Mendelian imputation of parental genotypes improves estimates of direct genetic effects
Open accessPublished version of a preprint
Abstract
Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment ( r = 0.739, standard error (s.e.) = 0.086) and cognitive ability ( r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Estimates of DGEs are less precise than estimates of population effects because they only use within-family genotype variation, which is half of the variation in a random-mating population.
“Furthermore, even when parental genotypes are available, estimates of DGEs are less precise than estimates of population effects because they only use within-family genotype variation, which is half of the variation in a random-mating population3.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The Main section says that random segregation within families enables unbiased estimates of direct genetic effects, and the Results section on genome-wide analyses says power to analyze individual-SNP direct effects was limited (page numbers unavailable). But the paper does not state the claimed comparison with population-effect precision or that within-family genotype variation is half of population variation. Its reported one-half effective sample size compares parent–offspring pairs with complete parental-genotype data, not direct-effect estimates with population-effect estimates. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAlthough population structure can introduce bias into imputed parental genotypes, the resulting bias in DGE estimates is negligible for samples of relatively homogeneous genetic ancestry typically used in GWAS.
“Although population structure can introduce bias into imputed parental genotypes, the resulting bias in DGE estimates is negligible for the samples of relatively homogeneous genetic ancestry typically used in GWAS3,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The Methods section, “Effect of population structure” (page not available), says population structure biases imputed parental genotypes and can bias direct-effect estimates. It says that for relatively homogeneous samples this bias is *likely* negligible at the individual-SNP level; the claim states negligibility without that uncertainty or scope limitation. The passage also does not establish that such samples are typically used in GWAS. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBecause segregation of chromosomes during meiosis is independent of environment, estimates of DGEs are free from confounding due to gene-environment correlation, including from IGEs and population-stratification.
“Because the segregation of chromosomes during meiosis is independent of environment, estimates of DGEs are free from confounding due to gene-environment correlation, including from IGEs and population-stratification3,9,15,16.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The Main section says random segregation during meiosis enables parent–offspring trio analyses to obtain unbiased, stratification-free estimates of direct genetic effects. But it also warns that sibling-based estimates can be biased by indirect genetic effects from siblings; the unqualified claim that estimates of direct genetic effects are free of this confounding is too broad. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLDSC was designed for GWAS summary statistics on population effects derived from samples of unrelated individuals.
“Since LDSC was designed for GWAS summary statistics on population effects derived from samples of unrelated individuals, we use the effective sample size for the required sample size input3,9.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The Results section describes population effects as estimates from standard GWAS, and Methods > GWASs in unrelated individuals describes an unrelated GWAS sample. The paper does not state that LDSC was designed for summary statistics from such samples; Methods > IBD inference mentions LD Score Regression software only as a way to calculate LD scores. No page numbers are available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFGWAS uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) from other factors contributing to genetic associations.
“Family-based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
sib-GWAS is biased by IGEs between siblings.
“Because genotype data on sibling pairs is more commonly available than complete genotype data on parents, sib-GWAS has been a popular approach9,13, although it is biased by IGEs between siblings3,15,16.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
FGWAS has been proposed as a solution to confounding in GWAS that also enables deeper investigation of gene-environment correlation and non-random mating.
“Family-based GWAS (FGWAS) has been proposed as a solution to the problem of confounding in GWAS3,9,14–16 that also enables deeper investigation of the impact of gene-environment correlation — including from IGEs — and non-random mating, including population structure and assortative mating.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
Genetic associations (including those from GWAS) can be affected by gene-environment correlations and non-random mating, such as indirect genetic effects, assortative mating, and population stratification, leading to biased inferences.
“However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
When applied to sibling pair data, the imputation approach increases effective sample size of DGE estimates by up to one third and of average NTCs by up to one half compared to sib-GWAS.
“When applied to sibling pair data, this approach increases the effective sample size of DGE estimates by up to one third and of average NTCs by up to one half compared to sib-GWAS3.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The imputation approach gives consistent and unbiased estimates of DGEs provided that imputed parental genotypes are unbiased.
“The imputation approach has been proven to give consistent and unbiased estimates of DGEs provided that the imputed parental genotypes are unbiased3.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
sib-GWAS uses genetic differences between siblings to estimate DGEs.
“An alternative study design, which we call sib-GWAS, uses genetic differences between siblings to estimate DGEs3,9.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
Young et al. presented an approach that enables sibling pair data, parent-offspring pair data, and complete parental genotype data to be jointly analyzed through imputation of missing parental genotypes.
