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Cited by 383Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Open access
Howe, Laurence J., Nivard, Michel G., Morris, Tim T., Hansen, Ailin F., Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael +190 more·2022·Nature Genetics
DOI 10.1038/s41588-022-01062-7PMC PMC9110300
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Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

AI snapshot

Written from the paper’s full text.

TL;DR

This study combined 178,086 siblings from 19 cohorts to compare population and within-sibship GWAS estimates for 25 phenotypes. Within-sibship estimates were smaller for several behavioral and anthropometric traits, with some downstream differences in heritability, genetic correlation, Mendelian randomization and polygenic adaptation analyses, while most molecular phenotypes were generally consistent.

Key findings

  • Within-sibship shrinkage was largest for number of children (67%; 95% CI 4%, 130%), age at first birth (52%; 30%, 75%), depressive symptoms (50%; 18%, 82%) and educational attainment (47%; 41%, 52%); shrinkage was also found for cognitive ability (22%; 6%, 37%), ever smoking (19%; 9%, 30%) and height (10%; 8%, 12%), with limited evidence for the remaining 17 phenotypes including BMI and SBP.
  • SNP heritability attenuated for educational attainment (population h2 = 0.13 vs within-sibship h2 = 0.04; 76% attenuation; difference P = 5.3 × 10−26), cognitive ability (0.24 vs 0.14; 44%; P = 0.011), ever smoking (0.10 vs 0.07; 25%; P = 0.029) and height (0.41 vs 0.34; 17%; P = 1.6 × 10−3).
  • Genetic correlations with educational attainment attenuated toward zero for BMI (rg = −0.32 to −0.05; difference P = 5.3 × 10−4), ever smoking (−0.41 to −0.14; P = 0.040) and CRP (−0.46 to −0.06; P = 0.039).
  • Within-sibship MR estimates differed from population MR for height on educational attainment (0.06 s.d. vs 0.02 s.d.; difference P = 1.2 × 10−3) and BMI on educational attainment (−0.19 s.d. vs −0.05 s.d.; P < 0.001), age at first birth (P = 2.3 × 10−3) and cognitive ability (P = 0.020); polygenic adaptation on taller height was supported by population (r = 0.022; 95% CI 0.014, 0.031) and within-sibship (r = 0.012; 0.003, 0.020) estimates.

Methods

  • Data came from 178,076 individuals with one or more genotyped siblings from 77,832 sibships in 19 cohorts; 25 phenotypes were analyzed, with phenotype sample sizes ranging from 13,375 to 163,748 (median 82,760). European-ancestry cohorts were meta-analyzed, and 13,856 individuals from the China Kadoorie Biobank were analyzed separately.
  • Population and within-sibship linear regression models were fit in each cohort; the within-sibship model included the mean sibship genotype as a covariate and used family-mean-centered genotype, with age, sex and principal components as covariates and standard errors clustered by sibship. Fixed-effects meta-analysis was performed in METAL.
  • Downstream analyses used LDSC and SumHer for SNP heritability and genetic correlations, within-sibship Mendelian randomization for height and BMI, and tSDS-based Spearman correlations for polygenic adaptation; simulations evaluated LDSC applicability and relatedness effects.

Limitations

  • Stated in the paper: within-sibship models do not control for indirect genetic effects of siblings; assortative mating can bias within-sibship heritability estimates downward; findings are limited to adult phenotypes; family data and statistical inefficiency constrain within-family GWAS; and power was limited for heterogeneity analyses.
  • Stated in the paper: LD score mismatch with 1000 Genomes LD scores in the Norwegian HUNT study and other studies likely inflated LDSC ratios in both population and within-sibship models, and cryptic relatedness likely inflated LDSC ratios in population models but not within-sibship data.
  • Evident from the text: the main meta-analysis was restricted to European-ancestry cohorts, with non-European evidence limited to the China Kadoorie Biobank, and several LDSC confounding estimates were imprecise.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41588-022-01062-7

Cites

63 of 63 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

45 of 53 cited sources checked · 25 supported · 1 flagged · accuracy not yet measured

  • Premisecitation

    Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents.

