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3. References

Author

Herd, Pamela and Moynihan, Donald P.

Published
2018

6. Index

Author

Herd, Pamela and Moynihan, Donald P.

Published
2018

7. Cover

Author

Herd, Pamela and Moynihan, Donald P.

Published
2018

8. Notes

Author

Herd, Pamela and Moynihan, Donald P.

Published
2018

16. Acknowledgments

Author

Herd, Pamela and Moynihan, Donald P.

Published
2018

21. Title Page

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

22. Copyright

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

23. Frontmatter

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

25. About the Series

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

26. Dedication

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

27. About the Authors

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

30. Acknowledgments

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

31. Chapter 5

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

32. Endnotes

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

37. Index

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

38. References

Author

Meyer, Madonna Harrington and Herd, Pamela

Published
2007

40. Genetic diversity fuels gene discovery for tobacco and alcohol use

Author

Saunders, Gretchen RB, Wang, Xingyan, Chen, Fang, Jang, Seon-Kyeong, Liu, Mengzhen, Wang, Chen, Gao, Shuang, Jiang, Yu, Khunsriraksakul, Chachrit, Otto, Jacqueline M, Addison, Clifton, Akiyama, Masato, Albert, Christine M, Aliev, Fazil, Alonso, Alvaro, Arnett, Donna K, Ashley-Koch, Allison E, Ashrani, Aneel A, Barnes, Kathleen C, Barr, R Graham, Bartz, Traci M, Becker, Diane M, Bielak, Lawrence F, Benjamin, Emelia J, Bis, Joshua C, Bjornsdottir, Gyda, Blangero, John, Bleecker, Eugene R, Boardman, Jason D, Boerwinkle, Eric, Boomsma, Dorret I, Boorgula, Meher Preethi, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Iona, Cho, Michael H, Choquet, Hélène, Cole, John W, Cornelis, Marilyn C, Cucca, Francesco, Curran, Joanne E, de Andrade, Mariza, Dick, Danielle M, Docherty, Anna R, Duggirala, Ravindranath, Eaton, Charles B, Ehringer, Marissa A, Esko, Tõnu, Faul, Jessica D, Fernandes Silva, Lilian, Fiorillo, Edoardo, Fornage, Myriam, Freedman, Barry I, Gabrielsen, Maiken E, Garrett, Melanie E, Gharib, Sina A, Gieger, Christian, Gillespie, Nathan, Glahn, David C, Gordon, Scott D, Gu, Charles C, Gu, Dongfeng, Gudbjartsson, Daniel F, Guo, Xiuqing, Haessler, Jeffrey, Hall, Michael E, Haller, Toomas, Harris, Kathleen Mullan, He, Jiang, Herd, Pamela, Hewitt, John K, Hickie, Ian, Hidalgo, Bertha, Hokanson, John E, Hopfer, Christian, Hottenga, JoukeJan, Hou, Lifang, Huang, Hongyan, Hung, Yi-Jen, Hunter, David J, Hveem, Kristian, Hwang, Shih-Jen, Hwu, Chii-Min, Iacono, William, Irvin, Marguerite R, Jee, Yon Ho, Johnson, Eric O, Joo, Yoonjung Y, Jorgenson, Eric, Justice, Anne E, Kamatani, Yoichiro, Kaplan, Robert C, Kaprio, Jaakko, Kardia, Sharon LR, and Keller, Matthew C

Subjects
Genetics, Alcoholism, Alcohol Use and Health, Prevention, Human Genome, Substance Misuse, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Humans, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Multifactorial Inheritance, Risk Factors, Tobacco Use, Alcohol Drinking, Transcriptome, Sample Size, Genetic Loci, Internationality, Europe, 23andMe Research Team, Biobank Japan Project, General Science & Technology
Abstract

