The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index; 20 of the 28 sources its claims cite were checked for support.
Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals
Open accessAbstract
Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study expands a GWAS of educational attainment to ~1.1 million individuals, identifying 1,271 autosomal lead SNPs and 10 X-chromosome lead SNPs. Joint multi-phenotype analysis yields polygenic scores explaining 11–13% of educational-attainment variance and 7–10% of cognitive-performance variance.
Key findings
- The primary EduYears GWAS (N = 1,131,881) identified 1,271 approximately independent genome-wide-significant SNPs (995 at the stricter P < 1×10−8 threshold); median lead-SNP effect size was 1.7 weeks of schooling per allele (5th–95th percentiles: 1.1–2.6 weeks).
- Lead SNPs showed heterogeneous effects across cohorts: the joint null of homogeneous cohort-level effects was rejected (P = 9.7×10−12), and the inverse-variance-weighted mean cross-cohort genetic correlation was 0.72 (SE = 0.14), distinguishable from one (P = 0.03).
- X-chromosome analyses (N = 694,894) identified 10 lead SNPs and estimated X-chromosome SNP heritability of ~0.3% in both men and women, consistent with partial dosage compensation.
- Polygenic prediction: an LDpred score had incremental R² of 12.7% in Add Health and 10.6% in HRS (weighted average 11.4%); MTAG-based scores explained 11–13% of EduYears variance and 7–10% of cognitive-performance variance. In an African-American HRS sample (N = 1,519), the score’s incremental R² was 1.6%, an 85% attenuation.
Methods
- Sample-size-weighted meta-analysis of 71 quality-controlled cohort-level results files using METAL, restricted to European-ancestry individuals with EduYears measured at age ≥30; ~10 million SNPs from 1000 Genomes phase 3, with LD Score regression intercept used to inflation-adjust test statistics.
- Additional analyses included within-family association in 22,135 sibling pairs, X-chromosome GWAS in UK Biobank and 23andMe, GWAS of cognitive performance (N = 257,841), math ability (N = 564,698), and highest math (N = 430,445), and MTAG joint analysis of the four phenotypes.
- Polygenic scores were constructed with LDpred and evaluated in Add Health (N = 4,775), HRS (N = 8,609), and WLS; biological annotation used DEPICT, stratified LD Score regression, and CAVIARBF fine-mapping.
Limitations
- The paper states that cross-cohort genetic correlation is imperfect for environmentally contingent phenotypes like EA, that within-family effect sizes were roughly 40% smaller than GWAS effect sizes with assortative-mating adjustment explaining at most one third of the deflation, and that GWAS estimates may be biased upward by correlation between EA and rearing environment.
- The paper states that the score has much lower predictive power in an African-American sample and is expected to have reduced power in other non-European-ancestry groups; it also cautions that the score should not be interpreted as a measure of genetic endowment without controls for environmental amplification.
- Evident from the text: all primary association analyses were restricted to European-ancestry individuals, and the X-chromosome heritability analysis could not distinguish whether lower heritability reflects smaller per-allele effects or haploidy/partial X-inactivation.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
44 Own result · 15 Own interpretation · 10 Own method · 7 Supported · 6 Partly supported · 2 Sources not found · 2 No full text of the sources · 2 Not checked yet · 1 Supported so far · 1 Not supported · 1 Couldn’t confirm · 1 No basis given · accuracy not yet measured
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“Of the 162 SNPs, 158 pass quality-control filters in our updated meta-analysis.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Replication of lead SNPs from Okbay et al.’s combined-stage analysis.