    “Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents7,17,30–34.”
    Quote in the paperSubstantial claim5 of 7 sources checked
    • cites [7] Brumpton, B. et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. Nat. Commun. 3519 (2020). source
    • cites [17] Young, A. I., Benonisdottir, S., Przeworski, M. & Kong, A. Deconstructing the sources of genotype-phenotype associations in humans. Science 365, 1396–1400 (2019). source
    • cites [30] Fulker, D. W., Cherny, S. S., Sham, P. C. & Hewitt, J. K. Combined linkage and association sib-pair analysis for quantitative traits. Am. J. Hum. Genet. 64, 259–267 (1999). source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [31] Abecasis, G. R., Cardon, L. R. & Cookson, W. O. A general test of association for quantitative traits in nuclear families. Am. J. Hum. Genet. 66, 279–292 (2000). source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [32] Pingault, J.-B. et al. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19, 566–580 (2018). source
    • cites [33] Neale, M. C. et al. Distinguishing population stratification from genuine allelic effects with Mx: association of ADH2 with alcohol consumption. Behav. Genet. 29, 233–243 (1999). source
    • cites [34] Curtis, D., Miller, M. B. & Sham, P. C. Combining the sibling disequilibrium test and transmission/disequilibrium test for multiallelic markers. Am. J. Hum. Genet. 64, 1785 (1999). source

      Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
  • Premisecitation

    Lee et al. found that within-sibship GWAS estimates for educational attainment variants were around 40% lower than estimates from unrelated individuals.

    “For example, Lee et al.14 found that within-sibship GWAS estimates for educational attainment variants were around 40% lower than estimates from unrelated individuals, indicating the presence of demographic and indirect genetic effects.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Lee et al. [14] Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018). source
  • Premisecitation

    GWAS associations for some phenotypes estimated from samples of unrelated individuals also capture effects of demography (assortative mating and population stratification) and indirect genetic effects of relatives.

    “However, there is growing evidence that GWAS associations for some phenotypes estimated from samples of unrelated individuals also capture effects of demography7,8 (assortative mating9–11 and population stratification12) and indirect genetic effects of relatives13–19 (Fig. 1).”
    Quote in the paperSubstantial claim6 of 6 sources checked
    • cites [7] Brumpton, B. et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. Nat. Commun. 3519 (2020). source
    • cites [8] Shen, H. & Feldman, M. W. Genetic nurturing, missing heritability, and causal analysis in genetic statistics. Proc. Natl Acad. Sci. USA 117, 25646–25654 (2020). source
    • cites [9] Howe, L. J. et al. Genetic evidence for assortative mating on alcohol consumption in the UK Biobank. Nat. Commun. https://doi.org/10.1038/s41467-019-12424-x (2019). source

      The Introduction reports genetic evidence of assortative mating on alcohol consumption, and the Discussion says assortative mating could create associations between genetic and environmental factors in offspring. This supports the assortative-mating component for which this paper is cited, but our copy does not establish that GWAS associations estimated from unrelated individuals capture those effects. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites [10] Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 0016 (2017). source

      The paper reports assortative mating at height- and BMI-associated loci and says it can create gametic phase disequilibrium, supporting the assortative-mating component of the cited sentence. It does not directly show that GWAS association estimates from unrelated individuals capture those effects. The citations for population stratification and relatives’ indirect genetic effects are attached to other parts of the sentence. (LLM, conf=0.79)

      Experimental — accuracy not yet measured
    • cites [11] Yengo, L. et al. Imprint of assortative mating on the human genome. Nat. Hum. Behav. 2, 948–954 (2018). source
    • cites [12] Haworth, S. et al. Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nat. Commun. 10, 333 (2019). source
  • Premisecitation

    WS-MR is largely robust against demographic and indirect genetic effects that could distort estimates from nonfamily designs.

    “WS-MR is largely robust against demographic and indirect genetic effects that could distort estimates from nonfamily designs7,25.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [7] Brumpton, B. et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. Nat. Commun. 3519 (2020). source
    • cites [25] Davies, N. M. et al. Within family Mendelian randomization studies. Hum. Mol. Genet. 28, R170–R179 (2019). source
  • Premisecitation

    Population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height.

    “However, this approach is sensitive to population stratification as illustrated by recent work using UK Biobank data which showed that population stratification in GWAS data likely confounded previous estimates of polygenic adaptation on height26,27.”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [26] Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019). source
    • cites [27] Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019). source
  • Classified as probably not a substantial claim. 20
  • May combine two claims. 27

  • Classified as probably not a substantial claim.

    Premisecitation

    GWAS associations can be interpreted as estimates of direct individual genetic effects, that is, the effect of inheriting a genetic variant (or a correlated variant) on a phenotype.

