Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods
Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritab...
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| Published in | Environmental health perspectives Vol. 130; no. 5; p. 55001 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Institute of Environmental Health Sciences
01.05.2022
Environmental Health Perspectives |
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| Online Access | Get full text |
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| Abstract | Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098. |
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| AbstractList | Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098. Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098. Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. |
| Audience | Academic |
| Author | Engel, Stephanie M Highland, Heather M Lilly, Adam G Howard, Annie Green Graff, Mariaelisa Avery, Christy L North, Kari E Lee, Moa P Downie, Carolina G Ballou, Anna F Collins, Jason M Gordon-Larsen, Penny Lu, Kun Buchanan, Victoria L Rager, Julia E Staley, Brooke S |
| Author_xml | – sequence: 1 givenname: Christy L orcidid: 0000-0002-1044-8162 surname: Avery fullname: Avery, Christy L organization: Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 2 givenname: Annie Green orcidid: 0000-0003-0837-8166 surname: Howard fullname: Howard, Annie Green organization: Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 3 givenname: Anna F orcidid: 0000-0002-6875-4410 surname: Ballou fullname: Ballou, Anna F organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 4 givenname: Victoria L orcidid: 0000-0001-9943-9950 surname: Buchanan fullname: Buchanan, Victoria L organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 5 givenname: Jason M orcidid: 0000-0002-2903-797X surname: Collins fullname: Collins, Jason M organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 6 givenname: Carolina G orcidid: 0000-0001-6972-9981 surname: Downie fullname: Downie, Carolina G organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 7 givenname: Stephanie M surname: Engel fullname: Engel, Stephanie M organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 8 givenname: Mariaelisa surname: Graff fullname: Graff, Mariaelisa organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 9 givenname: Heather M surname: Highland fullname: Highland, Heather M organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 10 givenname: Moa P surname: Lee fullname: Lee, Moa P organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 11 givenname: Adam G orcidid: 0000-0002-3740-1540 surname: Lilly fullname: Lilly, Adam G organization: Department of Sociology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 12 givenname: Kun surname: Lu fullname: Lu, Kun organization: Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 