clinical Sciences, Public Health and Health Services, General & Internal Medicine, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

ObjectivesTo develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.DesignAnalysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.SettingMultiple institutions that were members of international PRACTICAL consortium.ParticipantsAll consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.Main outcome measuresPrediction with hazard score of age of onset of aggressive cancer in validation set.ResultsIn the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.ConclusionsPolygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Published
2018

16. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

Author

Rothstein, Joseph, Pejaver, Vikas, Middha, Sumit, McDonnell, Shannon, Baheti, Saurabh, Musolf, Anthony, Li, Qing, Holzinger, Emily, Karyadi, Danielle, Cannon-Albright, Lisa, Teerlink, Craig, Stanford, Janet, Isaacs, William, Xu, Jianfeng, Cooney, Kathleen, Lange, Ethan, Schleutker, Johanna, Carpten, John, Powell, Isaac, Cussenot, Olivier, Cancel-Tassin, Geraldine, Giles, Graham, MacInnis, Robert, Maier, Christiane, Hsieh, Chih-Lin, Wiklund, Fredrik, Catalona, William, Foulkes, William, Mandal, Diptasri, Eeles, Rosalind, Kote-Jarai, Zsofia, Bustamante, Carlos, Schaid, Daniel, Hastie, Trevor, Ostrander, Elaine, Bailey-Wilson, Joan, Radivojac, Predrag, Thibodeau, Stephen, Whittemore, Alice, Sieh, Weiva, and Ioannidis, Nilah

Subjects
Area Under Curve, DNA Mutational Analysis, Disease, Exome, Gene Frequency, Humans, Mutation, Missense, ROC Curve, Software
Abstract

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies

Published
2016

17. Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

Author

Kar, Siddhartha P, Beesley, Jonathan, Olama, Ali Amin Al, Michailidou, Kyriaki, Tyrer, Jonathan, Kote-Jarai, ZSofia, Lawrenson, Kate, Lindstrom, Sara, Ramus, Susan J, Thompson, Deborah J, Investigators, ABCTB, Kibel, Adam S, Dansonka-Mieszkowska, Agnieszka, Michael, Agnieszka, Dieffenbach, Aida K, Gentry-Maharaj, Aleksandra, Whittemore, Alice S, Wolk, Alicja, Monteiro, Alvaro, Peixoto, Ana, Kierzek, Andrzej, Cox, Angela, Rudolph, Anja, Gonzalez-Neira, Anna, Wu, Anna H, Lindblom, Annika, Swerdlow, Anthony, Study, AOCS Study Group amp Australian Cancer, BioResource, APCB, Ziogas, Argyrios, Ekici, Arif B, Burwinkel, Barbara, Karlan, Beth Y, Nordestgaard, Børge G, Blomqvist, Carl, Phelan, Catherine, McLean, Catriona, Pearce, Celeste Leigh, Vachon, Celine, Cybulski, Cezary, Slavov, Chavdar, Stegmaier, Christa, Maier, Christiane, Ambrosone, Christine B, Høgdall, Claus K, Teerlink, Craig C, Kang, Daehee, Tessier, Daniel C, Schaid, Daniel J, Stram, Daniel O, Cramer, Daniel W, Neal, David E, Eccles, Diana, Flesch-Janys, Dieter, Edwards, Digna R Velez, Wokozorczyk, Dominika, Levine, Douglas A, Yannoukakos, Drakoulis, Sawyer, Elinor J, Bandera, Elisa V, Poole, Elizabeth M, Goode, Ellen L, Khusnutdinova, Elza, Høgdall, Estrid, Song, Fengju, Bruinsma, Fiona, Heitz, Florian, Modugno, Francesmary, Hamdy, Freddie C, Wiklund, Fredrik, Giles, Graham G, Olsson, Håkan, Wildiers, Hans, Ulmer, Hans-Ulrich, Pandha, Hardev, Risch, Harvey A, Darabi, Hatef, Salvesen, Helga B, Nevanlinna, Heli, Gronberg, Henrik, Brenner, Hermann, Brauch, Hiltrud, Anton-Culver, Hoda, Song, Honglin, Lim, Hui-Yi, McNeish, Iain, Campbell, Ian, Vergote, Ignace, Gronwald, Jacek, Lubiński, Jan, Stanford, Janet L, Benítez, Javier, Doherty, Jennifer A, Permuth, Jennifer B, Chang-Claude, Jenny, Donovan, Jenny L, Dennis, Joe, Schildkraut, Joellen M, Schleutker, Johanna, and Hopper, John L

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Breast Cancer, Aging, Urologic Diseases, Prostate Cancer, Ovarian Cancer, Rare Diseases, Human Genome, Cancer, Aetiology, 2.1 Biological and endogenous factors, Breast Neoplasms, Case-Control Studies, Chromosome Mapping, Datasets as Topic, Enhancer Elements, Genetic, Female, Gene Regulatory Networks, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Meta-Analysis as Topic, Organ Specificity, Ovarian Neoplasms, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Quantitative Trait Loci, Signal Transduction, ABCTB Investigators, AOCS Study Group & Australian Cancer Study, APCB BioResource, kConFab Investigators, NBCS Investigators, GENICA Network, PRACTICAL consortium, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

UnlabelledBreast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.SignificanceWe demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.

Published
2016

18. Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types.

Author

Kar, Siddhartha P, Beesley, Jonathan, Amin Al Olama, Ali, Michailidou, Kyriaki, Tyrer, Jonathan, Kote-Jarai, ZSofia, Lawrenson, Kate, Lindstrom, Sara, Ramus, Susan J, Thompson, Deborah J, ABCTB Investigators, Kibel, Adam S, Dansonka-Mieszkowska, Agnieszka, Michael, Agnieszka, Dieffenbach, Aida K, Gentry-Maharaj, Aleksandra, Whittemore, Alice S, Wolk, Alicja, Monteiro, Alvaro, Peixoto, Ana, Kierzek, Andrzej, Cox, Angela, Rudolph, Anja, Gonzalez-Neira, Anna, Wu, Anna H, Lindblom, Annika, Swerdlow, Anthony, AOCS Study Group & Australian Cancer Study (Ovarian Cancer), APCB BioResource, Ziogas, Argyrios, Ekici, Arif B, Burwinkel, Barbara, Karlan, Beth Y, Nordestgaard, Børge G, Blomqvist, Carl, Phelan, Catherine, McLean, Catriona, Pearce, Celeste Leigh, Vachon, Celine, Cybulski, Cezary, Slavov, Chavdar, Stegmaier, Christa, Maier, Christiane, Ambrosone, Christine B, Høgdall, Claus K, Teerlink, Craig C, Kang, Daehee, Tessier, Daniel C, Schaid, Daniel J, Stram, Daniel O, Cramer, Daniel W, Neal, David E, Eccles, Diana, Flesch-Janys, Dieter, Edwards, Digna R Velez, Wokozorczyk, Dominika, Levine, Douglas A, Yannoukakos, Drakoulis, Sawyer, Elinor J, Bandera, Elisa V, Poole, Elizabeth M, Goode, Ellen L, Khusnutdinova, Elza, Høgdall, Estrid, Song, Fengju, Bruinsma, Fiona, Heitz, Florian, Modugno, Francesmary, Hamdy, Freddie C, Wiklund, Fredrik, Giles, Graham G, Olsson, Håkan, Wildiers, Hans, Ulmer, Hans-Ulrich, Pandha, Hardev, Risch, Harvey A, Darabi, Hatef, Salvesen, Helga B, Nevanlinna, Heli, Gronberg, Henrik, Brenner, Hermann, Brauch, Hiltrud, Anton-Culver, Hoda, Song, Honglin, Lim, Hui-Yi, McNeish, Iain, Campbell, Ian, Vergote, Ignace, Gronwald, Jacek, Lubiński, Jan, Stanford, Janet L, Benítez, Javier, Doherty, Jennifer A, Permuth, Jennifer B, Chang-Claude, Jenny, Donovan, Jenny L, Dennis, Joe, Schildkraut, Joellen M, Schleutker, Johanna, and Hopper, John L

Subjects
ABCTB Investigators, AOCS Study Group & Australian Cancer Study, APCB BioResource, kConFab Investigators, NBCS Investigators, GENICA Network, PRACTICAL consortium, Humans, Breast Neoplasms, Ovarian Neoplasms, Prostatic Neoplasms, Genetic Predisposition to Disease, Case-Control Studies, Chromosome Mapping, Signal Transduction, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Female, Male, Gene Regulatory Networks, Meta-Analysis as Topic, Enhancer Elements, Genetic, Genome-Wide Association Study, Genetic Loci, Datasets as Topic, Polymorphism, Single Nucleotide, Enhancer Elements, Genetic, Urologic Diseases, Prostate Cancer, Human Genome, Rare Diseases, Genetics, Cancer, Breast Cancer, Aging, Ovarian Cancer, 2.1 Biological and endogenous factors, Oncology and Carcinogenesis
Abstract

UnlabelledBreast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.SignificanceWe demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.

Published
2016

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

Author

Gusev, Alexander, Shi, Huwenbo, Kichaev, Gleb, Pomerantz, Mark, Li, Fugen, Long, Henry W, Ingles, Sue A, Kittles, Rick A, Strom, Sara S, Rybicki, Benjamin A, Nemesure, Barbara, Isaacs, William B, Zheng, Wei, Pettaway, Curtis A, Yeboah, Edward D, Tettey, Yao, Biritwum, Richard B, Adjei, Andrew A, Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Chokkalingam, Anand P, John, Esther M, Murphy, Adam B, Signorello, Lisa B, Carpten, John, Leske, M Cristina, Wu, Suh-Yuh, Hennis, Anslem JM, Neslund-Dudas, Christine, Hsing, Ann W, Chu, Lisa, Goodman, Phyllis J, Klein, Eric A, Witte, John S, Casey, Graham, Kaggwa, Sam, Cook, Michael B, Stram, Daniel O, Blot, William J, Eeles, Rosalind A, Easton, Douglas, Kote-Jarai, Zsofia, Al Olama, Ali Amin, Benlloch, Sara, Muir, Kenneth, Giles, Graham G, Southey, Melissa C, Fitzgerald, Liesel M, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Henderson, Brian E, Schleutker, Johanna, Wahlfors, Tiina, Tammela, Teuvo LJ, Nordestgaard, Børge G, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Stanford, Janet L, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Teerlink, Craig, Brenner, Hermann, Dieffenbach, Aida K, Arndt, Volker, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Spurdle, Amanda, Clements, Judith A, Teixeira, Manuel R, Pandha, Hardev, Michael, Agnieszka, Paulo, Paula, Maia, Sofia, Kierzek, Andrzej, PRACTICAL consortium, Conti, David V, and Albanes, Demetrius

Subjects
PRACTICAL consortium, Cell Line, Tumor, Humans, Prostatic Neoplasms, Genetic Predisposition to Disease, Histones, Epigenesis, Genetic, Acetylation, Inheritance Patterns, Linkage Disequilibrium, Polymorphism, Single Nucleotide, African Americans, European Continental Ancestry Group, Male, Atlases as Topic, Genome-Wide Association Study, Genetic Loci, Cell Line, Tumor, Epigenesis, Genetic, Polymorphism, Single Nucleotide
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.