    “GWAS associations can be interpreted as estimates of direct individual genetic effects, that is, the effect of inheriting a genetic variant (or a correlated variant) on a phenotype4–6.”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [4] Morris, T. T., Davies, N. M., Hemani, G. & Davey Smith, G. Population phenomena inflate genetic associations of complex social traits. Sci. Adv. 6, eaay0328 (2020). source

      The Introduction (Estimation of genotype-phenotype associations; page unavailable) identifies individual-level variant substitution effects as the usual target of genotype–phenotype analyses and describes GWAS associations as per-SNP estimates. But the Discussion (page unavailable) cautions that a genetic association does not necessarily imply such an effect: population stratification, assortative mating, and dynastic effects can confound it. The claim omits that qualification on interpreting GWAS associations as direct effects. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [5] Fisher, R. A. The Genetical Theory of Natural Selection (Oxford Univ. Press, 1930). source
    • cites [6] Young A. I. et al. Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects. Preprint at biorXiv https://doi.org/10.1101/2020.07.02.185199 source

      The Abstract says GWAS associations include direct genetic effects but can also include indirect effects and confounding. The results section states that a standard GWAS estimates a population effect—the direct effect plus an average nontransmitted coefficient—not the direct effect alone. The Abstract also reports that standard GWAS estimates were inaccurate estimates of direct effects for educational attainment and cognitive ability. (LLM, conf=0.88)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    GWAS using siblings have been previously limited by available data, but are now feasible by combining well-established family studies with recent large biobanks.

    “GWAS using siblings (within-sibship GWAS) (Fig. 2) have been previously limited by available data, but are now feasible by combining well-established family studies with recent large biobanks that incidentally or by design contain thousands of sibships35–39.”
    Quote in the paperSubstantial claim3 of 5 sources checked
    • cites [35] Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203 (2018). source
    • cites [36] Krokstad, S. et al. Cohort profile: the HUNT Study, Norway. Int J. Epidemiol. 42, 968–-77 (2013). source
    • cites [37] Smith, B. H. et al. Cohort profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness. Int J. Epidemiol. 42, 689–700 (2013). source
    • cites [38] Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J. Epidemiol. 40, 1652–1666 (2011). source

      The Additional information section (page not supplied) reports that the China Kadoorie Biobank recruited 512,891 people and stored blood samples, supporting its identification as a large biobank. The Discussion describes its potential for genetic research, but our copy does not report thousands of sibships or address the feasibility of within-sibship GWAS; searches for “sibling” and “sibship” found no relevant passage. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [39] Brumpton, B. M. et al. The HUNT Study: a population-based cohort for genetic research. Preprint medRxiv https://doi.org/10.1101/2021.12.23.21268305 source

      The Causal inference and family effects section says a lack of genotyped family data previously limited these designs. The family-based design table reports more than 15,000 sibling pairs in HUNT and lists within-family GWAS, supporting their feasibility there. Our copy does not say that feasibility arose from combining established family studies with recent large biobanks. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    LDSC can use GWAS data to estimate SNP heritability, the proportion of phenotypic variation explained by common SNPs.

    “Linkage disequilibrium (LD) score regression (LDSC) can use GWAS data to estimate SNP heritability, the proportion of phenotypic variation explained by common SNPs20,23.”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [20] Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source

      The Results (Overview of Methods; page unavailable) say the LD Score regression slope can be rescaled to estimate heritability explained by SNPs used to calculate LD Scores. But this requires strong assumptions about rare-variant effects; the paper does not present it as an unqualified estimate of the proportion of phenotypic variation explained specifically by common SNPs. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [23] Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). source

  • May combine two claims.

    Premisecitation

    Twin studies have indicated effects of the common environment on many phenotypes for which shrinkage was observed, such as educational attainment, cognitive ability and smoking, potentially consistent with indirect genetic effects of parents.

    “Notably, twin studies have indicated effects of the common environment on many of the phenotypes for which we observed shrinkage, such as educational attainment44, cognitive ability45 and smoking46, potentially consistent with indirect genetic effects of parents.”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [44] Silventoinen, K. et al. Genetic and environmental variation in educational attainment: an individual-based analysis of 28 twin cohorts. Sci. Rep. 10, 12681 (2020). source
    • cites [45] Boomsma, D., Busjahn, A. & Peltonen, L. Classical twin studies and beyond. Nat. Rev. Genet. 3, 872–882 (2002). source
    • cites [46] Maes, H. H. et al. A genetic epidemiological mega analysis of smoking initiation in adolescents. Nicotine Tob. Res. 19, 401–409 (2017). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Polygenic adaptation was estimated using similar methods to a previous publication.

    “Polygenic adaptation was estimated using similar methods to a previous publication28.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [28] Field, Y. et al. Detection of human adaptation during the past 2000 years. Science https://doi.org/10.1126/science.aag0776 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Given previous concerns, we performed several sensitivity analyses for the height analysis.

    “Given previous concerns26,27, we performed several sensitivity analyses for the height analysis detailed in the Supplementary Methods.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [26] Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019). source
    • cites [27] Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019). source

  • May combine two claims.