“Young et al. presented an alternative approach that enables sibling pair data, parent-offspring pair data, and complete parental genotype data to be jointly analyzed in a unified analytical framework through imputation of missing parental genotypes3,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The primary explanation for positive LDSC estimates of correlation between DGEs and average NTCs for height and EA is likely assortative mating, which is strong for EA and height, although IGEs that are positively correlated with DGEs could also contribute.
“The primary explanation is likely AM, which is strong for EA and height25,31, although IGEs that are positively correlated with DGEs could also contribute3.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
The population effect estimated from β can differ substantially from δ+α when there is assortative mating.
“Unlike in the single variant case, the population effect estimated from β can differ substantially from δ+α — where α is the average NTC for the PGI — when there is AM3,7.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [3]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470 · see citing works
Self-citationFast and accurate genotype imputation in genome-wide association studies through pre-phasing
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737 · see citing works
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Advantages and pitfalls in the application of mixed-model association methods
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014) · Nature Genetics · doi:10.1038/ng.2876 · Cited by 981 · see citing works
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01016-z · Cited by 654 · see citing works
Self-citationDetection of sharing by descent, long-range phasing and haplotype imputation
Kong, A., Masson, G., Frigge, M. L., Gylfason, A., Zusmanovich, P., Thorleifsson, G., Olason, P. I., Ingason, A., Steinberg, S., Rafnar, T., et al. (2008) · Nature Genetics · doi:10.1038/ng.216 · Cited by 447 · see citing works
Self-citationComparing Within- and Between-Family Polygenic Score Prediction
Selzam, S., Ritchie, S. J., Pingault, J.-B., Reynolds, C. A., O’Reilly, P. F., & Plomin, R. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.06.006 · Cited by 289 · see citing works
Deconstructing the sources of genotype-phenotype associations in humans
Young, A. I., Benonisdottir, S., Przeworski, M., & Kong, A. (2019) · Science · doi:10.1126/science.aax3710 · Cited by 288 · see citing works
Self-citationGenetic correlates of social stratification in Great Britain
Abdellaoui, A., Hugh-Jones, D., Yengo, L., Kemper, K. E., Nivard, M. G., Veul, L., Holtz, Y., Zietsch, B. P., Frayling, T. M., Wray, N. R., et al. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0757-5 · Cited by 261 · see citing works
Problems with Using Polygenic Scores to Select Embryos
Turley, P., Meyer, M. N., Wang, N., Cesarini, D., Hammonds, E., Martin, A. R., Neale, B. M., Rehm, H. L., Wilkins-Haug, L., Benjamin, D. J., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmsr2105065 · Cited by 187 · see citing works
Self-citationRelatedness disequilibrium regression estimates heritability without environmental bias
Young, A. I., Frigge, M. L., Gudbjartsson, D. F., Thorleifsson, G., Bjornsdottir, G., Sulem, P., Masson, G., Thorsteinsdottir, U., Stefansson, K., & Kong, A. (2018) · Nature Genetics · doi:10.1038/s41588-018-0178-9 · Cited by 173 · see citing works
Self-citationGenetic predictors of participation in optional components of UK Biobank
Tyrrell, J., Zheng, J., Beaumont, R., Hinton, K., Richardson, T. G., Wood, A. R., Davey Smith, G., Frayling, T. M., & Tilling, K. (2021) · Nature Communications · doi:10.1038/s41467-021-21073-y · Cited by 162 · see citing works
Imprint of assortative mating on the human genome
Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., Gratten, J., Turley, P., Cesarini, D., Benjamin, D. J., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0476-3 · Cited by 148 · see citing works
Self-citationTrejo, S., & Domingue, B. W. (2018) · Biodemography and Social Biology · doi:10.1080/19485565.2019.1681257 · Cited by 69 · see citing works
Flagged first, then by citations
Research works printed but not linked: 33
- [1]Mills MC Rahal C A scientometric review of genome-wide association studies Commun. Biol. 2019 2 1 11 10.1038/s42003-018-0261-x 30623105 PMC6323052 · DOI