13 givenname: Julia E orcidid: 0000-0002-2882-5042 surname: Rager fullname: Rager, Julia E organization: Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 14 givenname: Brooke S orcidid: 0000-0003-1502-602X surname: Staley fullname: Staley, Brooke S organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 15 givenname: Kari E surname: North fullname: North, Kari E organization: Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 16 givenname: Penny orcidid: 0000-0001-5322-4188 surname: Gordon-Larsen fullname: Gordon-Larsen, Penny organization: Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35533073$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1093_exposome_osae002 crossref_primary_10_1016_j_envres_2023_115788 crossref_primary_10_1161_CIRCRESAHA_123_322002 |
| Cites_doi | 10.3390/toxics7030041 10.1038/s41588-019-0409-8 10.1038/nrg.2016.27 10.1093/bioinformatics/btu704 10.1038/s41588-018-0271-0 10.1002/gepi.22326 10.3389/fimmu.2018.02727 10.1186/s13059-017-1215-1 10.1093/aje/kwy204 10.1016/j.cotox.2020.07.005 10.1289/EHP140 10.3390/metabo9040076 10.1093/aje/kwz028 10.1038/nrg.2015.36 10.1038/nrg2764 10.1016/j.ajogmf.2021.100393 10.1097/EDE.0000000000000081 10.1093/aje/kwu283 10.1093/aje/kwx261 10.1016/j.scitotenv.2021.145759 10.1093/ije/dyw314 10.1534/genetics.118.301394 10.1016/j.envpol.2017.11.033 10.1086/367924 10.1097/EDE.0000000000000987 10.1097/EDE.0000000000000641 10.1038/s41467-018-07348-x 10.1038/s41370-017-0012-y 10.1038/ng.3656 10.1016/j.ajhg.2011.11.029 10.1016/j.toxrep.2015.11.009 10.1016/j.biopsych.2018.12.015 10.1016/B978-0-08-097086-8.42095-7 10.1089/cmb.2019.0325 10.1097/MOP.0000000000000327 10.1016/j.jclinepi.2015.08.001 10.1007/s00216-019-02166-6 10.1093/ije/dyr236 10.1186/s12863-015-0248-2 10.1126/science.aay3164 10.1101/2020.04.23.20077099 10.1101/362392 10.1016/j.metabol.2020.154292 10.1177/0962280206077743 10.1289/ehp.1308015 10.1093/hmg/ddv190 10.1093/ije/dyz046 10.1038/nrg3457 10.1016/j.dnarep.2014.03.031 10.1038/s41467-019-13770-6 10.1016/j.ajhg.2017.06.005 10.1146/annurev.genom.9.081307.164242 10.1093/hmg/ddv112 10.1158/1055-9965.EPI-05-0456 10.1289/EHP1712 10.1038/s41576-019-0127-1 10.1186/s12859-016-1122-6 10.1038/s41588-018-0152-6 10.1038/s41540-019-0099-y 10.1038/s41467-020-18489-3 10.1186/s12864-019-5957-x 10.1038/538161a 10.1021/es901480f 10.1021/acs.est.0c02657 10.1101/252270 10.1007/s00198-019-05118-z 10.1093/hmg/ddt239 10.1002/gepi.20168 10.1038/447655a 10.1038/s41588-018-0193-x 10.1001/jamanetworkopen.2021.12820 10.1001/jamacardio.2020.5398 10.1371/journal.pone.0226771 10.1038/s41598-017-19120-0 10.1093/aje/kwx258 10.1093/ije/dyt182 10.1016/j.ijheh.2016.08.001 10.1021/acs.est.7b04781 10.1146/annurev-pharmtox-010818-021315 10.1016/j.envint.2015.12.008 10.1038/ejhg.2017.51 10.1038/s41467-020-15193-0 10.1093/nar/gky1120 10.1371/journal.pgen.1008500 10.1016/j.ajhg.2010.11.011 10.1016/j.neuron.2010.10.003 