    Study design and conductcitation

    While assortative mating is unlikely to affect within-sibship GWAS estimates, it can bias within-sibship estimates of heritability downwards and so may have affected the LDSC SNP heritability and genetic correlation estimates.

    “Another limitation is that while assortative mating is unlikely to affect within-sibship GWAS estimates, it can bias within-sibship estimates of heritability downwards49 and so may have affected our LDSC SNP heritability and genetic correlation estimates.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [49] Kemper, K. E. et al. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals. Nat. Commun. 12, 1050 (2021). source
    • Rests on an assumption: Assortative mating is unlikely to affect within-sibship GWAS estimates.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Previous evidence for adaptation on height using siblings in UK Biobank was suggestive of some adaptation, but statistically inconclusive.

    “Previous evidence for adaptation on height using siblings in UK Biobank was suggestive of some adaptation, but statistically inconclusive26.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [26] Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019). source

      The Results (GWAS datasets; no page supplied) identify a UK Biobank sibling-pair analysis, and the Discussion says UK Biobank selection signals were absent or greatly attenuated, with weak significance in some analyses. Those passages support a cautious reading of the evidence, but they do not specifically say that the sibling-based result was suggestive yet statistically inconclusive. (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Sibling effects have been estimated to be modest compared with parental effects but could have impacted the GWAS estimates.

    “Sibling effects have been estimated to be modest compared with parental effects6,48 but could have impacted our GWAS estimates.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [6] Young A. I. et al. Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects. Preprint at biorXiv https://doi.org/10.1101/2020.07.02.185199 source
    • cites [48] Kong, A., Benonisdottir, S. and Young, A. I. Family analysis with Mendelian imputations. Preprint at biorXiv https://doi.org/10.1101/2020.07.02.185181 source

  • May combine two claims.

    Premisecitation

    Twin studies do not find strong evidence for common environmental effects on height, where shrinkage is more likely to be a consequence of assortative mating.

    “In contrast, twin studies do not find strong evidence for common environmental effects on height, where shrinkage is more likely to be a consequence of assortative mating10,46,47.”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [10] Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 0016 (2017). source

      The paper’s Abstract (page unavailable) reports evidence for assortative mating on height and predicts that it inflates height heritability relative to random mating. It does not report that twin studies find little common-environment effect on height or identify the claimed shrinkage as a consequence of assortative mating. (LLM, conf=0.79)

      Experimental — accuracy not yet measured
    • cites [46] Maes, H. H. et al. A genetic epidemiological mega analysis of smoking initiation in adolescents. Nicotine Tob. Res. 19, 401–409 (2017). source

      The Discussion (page not provided) addresses shared environmental effects and assortative mating for adolescent smoking initiation, not height. Searches for height, shrinkage, and assortative mating found no passage assessing whether twin studies find common environmental effects on height or attributing shrinkage to assortative mating. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
    • cites [47] Stulp, G., Simons, M. J., Grasman, S. & Pollet, T. V. Assortative mating for human height: A meta-analysis. Am. J. Hum. Biol. 29, e22917 (2017). source

      The Discussion (page not available) reports moderate assortative mating for height, but does not assess common environmental effects in twin studies or explain shrinkage. Searches for “shrinkage,” “common environmental effects,” and shared environment found no passage addressing those claims. (LLM, conf=0.72)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    UK Biobank within-sibship ratio estimates for BMI, SBP and educational attainment were largely consistent with zero confounding although confidence intervals were wide.

    “UK Biobank estimates were largely consistent with zero confounding although confidence intervals were wide (Supplementary Table 13).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the within-sibship model, the confounding estimate for educational attainment was 35% (95% CI 12%, 57%) and imprecise.

    “Confounding estimates were modest for height (10%; 95% CI 6%, 14%) and BMI (9%; 2%, 16%), while the estimate for educational attainment was imprecise (35%; 12%, 57%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Population MR estimates provided strong evidence that taller height and lower BMI increase educational attainment (0.06 s.d. increase per s.d. taller height; 0.19 s.d. decrease per s.d. higher BMI).

    “Population MR estimates provided strong evidence that taller height and lower BMI increase educational attainment (0.06 s.d. increase in education per s.d. taller height; 95% CI 0.04, 0.07; 0.19 s.d. decrease in education per s.d. higher BMI; 0.16, 0.22).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Simulations confirmed that relatedness beyond sibling pairs can lead to underestimating standard errors in the population model and has no effect on the standard errors of the within-sibship model.

    “We performed simulations, described below, confirming that such relatedness can lead to underestimating standard errors in the population model and has no effect on the standard errors of the within-sibship model.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The LDSC ratios in the population GWAS were higher than previous studies.

