Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-29T08:50:05.993Z Has data issue: false hasContentIssue false
  • English
  • Français

How nonshared environmental factors come to correlate with heredity

Published online by Cambridge University Press:  29 October 2020

Christopher R. Beam Open the ORCID record for Christopher R. Beam [Opens in a new window] ,
Patrizia Pezzoli Open the ORCID record for Patrizia Pezzoli [Opens in a new window] ,
Jane Mendle ,
S. Alexandra Burt ,
Michael C. Neale ,
Steven M. Boker Open the ORCID record for Steven M. Boker [Opens in a new window] ,
Pamela K. Keel  and
Kelly L. Klump
Christopher R. Beam*
Affiliation:
Department of Psychology, University of Southern California, Los Angeles, CA, USA
Patrizia Pezzoli
Affiliation:
Department of Psychology, Åbo Akademi University, Abo, Finland
Jane Mendle
Affiliation:
Department of Human Development, Cornell University, Ithaca, NY, USA
S. Alexandra Burt
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
Michael C. Neale
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
Steven M. Boker
Affiliation:
Department of Psychology, University of Virginia, Charlottesville, VA, USA
Pamela K. Keel
Affiliation:
Department of Psychology, Florida State University, Tallahassee, FL, USA
Kelly L. Klump
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
*
Author for Correspondence: Christopher R. Beam, Assistant Professor of Psychology and Gerontology, Department of Psychology, University of Southern California, 3620 McClintock Ave, Seeley G. Mudd, Room 501, Los Angeles, CA90089; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype–environment effects) can explain the emergence of observed differences over time. Phenotype–environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype–environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype–environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene–environment correlation over time, the advantages and challenges of including gene–environment correlation in longitudinal twin models, and recommendations for future research.