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

Published
2022

41. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Author

Okbay, Aysu, Wu, Yeda, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Nehzati, Seyed Moeen, Sidorenko, Julia, Kweon, Hyeokmoon, Goldman, Grant, Gjorgjieva, Tamara, Jiang, Yunxuan, Hicks, Barry, Tian, Chao, Hinds, David A, Ahlskog, Rafael, Magnusson, Patrik KE, Oskarsson, Sven, Hayward, Caroline, Campbell, Archie, Porteous, David J, Freese, Jeremy, Herd, Pamela, Watson, Chelsea, Jala, Jonathan, Conley, Dalton, Koellinger, Philipp D, Johannesson, Magnus, Laibson, David, Meyer, Michelle N, Lee, James J, Kong, Augustine, Yengo, Loic, Cesarini, David, Turley, Patrick, Visscher, Peter M, Beauchamp, Jonathan P, Benjamin, Daniel J, and Young, Alexander I

Subjects
Human Genome, Genetics, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, 23andMe Research Team, Social Science Genetic Association Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Published
2022

42. Resource profile and user guide of the Polygenic Index Repository

Author

Becker, Joel, Burik, Casper AP, Goldman, Grant, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Belsky, Daniel W, Karlsson Linnér, Richard, Ahlskog, Rafael, Kleinman, Aaron, Hinds, David A, Caspi, Avshalom, Corcoran, David L, Moffitt, Terrie E, Poulton, Richie, Sugden, Karen, Williams, Benjamin S, Harris, Kathleen Mullan, Steptoe, Andrew, Ajnakina, Olesya, Milani, Lili, Esko, Tõnu, Iacono, William G, McGue, Matt, Magnusson, Patrik KE, Mallard, Travis T, Harden, K Paige, Tucker-Drob, Elliot M, Herd, Pamela, Freese, Jeremy, Young, Alexander, Beauchamp, Jonathan P, Koellinger, Philipp D, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Meyer, Michelle N, Laibson, David, Cesarini, David, Benjamin, Daniel J, Turley, Patrick, and Okbay, Aysu

Subjects
Epidemiology, Health Sciences, 2.6 Resources and infrastructure (aetiology), Aetiology, Data Analysis, Databases, Genetic, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Polygenic score, Polygenic index, Repository, Measurement error, 23andMe Research Group, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Published
2021

43. Reporting guidelines for human microbiome research: the STORMS checklist

Author

Mirzayi, Chloe, Renson, Audrey, Zohra, Fatima, Elsafoury, Shaimaa, Geistlinger, Ludwig, Kasselman, Lora J, Eckenrode, Kelly, van de Wijgert, Janneke, Loughman, Amy, Marques, Francine Z, MacIntyre, David A, Arumugam, Manimozhiyan, Azhar, Rimsha, Beghini, Francesco, Bergstrom, Kirk, Bhatt, Ami, Bisanz, Jordan E, Braun, Jonathan, Bravo, Hector Corrada, Buck, Gregory A, Bushman, Frederic, Casero, David, Clarke, Gerard, Collado, Maria Carmen, Cotter, Paul D, Cryan, John F, Demmer, Ryan T, Devkota, Suzanne, Elinav, Eran, Escobar, Juan S, Fettweis, Jennifer, Finn, Robert D, Fodor, Anthony A, Forslund, Sofia, Franke, Andre, Furlanello, Cesare, Gilbert, Jack, Grice, Elizabeth, Haibe-Kains, Benjamin, Handley, Scott, Herd, Pamela, Holmes, Susan, Jacobs, Jonathan P, Karstens, Lisa, Knight, Rob, Knights, Dan, Koren, Omry, Kwon, Douglas S, Langille, Morgan, Lindsay, Brianna, McGovern, Dermot, McHardy, Alice C, McWeeney, Shannon, Mueller, Noel T, Nezi, Luigi, Olm, Matthew, Palm, Noah, Pasolli, Edoardo, Raes, Jeroen, Redinbo, Matthew R, Rühlemann, Malte, Balfour Sartor, R, Schloss, Patrick D, Schriml, Lynn, Segal, Eran, Shardell, Michelle, Sharpton, Thomas, Smirnova, Ekaterina, Sokol, Harry, Sonnenburg, Justin L, Srinivasan, Sujatha, Thingholm, Louise B, Turnbaugh, Peter J, Upadhyay, Vaibhav, Walls, Ramona L, Wilmes, Paul, Yamada, Takuji, Zeller, Georg, Zhang, Mingyu, Zhao, Ni, Zhao, Liping, Bao, Wenjun, Culhane, Aedin, Devanarayan, Viswanath, Dopazo, Joaquin, Fan, Xiaohui, Fischer, Matthias, Jones, Wendell, Kusko, Rebecca, Mason, Christopher E, Mercer, Tim R, Sansone, Susanna-Assunta, Scherer, Andreas, Shi, Leming, Thakkar, Shraddha, Tong, Weida, Wolfinger, Russ, Hunter, Christopher, Segata, Nicola, and Huttenhower, Curtis

