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Biosocial Surveys (2007)
Committee on Population (CPOP)

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. "11 Comments on the Utility of Social Science Surveys for the Discovery and Validation of Genes Influencing Complex Traits--Harald H.H. Göring." Biosocial Surveys. Washington, DC: The National Academies Press, 2007.

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Biosocial Surveys

disorder, and predictions that scientists will unravel the genetic mysteries of most conditions in only a few more years abound, often coupled with enormous promises for the prevention and cure of disease in the near future. Under these circumstances, it is not surprising that many individuals have a very deterministic perception of the action of genes and think that there is a gene for every condition, with the condition being fully and accurately determined by this gene, independently of anything else. Geneticists are not blameless for this situation, as they often do not correct such views, unintentionally promote them by using sloppy terminology consistent with such opinions, or even intentionally further them by making exaggerated claims about the future impact of their area of research, perhaps in an effort to improve funding. It is in this environment that many researchers in other fields have begun thinking about whether they should and can incorporate gene discovery into their own studies.

In this chapter, I comment on the utility of large-scale social science surveys for the discovery and validation of genes influencing conditions of interest to social scientists. I start with a brief overview of the nature of so-called complex traits and highlight some of the concepts behind study designs that are being used for the identification of genes. I attempt to contrast the traits for which gene-mapping studies have succeeded and the designs of gene discovery experiments to social science surveys, with a focus of the suitability of the latter for gene identification. I close with a few remarks on how such surveys may be useful for gene discovery and validation from my perspective.

ETIOLOGICAL ARCHITECTURE OF COMPLEX TRAITS

There is no accepted definition of what constitutes a so-called complex or multifactorial trait. The term is generally used to denote the opposite of a so-called Mendelian trait, in which a defect in a gene by itself can cause a specific phenotype (the focus is often on a disease). In contrast, the relationship between genotype and phenotype is not as deterministic in complex traits, for which individual genetic variants merely modulate the probability of presenting a particular phenotype.

For many traits, we have absolutely no idea about the identity of environmental factors and genes whose variants account for some of the variability in the phenotype in the population, and the designation of a trait as complex simply acknowledges the belief—based on common sense, failed gene mapping attempts, analogies with similar traits about which we have a better understanding, or evolutionary considerations—that a multitude of genetic and environmental factors must influence the phenotype. It may well turn out that a trait is not as complicated as first assumed, such as when gene mapping studies readily succeed in pinpointing the

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Front Matter (R1-R14)
Introduction--James W. Vaupel, Kenneth W. Wachter, and Maxine Weinstein (1-12)
Part I: What We've Learned So Far (13-14)
1 Biological Indicators and Genetic Information in Danish Twin and Oldest-Old Surveys--Kaare Christensen, Lise Bathum, and Lene Christiansen (15-41)
2 Whitehall II and ELSA: Integrating Epidemiological and Psychobiological Approaches to the Assessment of Biological Indicators--Michael Marmot and Andrew Steptoe (42-59)
3 The Taiwan Biomarker Project--Ming-Cheng Chang, Dana A. Glei, Noreen Goldman, and Maxine Weinstein (60-77)
4 Elastic Powers: The Integration of Biomarkers into the Health and Retirement Study--David Weir (78-95)
5 An Overview of Biomarker Research from Community and Population-Based Studies on Aging--Jennifer R. Harris, Tara L. Gruenewald, and Teresa Seeman (96-135)
6 The Women's Health Initiative: Lessons for the Population Study of Biomarkers--Robert B. Wallace (136-148)
7 Comments on Collecting and Utilizing Biological Indicators in Social Science Surveys--Duncan Thomas and Elizabeth Frankenberg (149-155)
8 Biomarkers in Social Science Research on Health and Aging: A Review of Theory and Practice--Douglas C. Ewbank (156-172)
Part II: The Potential and Pitfalls of Genetic Information (173-174)
9 Are Genes Good Markers of Biological Traits?--Mary Jane West-Eberhard (175-193)
10 Genetic Markers in Social Science Research: Opportunities and Pitfalls--George P. Vogler and Gerald E. McClearn (194-207)
11 Comments on the Utility of Social Science Surveys for the Discovery and Validation of Genes Influencing Complex Traits--Harald H.H. Göring (208-230)
12 Overview Thoughts on Genetics: Walking the Line Between Denial and Dreamland, or Genes Are Involved in Everything, But Not Everything Is "Genetic"--Kenneth M. Weiss (231-248)
Part III: New Ways of Collecting, Applying, and Thinking About Data (249-250)
13 Minimally Invasive and Innovative Methods for Biomeasure Collection in Population-Based Research--Stacy Tessler Lindau and Thomas W. McDade (251-277)
14 Nutrigenomics--John Milner, Elaine B. Trujillo, Christine M. Kaefer, and Sharon Ross (278-303)
15 Genoeconomics--Daniel J. Benjamin, Christopher F. Chabris, Edward L. Glaeser, Vilmundur Gudnason, Tamara B. Harris, David I. Laibson, Lenore J. Launer, and Shaun Purcell (304-335)
16 Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies--George Davey Smith and Shah Ebrahim (336-366)
17 Multilevel Investigations: Conceptual Mappings and Perspectives--John T. Cacioppo, Gary G. Berntson, and Ronald A. Thisted (367-380)
18 Genomics and Beyond: Improving Understanding and Analysis of Human (Social, Economic, and Demographic) Behavior--John Hobcraft (381-400)
Appendix: Biographical Sketches of Contributors (401-414)