Common polygenic variation enhances risk prediction for Alzheimer’s disease

The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic com...

Full description

Saved in:

Bibliographic Details
Published in	Brain (London, England : 1878) Vol. 138; no. 12; pp. 3673 - 3684
Main Authors	Escott-Price, Valentina, Sims, Rebecca, Bannister, Christian, Harold, Denise, Vronskaya, Maria, Majounie, Elisa, Badarinarayan, Nandini, Morgan, Kevin, Passmore, Peter, Holmes, Clive, Powell, John, Brayne, Carol, Gill, Michael, Mead, Simon, Goate, Alison, Cruchaga, Carlos, Lambert, Jean-Charles, van Duijn, Cornelia, Maier, Wolfgang, Ramirez, Alfredo, Holmans, Peter, Jones, Lesley, Hardy, John, Seshadri, Sudha, Schellenberg, Gerard D., Amouyel, Philippe, Williams, Julie
Format	Journal Article
Language	English
Published	England Oxford University Press 01.12.2015
Subjects	Alleles Alzheimer Disease - genetics Apolipoproteins E - genetics Case-Control Studies Genetic Predisposition to Disease - genetics Genetic Testing Genetic Variation - genetics Genome-Wide Association Study Genotype Humans Logistic Models Multifactorial Inheritance - genetics Original Risk ROC Curve Alzheimer’s disease polygenic score predictive model
Online Access	Get full text

Cover

Loading…

Abstract	The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.
AbstractList	The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. Heritability estimates for Alzheimer’s disease in genome-wide association studies increase substantially when weak effect loci are also considered. Escott-Price et al. investigate the polygenic architecture of Alzheimer’s disease and the accuracy of prediction models, and show that including the polygenic component of risk significantly improves accuracy of case prediction. Heritability estimates for Alzheimer’s disease in genome-wide association studies increase substantially when weak effect loci are also considered. Escott-Price et al. investigate the polygenic architecture of Alzheimer’s disease and the accuracy of prediction models, and show that including the polygenic component of risk significantly improves accuracy of case prediction. The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease ( P = 4.9 × 10 −26 ). This enrichment remained significant after APOE and other genome-wide associated regions were excluded ( P = 3.4 × 10 −19 ). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE , the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.
Author	Schellenberg, Gerard D. Badarinarayan, Nandini Goate, Alison Morgan, Kevin Vronskaya, Maria Maier, Wolfgang Hardy, John Sims, Rebecca Holmans, Peter Cruchaga, Carlos Brayne, Carol Majounie, Elisa Amouyel, Philippe Passmore, Peter Jones, Lesley Powell, John Bannister, Christian Seshadri, Sudha van Duijn, Cornelia Mead, Simon Gill, Michael Ramirez, Alfredo Escott-Price, Valentina Harold, Denise Williams, Julie Holmes, Clive Lambert, Jean-Charles
Author_xml	– sequence: 1 givenname: Valentina surname: Escott-Price fullname: Escott-Price, Valentina – sequence: 2 givenname: Rebecca surname: Sims fullname: Sims, Rebecca – sequence: 3 givenname: Christian surname: Bannister fullname: Bannister, Christian – sequence: 4 givenname: Denise surname: Harold fullname: Harold, Denise – sequence: 5 givenname: Maria surname: Vronskaya fullname: Vronskaya, Maria – sequence: 6 givenname: Elisa surname: Majounie fullname: Majounie, Elisa – sequence: 7 givenname: Nandini surname: Badarinarayan fullname: Badarinarayan, Nandini – sequence: 8 givenname: Kevin surname: Morgan fullname: Morgan, Kevin – sequence: 9 givenname: Peter surname: Passmore fullname: Passmore, Peter – sequence: 10 givenname: Clive surname: Holmes fullname: Holmes, Clive – sequence: 11 givenname: John surname: Powell fullname: Powell, John – sequence: 12 givenname: Carol surname: Brayne fullname: Brayne, Carol – sequence: 13 givenname: Michael surname: Gill fullname: Gill, Michael – sequence: 14 givenname: Simon surname: Mead fullname: Mead, Simon – sequence: 15 givenname: Alison surname: Goate fullname: Goate, Alison – sequence: 16 givenname: Carlos surname: Cruchaga fullname: Cruchaga, Carlos – sequence: 17 givenname: Jean-Charles surname: Lambert fullname: Lambert, Jean-Charles – sequence: 18 givenname: Cornelia surname: van Duijn fullname: van Duijn, Cornelia – sequence: 19 givenname: Wolfgang surname: Maier fullname: Maier, Wolfgang – sequence: 20 givenname: Alfredo surname: Ramirez fullname: Ramirez, Alfredo – sequence: 21 givenname: Peter surname: Holmans fullname: Holmans, Peter – sequence: 22 givenname: Lesley surname: Jones fullname: Jones, Lesley – sequence: 23 givenname: John surname: Hardy fullname: Hardy, John – sequence: 24 givenname: Sudha surname: Seshadri fullname: Seshadri, Sudha – sequence: 25 givenname: Gerard D. surname: Schellenberg fullname: Schellenberg, Gerard D. – sequence: 26 givenname: Philippe surname: Amouyel fullname: Amouyel, Philippe – sequence: 27 givenname: Julie surname: Williams fullname: Williams, Julie
