Multilevel Twin Models: Geographical Region as a Third Level Variable
Tóm tắt
The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.
Tài liệu tham khảo
Abdellaoui A, Hottenga JJ, De Knijff P, Nivard MG, Xiao X, Scheet P et al (2013) Population structure, migration, and diversifying selection in the Netherlands. Eur J Hum Genet 21(11):1277–1285. https://doi.org/10.1038/ejhg.2013.48
Abdellaoui A, Hugh-Jones D, Yengo L, Kemper KE, Nivard MG, Veul L et al (2019) Genetic correlates of social stratification in Great Britain. Nat Hum Behav 3(12):1332–1342. https://doi.org/10.1038/s41562-019-0757-5
Barton NH, Etheridge AM, Véber A (2017) The infinitesimal model: definition, derivation, and implications. Theor Popul Biol 118:50–73. https://doi.org/10.1016/j.tpb.2017.06.001
Baten J, Blum M (2014) Human height since 1820. In: van Zanden JL (ed) How was life?: Global well-being since 1820. OECD Publishing, Paris, pp 117–137
Baten J, Komlos J (1998) Height and the standard of living. J Econ Hist 58(3):866–870. https://doi.org/10.1017/S0022050700021239
Bentler PM, Stein JA (1992) Structural equation models in medical research. Stat Methods Med Res 1(2):159–181. https://doi.org/10.1177/096228029200100203
Boker S, Neale M, Maes H, Wilde M, Spiegel M, Brick T (2011) OpenMx: an open source extended structural equation modeling framework. Psychometrika 76(2):306–317. https://doi.org/10.1007/s11336-010-9200-6
Boomsma DI, Molenaar PC (1986) Using LISREL to analyze genetic and environmental covariance structure. Behav Genet 16(2):237–250. https://doi.org/10.1007/BF01070799
Boomsma DI, Orlebeke JF, van Baal GC (1992) The Dutch Twin Register: growth data on weight and height. Behav Genet 22(2):247–251. https://doi.org/10.1007/BF01067004
Boomsma DI, Wijmenga C, Slagboom EP, Swertz MA, Karssen LC, Abdellaoui A et al (2014) The Genome of the Netherlands: design, and project goals. Eur J Hum Genet 22(2):221–227. https://doi.org/10.1038/ejhg.2013.118
Chen G, Saad ZS, Nath AR, Beauchamp MS, Cox RW et al (2012) FMRI group analysis combining effect estimates and their variances. Neuroimage 60(1):747–765. https://doi.org/10.1016/j.neuroimage.2011.12.060
Colodro-Conde L, Couvy-Duchesne B, Whitfield JB, Streit F, Gordon S, Kemper KE et al (2018) Association between population density and genetic risk for schizophrenia. JAMA Psychiatry 75(9):901–910. https://doi.org/10.1001/jamapsychiatry.2018.1581
Eaves LJ, Last KA, Young PA, Martin NG (1978) Model-fitting approaches to the analysis of human behaviour. Heredity 41(3):249–320. https://doi.org/10.1038/hdy.1978.101
Estourgie-van Burk GF, Bartels M, Van Beijsterveldt TC, Delemarre-van de Waal HA, Boomsma DI et al (2006) Body size in five-year-old twins: heritability and comparison to singleton standards. Twin Res Hum Genet 9(5):646–655. https://doi.org/10.1375/twin.9.5.646
Falconer DS, McKay TFC (1996) Introduction to quantitative genetics. Burnt Mill Engld. https://doi.org/10.2307/2529912
Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb. 53:399–433. https://doi.org/10.1017/S0080456800012163
Gelman, A (2005) Analysis of variance—why it is more important than ever. The annals of statistics 33(1): 1-53.
