Bias and Precision of Parameter Estimates from Models Using Polygenic Scores to Estimate Environmental and Genetic Paren
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ORIGINAL RESEARCH
Bias and Precision of Parameter Estimates from Models Using Polygenic Scores to Estimate Environmental and Genetic Parental Influences Yongkang Kim1 · Jared V. Balbona1,2 · Matthew C. Keller1,2 Received: 7 August 2020 / Accepted: 20 November 2020 © The Author(s) 2020
Abstract In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing r2 values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ( r2 = .05 ), standard errors of these standardized estimates are reasonable ( < .05 ) for n = 16K trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., r2 > .025) as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS’s on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring. Keywords Vertical transmission (VT) · Nature of nurture · OpenMx · Structural equation modeling (SEM) · Assortative mating (AM)
Introduction Edited by Sarah Medland. Supplementary information The online version of this article (https://doi.org/10.1007/s10519-020-10033-9) contains supplementary material, which is available to authorized users. * Yongkang Kim [email protected] * Matthew C. Keller [email protected] 1
Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, USA
Department of Psychology & Neuroscience, University of Co
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