- [2]Meuwissen TH Hayes BJ Goddard ME Prediction of total genetic value using genome-wide dense marker maps Genetics 2001 157 1819 1829 10.1093/genetics/157.4.1819 11290733 PMC1461589 · DOI · Paper page
- [4]Kong A The nature of nurture: effects of parental genotypes Science 2018 359 424 428 10.1126/science.aan6877 29371463 · DOI · Paper page
- [5]Price AL Principal components analysis corrects for stratification in genome-wide association studies Nat. Genet. 2006 38 904 909 10.1038/ng1847 16862161 · DOI · Paper page
- [7]Berg JJ Reduced signal for polygenic adaptation of height in UK Biobank Elife 2019 8 1 47 10.7554/eLife.39725 PMC6428572 30895923 · DOI · Paper page
- [8]Zaidi AA Mathieson I Demographic history mediates the effect of stratification on polygenic scores Elife 2020 9 e61548 10.7554/eLife.61548 33200985 PMC7758063 · DOI · Paper page
- [10]Brumpton B Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses Nat. Commun. 2020 11 1 13 10.1038/s41467-020-17117-4 32665587 PMC7360778 · DOI · Paper page
- [11]Sohail M Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies Elife 2019 8 8 11 10.7554/eLife.39702 PMC6428571 30895926 · DOI · Paper page
- [13]Visscher PM Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings PLoS Genet. 2006 2 e41 10.1371/journal.pgen.0020041 16565746 PMC1413498 · DOI
- [14]Benyamin B Family-based genome-wide association studies Pharmacogenomics 2009 10 181 190 10.2217/14622416.10.2.181 19207019 · DOI
- [15]Weiner DJ Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders Nat. Genet. 2017 49 978 985 10.1038/ng.3863 28504703 PMC5552240 · DOI · Paper page
- [17]Mostafavi H Variable prediction accuracy of polygenic scores within an ancestry group Elife 2020 9 e48376 10.7554/eLife.48376 31999256 PMC7067566 · DOI · Paper page
- [18]Fulker DW Cherny SS Sham PC Hewitt JK Combined linkage and association sib-pair analysis for quantitative traits Am. J. Hum. Genet. 1999 64 259 267 10.1086/302193 9915965 PMC1377724 · DOI · Paper page
- [19]Hickey JM Kinghorn BP Tier B van der Werf JHJ Cleveland MA A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation Genet. Sel. Evol. 2012 44 9 10.1186/1297-9686-44-9 22462519 PMC3378429 · DOI
- [20]Sargolzaei M Chesnais JP Schenkel FS A new approach for efficient genotype imputation using information from relatives BMC Genomics 2014 15 478 10.1186/1471-2164-15-478 24935670 PMC4076979 · DOI
- [21]Whalen A Gorjanc G Hickey JM AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data Bioinformatics 2020 36 4369 4371 10.1093/bioinformatics/btaa499 32467963 PMC7520044 · DOI
- [22]Bycroft C The UK Biobank resource with deep phenotyping and genomic data Nature 2018 562 203 209 10.1038/s41586-018-0579-z 30305743 PMC6786975 · DOI · Paper page
- [23]Manichaikul A Robust relationship inference in genome-wide association studies Bioinformatics 2010 26 2867 2873 10.1093/bioinformatics/btq559 20926424 PMC3025716 · DOI · Paper page
- [24]Cavalli-Sforza LL Feldman MW Cultural versus biological inheritance: phenotypic transmission from parents to children (a theory of the effect of parental phenotypes on children’s phenotypes) Am. J. Hum. Genet. 1973 25 618 637 4797967 PMC1762580
- [25]Rice J Cloninger CR Reich T Multifactorial inheritance with cultural transmission and assortative mating. I. Description and basic properties of the unitary models Am. J. Hum. Genet. 1978 30 618 643 747189 PMC1685878
- [27]Cook JP Mahajan A Morris AP Fine-scale population structure in the UK Biobank: implications for genome-wide association studies Hum. Mol. Genet. 2020 29 2803 2811 10.1093/hmg/ddaa157 32691046 · DOI
- [29]Kruglyak L Daly MJ Reeve-Daly MP Lander ES Parametric and nonparametric linkage analysis: a unified multipoint approach Am. J. Hum. Genet. 1996 58 1347 8651312 PMC1915045
- [31]Gudbjartsson DF A frameshift deletion in the sarcomere gene MYL4 causes early-onset familial atrial fibrillation. Eur. Heart J. 2016 38 27 34 10.1093/eurheartj/ehw379 27742809 · DOI
- [32]Rubin, D. B. Multiple Imputation for Nonresponse in Surveys (John Wiley & Sons, 2004).
- [33]Munafò MR Tilling K Taylor AE Evans DM Davey Smith G Collider scope: when selection bias can substantially influence observed associations Int. J. Epidemiol. 2017 47 226 235 10.1093/ije/dyx206 PMC5837306 29040562 · DOI · Paper page
- [35]Benonisdottir, S. & Kong, A. The genetics of participation: method and analysis. Preprint in bioRxiv10.1101/2022.02.11.480067 (2022).