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26490334$$D View this record in MEDLINE/PubMed
BookMark	eNptUctOGzEUtSpQSWh3rNEsWTBgjx-xN0gooqUSiE27tjyea2I6Y0_tSVBY8Rv9vX4J0wRQqVhd6Z6XdM4U7YQYAKEDgk8IVvS0TsaHU3O_qoT8gCaECVxWhIsdNMEYi1IqjvfQNOc7jAmjlfiI9irBFKaUTdD1PHZdDEUf2_UtBG-LlUneDH78QViYYCEXyeefRZ-g8XYDuJiK8_ZhAb6D9Ofxdy4an8Fk-IR2nWkzfH6---jHl4vv88vy6ubrt_n5VWmpZEM545YRJlXdSMqVAywdFQ1W0tXSEmEqxnnVYBBcWCE5BsItUdypeuaoa4Duo7Otb7-sO2gshCGZVvfJdyatdTRev0WCX-jbuNJ8bKQiajQ4ejZI8dcS8qA7ny20rQkQl1mTGRWMUCn5SD38N-s15KXDkVBtCTbFnBM4bf2waXCM9q0mWP8dSm-G0tuhRtHxf6IX33fpTz1ymf4
CitedBy_id	crossref_primary_10_1093_sleep_zsaa137 crossref_primary_10_1111_bpa_12879 crossref_primary_10_1038_s41386_019_0595_1 crossref_primary_10_3389_fgene_2019_00726 crossref_primary_10_1016_j_neurobiolaging_2020_05_007 crossref_primary_10_3389_fnagi_2024_1369493 crossref_primary_10_3389_fnins_2022_1043626 crossref_primary_10_1016_j_arr_2022_101723 crossref_primary_10_1016_j_neurobiolaging_2019_09_007 crossref_primary_10_14283_jarlife_20221 crossref_primary_10_1038_s41467_022_35088_6 crossref_primary_10_1016_j_nbd_2023_106284 crossref_primary_10_1111_jnc_16184 crossref_primary_10_1186_s13195_021_00895_4 crossref_primary_10_1016_j_neurobiolaging_2019_09_001 crossref_primary_10_1038_s41380_020_0764_y crossref_primary_10_1038_s41398_017_0036_z crossref_primary_10_1016_j_xgen_2024_100555 crossref_primary_10_1001_jama_2019_9879 crossref_primary_10_1093_brain_awy327 crossref_primary_10_1212_WNL_0000000000003734 crossref_primary_10_1016_j_neurobiolaging_2020_09_014 crossref_primary_10_1038_s41380_023_02076_1 crossref_primary_10_3389_fnins_2022_827447 crossref_primary_10_1038_s41598_025_90277_9 crossref_primary_10_1016_j_nbd_2022_105924 crossref_primary_10_3233_JAD_210700 crossref_primary_10_1016_j_nbd_2020_104953 crossref_primary_10_2139_ssrn_3919689 crossref_primary_10_1016_j_neurobiolaging_2016_07_008 crossref_primary_10_14283_jpad_2024_29 crossref_primary_10_3389_fnagi_2023_1168638 crossref_primary_10_3233_JAD_215163 crossref_primary_10_3390_jcm12165189 crossref_primary_10_1002_dad2_12300 crossref_primary_10_1007_s00401_017_1685_y crossref_primary_10_1002_alz_13390 crossref_primary_10_1093_brain_awaa164 crossref_primary_10_1186_s12883_017_0978_z crossref_primary_10_3389_fgene_2019_00267 crossref_primary_10_1002_ana_25530 crossref_primary_10_1016_j_archger_2022_104853 crossref_primary_10_3233_JAD_230548 crossref_primary_10_1111_acel_13384 crossref_primary_10_3390_cells11121930 crossref_primary_10_3233_JAD_190568 crossref_primary_10_1002_alz_14455 crossref_primary_10_1159_000440841 crossref_primary_10_1016_j_neurol_2022_12_006 crossref_primary_10_3389_frdem_2023_1120206 crossref_primary_10_1186_s12883_017_0846_x crossref_primary_10_1038_s41380_022_01926_8 crossref_primary_10_7554_eLife_36011 crossref_primary_10_1016_j_neurobiolaging_2022_08_001 crossref_primary_10_1016_j_neurobiolaging_2018_04_009 crossref_primary_10_1136_jnnp_2018_318212 crossref_primary_10_3233_JAD_160707 crossref_primary_10_1016_S1474_4422_19_30287_X crossref_primary_10_1007_s12687_023_00645_z crossref_primary_10_1016_j_neuroimage_2016_01_049 crossref_primary_10_1093_ije_dyz080 crossref_primary_10_1080_03036758_2022_2098780 crossref_primary_10_1186_s13195_023_01343_1 crossref_primary_10_12779_dnd_2018_17_4_131 crossref_primary_10_1002_alz_13924 crossref_primary_10_1016_j_neurobiolaging_2020_05_015 crossref_primary_10_3390_ijms241612799 crossref_primary_10_1212_NXG_0000000000000576 crossref_primary_10_1016_j_ygeno_2020_01_015 crossref_primary_10_1136_bmjopen_2021_053936 crossref_primary_10_1016_j_nicl_2020_102359 crossref_primary_10_1016_j_ejpn_2019_12_001 crossref_primary_10_1038_s41582_020_00416_1 crossref_primary_10_1093_eurheartj_ehad293 crossref_primary_10_1093_braincomms_fcac314 crossref_primary_10_3390_genes12121859 crossref_primary_10_14283_jpad_2018_46 crossref_primary_10_1007_s40142_020_00190_y crossref_primary_10_1111_ahg_12520 crossref_primary_10_3390_biomedicines13030739 