How Close to the Mark Might Published Heritability Estimates Be?
DOI:
https://doi.org/10.15626/MP.2018.1479Keywords:
Heritability, heritability estimation, standard biometric model, quantitative genetics, , structural equation modeling, random forestAbstract
The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimation
employed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/
Metrics
References
Afifi, T. O., Asmundson, G. J., Taylor, S., & Jang, K. L. (2010). The role of genes and environment on trauma exposure and posttraumatic stress disorder symptoms: A review of twin studies. Clinical Psychology Review, 30(1), 101–112. DOI: https://doi.org/10.1016/j.cpr.2009.10.002
Benyamin, B., Pourcain, B., Davis, O. S., Davies, G., Hansell, N. K., Brion, M.-J., Kirkpatrick, R. M., Cents, R. A., Frani ́c, S., & Miller, M. B. (2014). Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Molecular Psychiatry, 19(2), 253–258. DOI: https://doi.org/10.1038/mp.2012.184
Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Jones, Z., Casalicchio, G., Gallo, M., Bossek, J., Studerus, E., Judt, L., Kuehn, T., Kerschke, P., Fendt, F., Probst, P., Sun, X., Thomas, J., Vieira, B., Beggel, L., . . . Coors, S. (2017). Mlr: Machine Learning in R. Retrieved May 15, 2017, from https://cran.r-project.org/web/packages/mlr/index.html
Bowles, S., Gintis, H., et al. (2001). The inheritance of economic status: Education, class and genetics [Publisher: Oxford University Press New York]. International encyclopedia of the social and behavioral sciences: Genetics, behavior and society, 6, 4132–141. DOI: https://doi.org/10.1016/B0-08-043076-7/03363-5
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324
Bronfenbrenner, U. (1999). Nature with nurture: A reinterpretation of the evidence [Publisher: Oxford University Press New York]. Race and IQ, 153–183. DOI: https://doi.org/10.1093/oso/9780195102208.003.0009
Calaway, R., Analytics, R., & Weston, S. (2015). Foreach: Provides Foreach Looping Construct for R. Retrieved May 15, 2017, from https://cran.r-project.org/web/packages/foreach/index.html
Chen, X., Kuja-Halkola, R., Rahman, I., Arpegård, J., Viktorin, A., Karlsson, R., Hägg, S., Svensson, P., Pedersen, N. L., & Magnusson, P. K. E. (2015). Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models. The American Journal of Human Genetics, 97(5), 708–714. https://doi.org/10.1016/j.ajhg.2015.10.004 DOI: https://doi.org/10.1016/j.ajhg.2015.10.004
Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18(1), 115–126. DOI: https://doi.org/10.1207/s15327906mbr1801_7
Dowle, M., Srinivasan, A., Gorecki, J., Short, T., Lianoglou, S., & Antonyan, E. (2017). Data.table: Extension of ’data.frame’. Retrieved May 15, 2017, from https://cran.r-project.org/web/packages/data.table/index.html
Eaves, L., & Erkanli, A. (2003). Markov Chain Monte Carlo Approaches to Analysis of Genetic and Environmental Components of Human Developmental Change and G × E Interaction. Behavior Genetics, 33(3), 279–299. https://doi.org/10.1023/A:1023446524917 DOI: https://doi.org/10.1023/A:1023446524917
Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics, 11, 446. http://dx.doi.org/10.1038/nrg2809 DOI: https://doi.org/10.1038/nrg2809
Evans, D. M. (2011). Gene-Gene Interaction and Epistasis. In Analysis of Complex Disease Association Studies (pp. 197–213). Elsevier. DOI: https://doi.org/10.1016/B978-0-12-375142-3.10012-4
Evans, D. M., Gillespie, N. A., & Martin, N. G. (2002). Biometrical genetics. Biological Psychology, 61(1-2), 33–51. DOI: https://doi.org/10.1016/S0301-0511(02)00051-0
Evans, L. M., Tahmasbi, R., Vrieze, S. I., Abecasis, G. R., Das, S., Gazal, S., Bjelland, D. W., De Candia, T. R., Goddard, M. E., Neale, B. M., Yang, J., Visscher, P. M., & Keller, M. C. (2018). Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nature Genetics, 50(5), 737–745. https://doi.org/10.1038/s41588-018-0108-x DOI: https://doi.org/10.1038/s41588-018-0108-x
Falconer, D. S. (1960). Introduction to quantitative genetics. Pearson Education Limited.