Keywords

affectdevelopmental behavioral geneticsgene–environment interplaylongitudinal modelingmood
Type
Regular Article
Information
Development and Psychopathology , Volume 34 , Issue 1 , February 2022 , pp. 321 - 333
Check for updates
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, D. B., Thiel, B., St. Jean, P., Elston, R. C., Infante, M. C., & Schork, N. J. (1998). Multiple phenotype modeling in gene-mapping studies of quantitative traits: Power advantages. American Journal of Human Genetics, 63, 1190–1201. doi: 10.1086/302038CrossRefGoogle ScholarPubMed
Anastasi, A. (1958). Heredity, environment, and the question “how?”. Psychological Review, 65, 197–208.CrossRefGoogle Scholar
Bäckström, T., Haage, D., Löfgren, M., Johansson, I. M., Strömberg, J., Nyberg, S., … Bengtsson, S. K. (2011). Paradoxical effects of GABA-A modulators may explain sex steroid induced negative mood symptoms in some persons. Neuroscience, 191, 46–54. doi: 10.1016/j.neuroscience.2011.03.061CrossRefGoogle ScholarPubMed
Bartels, M., Rietveld, M. J. H., Van Baal, G. C. M., & Boomsma, D. I. (2002). Genetic and environmental influences on the development of intelligence. Behavior Genetics, 32, 237–249.CrossRefGoogle ScholarPubMed
Bartels, M., van den Oord, E. J. C. G., Hudziak, J. J., Rietveld, M. J. H., van Beijsterveldt, C. E. M., & Boomsma, D. I. (2004). Genetic and environmental mechanisms underlying stability and change in problem behaviors at ages 3, 7, 10, and 12. Developmental Psychology, 40, 852–867. doi: 10.1037/0012-1649.40.5.852CrossRefGoogle ScholarPubMed
Beam, C. R., Emery, R. E., Reynolds, C. A., Gatz, M., Turkheimer, E., & Pedersen, N. L. (2016). Widowhood and the stability of late life depressive symptomatology in the Swedish adoption twin study of aging. Behavior Genetics, 46, 100–113. doi: 10.1007/s10519-015-9733-7CrossRefGoogle ScholarPubMed
Beam, C. R., & Sharp, E. S. (2020). Social dynamics of personality development in late adulthood. Manuscript in preparation.Google Scholar
Beam, C. R., & Turkheimer, E. (2013). Phenotype–environment correlations in longitudinal twin models. Development and Psychopathology, 25, 7–16. doi: 10.1017/S0954579412000867CrossRefGoogle ScholarPubMed
Beam, C. R., & Turkheimer, E. (2017). Gene-environment correlation as a source of stability and diversity in development. In Tolan, P. H., & Leventhal, B. (Eds.), Gene-environment transactions in developmental psychopathology: Role in intervention research (pp. 111–130). New York: Springer.CrossRefGoogle Scholar
Beam, C. R., Turkheimer, E., Dickens, W. T., & Davis, D. W. (2015). Twin differentiation of cognitive ability through phenotype to environment transmission: The Louisville Twin Study. Behavior Genetics, 45, 622–634. doi: 10.1007/s10519-015-9756-0CrossRefGoogle ScholarPubMed
Bell, R. Q. (1968). A reinterpretation of the direction of effects in studies of socialization. Psychological Review, 75(2), 81–95.CrossRefGoogle ScholarPubMed
Boker, S. M., & McArdle, J. J. (2014). Path analysis and path diagrams. Retrieved from https://onlinelibrary.wiley.com/doi/10.1002/9781118445112.stat06517CrossRefGoogle Scholar
Boomsma, D. I., & Molenaar, P. C. M. (1987). The genetic analysis of repeated measures. I. simplex models. Behavior Genetics, 17, 111–123.CrossRefGoogle ScholarPubMed
Briley, D. A., Livengood, J., Derringer, J., Tucker-Drob, E. M., Fraley, R. C., & Roberts, B. W. (2019). Interpreting behavior genetic models: Seven developmental processes to understand. Behavior Genetics, 49(2), 196–210. doi: 10.1007/s10519-018-9939-6CrossRefGoogle ScholarPubMed
Briley, D. A., & Tucker-Drob, E. M. (2013). Explaining the increasing heritability of cognitive ability across development: A meta-analysis of longitudinal twin and adoption studies. Psychological Science, 24, 1704–1713. doi: 10.1177/0956797613478618CrossRefGoogle ScholarPubMed
Briley, D. A., & Tucker-Drob, E. M. (2014). Genetic and environmental continuity in personality development: A meta-analysis. Psychological Bulletin, 140, 1303–1331.CrossRefGoogle ScholarPubMed
Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101, 568–586. doi: 10.1037/0033-295X.101.4.568CrossRefGoogle ScholarPubMed
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304. doi: 10.1177/0049124104268644CrossRefGoogle Scholar
Burt, S. A., Klahr, A. M., & Klump, K. L. (2015). Do non-shared environmental influences persist over time? An examination of days and minutes. Behavior Genetics, 45(1), 24–34.CrossRefGoogle ScholarPubMed