crossref_primary_10_1186_s12868_024_00914_8 crossref_primary_10_1016_j_neurobiolaging_2019_06_001 crossref_primary_10_1159_000452256 crossref_primary_10_3233_JAD_179904 crossref_primary_10_3233_JAD_191233 crossref_primary_10_1038_s41380_022_01531_9 crossref_primary_10_1093_jnen_nlaa024 crossref_primary_10_1186_s13195_024_01661_y crossref_primary_10_1002_dad2_12352 crossref_primary_10_1016_j_biopsych_2016_02_033 crossref_primary_10_1016_j_arr_2021_101402 crossref_primary_10_1016_S1474_4422_18_30053_X crossref_primary_10_1038_s41398_022_02055_0 crossref_primary_10_1038_s41598_018_36820_3 crossref_primary_10_1002_ana_26131 crossref_primary_10_1038_s41398_022_02093_8 crossref_primary_10_3389_fnins_2022_987677 crossref_primary_10_1093_braincomms_fcac125 crossref_primary_10_1080_15622975_2017_1375556 crossref_primary_10_1177_1073858420912404 crossref_primary_10_14283_jpad_2021_70 crossref_primary_10_1007_s11825_018_0193_3 crossref_primary_10_1002_ana_25164 crossref_primary_10_1007_s00401_020_02199_7 crossref_primary_10_1002_alz_13506 crossref_primary_10_1186_s13195_022_01079_4 crossref_primary_10_3389_fgene_2019_00519 crossref_primary_10_1038_s41398_018_0221_8 crossref_primary_10_1038_s41598_023_27551_1 crossref_primary_10_2217_epi_2017_0076 crossref_primary_10_15252_emmm_201910606 crossref_primary_10_2174_1874609814666210302085232 crossref_primary_10_1002_alz_13753 crossref_primary_10_1002_sim_8665 crossref_primary_10_1093_braincomms_fcad229 crossref_primary_10_1038_s41380_018_0030_8 crossref_primary_10_1002_dad2_12229 crossref_primary_10_37394_23209_2024_21_38 crossref_primary_10_1002_dad2_12226 crossref_primary_10_1136_jnnp_2017_317234 crossref_primary_10_1007_s13311_021_01152_0 crossref_primary_10_1038_s41593_020_0599_5 crossref_primary_10_3233_JAD_200578 crossref_primary_10_1016_j_neurol_2022_03_005 crossref_primary_10_1080_1028415X_2021_1966868 crossref_primary_10_1111_ene_15351 crossref_primary_10_1136_bmjopen_2020_042093 crossref_primary_10_1186_s12929_017_0362_8 crossref_primary_10_3233_JAD_220740 crossref_primary_10_1101_cshperspect_a041201 crossref_primary_10_3390_ijms241914765 crossref_primary_10_1002_alz_12870 crossref_primary_10_1371_journal_pone_0281440 crossref_primary_10_3233_JAD_215152 crossref_primary_10_1002_trc2_12386 crossref_primary_10_1136_bmjopen_2018_024404 crossref_primary_10_1212_WNL_0000000000005415 crossref_primary_10_1002_ana_24999 crossref_primary_10_1007_s11910_017_0787_1 crossref_primary_10_1038_nrneurol_2016_182 crossref_primary_10_1093_braincomms_fcaa167 crossref_primary_10_1038_s41467_023_38374_z crossref_primary_10_1186_s40246_023_00499_z crossref_primary_10_3390_ijms241310717 crossref_primary_10_1016_j_neurobiolaging_2021_08_005 crossref_primary_10_1016_j_nbd_2019_04_004 crossref_primary_10_1186_s13195_024_01559_9 crossref_primary_10_3143_geriatrics_57_374 crossref_primary_10_3233_JAD_220174 crossref_primary_10_1177_13872877251326283 crossref_primary_10_1038_s41467_021_24082_z crossref_primary_10_1136_bmjopen_2022_069243 crossref_primary_10_1038_ng_3916 crossref_primary_10_1101_mcs_a003491 crossref_primary_10_1101_cshperspect_a036525 crossref_primary_10_1176_appi_ajp_2017_17050529 crossref_primary_10_1001_jamanetworkopen_2022_47162 crossref_primary_10_1007_s11356_020_09964_x crossref_primary_10_1016_j_bcp_2021_114754 crossref_primary_10_1093_braincomms_fcad320 crossref_primary_10_1002_ajmg_b_32705 crossref_primary_10_1186_s12864_022_08617_2 crossref_primary_10_1016_j_neubiorev_2020_11_021 crossref_primary_10_1007_s12031_019_01372_2 crossref_primary_10_1186_s12863_023_01151_4 crossref_primary_10_1212_WNL_0000000000010306 crossref_primary_10_3390_ijms25052645 crossref_primary_10_1007_s40142_019_0158_0 crossref_primary_10_1016_j_nepig_2016_05_001 crossref_primary_10_3233_JAD_220599 crossref_primary_10_1038_s41380_018_0112_7 crossref_primary_10_1186_s13195_024_01414_x crossref_primary_10_1126_science_abb8575 crossref_primary_10_12688_wellcomeopenres_15359_1 crossref_primary_10_3233_JAD_160889 crossref_primary_10_1093_cercor_bhaa034 crossref_primary_10_1186_s13195_023_01362_y crossref_primary_10_1212_WNL_0000000000002922 crossref_primary_10_3389_fnagi_2018_00423 crossref_primary_10_1093_brain_awv335 crossref_primary_10_37881_1_811 crossref_primary_10_1093_braincomms_fcab083 crossref_primary_10_1016_j_clnu_2023_08_006 crossref_primary_10_1016_j_nbd_2022_105768 crossref_primary_10_14283_jpad_2020_64 crossref_primary_10_1016_j_jalz_2016_02_006 crossref_primary_10_1016_S1474_4422_17_30202_8 