What the sources say
- Disputedcites Okbay et al. [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 sourceExperimental — accuracy not yet measured
The paper states that its meta-analysis identified 74 approximately independent genome-wide significant loci, with one lead SNP defined for each locus (Abstract; page unavailable). It does not report 162 lead SNPs. The statement that 158 passed filters in the citing authors’ updated meta-analysis describes their own analysis, not a result attributed to this source. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our finding of an imperfect genetic correlation replicates earlier results from smaller samples16,17.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Heterogeneous Effect Sizes
What the sources say
- Supportedcites [16] de Vlaming R Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies. PLOS Genet. 13, e1006495 (2017). 28095416 PMC5240919 source
The Results section reports an earlier estimate of imperfect genetic correlation for years of schooling: approximately 0.8 across studies and between females and males. Table 1 gives the sample size as 20,619, supporting the citation’s reference to a result from a smaller sample. (LLM, conf=0.86)
- Partly supportedcites [17] Tropf FC Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav (2017). doi: 10.1038/s41562-017-0195-1 PMC5642946 29051922 sourceExperimental — accuracy not yet measured
The Discussion reports limited evidence that genetic effects on reproductive behaviour in one country predict those in another, consistent with an imperfect cross-population genetic correlation. But it also says the study lacked the power to estimate genetic correlations directly; the correlations of 0.5–0.8 were used in simulations, not measured as a replicated finding. (LLM, conf=0.83)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“However, theoretical projections that failed to consider heterogeneity of effect sizes were optimistic4.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > DISCUSSION
What the sources say
- Could not verifycites [4] Rietveld CA GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science (80−.). 340, 1467– 1471 (2013). PMC3751588 23722424 sourceExperimental — accuracy not yet measured
The passage labeled Abstract discusses projections for polygenic-score performance and reports small SNP effects, but it does not evaluate projections that ignored heterogeneity of effect sizes. Searches for that assumption and for “optimistic” found no passage making the attributed point. (LLM, conf=0.70)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In the subsample of our data (N = 726,808) that did not contribute to the earlier study’s analyses, the SNPs replicate at a rate that closely matches theoretical projections derived from our Bayesian framework (Supplementary Figure 3).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Primary GWAS of EduYears
What the sources say
- Partly supportedcites [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 sourceExperimental — accuracy not yet measured
The paper’s replication discussion (page unavailable in our copy) reports that SNPs from a previous educational-attainment study replicated in new cohorts, and compares replication of its 74 lead SNPs with expected numbers. Extended Data Figure 4 gives a replication sample of N = 111,349. These passages do not support the claimed 162 associations, N = 726,808 subsample, or projections from the citing authors’ Bayesian framework. (LLM, conf=0.84)
plaincorrelationaln = 726,808- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“reported that a 10-million-SNP linear predictor (hereafter, polygenic score) had an out-of-sample predictive power of 3.2%10.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > INTRODUCTION
What the sources say
- Supportedcites [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 source
The paper reports that a polygenic score constructed from all measured SNPs had mean predictive power of 3.2% across two holdout samples (Abstract; page unavailable). It describes its GWAS as covering approximately 9.3 million SNPs, consistent with the citing sentence’s rounded description of a 10-million-SNP score. (LLM, conf=0.88)
plaincorrelationaln = 293,723- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The largest (N = 293,723) GWAS of EA to date identified 74 approximately independent SNPs at genome-wide significance (hereafter, lead SNPs)”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > INTRODUCTION
What the sources say
- Supportedcites [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 source
The paper’s Abstract reports an educational-attainment GWAS sample of 293,723 and 74 approximately independent genome-wide significant loci. It defines one lead SNP per locus, supporting the citing sentence’s description of 74 lead SNPs. (LLM, conf=0.94)
plainn = 293,723- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our and others’ findings16,17 suggest that imperfect genetic correlation across cohorts will be the norm for phenotypes that, like EA, are environmentally contingent.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > DISCUSSION
What the sources say
- Partly supportedcites [16] de Vlaming R Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies. PLOS Genet. 13, e1006495 (2017). 28095416 PMC5240919 sourceExperimental — accuracy not yet measured
The Results (Empirical results for SNP-based heritability and CGR; page unavailable) report an average cross-study genetic correlation of about 0.8 for years of schooling. The Discussion (page unavailable) identifies differing environments and gene–environment interactions as possible explanations, but says the study cannot disentangle the causes. It does not establish that imperfect correlations will be the norm across environmentally contingent phenotypes generally. (LLM, conf=0.84)