    “The LDSC ratios in the population GWAS were also higher than previous studies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The weak evidence for within-sibship shrinkage in the association between BMI genetic variants and BMI is in contrast to strong evidence from MR and genetic correlation analyses that the association between BMI genetic variants and educational attainment attenuates.

    “The weak evidence for within-sibship shrinkage in the association between BMI genetic variants and BMI is in contrast to the strong evidence from MR analyses (and genetic correlation analyses) that the association between BMI genetic variants and educational attainment does attenuate.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Seemingly unrelated regression standard errors were consistent with the jackknife approach standard errors.

    “Seemingly unrelated regression standard errors were consistent with the jackknife approach standard errors.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the China Kadoorie Biobank, there was minimal evidence of shrinkage for height genetic variants but suggestive evidence of shrinkage for variants associated with smoking initiation.

    “In this sample, we found minimal evidence of shrinkage for height genetic variants but—consistent with the European meta-analysis—suggestive evidence of shrinkage for variants associated with smoking initiation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    There was little evidence of heterogeneity in shrinkage estimates at genetic variants strongly associated (P < 1 × 10−5) with height and educational attainment, although power was limited.

    “We found little evidence of heterogeneity in shrinkage estimates at genetic variants strongly associated (P < 1 × 10−5) with height and educational attainment, although power was limited by available samples.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Estimates failed to converge for genetic correlation analyses involving age at first birth and age at menopause, so these phenotypes were not analyzed.

    “Estimates failed to converge for genetic correlation analyses involving age at first birth and age at menopause, so these phenotypes were not analyzed here.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    WS-MR estimates for height and BMI on educational attainment were greatly attenuated (height: 0.02 s.d. increase; BMI: 0.05 s.d. decrease).

    “In contrast, WS-MR estimates for these relationships were greatly attenuated (height: 0.02 s.d. increase; −0.01, 0.04; difference P = 1.2 × 10−3; BMI: 0.05 s.d. decrease; 0.01, 0.09; difference P = 2.8 × 10−7).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Similar attenuation was observed from population MR and WS-MR estimates for BMI on age at first birth (difference P = 2.3 × 10−3) and cognitive ability (difference P = 0.020).

    “We also observed similar attenuation from population MR and WS-MR estimates for BMI on age at first birth (difference P = 2.3 × 10−3) and cognitive ability (difference P = 0.020); phenotypes highly correlated with education.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Standard errors for shrinkage estimates decreased by around 15% when the covariance was modeled.

    “We found that standard errors for shrinkage estimates decreased by around 15% when the covariance was modeled (Supplementary Table 10).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There were differences between within-sibship and population MR estimates of height on educational attainment and BMI on educational attainment, cognitive ability and age at first birth.

    “However, we observed differences between within-sibship and population MR estimates of height (on educational attainment) and BMI (on educational attainment, cognitive ability and age at first birth).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Using within-sibship GWAS estimates from a larger (~4-fold) sample of siblings, there was strong evidence of polygenic adaptation on increased height and some evidence of adaptation on number of children and HDL-cholesterol.

    “Here, using within-sibship GWAS estimates from a larger (~4-fold) sample of siblings, we found strong evidence of polygenic adaptation on increased height and some evidence of adaptation on number of children and HDL-cholesterol.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The observed SNP heritability attenuations were consistent with theoretical expectation, suggesting that lower within-sibship SNP heritability estimates are explained by genetic association estimate shrinkage.

    “The observed attenuations were consistent with theoretical expectation (Supplementary Table 4), suggesting that the lower within-sibship SNP heritability estimates are explained by genetic association estimate shrinkage.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisenone given

    LDSC requires nonzero heritability to generate meaningful rg estimates.

    “LDSC requires nonzero heritability to generate meaningful rg estimates, so we restricted analyses to the 22 phenotypes with SNP heritability point estimates greater than zero in both population and within-sibship models (that is, omitted physical activity and ratio of forced expiratory volume (FEV1)/forced vital capacity (FEV1FVC)).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The findings are limited to adult phenotypes.

    “Our findings are also limited to adult phenotypes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    This clustering accounts for sibling relationships but does not account for further relatedness present in each sample.

    “Note that this clustering accounts for sibling relationships but does not account for further relatedness present in each sample.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    We do not have an estimate of the covariance term between the population and within-sibship scores because the two GWAS were fit in separate regression models.

    “However, we do not have an estimate of this covariance term because the two GWAS were fit in separate regression models.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    GWAS analyses were performed uniformly across individual studies using automated scripts and a preregistered analysis plan.

    “GWAS analyses were performed uniformly across individual studies using automated scripts and a preregistered analysis plan (https://github.com/LaurenceHowe/SiblingGWAS).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    Uniform shrinkage across the genome would lead to GWAS Z scores being multiplied by a factor (1 − k), where k is the shrinkage coefficient, and χ2 statistics being multiplied by (1 − k)2.