crossref_primary_10_1016_S2215_0366_17_30333_4 crossref_primary_10_1093_hmg_ddz163 crossref_primary_10_1093_nargab_lqab069 crossref_primary_10_1212_WNL_0000000000209452 crossref_primary_10_3233_JAD_170909 crossref_primary_10_1002_dad2_12142 crossref_primary_10_1038_s10038_022_01047_8 crossref_primary_10_3389_fgene_2025_1507395 crossref_primary_10_1093_brain_awy277 crossref_primary_10_1093_braincomms_fcz047 crossref_primary_10_1007_s11042_024_20299_4 crossref_primary_10_1093_gerona_glab030 crossref_primary_10_1161_CIRCRESAHA_120_315928 crossref_primary_10_1016_j_nicl_2020_102527 crossref_primary_10_3389_fdgth_2020_00014 crossref_primary_10_1002_gepi_22117 crossref_primary_10_1002_alz_13515 crossref_primary_10_1089_neur_2024_0048 crossref_primary_10_1016_j_jalz_2019_04_005 crossref_primary_10_1038_s41598_024_75377_2 crossref_primary_10_1080_1028415X_2023_2187952 crossref_primary_10_1016_j_conb_2019_11_024 crossref_primary_10_1097_MOL_0000000000000291 crossref_primary_10_1016_j_semcdb_2021_08_008 crossref_primary_10_1186_s13024_021_00452_5 crossref_primary_10_1016_j_pnpbp_2024_111157 crossref_primary_10_3390_brainsci11030332 crossref_primary_10_1016_j_jalz_2017_08_013 crossref_primary_10_1016_j_jalz_2019_07_010 crossref_primary_10_1016_S0140_6736_20_32205_4 crossref_primary_10_3389_fnagi_2021_725246 crossref_primary_10_4049_jimmunol_1901068 crossref_primary_10_1016_j_neurobiolaging_2020_03_012 crossref_primary_10_1016_j_neurobiolaging_2020_11_009 crossref_primary_10_1016_j_tins_2020_10_002 crossref_primary_10_1155_2021_3359103 crossref_primary_10_3233_JAD_161070 crossref_primary_10_1007_s11920_018_0871_5 crossref_primary_10_1016_j_trci_2019_08_005 crossref_primary_10_1093_hmg_ddz187 crossref_primary_10_1093_brain_awz384 crossref_primary_10_1002_sim_9214 crossref_primary_10_1002_dad2_12482 crossref_primary_10_1002_trc2_12211 crossref_primary_10_1016_j_neuroimage_2021_118304 crossref_primary_10_1212_NXG_0000000000200120 crossref_primary_10_1016_j_neurobiolaging_2020_11_002 crossref_primary_10_1038_s41582_022_00714_w crossref_primary_10_3233_JAD_231252 crossref_primary_10_1007_s40142_019_0161_5 crossref_primary_10_1186_s40246_024_00704_7 crossref_primary_10_3389_fgene_2021_647436 crossref_primary_10_3389_fnins_2019_00220 crossref_primary_10_1080_23808993_2017_1286227 crossref_primary_10_1111_ene_13439 crossref_primary_10_1007_s00401_022_02419_2 crossref_primary_10_1016_j_smrv_2018_08_001 crossref_primary_10_1038_s41398_019_0485_7 crossref_primary_10_1186_s13195_024_01664_9 crossref_primary_10_1186_s13195_023_01277_8 crossref_primary_10_3390_genes12081247 crossref_primary_10_1212_NXG_0000000000000506 crossref_primary_10_1038_s41598_022_17058_6 crossref_primary_10_1016_j_bpsc_2017_08_004 crossref_primary_10_1016_j_neurobiolaging_2020_08_022 crossref_primary_10_1016_j_tig_2019_02_005 crossref_primary_10_1016_S1474_4422_19_30033_X crossref_primary_10_1016_j_jbi_2023_104569 crossref_primary_10_1093_brain_awae176 crossref_primary_10_3389_fnagi_2022_849443 crossref_primary_10_1021_acschemneuro_8b00435 crossref_primary_10_1016_j_neuron_2019_01_056 crossref_primary_10_3233_JAD_170027 crossref_primary_10_1016_j_nbd_2023_106357 crossref_primary_10_1002_alz_14444 crossref_primary_10_1002_acn3_716 crossref_primary_10_3389_fnins_2021_650220 crossref_primary_10_1016_j_neurobiolaging_2022_02_004 crossref_primary_10_1093_brain_awaa364 crossref_primary_10_1093_geront_gnaa073 crossref_primary_10_1016_j_mcp_2016_09_001 crossref_primary_10_1016_j_neurobiolaging_2018_12_002 crossref_primary_10_1016_j_neurobiolaging_2016_07_018 crossref_primary_10_1093_brain_awy141 crossref_primary_10_1016_j_banm_2021_02_003 crossref_primary_10_1016_j_patter_2021_100303 crossref_primary_10_1186_s13195_020_00754_8 crossref_primary_10_1038_s41467_020_18534_1 crossref_primary_10_3233_JAD_180713 crossref_primary_10_1371_journal_pmed_1002258 crossref_primary_10_1038_s41386_021_01258_1 crossref_primary_10_1016_j_neurobiolaging_2020_04_024 crossref_primary_10_3233_JAD_230236 crossref_primary_10_1186_s12888_022_03717_5 crossref_primary_10_1016_j_nbd_2020_104976 crossref_primary_10_1186_s13195_017_0240_3 crossref_primary_10_3389_fmed_2018_00108 crossref_primary_10_1111_cts_13574 crossref_primary_10_1136_bmjopen_2023_082902 crossref_primary_10_1002_dad2_12164 crossref_primary_10_1093_braincomms_fcab308 crossref_primary_10_1016_j_gpb_2016_01_006 crossref_primary_10_1016_j_neurobiolaging_2017_09_035 crossref_primary_10_1016_j_parkreldis_2016_05_030 crossref_primary_10_1111_nan_12650 crossref_primary_10_1371_journal_pgen_1010294
Cites_doi	10.1016/j.jalz.2014.11.001 10.1016/j.biopsych.2013.01.011 10.1038/ng.440 10.1038/ng.2563 10.1002/ajmg.b.31216 10.1371/journal.pgen.1000529 10.1016/S0022-3956(02)00020-1 10.1371/journal.pbio.1000294 10.1038/ng.803 10.1001/jama.2010.574 10.1002/ana.24335 10.1093/hmg/dds491 10.1038/mp.2010.65 10.1086/519795 10.1111/bjd.12443 10.1093/hmg/dds476 10.1038/nature12975 10.1016/j.jalz.2014.05.1757 10.1371/journal.pone.0094661 10.1176/appi.ajp.2011.11040551 10.1002/gepi.20533 10.1038/ng.2802 10.1212/WNL.0b013e31828726f5 10.1038/ng.801 10.1038/ng.439 10.1038/nature08185 10.1038/nrneurol.2009.215
ContentType	Journal Article
Contributor	Younkin, Steven G Heuser, Isabella Rossor, Martin Kornhuber, Johannes Taylor, Sarah Nowotny, Petra Gurling, Hugh Collinge, John van den Bussche, Hendrik Proitsi, Petroula Heun, Reiner Russo, Giancarlo Fox, Nick Jones, Nicola ODonovan, Michael Lawlor, Brian Lynch, Aoibhinn Mann, David Jessen, Frank Drichel, Dmitriy Stretton, Alexandra Lovestone, Simon Wiltfang, Jens Deloukas, Panagiotis Hamshere, Marian Mühleisen, Thomas W Morris, John C McQuillin, Andrew Al-Chalabi, Ammar Singleton, Andrew B Gerrish, Amy Becker, Tim Moebus, Susanne Smith, A David Frölich, Lutz Livingston, Gill Shaw, Christopher E Nöthen, Markus M Pahwa, Jaspreet Singh Hollingworth, Paul Lacour, André Thomas, Charlene Gwilliam, Rhian Morgan, Angharad Kehoe, Patrick G Bass, Nicholas J Pankratz, V Shane Dowzell, Kimberley Schürmann, Britta Herold, Christine Johnston, Janet Rubinsztein, David C Hampel, Harald Kauwe, John S K Owen, Michael J Carrasquillo, Minerva M Lupton, Michelle K Chapman, Jade Dichgans, Martin Guerreiro, Rita Love, Seth Mayo, Kevin Williams, Amy Hüll, Michael Todd,
Contributor_xml	– sequence: 1 givenname: Richard surname: Abraham fullname: Abraham, Richard – sequence: 2 givenname: Paul surname: Hollingworth fullname: Hollingworth, Paul – sequence: 3 givenname: Amy surname: Gerrish fullname: Gerrish, Amy – sequence: 4 givenname: Jade surname: Chapman fullname: Chapman, Jade – sequence: 5 givenname: Giancarlo surname: Russo fullname: Russo, Giancarlo – sequence: 6 givenname: Marian surname: Hamshere fullname: Hamshere, Marian – sequence: 7 givenname: Jaspreet Singh surname: Pahwa fullname: Pahwa, Jaspreet Singh – sequence: 8 givenname: Kimberley surname: Dowzell fullname: Dowzell, Kimberley – sequence: 9 givenname: Amy surname: Williams fullname: Williams, Amy – sequence: 10 givenname: Nicola surname: Jones fullname: Jones, Nicola – sequence: 11 givenname: Charlene surname: Thomas fullname: Thomas, Charlene – sequence: 12 givenname: Alexandra surname: Stretton fullname: Stretton, Alexandra – sequence: 13 givenname: Angharad surname: Morgan fullname: Morgan, Angharad – sequence: 14 givenname: Sarah surname: Taylor fullname: Taylor, Sarah – sequence: 15 givenname: Simon surname: Lovestone fullname: Lovestone, Simon – sequence: 16 givenname: Petroula surname: Proitsi fullname: Proitsi, Petroula – sequence: 17 givenname: Michelle K surname: Lupton fullname: Lupton, Michelle K – sequence: 18 givenname: David C surname: Rubinsztein fullname: Rubinsztein, David C – sequence: 19 givenname: Brian surname: Lawlor fullname: Lawlor, Brian – sequence: 20 givenname: Aoibhinn surname: Lynch fullname: Lynch, Aoibhinn – sequence: 21 givenname: Kristelle surname: Brown fullname: Brown, Kristelle – sequence: 22 givenname: David surname: Craig fullname: Craig, David – sequence: 23 givenname: Bernadette surname: McGuinness fullname: McGuinness, Bernadette – sequence: 24 givenname: Stephen surname: Todd fullname: Todd, Stephen – sequence: 25 givenname: Janet surname: Johnston fullname: Johnston, Janet – sequence: 26 givenname: David surname: Mann fullname: Mann, David – sequence: 27 givenname: A David surname: Smith fullname: Smith, A David – sequence: 28 givenname: Seth surname: Love fullname: Love, Seth – sequence: 29 givenname: Patrick G surname: Kehoe fullname: Kehoe, Patrick G – sequence: 30 givenname: Nick surname: Fox fullname: Fox, Nick – sequence: 31 givenname: Martin surname: Rossor fullname: Rossor, Martin – sequence: 32 givenname: John surname: Collinge fullname: Collinge, John – sequence: 33 givenname: Frank surname: Jessen fullname: Jessen, Frank – sequence: 34 givenname: Reiner surname: Heun fullname: Heun, Reiner – sequence: 35 givenname: Britta surname: Schürmann fullname: Schürmann, Britta – sequence: 36 givenname: Tim surname: Becker fullname: Becker, Tim – sequence: 37 givenname: Christine