- [38]Border, R. et al. Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Preprint at bioRxiv10.1101/2022.03.21.485215 (2022). PMC9901291 36395242
- [39]Hwang L-D Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs. PLoS Genet. 2020 16 e1009154 10.1371/journal.pgen.1009154 33104719 PMC7646364 · DOI
- [41]Nagylaki T Assortative mating for a quantitative character J. Math. Biol. 1982 16 57 74 10.1007/BF00275161 7161578 · DOI
- [42]Balbona JV Kim Y Keller MC Estimation of parental effects using polygenic scores Behav. Genet. 2021 51 264 278 10.1007/s10519-020-10032-w 33387133 PMC8093180 · DOI
- [44]McCarthy S A reference panel of 64,976 haplotypes for genotype imputation Nat. Genet. 2016 48 1279 1283 10.1038/ng.3643 27548312 PMC5388176 · DOI · Paper page
- [47]Vilhjálmsson BJ Modeling linkage disequilibrium increases accuracy of polygenic risk scores Am. J. Hum. Genet. 2015 97 576 592 10.1016/j.ajhg.2015.09.001 26430803 PMC4596916 · DOI · Paper page
- [48]Loh P-R Efficient Bayesian mixed-model analysis increases association power in large cohorts Nat. Genet. 2015 47 284 290 10.1038/ng.3190 25642633 PMC4342297 · DOI · Paper page
Cited by
Sorted by citations
Genome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
Howe, L. J., Nivard, M. G., Morris, T. T., Hansen, A. F., Rasheed, H., Cho, Y., Chittoor, G., Ahlskog, R., Lind, P. A., Palviainen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01062-7 · Cited by 377 · see citing works
Self-citationWang, B., Baldwin, J. R., Schoeler, T., Cheesman, R., Barkhuizen, W., Dudbridge, F., Bann, D., Morris, T. T., & Pingault, J.-B. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.07.010 · Cited by 103 · see citing works
Family-GWAS reveals effects of environment and mating on genetic associations
Tan, T., Jayashankar, H., Guan, J., Nehzati, S. M., Mir, M., Bennett, M., Agerbo, E., Ahlskog, R., de Andrade Anapaz, V. P., Benonisdottir, S., et al. (2024) · doi:10.1101/2024.10.01.24314703 · Cited by 47 · see citing works
Self-citationEstimation of indirect genetic effects and heritability under assortative mating
Young, A. S. (2023) · doi:10.1101/2023.07.10.548458 · Cited by 32 · see citing works
Self-citationVeller, C., & Coop, G. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002511 · Cited by 78 · see citing works
“We then turn to sibling indirect effects, which are known to bias estimates of direct effects in sibling-based GWASs [2,34].”
InfluentialOvercoming Attenuation Bias in Regressions using Polygenic Indices: A Comparison of ApproachesPreprint
van Kippersluis, H., Biroli, P., Pereira, R. D., Galama, T. J., von Hinke, S., Meddens, S. F. W., Muslimova, D., Slob, E. A. W., de Vlaming, R., & Rietveld, C. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.09.439157 · Cited by 11 · see citing works
Confounding fuels misinterpretation in human genetics
Benning, J. W., Carlson, J., Smith, O. S., Shaw, R. G., & Harpak, A. (2025) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.1615 · Cited by 5 · see citing works
Development and validation of polygenic scores for within-family prediction of disease risksPreprint
Moore, S., Davidson, I., Anomaly, J., Li, J. H., Ahangari, M. M., Moissiy, L., Christensen, M., Young, A. I., Stern, D., & Wolfram, T. (2025) · medRxiv · doi:10.1101/2025.08.06.25333145 · Cited by 8 · see citing works
“We leveraged first-degree relatives in the UK Biobank cohort to impute parental genotypes using the snipar package 18 .”
Self-citationSchwaba, T., Nivard, M. G., Tucker–Drob, E. M., & Boomsma, D. I. (2025) · Annual Review of Psychology · doi:10.1146/annurev-psych-013125-042402 · Cited by 3 · see citing works
“…personality genomics, researchers have combined twin and family methods with GWAS by sampling related individuals from family units and estimating genome-wide associations while controlling for the genotypes of an individual’s family members (Howe et al. 2022, Tan et al. 2024, Young et al. 2022).”