    “Uniform shrinkage across the genome would lead to GWAS Z scores being multiplied by a factor (1 − k), where k is the shrinkage coefficient, and χ2 statistics being multiplied by (1 − k)2.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Within-sibship shrinkage in GWAS estimates and LDSC heritability estimates were largely consistent, suggesting any such bias is unlikely to have impacted the conclusions.

    “However, the within-sibship shrinkage in GWAS estimates and LDSC heritability estimates were largely consistent, suggesting any such bias is unlikely to have impacted our conclusions.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Standard errors from both estimators were clustered over families at the sibling level to account for nonrandom clustering of siblings within families.

    “Standard errors from both estimators were clustered over families at the sibling level to account for nonrandom clustering of siblings within families.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisenone given

    Assortative mating and indirect genetic effects tend to influence associations proportional to the direct effect, whereas population stratification is likely to have larger effects on ancestrally informative markers.

    “Both of these tend to influence associations proportional to the direct effect, whereas population stratification is likely to have larger effects on ancestrally informative markers.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    We used the LDAK-Thin model with the precomputed tagging file over the BLD-LDAK model because of the limited power of our datasets.

    “We used the LDAK-Thin model with the precomputed tagging file over the BLD-LDAK model because of the limited power of our datasets (the BLD-LDAK model includes additional parameters so generates less precise estimates).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Cryptic relatedness likely inflated LDSC ratios in the population models but not in the within-sibling data.

    “Thus, cryptic relatedness likely inflated LDSC ratios in the population models but not in the within-sibling data.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    We estimated only pairwise genetic correlations between educational attainment and all other phenotypes because of previous evidence that educational attainment is influenced by demographic and indirect genetic effects and, given the limited statistical power, to reduce the multiple testing burden.

    “We estimated only pairwise genetic correlations between educational attainment and all other phenotypes because of previous evidence that educational attainment is influenced by demographic and indirect genetic effects and, given the limited statistical power, to reduce the multiple testing burden.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Educational attainment is influenced by demographic and indirect genetic effects.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The automated pipeline restricted analyses to common genetic variants (MAF > 0.01) and removed poorly imputed variants (INFO < 0.3).

    “The automated pipeline restricted analyses to common genetic variants (minor allele frequency (MAF) > 0.01) and removed poorly imputed variants (INFO: information score < 0.3).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Within-family GWAS are limited by both available family data and statistical inefficiency (homozygosity within families).

    “Within-family GWAS are limited by both available family data and statistical inefficiency (homozygosity within families).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    We restricted genetic correlation analyses to the 22 phenotypes with SNP heritability point estimates greater than zero in both population and within-sibship models.

    “LDSC requires nonzero heritability to generate meaningful rg estimates, so we restricted analyses to the 22 phenotypes with SNP heritability point estimates greater than zero in both population and within-sibship models (that is, omitted physical activity and ratio of forced expiratory volume (FEV1)/forced vital capacity (FEV1FVC)).”
    Quote in the paperSubstantial claim
41 of 41

  • The authors state this as an assumption.

    Within-sibship models capture demographic and indirect genetic effects of parents in the family mean genotype covariate, so the within-sibship estimate is not influenced by these sources.

    “Demography and indirect genetic effects of parents (N) will be captured by GF. The association between GC and X will not be influenced by these sources of association but could be affected by indirect effects of the siblings themselves, which are not controlled for.”

  • The paper relies on this without support.

    Assortative mating is unlikely to affect within-sibship GWAS estimates.

    “while assortative mating is unlikely to affect within-sibship GWAS estimates”

  • The authors state this as an assumption.

    If biases affect the population and within-sibship SNP heritability estimates equally, then the difference between the two estimates will be unbiased.

    “If we assume that biases affect the estimates equally then the difference between the two estimates will be unbiased.”

  • The authors state this as an assumption.

    All other coefficients remain consistent when writing the expectation of hWS² as a function of k and hPop².

    “Therefore, assuming all other coefficients remain consistent, the expectation of hWS² can be written as a function of k and hPop².”

  • The paper relies on this without support.

    Effects from demographic and familial pathways inflate (rather than shrink) population-based estimates.

    “In general, these effects have been shown to inflate (rather than shrink) population-based estimates, so we estimated within-sibship shrinkage (the % difference from population to within-sibship estimates).”

  • The paper relies on this without support.

    Educational attainment is influenced by demographic and indirect genetic effects.

    “We estimated only pairwise genetic correlations between educational attainment and all other phenotypes because of previous evidence that educational attainment is influenced by demographic and indirect genetic effects and, given the limited statistical power, to reduce the multiple testing burden.”