surname: Herold fullname: Herold, Christine – sequence: 38 givenname: André surname: Lacour fullname: Lacour, André – sequence: 39 givenname: Dmitriy surname: Drichel fullname: Drichel, Dmitriy – sequence: 40 givenname: Hendrik surname: van den Bussche fullname: van den Bussche, Hendrik – sequence: 41 givenname: Isabella surname: Heuser fullname: Heuser, Isabella – sequence: 42 givenname: Johannes surname: Kornhuber fullname: Kornhuber, Johannes – sequence: 43 givenname: Jens surname: Wiltfang fullname: Wiltfang, Jens – sequence: 44 givenname: Martin surname: Dichgans fullname: Dichgans, Martin – sequence: 45 givenname: Lutz surname: Frölich fullname: Frölich, Lutz – sequence: 46 givenname: Harald surname: Hampel fullname: Hampel, Harald – sequence: 47 givenname: Michael surname: Hüll fullname: Hüll, Michael – sequence: 48 givenname: Dan surname: Rujescu fullname: Rujescu, Dan – sequence: 49 givenname: John S K surname: Kauwe fullname: Kauwe, John S K – sequence: 50 givenname: Petra surname: Nowotny fullname: Nowotny, Petra – sequence: 51 givenname: John C surname: Morris fullname: Morris, John C – sequence: 52 givenname: Kevin surname: Mayo fullname: Mayo, Kevin – sequence: 53 givenname: Gill surname: Livingston fullname: Livingston, Gill – sequence: 54 givenname: Nicholas J surname: Bass fullname: Bass, Nicholas J – sequence: 55 givenname: Hugh surname: Gurling fullname: Gurling, Hugh – sequence: 56 givenname: Andrew surname: McQuillin fullname: McQuillin, Andrew – sequence: 57 givenname: Rhian surname: Gwilliam fullname: Gwilliam, Rhian – sequence: 58 givenname: Panagiotis surname: Deloukas fullname: Deloukas, Panagiotis – sequence: 59 givenname: Ammar surname: Al-Chalabi fullname: Al-Chalabi, Ammar – sequence: 60 givenname: Christopher E surname: Shaw fullname: Shaw, Christopher E – sequence: 61 givenname: Andrew B surname: Singleton fullname: Singleton, Andrew B – sequence: 62 givenname: Rita surname: Guerreiro fullname: Guerreiro, Rita – sequence: 63 givenname: Thomas W surname: Mühleisen fullname: Mühleisen, Thomas W – sequence: 64 givenname: Markus M surname: Nöthen fullname: Nöthen, Markus M – sequence: 65 givenname: Susanne surname: Moebus fullname: Moebus, Susanne – sequence: 66 givenname: Karl-Heinz surname: Jöckel fullname: Jöckel, Karl-Heinz – sequence: 67 givenname: Norman surname: Klopp fullname: Klopp, Norman – sequence: 68 givenname: H-Erich surname: Wichmann fullname: Wichmann, H-Erich – sequence: 69 givenname: Minerva M surname: Carrasquillo fullname: Carrasquillo, Minerva M – sequence: 70 givenname: V Shane surname: Pankratz fullname: Pankratz, V Shane – sequence: 71 givenname: Steven G surname: Younkin fullname: Younkin, Steven G – sequence: 72 givenname: Michael surname: ODonovan fullname: ODonovan, Michael – sequence: 73 givenname: Michael J surname: Owen fullname: Owen, Michael J
Copyright	The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com. The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2015
Copyright_xml	– notice: The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com. – notice: The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2015
CorporateAuthor	GERAD/PERADES IGAP consortia
CorporateAuthor_xml	– name: GERAD/PERADES – name: IGAP consortia
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM
DOI	10.1093/brain/awv268
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	1460-2156
EndPage	3684
ExternalDocumentID	PMC5006219 26490334 10_1093_brain_awv268
Genre	Research Support, Non-U.S. Gov't Journal Article
GrantInformation_xml	– fundername: Medical Research Council grantid: MR/L023784/2 – fundername: Medical Research Council grantid: MR/L023784/1 – fundername: NIA NIH HHS grantid: U01 AG049505 – fundername: NIA NIH HHS grantid: P30 AG010124 – fundername: NIA NIH HHS grantid: R01 AG008122 – fundername: NIA NIH HHS grantid: U24 AG021886 – fundername: Medical Research Council grantid: MC_U123160651 – fundername: NIA NIH HHS grantid: R01 AG033193 – fundername: Medical Research Council grantid: MR/L501529/1 – fundername: NIA NIH HHS grantid: U01 AG032984