Goldstein H (2011) Multilevel statistical models. Wiley, Chichester
Guo G, Wang J (2002) The mixed or multilevel model for behavior genetic analysis. Behav Genet 32(1):37–49. https://doi.org/10.1023/A:1014455812027
Heath AC, Neale MC, Hewitt JK, Eaves LJ, Fulker DW et al (1989) Testing structural equation models for twin data using LISREL. Behav Genet 19(1):9–35. https://doi.org/10.1007/BF01065881
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417–441. https://doi.org/10.1037/h0071325
Hunter, MD (2020). Multilevel modeling in classical twin and modern molecular behavior genetics. Behavior Genetics. This Issue
Jelenkovic A, Sund R, Hur YM, Yokoyama Y, Hjelmborg JVB, Möller S et al (2016) Genetic and environmental influences on height from infancy to early adulthood: an individual-based pooled analysis of 45 twin cohorts. Sci Rep 6(1):1–13. https://doi.org/10.1038/srep28496
Joshi PK, Esko T, Mattsson H, Eklund N, Gandin I, Nutile T et al (2015) Directional dominance on stature and cognition in diverse human populations. Nature 523(7561):459–462. https://doi.org/10.1038/nature14618
Karp R, Martin R, Sewell T, Manni J, Heller A (1992) Growth and academic achievement in inner-city kindergarten children: the relationship of height, weight, cognitive ability, and neurodevelopmental level. Clin Pediatr 31(6):336–340. https://doi.org/10.1177/000992289203100604
Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38(4):963–974. https://doi.org/10.2307/2529876
Ligthart L, van Beijsterveldt CE, Kevenaar ST, de Zeeuw E, van Bergen E, Bruins S et al (2019) The Netherlands twin register: longitudinal research based on twin and twin-family designs. Twin Res Hum Genet 22(6):623–636. https://doi.org/10.1017/thg.2019.93
Liu EY, Li M, Wang W, Li Y (2013) MaCH-Admix: genotype imputation for admixed populations. Genet Epidemiol 37(1):25–37. https://doi.org/10.1002/gepi.21690
Longford NT (1993) Regression analysis of multilevel data with measurement error. Br J Math Stat Psychol 46(2):301–311. https://doi.org/10.1111/j.2044-8317.1993.tb01018.x
Martin NG, Eaves LJ (1977) The genetical analysis of covariance structure. Heredity 38(1):79–95. https://doi.org/10.1038/hdy.1977.9
McArdle JJ, Prescott CA (2005) Mixed-effects variance components models for biometric family analyses. Behav Genet 35(5):631–652. https://doi.org/10.1007/s10519-005-2868-1
Neale MC, Cardon LR (1992) Methodology for genetic studies of twins and families. NATO ASI Series. Kluwer Academic Press, Dordrecht. https://doi.org/10.1007/978-94-015-8018-2
Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM et al (2016) OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81(2):535–549. https://doi.org/10.1007/s11336-014-9435-8
Paterson L, Goldstein H (1991) New statistical methods for analysing social structures: an introduction to multilevel models. Br Edu Res J 17(4):387–393. https://doi.org/10.1080/0141192910170408
Postcodebijadres (2020) Postcodekaart van Nederland. Retrieved from. https://postcodebijadres.nl/postcodes-nederland. Accessed July 29 2020
Prince AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909. https://doi.org/10.1038/ng1847
Pritikin JN, Hunter MD, von Oertzen T, Brick TR, Boker SM (2017) Many-level multilevel structural equation modeling: an efficient evaluation strategy. Struct Eq Model 24(5):684–698. https://doi.org/10.1080/10705511.2017.1293542
Privé F, Luu K, Blum MG, McGrath JJ, Vilhjálmsson BJ (2020) Efficient toolkit implementing best practices for principal component analysis of population genetic data. Bioinformatics 36(16):4449–4457. https://doi.org/10.1093/bioinformatics/btaa520
R Development Core Team (2020) R: a language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org (ISBN 3-900051-07-0)
Rabe-Hesketh S, Skrondal A, Gjessing HK (2008) Biometrical modeling of twin and family data using standard mixed model software. Biometrics 64(1):280–288. https://doi.org/10.1111/j.1541-0420.2007.00803.x
Reich D, Price AL, Patterson N (2008) Principal component analysis of genetic data. Nat Genet 40(5):491–492. https://doi.org/10.1038/ng0508-491
Rijsdijk FV, Sham PC (2002) Analytic approaches to twin data using structural equation models. Brief Bioinform 3(2):119–133. https://doi.org/10.1093/bib/3.2.119
Schalekamp JC (2009) Bataven en Buitenlanders: 20 Eeuwen Immigratie in Nederland. Wind Publishers, Huizen, pp 15–40
Scharpf RB, Ruczinski I, Carvalho B, Doan B, Chakravarti A, Irizarry RA (2011) A multilevel model to address batch effects in copy number estimation using SNP arrays. Biostatistics 12(1):33–50. https://doi.org/10.1093/biostatistics/kxq043
Sellström E, Bremberg S (2006) Is there a “school effect” on pupil outcomes? A review of multilevel studies. J Epidemiol Commun Health 60(2):149. https://doi.org/10.1136/jech.2005.036707
Silventoinen K, Krueger RF, Bouchard TJ, Kaprio J, McGue M (2004) Heritability of body height and educational attainment in an international context: comparison of adult twins in Minnesota and Finland. A J H um Biol 16(5):544–555. https://doi.org/10.1002/ajhb.20060
Silventoinen K, Bartels M, Posthuma D, Estourgie-van Burk GF, Willemsen G, van Beijsterveldt TC et al (2007) Genetic regulation of growth in height and weight from 3 to 12 years of age: a longitudinal study of Dutch twin children. Twin Res Hum Genet 10(2):354–363. https://doi.org/10.1375/twin.10.2.354
Spears D (2012) Height and cognitive achievement among Indian children. Econ Hum Biol 10(2):210–219. https://doi.org/10.1016/j.ehb.2011.08.005
Steiger JH, Shapiro A, Browne MW (1985) On the multivariate asymptotic distribution of sequential Chi square statistics. Psychometrika 50(3):253–263. https://doi.org/10.1007/BF02294104
The Genome of the Netherlands Consortium, Francioli LC, Menelaou A, Pulit SL, Van Dijk F, Palamara PF, Elbers CC et al (2014) Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet 46(8):818. https://doi.org/10.1038/ng.3021
Van den Oord EJCG (2001) Estimating effects of latent and measured genotypes in multilevel models. Stat Methods Med Res 10:393–407.