Burt, S. A., & Klump, K. L. (2013). The Michigan State University Twin Registry (MSUTR): An update. Twin Research and Human Genetics, 16, 344–350. doi: 10.1017/thg.2012.87CrossRefGoogle ScholarPubMed
Burt, S. A., Plaisance, K. S., & Hambrick, D. Z. (2019). Understanding “what could be”: A call for “experimental behavioral genetics”. Behavior Genetics, 49, 235–243. doi: 10.1007/s10519-018-9918-yCrossRefGoogle Scholar
Cole, S. W. (2009). Social regulation of human gene expression. Current Directions in Psychological Science, 18, 132–137.CrossRefGoogle ScholarPubMed
de Kort, J. M., Dolan, C. V., & Boomsma, D. I. (2012). Accommodation of genotype-environment covariance in a longitudinal twin design. Netherlands Journal of Psychology, 67, 81–90.Google Scholar
de Kort, J. M., Dolan, C. V., Kan, K.-J., van Beijsterveldt, C. E. M., Bartels, M., & Boomsma, D. I. (2014). Can GE-covariance originating in phenotype to environment transmission account for the Flynn effect? Journal of Intelligence, 2, 82–105. doi: 10.3390/jintelligence2030082.CrossRefGoogle Scholar
Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108, 346–369.CrossRefGoogle ScholarPubMed
Dickens, W. T., Turkheimer, E. T., & Beam, C. R. (2011). The social dynamics of the expression of genes for cognitive ability. In Kendler, K. S., Jaffee, S., & Romer, D. (Eds.), The dynamic genome and mental health: The role of genes and environments in youth development (pp. 103–127). New York: Oxford University Press.Google Scholar
Dolan, C. V., De Kort, J. M., Van Beijsterveldt, T. C. E. M., Bartels, M., & Boomsma, D. I. (2014). GE covariance through phenotype to environment transmission: An assessment in longitudinal twin data and application to childhood anxiety. Behavior Genetics, 44, 240–253. doi: 10.1007/s10519-014-9659-5CrossRefGoogle ScholarPubMed
Dolan, C. V., Huijskens, R. C. A., Minică, C. C., Neale, M. C., & Boomsma, D. I. (2019). Incorporating polygenic scores in the twin model to estimate genotype-environment covariance: exploration of statistical power. bioRxiv, 1–32. doi: 10.1101/702738Google Scholar
Eaves, L. J., Krystyna, L., Martin, N. G., & Jinks, J. L. (1977). A progressive approach to non-additivity and genotype-environmental covariance in the analysis of human differences. British Journal of Mathematical and Statistical Psychology, 30, 1–42.CrossRefGoogle Scholar
Eaves, L. J., & Long, J., & Heath AC. (1986). A theory of developmental change in quantitative phenotypes applied to cognitive development. Behavior Genetics, 16, 143–162.CrossRefGoogle ScholarPubMed
Enders, C. K. (2010). Applied missing data analysis. New York, NY: The Guilford Press.Google Scholar
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138.CrossRefGoogle Scholar
Evans, D. M. (2002). The power of multivariate quantitative-trait loci linkage analysis is influenced by the correlation between variables. American Journal of Human Genetics, 70, 1599–1602. doi: 10.1086/340850CrossRefGoogle ScholarPubMed
Farage, M. A., Neill, S., & MacLean, A. B. (2009). Physiological changes associated with the menstrual cycle a review. Obstetrical and Gynecological Survey, 64, 58–72. doi: 10.1097/OGX.0b013e3181932a37CrossRefGoogle ScholarPubMed
Fischbein, S. (1978). Heredity-environment interaction in the development of twins. International Journal of Behavioral Development, 1, 313–322. doi: 10.1177/016502547800100402CrossRefGoogle Scholar
Fuller, J. L., & Thompson, W. R. (1960). Behavior genetics. New York: John Wiley & Sons, Inc.CrossRefGoogle Scholar
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.CrossRefGoogle Scholar
Gottlieb, G. (2003). On making behavioral genetics truly developmental. Human Development, 46, 337–355. doi: 10.1159/000073306CrossRefGoogle Scholar
Halbreich, U., Freeman, E. W., Rapkin, A. J., Cohen, L. S., Grubb, G. S., Bergeron, R., … Constantine, G. D. (2012). Continuous oral levonorgestrel/ethinyl estradiol for treating premenstrual dysphoric disorder. Contraception, 85, 19–27. doi: 10.1016/j.contraception.2011.05.008CrossRefGoogle ScholarPubMed
Johnson, W. (2007). Genetic and environmental influences on behavior: Capturing all the interplay. Psychological Review, 114, 423–440. doi: 10.1037/0033-295X.114.2.423CrossRefGoogle ScholarPubMed
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37, 615–626. doi: 10.1017/S0033291706009524CrossRefGoogle ScholarPubMed
Kiesner, J., Mendle, J., Eisenlohr-Moul, T. A., & Pastore, M. (2016). Cyclical symptom change across the menstrual cycle: Attributional, affective, and physical symptoms. Clinical Psychological Science, 4(5), 882–894.CrossRefGoogle Scholar
