Bias and precision of parameter estimates from models using polygenic scores to estimate environmental and genetic parental influences Article Swipe
Yongkang Kim
,
Jared V. Balbona
,
Matthew C. Keller
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.11.246827
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.11.246827
In a companion paper (Balbona et al. (2020)), 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 r 2 values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ( r 2 = .05), standard errors of these standardized estimates were reasonable (< .05) for n = 16 K trios, and smaller sample sizes (e.g., down to 4 K ) 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., r 2 > .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.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.08.11.246827
- https://www.biorxiv.org/content/biorxiv/early/2020/08/12/2020.08.11.246827.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3048377512
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- DOI
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https://doi.org/10.1101/2020.08.11.246827Digital Object Identifier
- Title
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Bias and precision of parameter estimates from models using polygenic scores to estimate environmental and genetic parental influencesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-08-12Full publication date if available
- Authors
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Yongkang Kim, Jared V. Balbona, Matthew C. KellerList of authors in order
- Landing page
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https://doi.org/10.1101/2020.08.11.246827Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2020/08/12/2020.08.11.246827.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2020/08/12/2020.08.11.246827.full.pdfDirect OA link when available
- Concepts
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Assortative mating, Trait, Statistics, Biology, Offspring, Sample size determination, Econometrics, Genetics, Mathematics, Mating, Computer science, Pregnancy, Programming languageTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.score. | 196 |
| abstract_inverted_index.scores | 19, 84, 266, 296 |
| abstract_inverted_index.series | 12 |
| abstract_inverted_index.should | 301 |
| abstract_inverted_index.study, | 80 |
| abstract_inverted_index.trait, | 27 |
| abstract_inverted_index.traits | 30, 269, 315 |
| abstract_inverted_index.trios, | 229 |
| abstract_inverted_index.unmet. | 167 |
| abstract_inverted_index.values | 119, 192 |
| abstract_inverted_index.account | 61 |
| abstract_inverted_index.current | 79 |
| abstract_inverted_index.effects | 181 |
| abstract_inverted_index.genetic | 54, 92, 180 |
| abstract_inverted_index.genomic | 284, 309 |
| abstract_inverted_index.mating. | 102 |
| abstract_inverted_index.maximum | 123 |
| abstract_inverted_index.parents | 88, 257, 289, 328 |
| abstract_inverted_index.perform | 312 |
| abstract_inverted_index.smaller | 231 |
| abstract_inverted_index.utility | 293 |
| abstract_inverted_index.(2020)), | 8 |
| abstract_inverted_index.(Balbona | 5 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.GWAS’s | 313 |
| abstract_inverted_index.Standard | 168 |
| abstract_inverted_index.additive | 53 |
| abstract_inverted_index.alleles, | 24 |
| abstract_inverted_index.approach | 253 |
| abstract_inverted_index.assuming | 97 |
| abstract_inverted_index.biobanks | 310 |
| abstract_inverted_index.compared | 116 |
| abstract_inverted_index.datasets | 282 |
| abstract_inverted_index.decrease | 182 |
| abstract_inverted_index.effects. | 55 |
| abstract_inverted_index.estimate | 32, 59 |
| abstract_inverted_index.explains | 202 |
| abstract_inverted_index.modestly | 272 |
| abstract_inverted_index.parental | 29, 41, 299 |
| abstract_inverted_index.quantify | 134 |
| abstract_inverted_index.relevant | 268, 319 |
| abstract_inverted_index.requires | 264 |
| abstract_inverted_index.standard | 214 |
| abstract_inverted_index.unbiased | 150 |
| abstract_inverted_index.vertical | 74, 94, 176 |
| abstract_inverted_index.(vertical | 48 |
| abstract_inverted_index.companion | 3, 109 |
| abstract_inverted_index.estimated | 172 |
| abstract_inverted_index.estimates | 125, 145, 219 |
| abstract_inverted_index.expected, | 157 |
| abstract_inverted_index.gene-gene | 64 |
| abstract_inverted_index.influence | 258 |
| abstract_inverted_index.offspring | 26, 46, 90 |
| abstract_inverted_index.parameter | 144 |
| abstract_inverted_index.parenting | 321 |
| abstract_inverted_index.polygenic | 18, 83, 195, 200, 242, 265, 295 |
| abstract_inverted_index.precision | 138 |
| abstract_inverted_index.simulated | 82, 131 |
| abstract_inverted_index.software, | 114 |
| abstract_inverted_index.variances | 173 |
| abstract_inverted_index.variation | 34, 208 |
| abstract_inverted_index.additional | 305 |
| abstract_inverted_index.estimates. | 140 |
| abstract_inverted_index.increasing | 184, 189 |
| abstract_inverted_index.influences | 39, 300 |
| abstract_inverted_index.introduced | 10 |
| abstract_inverted_index.likelihood | 124 |
| abstract_inverted_index.motivation | 306 |
| abstract_inverted_index.offspring, | 260 |
| abstract_inverted_index.offspring. | 291, 330 |
| abstract_inverted_index.oversample | 324 |
| abstract_inverted_index.parameters | 121 |
| abstract_inverted_index.phenotypes | 86 |
| abstract_inverted_index.phenotypic | 286 |
| abstract_inverted_index.predictive | 273 |
| abstract_inverted_index.reasonable | 221 |
| abstract_inverted_index.relatives, | 326 |
| abstract_inverted_index.scenarios, | 96 |
| abstract_inverted_index.assortative | 71, 101 |
| abstract_inverted_index.assumptions | 152, 165 |
| abstract_inverted_index.covariation | 67 |
| abstract_inverted_index.elucidating | 298 |
| abstract_inverted_index.information | 287 |
| abstract_inverted_index.predictive. | 246 |
| abstract_inverted_index.transmitted | 21 |
| abstract_inverted_index.instantiated | 104 |
| abstract_inverted_index.particularly | 327 |
| abstract_inverted_index.standardized | 218 |
| abstract_inverted_index.transmission | 75, 95, 177 |
| abstract_inverted_index.environmental | 38 |
| abstract_inverted_index.respectively. | 76 |
| abstract_inverted_index.transmission) | 49 |
| abstract_inverted_index.understanding | 255 |
| abstract_inverted_index.nontransmitted | 23 |
| abstract_inverted_index.gene-environment | 66 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5043052935, https://openalex.org/A5028780443 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I188538660 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.59358639 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |