Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification
Abstract Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for B...
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| Format: | Article |
| Language: | English |
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BMC
2019-06-01
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| Series: | BMC Cancer |
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| Online Access: | http://link.springer.com/article/10.1186/s12885-019-5783-1 |
| _version_ | 1811294142288887808 |
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| author | Kristi Läll Maarja Lepamets Marili Palover Tõnu Esko Andres Metspalu Neeme Tõnisson Peeter Padrik Reedik Mägi Krista Fischer |
| author_facet | Kristi Läll Maarja Lepamets Marili Palover Tõnu Esko Andres Metspalu Neeme Tõnisson Peeter Padrik Reedik Mägi Krista Fischer |
| author_sort | Kristi Läll |
| collection | DOAJ |
| description | Abstract Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women. |
| first_indexed | 2024-04-13T05:13:04Z |
| format | Article |
| id | doaj.art-8f3ffbee83b84b92a801a96e634515d1 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-13T05:13:04Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-8f3ffbee83b84b92a801a96e634515d12022-12-22T03:00:59ZengBMCBMC Cancer1471-24072019-06-011911910.1186/s12885-019-5783-1Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratificationKristi Läll0Maarja Lepamets1Marili Palover2Tõnu Esko3Andres Metspalu4Neeme Tõnisson5Peeter Padrik6Reedik Mägi7Krista Fischer8Estonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuInstitute of Clinical Medicine, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuEstonian Genome Center, Institute of Genomics, University of TartuAbstract Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.http://link.springer.com/article/10.1186/s12885-019-5783-1Polygenic risk scoreGenetic predisposition to diseaseBreast cancerRisk stratificationPersonalized medicine |
| spellingShingle | Kristi Läll Maarja Lepamets Marili Palover Tõnu Esko Andres Metspalu Neeme Tõnisson Peeter Padrik Reedik Mägi Krista Fischer Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification BMC Cancer Polygenic risk score Genetic predisposition to disease Breast cancer Risk stratification Personalized medicine |
| title | Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification |
| title_full | Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification |
| title_fullStr | Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification |
| title_full_unstemmed | Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification |
| title_short | Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification |
| title_sort | polygenic prediction of breast cancer comparison of genetic predictors and implications for risk stratification |
| topic | Polygenic risk score Genetic predisposition to disease Breast cancer Risk stratification Personalized medicine |
| url | http://link.springer.com/article/10.1186/s12885-019-5783-1 |
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