Published
2016

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

Author

Gusev, Alexander, Shi, Huwenbo, Kichaev, Gleb, Pomerantz, Mark, Li, Fugen, Long, Henry W, Ingles, Sue A, Kittles, Rick A, Strom, Sara S, Rybicki, Benjamin A, Nemesure, Barbara, Isaacs, William B, Zheng, Wei, Pettaway, Curtis A, Yeboah, Edward D, Tettey, Yao, Biritwum, Richard B, Adjei, Andrew A, Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Chokkalingam, Anand P, John, Esther M, Murphy, Adam B, Signorello, Lisa B, Carpten, John, Leske, M Cristina, Wu, Suh-Yuh, Hennis, Anslem JM, Neslund-Dudas, Christine, Hsing, Ann W, Chu, Lisa, Goodman, Phyllis J, Klein, Eric A, Witte, John S, Casey, Graham, Kaggwa, Sam, Cook, Michael B, Stram, Daniel O, Blot, William J, Eeles, Rosalind A, Easton, Douglas, Kote-Jarai, ZSofia, Al Olama, Ali Amin, Benlloch, Sara, Muir, Kenneth, Giles, Graham G, Southey, Melissa C, Fitzgerald, Liesel M, Gronberg, Henrik, Wiklund, Fredrik, Aly, Markus, Henderson, Brian E, Schleutker, Johanna, Wahlfors, Tiina, Tammela, Teuvo LJ, Nordestgaard, Børge G, Key, Tim J, Travis, Ruth C, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Pharoah, Paul, Pashayan, Nora, Khaw, Kay-Tee, Stanford, Janet L, Thibodeau, Stephen N, McDonnell, Shannon K, Schaid, Daniel J, Maier, Christiane, Vogel, Walther, Luedeke, Manuel, Herkommer, Kathleen, Kibel, Adam S, Cybulski, Cezary, Wokolorczyk, Dominika, Kluzniak, Wojciech, Cannon-Albright, Lisa, Teerlink, Craig, Brenner, Hermann, Dieffenbach, Aida K, Arndt, Volker, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Slavov, Chavdar, Kaneva, Radka, Mitev, Vanio, Batra, Jyotsna, Spurdle, Amanda, Clements, Judith A, Teixeira, Manuel R, Pandha, Hardev, Michael, Agnieszka, Paulo, Paula, Maia, Sofia, Kierzek, Andrzej, Conti, David V, Albanes, Demetrius, and Berg, Christine

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Human Genome, Urologic Diseases, Aging, Prostate Cancer, Cancer, Aetiology, 2.1 Biological and endogenous factors, Acetylation, Black or African American, Atlases as Topic, Cell Line, Tumor, Epigenesis, Genetic, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Histones, Humans, Inheritance Patterns, Linkage Disequilibrium, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, White People, PRACTICAL consortium
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.

Published
2016

21. Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions

Author

Han, Ying, Hazelett, Dennis J, Wiklund, Fredrik, Schumacher, Fredrick R, Stram, Daniel O, Berndt, Sonja I, Wang, Zhaoming, Rand, Kristin A, Hoover, Robert N, Machiela, Mitchell J, Yeager, Merideth, Burdette, Laurie, Chung, Charles C, Hutchinson, Amy, Yu, Kai, Xu, Jianfeng, Travis, Ruth C, Key, Timothy J, Siddiq, Afshan, Canzian, Federico, Takahashi, Atsushi, Kubo, Michiaki, Stanford, Janet L, Kolb, Suzanne, Gapstur, Susan M, Diver, W Ryan, Stevens, Victoria L, Strom, Sara S, Pettaway, Curtis A, Olama, Ali Amin Al, Kote-Jarai, Zsofia, Eeles, Rosalind A, Yeboah, Edward D, Tettey, Yao, Biritwum, Richard B, Adjei, Andrew A, Tay, Evelyn, Truelove, Ann, Niwa, Shelley, Chokkalingam, Anand P, Isaacs, William B, Chen, Constance, Lindstrom, Sara, Le Marchand, Loic, Giovannucci, Edward L, Pomerantz, Mark, Long, Henry, Li, Fugen, Ma, Jing, Stampfer, Meir, John, Esther M, Ingles, Sue A, Kittles, Rick A, Murphy, Adam B, Blot, William J, Signorello, Lisa B, Zheng, Wei, Albanes, Demetrius, Virtamo, Jarmo, Weinstein, Stephanie, Nemesure, Barbara, Carpten, John, Leske, M Cristina, Wu, Suh-Yuh, Hennis, Anselm JM, Rybicki, Benjamin A, Neslund-Dudas, Christine, Hsing, Ann W, Chu, Lisa, Goodman, Phyllis J, Klein, Eric A, Zheng, S Lilly, Witte, John S, Casey, Graham, Riboli, Elio, Li, Qiyuan, Freedman, Matthew L, Hunter, David J, Gronberg, Henrik, Cook, Michael B, Nakagawa, Hidewaki, Kraft, Peter, Chanock, Stephen J, Easton, Douglas F, Henderson, Brian E, Coetzee, Gerhard A, Conti, David V, and Haiman, Christopher A