Novel estimators for family-based genome-wide association studies increase power and robustness
Guan, J., Nehzati, S. M., Benjamin, D. J., & Young, A. I. (2022) · doi:10.1101/2022.10.24.513611 · Cited by 2 · see citing works
“Since E[gij |gp(i), gm(i)] = gpar(i)/2, and variation in offspring genotype around this expectation is due to random Mendelian segregations, estimates of direct genetic effects from fitting Model 1 are free from confounding due to IGEs and non-random mating[8].”
BackgroundMethodInfluentialSelf-citationThe Power to Resolve Cultural Transmission and Sibling Interaction Using Polygenic Scores
Bernardo, J. B., Pahnke, C. K. L., van Bergen, E., & Dolan, C. V. (2025) · Behavior Genetics · doi:10.1007/s10519-025-10244-y · Cited by 1 · see citing works
“Later studies demonstrated that the T/NT regression model is equivalent to the regression model in which the offspring phenotype is regressed on the offspring PGS and the parental PGSs (Okbay et al. 2022; Tubbs et al. 2020 ).”
InfluentialXiao, H., Zhou, Z.-C., Ma, Y.-J., Li, X.-Y., Ding, K.-X., Wu, Y.-Q., Wu, T., Hu, Y.-H., & Chen, D.-F. (2025) · Human Genetics · doi:10.1007/s00439-025-02780-8 · Cited by 1 · see citing works
“On the other hand, previous investigations showed that clinical phenotypes, such as lipids (Howe et al. 2022 ), FEV1 (Young et al. 2022 ), and type 2 diabetes (Sunde et al. 2024), appear less influenced by non-direct genetic effects.”
Krätschmer, I., & Robinson, M. R. (2026) · Genetics · doi:10.1093/genetics/iyag042 · Cited by 2 · see citing works
“However, it is more interesting to look at the relationship of the phenotypic and genotypic differences in siblings as the difference in siblings provides an estimate of direct genetics effects unbiased of indirect parental effects (Brumpton et al. 2020; Morris et al. 2020; Young et al. 2022).”
Boardman, J. D., Harris, K. M., & Finch, B. K. (2024) · Biodemography and Social Biology · doi:10.1080/19485565.2024.2355891 · Cited by 1 · see citing works
“Although PGS effects contain environmental and population artifacts, these confounds can be controlled for using either within-sibship-based designs (Howe et al. 2022) or by imputing and controlling for parental genotypes (Young et al. 2022).”
BackgroundYoung, E. A., Erten, E. Y., Postma, E., Lummaa, V., van der Vegt, B., de Bock, G. H., & Dugdale, H. L. (2026) · Molecular Biology and Evolution · doi:10.1093/molbev/msag222
“As recommended by HESS, we also only used sites available in the Genomes phase1 v3 reference panels (The 1000 Genomes Project Consortium 2010) using reference SNP IDs and alleles to merge the data ( n = 8,676,905 SNPs We used the 1,703 LD-independent blocks, on average 1.6 Mb in length, based upon European LD patterns…”
InfluentialEvans, D. M., Smith, G. D., & Moen, G.-H. (2023) · BMC Research Notes · doi:10.1186/s13104-025-07326-9
“…to mention several recent methodological extensions of genetic association studies that permit estimation of maternal, paternal and offspring genetic effects when parent-child duos/trios are not readily available, and that can be used in downstream cross-generational MR studies [2, 10, 18–23].”
- Authors
- Young, A. I., Nehzati, S. M., Benonisdottir, S., Okbay, A., Jayashankar, H., Lee, C., Cesarini, D., Benjamin, D. J., Turley, P., & Kong, A.
- Affiliations
- University of Southern California (US)
- Vrije Universiteit Amsterdam (NL)
- National Bureau of Economic Research (US)
- University of Oxford (GB)
- Corresponding author
- Young, Alexander I.
- Kong, Augustine
- Venue
- Nature Genetics
- Published
- 2022-06
- Volume
- 54
- Issue
- 6
- Pages
- 897-905
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC9197765
- OpenAlex
- W4281917178
- Cited by
- 144
- References
- 0
- Citations by year
- 2026: 32 · 2025: 30 · 2024: 29 · 2023: 19 · 2022: 19 · 2021: 12 · 2020: 3
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Li Ka Shing Foundation
- Netherlands Organisation for Scientific Research | Nationaal Regieorgaan Praktijkgericht Onderzoek SIA (016.Veni.198.058)
- Foundation for the National Institutes of Health (R24-AG065184, R01-AG042568, K99-AG062787-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- Mendelian randomization, direct genetic effects, indirect genetic effects, confounding, educational attainment, cognitive ability
- MeSH terms
- Genotype, Humans, Parents, Software, Polymorphism, Single Nucleotide, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