Klump, K. L., & Burt, S. A. (2006). The Michigan State University Twin Registry (MSUTR): Genetic, environmental and neurobiological influences on behavior across development. Twin Research and Human Genetics, 9, 971–977. doi: 10.1375/183242706779462868CrossRefGoogle ScholarPubMed
Klump, K. L., Burt, S. A., Mcgue, M., & Iacono, W. G. (2007). Changes in genetic and environmental influences on disordered eating across adolescence. Archives of General Psychiatry, 64, 1409–1415.CrossRefGoogle ScholarPubMed
Klump, K. L., Fowler, N., Mayhall, L., Sisk, C. L., Culbert, K. M., & Burt, S. A. (2018). Estrogen moderates genetic influences on binge eating during puberty: Disruption of normative processes? Journal of Abnormal Psychology, 127(5), 458–470.CrossRefGoogle ScholarPubMed
Klump, K. L., Hildebrandt, B. A., O’Connor, S. M., Keel, P. K., Neale, M., Sisk, C. L., … Burt, S. A. (2015). Changes in genetic risk for emotional eating across the menstrual cycle: A longitudinal study. Psychological Medicine, 45(15), 3227–3237.CrossRefGoogle ScholarPubMed
Klump, K. L., Keel, P. K., Racine, S. E., Burt, A. A., Neale, M., Sisk, C. L., … Hu, J. Y. (2013). The interactive effects of estrogen and progesterone on changes in emotional eating across the menstrual cycle. Journal of Abnormal Psychology, 122, 131–137. doi: 10.1037/a0029524CrossRefGoogle ScholarPubMed
Klump, K. L., Racine, S. E., Hildebrandt, B., Burt, S. A., Neale, M., Sisk, C. L., … Keel, P. K. (2014). Influences of ovarian hormones on dysregulated eating: A comparison of associations in women with versus women without binge episodes. Clinical Psychological Science, 2(5), 545–559.CrossRefGoogle ScholarPubMed
Lickliter, R., & Harshaw, C. (2010). Canalization and malleability reconsidered: The developmental basis of phenotypic stability and variability. In Hood, K. E., Halpern, C. T., Greenberg, G. & Lerner, R. M. (Eds.), Handbook of developmental science, behavior, and genetics (pp. 491–525). West Sussex: Wiley-Blackwell.CrossRefGoogle Scholar
McArdle, J. J., & Prescott, C. A. (2005). Mixed-effects variance components models for biometric family analyses. Behavior Genetics, 35, 631–652. doi: 10.1007/s10519-005-2868-1.CrossRefGoogle ScholarPubMed
McCartney, K., Harris, M. J., & Bernieri, F. (1990). Growing up and growing apart: A developmental meta-analysis of twin studies. Psychological Bulletin, 107, 226–237.CrossRefGoogle ScholarPubMed
McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research: The utility of twins. Perspectives on Psychological Science, 5, 546–556. doi: 10.1177/1745691610383511.CrossRefGoogle Scholar
Montag, C., Hahn, E., Reuter, M., Spinath, F. M., Davis, K., & Panksepp, J. (2016). The role of nature and nurture for individual differences in primary emotional systems: Evidence from a twin study. PLoS ONE, 11, 1–15. doi: 10.1371/journal.pone.0151405.Google ScholarPubMed
Moscati, A., Verhulst, B., McKee, K., Silberg, J., & Eaves, L. (2018). Cross-lagged analysis of interplay between differential traits in sibling pairs: Validation and application to parenting behavior and ADHD symptomatology. Behavior Genetics, 48, 22–33. doi: 10.1007/s10519-017-9882-y.CrossRefGoogle ScholarPubMed
Moulder, R. G., Boker, S. M., Ramseyer, F., & Tschacher, W. (2018). Determining synchrony between behavioral time series: An application of surrogate data generation for establishing falsifiable null-hypotheses. Psychological Methods, 23, 757–773. doi: 10.1037/met0000172CrossRefGoogle ScholarPubMed
Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus user's guide. (8th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families. Dordrecht, The Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Neale, M. C., & McArdle, J. J. (2000). Structured latent growth curves for twin data. Twin Research and Human Genetics, 3(3), 165–177.CrossRefGoogle ScholarPubMed
Nivard, M. G., Dolan, C. V., Kendler, K. S., Kan, K. J., Willemsen, G., Van Beijsterveldt, C. E. M., … Boomsma, D. I. (2015). Stability in symptoms of anxiety and depression as a function of genotype and environment: A longitudinal twin study from ages 3 to 63 years. Psychological Medicine, 45, 1039–1049. doi: 10.1017/S003329171400213XCrossRefGoogle ScholarPubMed
Östlund, H., Keller, E., & Hurd, Y. L. (2003). Estrogen receptor gene expression in relation to neuropsychiatric disorders. Annals of the New York Academy of Sciences, 1007, 54–63.CrossRefGoogle ScholarPubMed
Petrill, S. A., Lipton, P. A., Hewitt, J. K., Plomin, R., Cherny, S. S., Corley, R., & DeFries, J. C. (2004). Genetic and environmental contributions to general cognitive ability through the first 16 years of life. Developmental Psychology, 40, 805–812. doi: 10.1037/0012-1649.40.5.805CrossRefGoogle ScholarPubMed