10.1038/s41598-019-52482-1 10.1002/gepi.22013 10.1289/ehp.1205305 10.1289/EHP1011 10.1001/jama.300.11.1303 10.1101/2020.10.21.347773 10.1001/jamacardio.2020.4317 10.1038/ng.2354 10.1212/WNL.0000000000007279 10.1038/ng.3367 10.1146/annurev-genom-083117-021602 10.1093/oxfordjournals.aje.a115184 10.1289/EHP412 10.1038/s41467-018-04362-x 10.1186/s12940-016-0141-0 10.1038/s41416-020-01236-2 10.1161/CIRCGENETICS.108.829747 10.1002/cphg.80 10.1093/nar/gkw1133 10.1016/j.envint.2016.11.026 10.1136/bmj.k601 10.1016/j.reprotox.2020.02.004 10.1534/genetics.115.185967 10.1038/s41586-019-1310-4 10.1007/s00439-012-1199-6 10.1111/imm.12195 10.1016/j.ajhg.2021.02.014 10.1002/gepi.22328 10.1016/j.envint.2019.05.071 10.1093/aje/kwj039 10.1371/journal.pgen.1006711 10.1093/aje/kwv325 10.1038/ng.3506 10.1371/journal.pmed.1002548 10.1101/2021.11.19.21266436 10.1093/hmg/ddy163 10.1186/s12859-016-1273-5 10.1080/01621459.2015.1021005 10.1056/NEJMoa055218 10.1158/1055-9965.EPI-16-0106 10.1002/etc.3025 10.1289/ehp.1307204 10.1289/ehp.0901541 10.1289/EHP6803 10.1111/apt.13154 10.1016/j.ajhg.2020.09.003 10.1021/acs.est.8b04752 |
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| References_xml | – ident: e_1_3_2_66_2 doi: 10.3390/toxics7030041 – ident: e_1_3_2_124_2 doi: 10.1038/s41588-019-0409-8 – ident: e_1_3_2_48_2 doi: 10.1038/nrg.2016.27 – ident: e_1_3_2_22_2 doi: 10.1093/bioinformatics/btu704 – ident: e_1_3_2_132_2 doi: 10.1038/s41588-018-0271-0 – ident: e_1_3_2_26_2 doi: 10.1002/gepi.22326 – ident: e_1_3_2_120_2 doi: 10.3389/fimmu.2018.02727 – ident: e_1_3_2_119_2 doi: 10.1186/s13059-017-1215-1 – ident: e_1_3_2_81_2 doi: 10.1093/aje/kwy204 – ident: e_1_3_2_53_2 doi: 10.1016/j.cotox.2020.07.005 – ident: e_1_3_2_4_2 doi: 10.1289/EHP140 – ident: e_1_3_2_115_2 doi: 10.3390/metabo9040076 – ident: e_1_3_2_38_2 doi: 10.1093/aje/kwz028 – ident: e_1_3_2_89_2 doi: 10.1038/nrg.2015.36 – ident: e_1_3_2_133_2 doi: 10.1038/nrg2764 – ident: e_1_3_2_63_2 doi: 10.1016/j.ajogmf.2021.100393 – ident: e_1_3_2_78_2 doi: 10.1097/EDE.0000000000000081 – ident: e_1_3_2_87_2 doi: 10.1093/aje/kwu283 – ident: e_1_3_2_107_2 doi: 10.1093/aje/kwx261 – ident: e_1_3_2_55_2 doi: 10.1016/j.scitotenv.2021.145759 – ident: e_1_3_2_86_2 doi: 10.1093/ije/dyw314 – ident: e_1_3_2_131_2 doi: 10.1534/genetics.118.301394 – ident: e_1_3_2_57_2 doi: 10.1016/j.envpol.2017.11.033 – ident: e_1_3_2_101_2 doi: 10.1086/367924 – ident: e_1_3_2_10_2 doi: 10.1097/EDE.0000000000000987 – ident: e_1_3_2_14_2 doi: 10.1097/EDE.0000000000000641 – ident: e_1_3_2_17_2 doi: 10.1038/s41467-018-07348-x – ident: e_1_3_2_65_2 doi: 10.1038/s41370-017-0012-y – ident: e_1_3_2_21_2 doi: 10.1038/ng.3656 – ident: e_1_3_2_30_2 doi: 10.1016/j.ajhg.2011.11.029 – ident: e_1_3_2_67_2 doi: 10.1016/j.toxrep.2015.11.009 – ident: e_1_3_2_50_2 doi: 10.1016/j.biopsych.2018.12.015 – start-page: 255 volume-title: International Encyclopedia of the