Fedko, I. O., Hottenga, J.-J., Helmer, Q., Mbarek, H., Huider, F., Amin, N., Beulens, J. W., Bremmer, M. A., Elders, P. J., Galesloot, T. E., Kiemeney, L. A., Van Loo, H. M., Picavet, H. S. J., Rutters, F., Van Der Spek, A., Van De Wiel, A. M., Van Duijn, C., De Geus, E. J. C., Feskens, E. J. M., . . . Bot, M. (2021). Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report). Psychological Medicine, 51(8), 1345–1354. https://doi.org/10.1017/S0033291720000100 DOI: https://doi.org/10.1017/S0033291720000100
Fisher, R. A. (1919). XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399–433. https://doi.org/10.1017/S0080456800012163 DOI: https://doi.org/10.1017/S0080456800012163
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5). https://doi.org/10.1214/aos/1013203451 DOI: https://doi.org/10.1214/aos/1013203451
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24(1), 44–65. DOI: https://doi.org/10.1080/10618600.2014.907095
Gregersen, J. W., Kranc, K. R., Ke, X., Svendsen, P., Madsen, L. S., Thomsen, A. R., Cardon, L. R., Bell, J. I., & Fugger, L. (2006). Functional epistasis on a common MHC haplotype associated with multiple sclerosis. Nature, 443(7111), 574–577. https://doi.org/10.1038/nature05133 DOI: https://doi.org/10.1038/nature05133
Grotzinger, A. D., Rhemtulla, M., De Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R. E., McIntosh, A. M., Deary, I. J., Koellinger, P. D., Harden, K. P., Nivard, M. G., & Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513–525. https://doi.org/10.1038/s41562-019-0566-x DOI: https://doi.org/10.1038/s41562-019-0566-x
Hare, R. D. (1991). The Hare psychopathy checklist-revised: Manual. Multi-Health Systems, Incorporated. DOI: https://doi.org/10.1037/t01167-000
Hare, R. D. (1996). Psychopathy: A Clinical Construct Whose Time Has Come. Criminal Justice and Behavior, 23(1), 25–54. https://doi.org/10.1177/0093854896023001004 DOI: https://doi.org/10.1177/0093854896023001004
He, L., Sillanpää, M. J., Silventoinen, K., Kaprio, J., & Pitkäniemi, J. (2016). Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models. Genetics, 202(4), 1313–1328. https://doi.org/10.1534/genetics.115.183905 DOI: https://doi.org/10.1534/genetics.115.183905
Heath, A. C., Neale, M. C., Hewitt, J. K., Eaves, L. J., & Fulker, D. W. (1989). Testing structural equation models for twin data using LISREL. Behavior Genetics, 19(1), 9–35. https://doi.org/10.1007/BF01065881 DOI: https://doi.org/10.1007/BF01065881
Herzig, A. F., Nutile, T., Ruggiero, D., Ciullo, M., Perdry, H., & Leutenegger, A.-L. (2018). Detecting the dominance component of heritability in isolated and outbred human populations. Scientific Reports, 8(1), 18048. https://doi.org/10.1038/s41598-018-36050-7 DOI: https://doi.org/10.1038/s41598-018-36050-7
Hill, W. D., Harris, S. E., & Deary, I. J. (2019). What genome-wide association studies reveal about the association between intelligence and mental health. Current Opinion in Psychology, 27, 25–30. https://doi.org/10.1016/j.copsyc.2018.07.007 DOI: https://doi.org/10.1016/j.copsyc.2018.07.007
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits. PLOS Genetics, 4(2), e1000008. https://doi.org/10.1371/journal.pgen.1000008 DOI: https://doi.org/10.1371/journal.pgen.1000008
Hohman, T. J., Koran, M. E., Thornton-Wells, T., & for the Alzheimer’s Neuroimaging Initiative. (2013). Epistatic Genetic Effects among Alzheimer’s Candidate Genes. PLoS ONE, 8(11), e80839. https://doi.org/10.1371/journal.pone.0080839 DOI: https://doi.org/10.1371/journal.pone.0080839
Holzinger, K. J. (1929). The relative effect of nature and nurture influences on twin differences. Journal of Educational Psychology, 20(4), 241–248. https://doi.org/10.1037/h0072484 DOI: https://doi.org/10.1037/h0072484
Hsu, S. D. (2014). On the genetic architecture of intelligence and other quantitative traits. arXiv preprint arXiv:1408.3421. https://arxiv.org/pdf/1408.3421.pdf
Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118
Jinks, J. L., & Fulker, D. W. (1970). Comparison of the biometrical genetical, MAVA, and classical approaches to the analysis of human behavior. Psychological Bulletin, 73(5), 311–349. https://doi.org/10.1037/h0029135 DOI: https://doi.org/10.1037/h0029135
Keller, M. C., & Coventry, W. L. (2005). Quantifying and Addressing Parameter Indeterminacy in the Classical Twin Design. Twin Research and Human Genetics, 8(3), 201–213. https://doi.org/10.1375/twin.8.3.201 DOI: https://doi.org/10.1375/twin.8.3.201
Keller, M. C., Medland, S. E., & Duncan, L. E. (2010). Are Extended Twin Family Designs Worth the Trouble? A Comparison of the Bias, Precision, and Accuracy of Parameters Estimated in Four Twin Family Models. Behavior Genetics, 40(3), 377–393. https://doi.org/10.1007/s10519-009-9320-x DOI: https://doi.org/10.1007/s10519-009-9320-x
Kempthorne, O. (1978). A Biometrics Invited Paper: Logical, Epistemological and Statistical Aspects of Nature-Nurture Data Interpretation. Biometrics, 34(1), 1–23. https://doi.org/10.2307/2529584 DOI: https://doi.org/10.2307/2529584
Long, J. S. (1981). Estimation and hypothesis testing in linear models containing measurement error: A review of Jöreskog’s model for the analysis of covariance structures. In P. V. Marsden (Ed.), Linear models in social research (pp. 209–256). Sage.
Lynch, M., & Walsh, B. (1998). Genetics and analysis of quantitative traits. Sinauer.
MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114(1), 185–199. https://doi.org/10.1037/0033-2909.114.1.185 DOI: https://doi.org/10.1037//0033-2909.114.1.185
Marchini, J., Donnelly, P., & Cardon, L. R. (2005). Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature genetics, 37(4), 413. DOI: https://doi.org/10.1038/ng1537
Neale, M., & Maes, H. (2004). Methodology for genetic studies of twins and families. Virginia Commonwealth University, Department of Psychiatry.
Nichols, R. C. (1965). The national merit twin study. Methods and goals in human behavior genetic, 231–244. https://genepi.qimr.edu.au/contents/p/staff/1965_Nichols_Vand_Meth&Goals_231.pdf DOI: https://doi.org/10.1016/B978-1-4832-3217-1.50019-X
Nikolas, M. A., & Burt, S. A. (2010). Genetic and environmental influences on ADHD symptom dimensions of inattention and hyperactivity: A meta-analysis. Journal of Abnormal Psychology, 119(1), 1–17. https://doi.org/10.1037/a0018010 DOI: https://doi.org/10.1037/a0018010
Nolte, I. M., Van Der Most, P. J., Alizadeh, B. Z., De Bakker, P. I., Boezen, H. M., Bruinenberg, M., Franke, L., Van Der Harst, P., Navis, G., Postma, D. S., Rots, M. G., Stolk, R. P., Swertz, M. A., Wolffenbuttel, B. H., Wijmenga, C., & Snieder, H. (2017). Missing heritability: Is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study. European Journal of Human Genetics, 25(7), 877–885. https://doi.org/10.1038/ejhg.2017.50 DOI: https://doi.org/10.1038/ejhg.2017.50
Polderman, T. J. C., 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. https://doi.org/10.1038/ng.3285 DOI: https://doi.org/10.1038/ng.3285
Revelle, W. (2016). psych: Procedures for Personality and Psychological Research. Northwestern University. Evanston, Illinois, USA. Retrieved from https://CRAN.R-project.org/package=psych
Revolution Analytics & Weston, S. (2022). doMC: Foreach Parallel Adaptor for ’parallel’. https://CRAN.R-project.org/package=doMC
Rijsdijk, F. V. (2002). Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics, 3(2), 119–133. https://doi.org/10.1093/bib/3.2.119 DOI: https://doi.org/10.1093/bib/3.2.119
Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L. R., Dupont, W. D., Parl, F. F., & Moore, J. H. (2001). Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer. The American Journal of Human Genetics, 69(1), 138–147. https://doi.org/10.1086/321276 DOI: https://doi.org/10.1086/321276
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02 DOI: https://doi.org/10.18637/jss.v048.i02