Plomin, R. (1986). Multivariate analysis and developmental behavioral genetics: Developmental change as well as continuity. Behavior Genetics, 16, 25–43.CrossRefGoogle ScholarPubMed
Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84, 309–322. doi: 10.1037/0033-2909.84.2.309CrossRefGoogle ScholarPubMed
Polderman, T. J., Benyamin, B., De Leeuw, C. A., Sullivan, P. F., Van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47(7), 702–709.CrossRefGoogle ScholarPubMed
Poromaa, I. S., & Gingnell, M. (2014). Menstrual cycle influence on cognitive function and emotion processing from a reproductive perspective. Frontiers in Neuroscience, 8, 1–16. doi: 10.3389/fnins.2014.00380Google Scholar
Purcell, S. (2002). Variance components models for gene–environment interaction in twin analysis. Twin Research, 5, 554–571. doi: 10.1375/twin.5.6.554CrossRefGoogle ScholarPubMed
Roberts, B. W., & Mroczek, D. K. (2008). Personality trait change in adulthood. Current Directions in Psychological Science, 17, 31–35. doi: 10.1016/j.pestbp.2011.02.012.InvestigationsCrossRefGoogle ScholarPubMed
Röcke, C., Li, S. C., & Smith, J. (2009). Intraindividual variability in positive and negative affect over 45 days: Do older adults fluctuate less than young adults? Psychology and Aging, 24, 863–878. doi: 10.1037/a0016276CrossRefGoogle ScholarPubMed
Romans, S., Clarkson, R., Einstein, G., Petrovic, M., & Stewart, D. (2012). Mood and the menstrual cycle: A review of prospective data studies. Gender Medicine, 9, 361–384. doi: 10.1016/j.genm.2012.07.003CrossRefGoogle ScholarPubMed
Røysamb, E., & Tambs, K. (2016). The beauty, logic and limitations of twin studies. Norsk Epidemiologi, 26, 35–46. doi: 10.5324/nje.v26i1-2.2014CrossRefGoogle Scholar
Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–519.CrossRefGoogle Scholar
Scarr, S. (1992). Developmental theories for the 1990s: Development and individual differences. Child Development, 63(1), 1–19.CrossRefGoogle ScholarPubMed
Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype-environment effects. Child Development, 54, 424–435.Google ScholarPubMed
Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343.CrossRefGoogle Scholar
Stone, A. A., Marco, C. A., Cruise, C. E., Cox, D. S., & Neale, J. M. (1996). Are stress-induced immunological changes mediated by mood? A closer look at how both desirable and undesirable daily events influence sIgA antibody. International Journal of Behavioral Medicine, 3, 1–13. doi: 10.1207/s15327558ijbm0301_1CrossRefGoogle Scholar
Tucker-Drob, E. M., & Briley, D. A. (2014). Continuity of genetic and environmental influences on cognition across the life span: a meta-analysis of longitudinal twin and adoption studies. Psychological Bulletin, 140, 949–79. doi: 10.1037/a0035893CrossRefGoogle ScholarPubMed
Turkheimer, E., & Waldron, M. (2000). Nonshared environment: A theoretical, methodological, and quantitative review. Psychological Bulletin, 126, 78–108. doi: 10.1037/0033-2909.126.1.78CrossRefGoogle ScholarPubMed
van den Berg, S. M., Beem, L., & Boomsma, D. I. (2006). Fitting genetic models using Markov Chain Monte Carlo algorithms with BUGS. Twin Research and Human Genetics, 9(3), 334–342.CrossRefGoogle ScholarPubMed
Wachs, T. D. (1983). The use and abuse of environment in behavior-genetic research. Child Development, 54, 396–407.CrossRefGoogle ScholarPubMed
Wall, E. H., Hewitt, S. C., Case, L. K., Lin, C. Y., Korach, K. S., & Teuscher, C. (2014). The role of genetics in estrogen responses: A critical piece of an intricate puzzle. FASEB Journal, 28, 5042–5054. doi: 10.1096/fj.14-260307CrossRefGoogle ScholarPubMed
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070.CrossRefGoogle ScholarPubMed
West, M. J., & King, A. P. (1987). Settling nature and nurture into an ontogenetic niche. Developmental Psychobiology, 20, 549–562. doi: 10.1002/dev.420200508CrossRefGoogle ScholarPubMed
Winship, C., & Korenman, S. (1999). Economic success and the evolution of schooling and mental ability. In Mayer, S., & Peterson, P. E. (Eds.), Earning and learning: How schools matter (pp. 49–78). Washington DC: Brookings.Google Scholar
Supplementary material: File

Beam et al. supplementary material

Beam et al. supplementary material 1

Download Beam et al. supplementary material(File)
File 122.2 KB
Supplementary material: PDF

Beam et al. supplementary material

Beam et al. supplementary material 2

Download Beam et al. supplementary material(PDF)
PDF 80.2 KB