  • The paper relies on this without support.

    There is previous evidence for shrinkage on height and educational attainment.

    “These phenotypes were chosen because of previous evidence for shrinkage on these phenotypes and available data.”

  • The paper relies on this without support.

    Cryptic relatedness, defined as nonindependence between close relatives in the study sample, leads to inflated precision in GWAS associations.

    “One source of inflation in GWAS associations is cryptic relatedness: nonindependence between close relatives in the study sample results which leads to inflated precision.”

  • The paper relies on this without support.

    Inflated signal relating to cryptic relatedness may result in confounded signal, which is detected by the LD score intercept/ratio.

    “Inflated signal relating to cryptic relatedness may result in confounded signal, which is detected by the LD score intercept/ratio.”
9 of 9

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?

  • Howe et al. produced sib-GWAS estimates of DGEs on 24 phenotypes from a meta-analysis of European genetic ancestry cohorts.

    “The sib-GWAS approach was used by Howe et al.9, which produced sib-GWAS estimates of DGEs on 24 phenotypes from a meta-analysis of European genetic ancestry cohorts.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [9]

    The Results describe within-sibship GWAS, and the Main figure caption says GWAS data from 18 European-ancestry cohorts were meta-analyzed. However, Methods > Meta-analysis says the meta-analyses covered 25 phenotypes, not 24. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • Because 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 [9]

    The Main text (Fig. 2; page not provided) says within-sibship estimates use the component of genotype arising from segregation at meiosis and are not influenced by demographic effects, including population stratification, or parental indirect genetic effects. But it explicitly says sibling indirect genetic effects are not controlled for, so the claim that estimates of direct genetic effects are free from confounding by indirect genetic effects generally is too broad. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • We do not replicate the Howe et al. result for cognitive performance: we find similar SNP heritability from DGEs (0.188, S.E. 0.027) and population effects (0.186, S.E. 0.016), whereas Howe et al. found substantially smaller heritability from DGEs (0.14, S.E. 0.043) than from population effects (0.24, S.E. 0.031).

    “However, we do not replicate the Howe et al. result for cognitive performance: we find similar SNP heritability from DGEs (0.188, S.E. 0.027) and population effects (0.186, S.E. 0.016), whereas Howe et al. found substantially smaller heritability from DGEs (0.14, S.E. 0.043) than from population effects (0.24, S.E. 0.031).”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [9]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Genome-wide summary statistics on DGEs from FGWAS can be used as inputs to LDSC, giving SNP heritability estimates that remove contributions from IGEs, population stratification, and inflation due to AM, bringing them closer to traditional definitions of heritability.

    “Genome-wide summary statistics on DGEs from FGWAS can be used instead as inputs to LDSC9, thereby giving SNP heritability estimates that remove contributions from IGEs, population stratification, and inflation due to AM, bringing them closer to traditional definitions of heritability.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [9]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Polygenic indices based on direct genetic effects from FGWAS have favorable properties due to the removal of confounding from PGI weights, making them suited to applications that are sensitive to confounding.

    “Polygenic predictors (called polygenic indices, PGIs, or polygenic scores) based on DGE estimates from FGWAS – hereafter, DGE PGIs – have favorable properties due to the removal of confounding from PGI weights, making them suited to applications that are sensitive to confounding4,5,9,13,23.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [9]

    The Discussion (page unavailable) says family-based estimates are likely to be less biased for phenotypes sensitive to demographic and indirect genetic effects, supporting their use in confounding-sensitive applications. But the Main text’s Fig. 2 explanation says within-sibship estimates can still be affected by siblings’ indirect effects, and the Discussion likewise notes that limitation. The paper therefore supports reduced confounding in the estimates used for PGI weights, not its complete removal. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Sibling total differs between the abstract and main text

    Minor

    The abstract reports 178,086 siblings, whereas the main text repeatedly reports 178,076-a difference of 10 individuals.

  • Polygenic-adaptation correlation is described using two different GWAS measures

    Minor

    The results describe correlating tSDS with GWAS P values, but the methods say the correlation used absolute GWAS Z scores. These measures have opposite rank order, so they would give correlations with opposite signs.