Subjects
Cancer, Human Genome, Biotechnology, Genetics, Urologic Diseases, Prostate Cancer, 2.1 Biological and endogenous factors, Aetiology, Asian People, Black People, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Hispanic or Latino, Humans, Linkage Disequilibrium, Male, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Quantitative Trait Loci, White People, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genome-encoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10(-4)-5.6 × 10(-3)) and in 30 regions we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10(-6)) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.

Published
2015

22. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Allen, Hana Lango, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Åsa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Body Height, Body Mass Index, Body Size, Body Weight, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Obesity, Polymorphism, Single Nucleotide, White People, MAGIC, Procardis Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

23. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Lango Allen, Hana, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Asa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
MAGIC, Procardis Consortium, Humans, Obesity, Genetic Predisposition to Disease, Body Weight, Body Mass Index, Body Size, Body Height, Chromosome Mapping, Polymorphism, Single Nucleotide, European Continental Ancestry Group, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Prevention, Genetics, Nutrition, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and Endocrine, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

24. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Lango Allen, Hana, Estrada, Karol, Lettre, Guillaume, Berndt, Sonja I, Weedon, Michael N, Rivadeneira, Fernando, Willer, Cristen J, Jackson, Anne U, Vedantam, Sailaja, Raychaudhuri, Soumya, Ferreira, Teresa, Wood, Andrew R, Weyant, Robert J, Segrè, Ayellet V, Speliotes, Elizabeth K, Wheeler, Eleanor, Soranzo, Nicole, Park, Ju-Hyun, Yang, Jian, Gudbjartsson, Daniel, Heard-Costa, Nancy L, Randall, Joshua C, Qi, Lu, Vernon Smith, Albert, Mägi, Reedik, Pastinen, Tomi, Liang, Liming, Heid, Iris M, Luan, Jian’an, Thorleifsson, Gudmar, Winkler, Thomas W, Goddard, Michael E, Sin Lo, Ken, Palmer, Cameron, Workalemahu, Tsegaselassie, Aulchenko, Yurii S, Johansson, Åsa, Carola Zillikens, M, Feitosa, Mary F, Esko, Tõnu, Johnson, Toby, Ketkar, Shamika, Kraft, Peter, Mangino, Massimo, Prokopenko, Inga, Absher, Devin, Albrecht, Eva, Ernst, Florian, Glazer, Nicole L, Hayward, Caroline, Hottenga, Jouke-Jan, Jacobs, Kevin B, Knowles, Joshua W, Kutalik, Zoltán, Monda, Keri L, Polasek, Ozren, Preuss, Michael, Rayner, Nigel W, Robertson, Neil R, Steinthorsdottir, Valgerdur, Tyrer, Jonathan P, Voight, Benjamin F, Wiklund, Fredrik, Xu, Jianfeng, Hua Zhao, Jing, Nyholt, Dale R, Pellikka, Niina, Perola, Markus, Perry, John RB, Surakka, Ida, Tammesoo, Mari-Liis, Altmaier, Elizabeth L, Amin, Najaf, Aspelund, Thor, Bhangale, Tushar, Boucher, Gabrielle, Chasman, Daniel I, Chen, Constance, Coin, Lachlan, Cooper, Matthew N, Dixon, Anna L, Gibson, Quince, Grundberg, Elin, Hao, Ke, Juhani Junttila, M, Kaplan, Lee M, Kettunen, Johannes, König, Inke R, Kwan, Tony, Lawrence, Robert W, Levinson, Douglas F, Lorentzon, Mattias, McKnight, Barbara, Morris, Andrew P, Müller, Martina, Suh Ngwa, Julius, Purcell, Shaun, Rafelt, Suzanne, Salem, Rany M, and Salvi, Erika