GroupedDBID	--- -E4 -~X .2P .55 .GJ .I3 .XZ .ZR 0R~ 1CY 1TH 23N 2WC 354 3O- 4.4 41~ 482 48X 53G 5GY 5RE 5VS 5WA 5WD 6PF 70D AABZA AACZT AAGKA AAIMJ AAJKP AAJQQ AAMDB AAMVS AAOGV AAPGJ AAPNW AAPQZ AAPXW AAQQT AARHZ AAUAY AAUQX AAVAP AAVLN AAWDT AAWTL AAYJJ AAYXX ABDFA ABDPE ABEJV ABEUO ABGNP ABIME ABIVO ABIXL ABJNI ABKDP ABLJU ABMNT ABNGD ABNHQ ABNKS ABPIB ABPQP ABPTD ABQLI ABQNK ABSMQ ABVGC ABWST ABXVV ABXZS ABZBJ ABZEO ACBNA ACFRR ACGFS ACIWK ACPQN ACPRK ACUFI ACUKT ACUTJ ACUTO ACVCV ACYHN ACZBC ADBBV ADEYI ADEZT ADGKP ADGZP ADHKW ADHZD ADIPN ADMTO ADNBA ADOCK ADQBN ADRTK ADVEK ADYVW ADZXQ AEGPL AEHUL AEJOX AEKPW AEKSI AELWJ AEMDU AEMQT AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFQV AFFZL AFGWE AFIYH AFOFC AFSHK AFXAL AFYAG AGINJ AGKEF AGKRT AGMDO AGORE AGQPQ AGQXC AGSYK AGUTN AHGBF AHMBA AHMMS AHXPO AI. AIJHB AJBYB AJDVS AJEEA AJNCP AKWXX ALMA_UNASSIGNED_HOLDINGS ALUQC ALXQX ANFBD APIBT APJGH APWMN AQDSO AQKUS ARIXL ASAOO ASPBG ATDFG ATGXG ATTQO AVNTJ AVWKF AXUDD AYOIW AZFZN BAWUL BAYMD BCRHZ BEYMZ BHONS BQDIO BR6 BSWAC BTRTY BVRKM BZKNY C1A C45 CAG CDBKE CITATION COF CS3 CXTWN CZ4 DAKXR DFGAJ DIK DILTD DU5 D~K E3Z EBS EE~ EIHJH EJD ELUNK EMOBN ENERS F5P F9B FECEO FEDTE FHSFR FLUFQ FOEOM FOTVD FQBLK GAUVT GJXCC GX1 H13 H5~ HAR HVGLF HW0 HZ~ IOX J21 J5H JXSIZ KAQDR KBUDW KOP KQ8 KSI KSN L7B M-Z MBLQV MBTAY MHKGH ML0 MVM N4W N9A NGC NLBLG NOMLY NOYVH NTWIH NU- NVLIB O0~ O9- OAUYM OAWHX OBFPC OBOKY OCZFY ODMLO OHH OHT OJQWA OJZSN OK1 OPAEJ OVD OWPYF O~Y P2P PAFKI PB- PEELM PQQKQ Q1. Q5Y QBD R44 RD5 RIG RNI ROL ROX ROZ RUSNO RW1 RXO RZF RZO TCN TCURE TEORI TJX TLC TMA TR2 VH1 VVN W8F WH7 WOQ X7H X7M XJT XOL YAYTL YKOAZ YQJ YSK YXANX ZCG ZGI ZKB ZKX ZXP ~91 CGR CUY CVF ECM EIF M49 NPM 7X8 5PM
ID	FETCH-LOGICAL-c384t-75c41489bd8359fe08f36d098fb8c16a24552d0e656c6850e15c195f9b7f3fde3
ISSN	0006-8950 1460-2156
IngestDate	Thu Aug 21 17:19:23 EDT 2025 Fri Jul 11 04:42:26 EDT 2025 Sat May 31 02:05:01 EDT 2025 Tue Jul 01 00:46:07 EDT 2025 Thu Apr 24 22:55:54 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	12
Keywords	Alzheimer’s disease polygenic score predictive model
Language	English
License	The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c384t-75c41489bd8359fe08f36d098fb8c16a24552d0e656c6850e15c195f9b7f3fde3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Data used in the preparation of this article were obtained from the Genetic and Environmental Risk for Alzheimer’s disease (GERAD) (which now incorporates the Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease using multiple powerful cohorts, focused Epigenetics and Stem cell metabolomics, PERADES consortium) and the International Genomics of Alzheimer’s Disease (IGAP) Consortia. For details of these consortia, see Appendix I and the Supplementary material.
OpenAccessLink	https://kclpure.kcl.ac.uk/portal/en/publications/7f81fabb-8ac0-426b-b817-edf08722a2c9
PMID	26490334
PQID	1736413885
PQPubID	23479
PageCount	12
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_5006219 proquest_miscellaneous_1736413885 pubmed_primary_26490334 crossref_citationtrail_10_1093_brain_awv268 crossref_primary_10_1093_brain_awv268
ProviderPackageCode	CITATION AAYXX
PublicationCentury	2000
PublicationDate	2015-12-01
PublicationDateYYYYMMDD	2015-12-01
PublicationDate_xml	– month: 12 year: 2015 text: 2015-12-01 day: 01
PublicationDecade	2010
PublicationPlace	England
PublicationPlace_xml	– name: England
PublicationTitle	Brain (London, England : 1878)
PublicationTitleAlternate	Brain
PublicationYear	2015
Publisher	Oxford University Press
Publisher_xml	– name: Oxford University Press
References	2016040708380356000_138.12.3673.15 2016040708380356000_138.12.3673.16 2016040708380356000_138.12.3673.17 2016040708380356000_138.12.3673.18 2016040708380356000_138.12.3673.11 2016040708380356000_138.12.3673.12 2016040708380356000_138.12.3673.13 2016040708380356000_138.12.3673.14 2016040708380356000_138.12.3673.1 2016040708380356000_138.12.3673.2 2016040708380356000_138.12.3673.3 2016040708380356000_138.12.3673.10 2016040708380356000_138.12.3673.4 2016040708380356000_138.12.3673.5 2016040708380356000_138.12.3673.6 2016040708380356000_138.12.3673.7 2016040708380356000_138.12.3673.8 2016040708380356000_138.12.3673.9 Moskvina (2016040708380356000_138.12.3673.20) 2013; 70 2016040708380356000_138.12.3673.19 2016040708380356000_138.12.3673.26 2016040708380356000_138.12.3673.27 2016040708380356000_138.12.3673.28 2016040708380356000_138.12.3673.22 2016040708380356000_138.12.3673.23 2016040708380356000_138.12.3673.24 2016040708380356000_138.12.3673.25 2016040708380356000_138.12.3673.21