Social & Behavioral Sciences year: 2015 ident: e_1_3_2_9_2 doi: 10.1016/B978-0-08-097086-8.42095-7 contributor: fullname: Hill J – ident: e_1_3_2_51_2 doi: 10.1089/cmb.2019.0325 – ident: e_1_3_2_60_2 doi: 10.1097/MOP.0000000000000327 – ident: e_1_3_2_75_2 doi: 10.1016/j.jclinepi.2015.08.001 – ident: e_1_3_2_59_2 doi: 10.1007/s00216-019-02166-6 – ident: e_1_3_2_3_2 doi: 10.1093/ije/dyr236 – ident: e_1_3_2_25_2 doi: 10.1186/s12863-015-0248-2 – ident: e_1_3_2_8_2 doi: 10.1126/science.aay3164 – ident: e_1_3_2_49_2 doi: 10.1101/2020.04.23.20077099 – ident: e_1_3_2_79_2 doi: 10.1101/362392 – ident: e_1_3_2_113_2 doi: 10.1016/j.metabol.2020.154292 – ident: e_1_3_2_73_2 doi: 10.1177/0962280206077743 – ident: e_1_3_2_61_2 doi: 10.1289/ehp.1308015 – ident: e_1_3_2_110_2 doi: 10.1093/hmg/ddv190 – ident: e_1_3_2_13_2 doi: 10.1093/ije/dyz046 – ident: e_1_3_2_16_2 doi: 10.1038/nrg3457 – ident: e_1_3_2_54_2 doi: 10.1016/j.dnarep.2014.03.031 – ident: e_1_3_2_127_2 doi: 10.1038/s41467-019-13770-6 – ident: e_1_3_2_18_2 doi: 10.1016/j.ajhg.2017.06.005 – ident: e_1_3_2_19_2 doi: 10.1146/annurev.genom.9.081307.164242 – ident: e_1_3_2_126_2 doi: 10.1093/hmg/ddv112 – ident: e_1_3_2_2_2 doi: 10.1158/1055-9965.EPI-05-0456 – ident: e_1_3_2_7_2 doi: 10.1289/EHP1712 – ident: e_1_3_2_29_2 doi: 10.1038/s41576-019-0127-1 – ident: e_1_3_2_99_2 doi: 10.1186/s12859-016-1122-6 – ident: e_1_3_2_111_2 doi: 10.1038/s41588-018-0152-6 – ident: e_1_3_2_114_2 doi: 10.1038/s41540-019-0099-y – ident: e_1_3_2_128_2 doi: 10.1038/s41467-020-18489-3 – ident: e_1_3_2_28_2 doi: 10.1186/s12864-019-5957-x – ident: e_1_3_2_130_2 doi: 10.1038/538161a – ident: e_1_3_2_71_2 doi: 10.1021/es901480f – ident: e_1_3_2_118_2 doi: 10.1021/acs.est.0c02657 – ident: e_1_3_2_52_2 doi: 10.1101/252270 – ident: e_1_3_2_90_2 doi: 10.1007/s00198-019-05118-z – ident: e_1_3_2_43_2 doi: 10.1093/hmg/ddt239 – ident: e_1_3_2_100_2 doi: 10.1002/gepi.20168 – ident: e_1_3_2_109_2 doi: 10.1038/447655a – ident: e_1_3_2_44_2 doi: 10.1038/s41588-018-0193-x – ident: e_1_3_2_83_2 doi: 10.1001/jamanetworkopen.2021.12820 – ident: e_1_3_2_46_2 doi: 10.1001/jamacardio.2020.5398 – ident: e_1_3_2_92_2 doi: 10.1371/journal.pone.0226771 – ident: e_1_3_2_102_2 doi: 10.1038/s41598-017-19120-0 – ident: e_1_3_2_108_2 doi: 10.1093/aje/kwx258 – ident: e_1_3_2_12_2 doi: 10.1093/ije/dyt182 – ident: e_1_3_2_37_2 doi: 10.1016/j.ijheh.2016.08.001 – ident: e_1_3_2_58_2 doi: 10.1021/acs.est.7b04781 – ident: e_1_3_2_6_2 doi: 10.1146/annurev-pharmtox-010818-021315 – ident: e_1_3_2_56_2 doi: 10.1016/j.envint.2015.12.008 – ident: e_1_3_2_24_2 doi: 10.1038/ejhg.2017.51 – ident: e_1_3_2_129_2 doi: 10.1038/s41467-020-15193-0 – ident: e_1_3_2_31_2 doi: 10.1093/nar/gky1120 – ident: e_1_3_2_27_2 doi: 10.1371/journal.pgen.1008500 – ident: e_1_3_2_41_2 doi: 10.1016/j.ajhg.2010.11.011 – ident: e_1_3_2_35_2 doi: 10.1016/j.neuron.2010.10.003 – ident: e_1_3_2_85_2 doi: 