2 of 2

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Authors
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., van der Zee, M. D., Cheesman, R., Mangino, M., Wang, Y., Li, S., Klaric, L., Ratliff, S. M., Bielak, L. F., Nygaard, M., Giannelis, A., Willoughby, E. A., Reynolds, C. A., Balbona, J. V., Andreassen, O. A., Ask, H., Baras, A., Bauer, C. R., Boomsma, D. I., Campbell, A., Campbell, H., Chen, Z., Christofidou, P., Corfield, E., Dahm, C. C., Dokuru, D. R., Evans, L. M., de Geus, E. J. C., Giddaluru, S., Gordon, S. D., Harden, K. P., Hill, W. D., Hughes, A., Kerr, S. M., Kim, Y., Kweon, H., Latvala, A., Lawlor, D. A., Li, L., Lin, K., Magnus, P., Magnusson, P. K. E., Mallard, T. T., Martikainen, P., Mills, M. C., Njølstad, P. R., Overton, J. D., Pedersen, N. L., Porteous, D. J., Reid, J., Silventoinen, K., Southey, M. C., Stoltenberg, C., Tucker-Drob, E. M., Wright, M. J., Social Science Genetic Association Consortium, Kweon, H., Koellinger, P. D., Benjamin, D. J., Turley, P., Within Family Consortium, 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., van der Zee, M. D., Cheesman, R., Mangino, M., Wang, Y., Li, S., Klaric, L., Ratliff, S. M., Bielak, L. F., Nygaard, M., Giannelis, A., Willoughby, E. A., Reynolds, C. A., Balbona, J. V., Andreassen, O. A., Ask, H., Boomsma, D. I., Campbell, A., Campbell, H., Chen, Z., Christofidou, P., Corfield, E., Dahm, C. C., Dokuru, D. R., Evans, L. M., de Geus, E. J. C., Giddaluru, S., Gordon, S. D., Harden, K. P., Hill, W. D., Hughes, A., Kerr, S. M., Kim, Y., Latvala, A., Lawlor, D. A., Li, L., Lin, K., Magnus, P., Magnusson, P. K. E., Mallard, T. T., Martikainen, P., Mills, M. C., Njølstad, P. R., Pedersen, N. L., Porteous, D. J., Silventoinen, K., Southey, M. C., Stoltenberg, C., Tucker-Drob, E. M., Wright, M. J., Hewitt, J. K., Keller, M. C., Stallings, M. C., Lee, J. J., Christensen, K., Kardia, S. L. R., Peyser, P. A., Smith, J. A., Wilson, J. F., Hopper, J. L., Hägg, S., Spector, T. D., Pingault, J.-B., Plomin, R., Havdahl, A., Bartels, M., Martin, N. G., Oskarsson, S., Justice, A. E., Millwood, I. Y., Hveem, K., Naess, Ø., Willer, C. J., Åsvold, B. O., Kaprio, J., Medland, S. E., Walters, R. G., Evans, D. M., Smith, G. D., Hayward, C., Brumpton, B., Hemani, G., Davies, N. M., Hewitt, J. K., Keller, M. C., Stallings, M. C., Lee, J. J., Christensen, K., Kardia, S. L. R., Peyser, P. A., Smith, J. A., Wilson, J. F., Hopper, J. L., Hägg, S., Spector, T. D., Pingault, J.-B., Plomin, R., Havdahl, A., Bartels, M., Martin, N. G., Oskarsson, S., Justice, A. E., Millwood, I. Y., Hveem, K., Naess, Ø., Willer, C. J., Åsvold, B. O., Koellinger, P. D., Kaprio, J., Medland, S. E., Walters, R. G., Benjamin, D. J., Turley, P., Evans, D. M., Davey Smith, G., Hayward, C., Brumpton, B., Hemani, G., & Davies, N. M.
Affiliations
  • Regeneron (United States) (US)
  • Geisinger Health System (US)
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2022-05
Volume
54
Issue
5
Pages
581-592
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-022-01062-7
PMC
PMC9110300
OpenAlex
W4229455035
Semantic Scholar
fd882733399892e67d8bbc69a36844797ffc0aec
Cited by
377
References
59
Citations by year
2026: 69 · 2025: 88 · 2024: 102 · 2023: 81 · 2022: 34 · 2021: 1 · 2019: 1
FWCI
46.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Explore Bristol Research (publishedVersion, cc-by)
  • VU Research Portal (publishedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
  • UCL Discovery (University College London) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Työväentutkimus Vuosikirja (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Research Portal (King's College London) (submittedVersion, other-oa)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
  • University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Apollo
  • Apollo (cc-by)
Funders
  • RCUK | Medical Research Council (MC_UU_00011)
  • Jacobs Foundation
  • RCUK | MRC | Medical Research Foundation (MC_UU_00011)
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
direct genetic effects, indirect genetic effects, population stratification, assortative mating, heritability, genetic correlation, polygenic adaptation, height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms, smoking, body mass index, LDL cholesterol, Mendelian randomization
MeSH terms
Humans, Phenotype, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Mendelian Randomization Analysis

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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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-29.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-29.

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