Subjects
Human Genome, Genetics, Computational Biology, Dysbiosis, Humans, Microbiota, Observational Studies as Topic, Research Design, Translational Science, Biomedical, Genomic Standards Consortium, Massive Analysis and Quality Control Society, Medical and Health Sciences, Immunology
Abstract

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Published
2021

44. TACKLING ADMINISTRATIVE BURDENS: THE LEGAL FRAMEWORK AND INNOVATIVE PRACTICES

Author

Herd, Pamela, Moynihan, Donald, and Widman, Amy

Subjects
Government regulation, Company business management, Cost benefit analysis, Information accessibility, Administrative procedure -- Laws, regulations and rules -- Research -- Evaluation, Administrative agencies -- Management -- Capacity -- Research, Cost benefit analysis -- Research, Government attorneys -- Powers and duties -- Research, Best practices -- Analysis -- Research, Corporate culture -- Management -- Evaluation -- Research, Cooperation (Economics) -- Methods -- Political aspects -- Research, Information management -- Methods -- Research -- Laws, regulations and rules, Paperwork Reduction Act of 1995
Abstract

INTRODUCTION I. DEFINITIONS II. METHODOLOGY III. BACKGROUND A. Legal Framework 1. Agency Prioritization 2. Data Collection 3. Data Management 4. Simplification B. Policy Architecture C. Literature Review IV. EXISTING AGENCY [...]

Published
2024

50. Personality Predicts Mortality Risk: An Integrative Data Analysis of 15 International Longitudinal Studies.

Author

Graham, Eileen, Rutsohn, Joshua, Turiano, Nicholas, Bendayan, Rebecca, Batterham, Philip, Gerstorf, Denis, Katz, Mindy, Reynolds, Chandra, Sharp, Emily, Yoneda, Tomiko, Bastarache, Emily, Elleman, Lorien, Zelinski, Elizabeth, Johansson, Boo, Kuh, Diana, Barnes, Lisa, Bennett, David, Deeg, Dorly, Lipton, Richard, Pedersen, Nancy, Piccinin, Andrea, Spiro, Avron, Muniz-Terrera, Graciela, Willis, Sherry, Schaie, K, Roan, Carol, Herd, Pamela, Hofer, Scott, and Mroczek, Daniel

Subjects
Generalizability, Health Behaviors, Mortality, Personality, Replicability
Abstract

This study examined the Big Five personality traits as predictors of mortality risk, and smoking as a mediator of that association. Replication was built into the fabric of our design: we used a Coordinated Analysis with 15 international datasets, representing 44,094 participants. We found that high neuroticism and low conscientiousness, extraversion, and agreeableness were consistent predictors of mortality across studies. Smoking had a small mediating effect for neuroticism. Country and baseline age explained variation in effects: studies with older baseline age showed a pattern of protective effects (HR

Published
2017

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