10.1038/s41598-019-52482-1 – ident: e_1_3_2_82_2 doi: 10.1002/gepi.22013 – ident: e_1_3_2_11_2 doi: 10.1289/ehp.1205305 – ident: e_1_3_2_62_2 doi: 10.1289/EHP1011 – ident: e_1_3_2_72_2 doi: 10.1001/jama.300.11.1303 – ident: e_1_3_2_77_2 doi: 10.1101/2020.10.21.347773 – ident: e_1_3_2_80_2 doi: 10.1001/jamacardio.2020.4317 – ident: e_1_3_2_23_2 doi: 10.1038/ng.2354 – ident: e_1_3_2_96_2 doi: 10.1212/WNL.0000000000007279 – ident: e_1_3_2_122_2 doi: 10.1038/ng.3367 – ident: e_1_3_2_20_2 doi: 10.1146/annurev-genom-083117-021602 – ident: e_1_3_2_105_2 doi: 10.1093/oxfordjournals.aje.a115184 – ident: e_1_3_2_134_2 doi: 10.1289/EHP412 – ident: e_1_3_2_112_2 doi: 10.1038/s41467-018-04362-x – ident: e_1_3_2_70_2 doi: 10.1186/s12940-016-0141-0 – ident: e_1_3_2_84_2 doi: 10.1038/s41416-020-01236-2 – ident: e_1_3_2_33_2 doi: 10.1161/CIRCGENETICS.108.829747 – ident: e_1_3_2_94_2 doi: 10.1002/cphg.80 – ident: e_1_3_2_32_2 doi: 10.1093/nar/gkw1133 – ident: e_1_3_2_15_2 doi: 10.1016/j.envint.2016.11.026 – ident: e_1_3_2_45_2 doi: 10.1136/bmj.k601 – ident: e_1_3_2_64_2 doi: 10.1016/j.reprotox.2020.02.004 – ident: e_1_3_2_123_2 doi: 10.1534/genetics.115.185967 – ident: e_1_3_2_74_2 doi: 10.1038/s41586-019-1310-4 – ident: e_1_3_2_39_2 doi: 10.1007/s00439-012-1199-6 – ident: e_1_3_2_93_2 doi: 10.1111/imm.12195 – ident: e_1_3_2_40_2 doi: 10.1016/j.ajhg.2021.02.014 – ident: e_1_3_2_104_2 doi: 10.1002/gepi.22328 – ident: e_1_3_2_88_2 doi: 10.1016/j.envint.2019.05.071 – ident: e_1_3_2_103_2 doi: 10.1093/aje/kwj039 – ident: e_1_3_2_125_2 doi: 10.1371/journal.pgen.1006711 – ident: e_1_3_2_5_2 doi: 10.1093/aje/kwv325 – ident: e_1_3_2_121_2 doi: 10.1038/ng.3506 – ident: e_1_3_2_47_2 doi: 10.1371/journal.pmed.1002548 – ident: e_1_3_2_95_2 doi: 10.1101/2021.11.19.21266436 – ident: e_1_3_2_76_2 doi: 10.1093/hmg/ddy163 – ident: e_1_3_2_98_2 doi: 10.1186/s12859-016-1273-5 – ident: e_1_3_2_97_2 doi: 10.1080/01621459.2015.1021005 – ident: e_1_3_2_91_2 doi: 10.1056/NEJMoa055218 – ident: e_1_3_2_34_2 doi: 10.1158/1055-9965.EPI-16-0106 – ident: e_1_3_2_117_2 doi: 10.1002/etc.3025 – ident: e_1_3_2_36_2 doi: 10.1289/ehp.1307204 – ident: e_1_3_2_42_2 doi: 10.1289/ehp.0901541 – ident: e_1_3_2_68_2 doi: 10.1289/EHP6803 – ident: e_1_3_2_69_2 doi: 10.1111/apt.13154 – ident: e_1_3_2_106_2 doi: 10.1016/j.ajhg.2020.09.003 – ident: e_1_3_2_116_2 doi: 10.1021/acs.est.8b04752 |
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| Snippet | Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently... |
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| SubjectTerms | Biomarkers Environmental Exposure - analysis Environmental health Exposome Genetic aspects Medical research Medicine, Experimental Methods Research Design Toxicogenomics |
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| Title | Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods |
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