References_xml	– ident: 2016040708380356000_138.12.3673.27 doi: 10.1016/j.jalz.2014.11.001 – ident: 2016040708380356000_138.12.3673.18 doi: 10.1016/j.biopsych.2013.01.011 – ident: 2016040708380356000_138.12.3673.7 doi: 10.1038/ng.440 – ident: 2016040708380356000_138.12.3673.19 doi: 10.1038/ng.2563 – ident: 2016040708380356000_138.12.3673.25 doi: 10.1002/ajmg.b.31216 – ident: 2016040708380356000_138.12.3673.11 doi: 10.1371/journal.pgen.1000529 – ident: 2016040708380356000_138.12.3673.28 doi: 10.1016/S0022-3956(02)00020-1 – ident: 2016040708380356000_138.12.3673.3 doi: 10.1371/journal.pbio.1000294 – volume: 70 start-page: 1268 year: 2013 ident: 2016040708380356000_138.12.3673.20 article-title: Analysis of genome-wide association studies of Alzheimer disease and of Parkinson disease to determine if these 2 diseases share a common genetic risk publication-title: JAMA Neurol – ident: 2016040708380356000_138.12.3673.10 doi: 10.1038/ng.803 – ident: 2016040708380356000_138.12.3673.24 doi: 10.1001/jama.2010.574 – ident: 2016040708380356000_138.12.3673.5 doi: 10.1002/ana.24335 – ident: 2016040708380356000_138.12.3673.16 doi: 10.1093/hmg/dds491 – ident: 2016040708380356000_138.12.3673.2 doi: 10.1038/mp.2010.65 – ident: 2016040708380356000_138.12.3673.22 doi: 10.1086/519795 – ident: 2016040708380356000_138.12.3673.9 doi: 10.1111/bjd.12443 – ident: 2016040708380356000_138.12.3673.1 doi: 10.1093/hmg/dds476 – ident: 2016040708380356000_138.12.3673.23 doi: 10.1038/nature12975 – ident: 2016040708380356000_138.12.3673.12 doi: 10.1016/j.jalz.2014.05.1757 – ident: 2016040708380356000_138.12.3673.4 doi: 10.1371/journal.pone.0094661 – ident: 2016040708380356000_138.12.3673.26 doi: 10.1176/appi.ajp.2011.11040551 – ident: 2016040708380356000_138.12.3673.17 doi: 10.1002/gepi.20533 – ident: 2016040708380356000_138.12.3673.15 doi: 10.1038/ng.2802 – ident: 2016040708380356000_138.12.3673.8 doi: 10.1212/WNL.0b013e31828726f5 – ident: 2016040708380356000_138.12.3673.21 doi: 10.1038/ng.801 – ident: 2016040708380356000_138.12.3673.14 doi: 10.1038/ng.439 – ident: 2016040708380356000_138.12.3673.13 doi: 10.1038/nature08185 – ident: 2016040708380356000_138.12.3673.6 doi: 10.1038/nrneurol.2009.215
SSID	ssj0014326
Score	2.612764
Snippet	The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic... Heritability estimates for Alzheimer’s disease in genome-wide association studies increase substantially when weak effect loci are also considered....
SourceID	pubmedcentral proquest pubmed crossref
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	3673
SubjectTerms	Alleles Alzheimer Disease - genetics Apolipoproteins E - genetics Case-Control Studies Genetic Predisposition to Disease - genetics Genetic Testing Genetic Variation - genetics Genome-Wide Association Study Genotype Humans Logistic Models Multifactorial Inheritance - genetics Original Risk ROC Curve
Title	Common polygenic variation enhances risk prediction for Alzheimer’s disease
URI	https://www.ncbi.nlm.nih.gov/pubmed/26490334 https://www.proquest.com/docview/1736413885 https://pubmed.ncbi.nlm.nih.gov/PMC5006219
Volume	138
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
link	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JbtswECWMFCh6KbrX3cAC7SlQIomLqGOQBWkLFz0kRW4CRZGwAFc2vKRoTv2Nfkv_pl_SoUjTUhagzUUwZFqU-J5HM8NZEHqngAhVyapIM8EjWsV5JIE4kVKKVkZww9sMudFnfnxKP56xs8HgdydqabUsd9TFtXklt0EVzgGuNkv2P5ANF4UT8BnwhSMgDMd_wthmdwB6s-nkB4yp1fY5WL4OUd2MLZ4LFzs-m9v9mBBWuDe5GOva9k3xoQ75ordTEwoeybq5tueHcySITHQcCYcLW-YhCn3kv0r7QvPduVsnTu3dOg7RjQ-1sdV7fULi2AudjWSc-77WB_CE_v68lyJhlyI-bsh-7ElmHoncFaHd0U4YUx5HoJLwnrQmokvLtCN8CXddUfyLnHDXfO7KS8IV0CrnrevlSH4_T11nnw5jZt9ayoC2mMfEe1v7Zbm_jPaZTUC1JWfvpGCj2PYZBx8-hS0sStpef-HBfNYFTL7bTr3rJrbVqP0sfdXoir1zOWy3owedPED3vQGD9xwbH6KBbh6huyMfovEYjRwpcSAlDqTEa1JiS0q8ISUG2HAg5Z-fvxbY0_EJOj06PNk_jnzPjkgRQZdRxhQFCzsvK1Dtc6NjYQgHCSBMKVTCZUoZS6tYgxmhuGCxTphKcmbyMjPEVJo8RVvNtNHPERZGGi4rWqo0oVJWQmbWmCE5y2ENmBqi7fVyFcoXtLd9VSaFC6wgRbvOhVvnIXofRs9cIZcbxr1dr3wBktZun8lGT1eLIskIB5VPCDZEzxwS4UprCIco62EUBtgq7v1vmnrcVnP3LHpx61--RPc2_7hXaGs5X-nXoCkvyzctI_8C8J7FYw
linkProvider	Flying Publisher
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Common+polygenic+variation+enhances+risk+prediction+for+Alzheimer%E2%80%99s+disease&rft.jtitle=Brain+%28London%2C+England+%3A+1878%29&rft.au=Escott-Price%2C+Valentina&rft.au=Sims%2C+Rebecca&rft.au=Bannister%2C+Christian&rft.au=Harold%2C+Denise&rft.date=2015-12-01&rft.pub=Oxford+University+Press&rft.issn=0006-8950&rft.eissn=1460-2156&rft.volume=138&rft.issue=12&rft.spage=3673&rft.epage=3684&rft_id=info:doi/10.1093%2Fbrain%2Fawv268&rft_id=info%3Apmid%2F26490334&rft.externalDocID=PMC5006219
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0006-8950&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0006-8950&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0006-8950&client=summon