Subjects
Biological Sciences, Genetics, Health Sciences, Mathematical Sciences, Statistics, Human Genome, Clinical Research, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Body Height, Chromosomes, Human, Pair 3, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Metabolic Networks and Pathways, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P

Published
2010

25. Hundreds of variants clustered in genomic loci and biological pathways affect human height.

Author

Lango Allen, Hana, Estrada, Karol, Lettre, Guillaume, Berndt, Sonja I, Weedon, Michael N, Rivadeneira, Fernando, Willer, Cristen J, Jackson, Anne U, Vedantam, Sailaja, Raychaudhuri, Soumya, Ferreira, Teresa, Wood, Andrew R, Weyant, Robert J, Segrè, Ayellet V, Speliotes, Elizabeth K, Wheeler, Eleanor, Soranzo, Nicole, Park, Ju-Hyun, Yang, Jian, Gudbjartsson, Daniel, Heard-Costa, Nancy L, Randall, Joshua C, Qi, Lu, Vernon Smith, Albert, Mägi, Reedik, Pastinen, Tomi, Liang, Liming, Heid, Iris M, Luan, Jian'an, Thorleifsson, Gudmar, Winkler, Thomas W, Goddard, Michael E, Sin Lo, Ken, Palmer, Cameron, Workalemahu, Tsegaselassie, Aulchenko, Yurii S, Johansson, Asa, Zillikens, M Carola, Feitosa, Mary F, Esko, Tõnu, Johnson, Toby, Ketkar, Shamika, Kraft, Peter, Mangino, Massimo, Prokopenko, Inga, Absher, Devin, Albrecht, Eva, Ernst, Florian, Glazer, Nicole L, Hayward, Caroline, Hottenga, Jouke-Jan, Jacobs, Kevin B, Knowles, Joshua W, Kutalik, Zoltán, Monda, Keri L, Polasek, Ozren, Preuss, Michael, Rayner, Nigel W, Robertson, Neil R, Steinthorsdottir, Valgerdur, Tyrer, Jonathan P, Voight, Benjamin F, Wiklund, Fredrik, Xu, Jianfeng, Zhao, Jing Hua, Nyholt, Dale R, Pellikka, Niina, Perola, Markus, Perry, John RB, Surakka, Ida, Tammesoo, Mari-Liis, Altmaier, Elizabeth L, Amin, Najaf, Aspelund, Thor, Bhangale, Tushar, Boucher, Gabrielle, Chasman, Daniel I, Chen, Constance, Coin, Lachlan, Cooper, Matthew N, Dixon, Anna L, Gibson, Quince, Grundberg, Elin, Hao, Ke, Juhani Junttila, M, Kaplan, Lee M, Kettunen, Johannes, König, Inke R, Kwan, Tony, Lawrence, Robert W, Levinson, Douglas F, Lorentzon, Mattias, McKnight, Barbara, Morris, Andrew P, Müller, Martina, Suh Ngwa, Julius, Purcell, Shaun, Rafelt, Suzanne, Salem, Rany M, and Salvi, Erika

Subjects
Chromosomes, Human, Pair 3, Humans, Genetic Predisposition to Disease, Body Height, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Genome, Human, Metabolic Networks and Pathways, Genome-Wide Association Study, Genetic Loci, Chromosomes, Human, Pair 3, Polymorphism, Single Nucleotide, Genome, Genetics, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, General Science & Technology
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P

Published
2010

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