Schönemann, P. H. (1989). New questions about old heritability estimates. Bulletin of the Psychonomic Society, 27(2), 175–178. https://doi.org/10.3758/BF03329932 DOI: https://doi.org/10.3758/BF03329932
Schönemann, P. H. (1997). On models and muddles of heritability. Genetica, 99(2), 97–108. https://doi.org/10.1023/A:1018358504373 DOI: https://doi.org/10.1007/BF02259513
Schwabe, I., Janss, L., & Van Den Berg, S. M. (2017). Can We Validate the Results of Twin Studies? A Census-Based Study on the Heritability of Educational Achievement. Frontiers in Genetics, 8, 160. https://doi.org/10.3389/fgene.2017.00160 DOI: https://doi.org/10.3389/fgene.2017.00160
Shalizi, C. (2007). Yet More on the Heritability and Malleability of IQ. [Retrieved September 27, 2007]. http://bactra.org/weblog/520.html
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., Amin, N., Breen, G., Cesarini, D., Chabris, C. F., Iacono, W. G., Ikram, M. A., Johannesson, M., Koellinger, P., Lee, J. J., . . . Posthuma, D. (2017). Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics, 49(7), 1107–1112. https://doi.org/10.1038/ng.3869 DOI: https://doi.org/10.1038/ng.3869
Strange, A., Capon, F., Donnelly, P., & Trembath, R. (2010). A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1. Nature Genetics, 42, 985–990. DOI: https://doi.org/10.1038/ng.694
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. https://doi.org/10.1186/1471-2105-9-307 DOI: https://doi.org/10.1186/1471-2105-9-307
Tomarken, A. J., & Waller, N. G. (2003). Potential problems with "well fitting" models. Journal of Abnormal Psychology, 112(4), 578–598. https://doi.org/10.1037/0021-843X.112.4.578 DOI: https://doi.org/10.1037/0021-843X.112.4.578
Van Houtem, C., Laine, M., Boomsma, D., Ligthart, L., Van Wijk, A., & De Jongh, A. (2013). A review and meta-analysis of the heritability of specific phobia subtypes and corresponding fears. Journal of Anxiety Disorders, 27(4), 379–388. https://doi.org/10.1016/j.janxdis.2013.04.007 DOI: https://doi.org/10.1016/j.janxdis.2013.04.007
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics, 101(1), 5–22. https://doi.org/10.1016/j.ajhg.2017.06.005 DOI: https://doi.org/10.1016/j.ajhg.2017.06.005
Vitzthum, V. J. (2003). A number no greater than the sum of its parts: The use and abuse of heritability. Human Biology, 75(4), 539–558. https://doi.org/10.1353/hub.2003.0064 DOI: https://doi.org/10.1353/hub.2003.0064
Wei, W.-H., Hemani, G., & Haley, C. S. (2014). Detecting epistasis in human complex traits. Nature Reviews Genetics, 15(11), 722–733. https://doi.org/10.1038/nrg3747 DOI: https://doi.org/10.1038/nrg3747
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A grammar of data manipulation. https://CRAN.R-project.org/package=dplyr
Wright, M. N., & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 1–17. https://doi.org/10.18637/jss.v077.i01 DOI: https://doi.org/10.18637/jss.v077.i01
Wright, S. (1921). Systems of mating. I. The biometric relations between parent and offspring. Genetics, 6(2), 111–123. https://doi.org/10.1093/genetics/6.2.111 DOI: https://doi.org/10.1093/genetics/6.2.111
Zhu, Z., Bakshi, A., Vinkhuyzen, A. A., Hemani, G., Lee, S. H., Nolte, I. M., van Vliet-Ostaptchouk, J. V., Snieder, H., Esko, T., & Milani, L. (2015). Dominance genetic variation contributes little to the missing heritability for human complex traits. The American Journal of Human Genetics, 96(3), 377–385. DOI: https://doi.org/10.1016/j.ajhg.2015.01.001
Zuk, O., Hechter, E., Sunyaev, S. R., & Lander, E. S. (2012). The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences, 109(4), 1193–1198. https://doi.org/10.1073/pnas.1119675109 DOI: https://doi.org/10.1073/pnas.1119675109
Published
Issue
Section
License
Copyright (c) 2024 Michael Maraun, Moritz Heene, Philipp Sckopke
This work is licensed under a Creative Commons Attribution 4.0 International License.