- Partly supportedcites [17] Tropf FC Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav (2017). doi: 10.1038/s41562-017-0195-1 PMC5642946 29051922 sourceExperimental — accuracy not yet measured
The Results (Best model by phenotype; page unavailable) find that birth cohort moderates genetic effects on educational attainment, supporting that example. But the same section finds no birth-cohort heterogeneity for the reproductive phenotypes, and the Discussion says genetic correlations across cohorts were not directly estimated. The paper therefore does not establish that imperfect correlations will be the norm for environmentally contingent phenotypes generally. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“we applied the bioinformatics tool DEPICT19 and found that, relative to other genes, genes near our lead SNPs are overwhelmingly enriched for expression in the central nervous system (Fig. 3A, Supplementary Table 6).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Supportedcites [19] Pers TH Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun 6, 5890 (2015). 25597830 PMC4420238 source
The citation identifies DEPICT as the tool the citing authors applied, rather than attributing their central-nervous-system finding to this paper. The Discussion describes DEPICT as a tool for tissue/cell-type enrichment analysis, and Methods explains that it takes independent associated SNPs as input. (LLM, conf=0.94)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“According to our LD Score regression13 estimates, only a small share (~5%) of this inflation is attributable to bias (Supplementary Figure 2, Supplementary Table 1).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Primary GWAS of EduYears
What the sources say
- Supportedcites [13] Bulik-Sullivan BK LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet 47, 291– 295 (2015). 25642630 PMC4495769 source
The citation identifies LD Score regression as the method used for the citing authors’ own estimates, rather than attributing their ~5% result to this paper. The Results (Simulations with Confounding and Polygenicity; page unavailable) state that the method can partition test-statistic inflation between bias and polygenicity. (LLM, conf=0.90)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Because the discovery sample used to construct the score consisted of individuals of European ancestry, we would not expect the predictive power of our score to be as high in other ancestry groups7,26,27.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Polygenic Prediction
What the sources say
- Supportedcites [7] Domingue BW, Belsky DW, Conley D, Harris KM & Boardman JD Polygenic Influence on Educational Attainment: New evidence from The National Longitudinal Study of Adolescent to Adult Health. AERA Open 1, 1– 13 (2015). 28164148 PMC5291340 source
The Materials > Sample section says the original GWAS used to construct the score was conducted in people of European descent and that its replication in African American samples may be compromised by ancestry-related differences in linkage disequilibrium. The Introduction also describes the potential racial specificity of GWAS findings, supporting the citing sentence’s expectation of lower predictive power in other ancestry groups. (LLM, conf=0.90)
- Not checked — no full text of the sourcecites [26] Martin AR Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am. J. Hum. Genet 100, 635– 649 (2017). 28366442 PMC5384097 source
- Supportedcites [27] Scutari M, Mackay I & Balding D Using Genetic Distance to Infer the Accuracy of Genomic Prediction. PLoS Genet. 12, e1006288 (2016). 27589268 PMC5010218 source
The Introduction states that genomic models have lower average predictive accuracy when the target population is more distantly related to the training population. It also notes that causal variants can differ between human ancestry groups. The European ancestry of the citing authors’ discovery sample is their own context, not a finding attributed to this source. (LLM, conf=0.88)
hedgedconditionaln = 1,519- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
- Probably not a substantial claim. 20
- It may state two separate claims as one. 19
- The quote only partly states this claim. 1
Every source the paper cites for this claim was checked and supports it.
In the paper
“which encodes the pore-forming subunit of a voltage-gated calcium channel that has been implicated in the trafficking of NMDA-type glutamate receptors24.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Supportedcites [24] Wang G CaV3.2 calcium channels control NMDA receptor-mediated transmission: a new mechanism for absence epilepsy. Genes Dev. 29, 1535– 51 (2015). 26220996 PMC4526737 source
The Discussion (‘Pathological function of CaV3.2 channels’; page not provided) links CACNA1H to CaV3.2 channels. The Results (‘CaV3.2 channels regulate glutamatergic transmission at various synapses’; page not provided) report that CaV3.2 regulates the synaptic incorporation of NMDA receptors, supporting the claim’s reference to receptor trafficking. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“(For height, the analogous estimate is only 6%.)”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Within-Family Association Analyses
What the sources say
- Partly supportedcites [15] Kong A The nature of nurture: effects of parental genotypes. bioRxiv 219261 (2017). doi: 10.1101/219261 29371463 sourceExperimental — accuracy not yet measured
The section ‘The nature of genetic nurture and other polygenic scores’ (page unavailable) reports an association between height and a non-transmitted height polygenic score, but gives no 6% estimate; it says most of the observed effect is estimated to be due to assortative-mating confounding. The height row of Table 1 (page unavailable) uses an educational-attainment polygenic score and does not establish the claimed 6% figure. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“At the standard false discovery rate (FDR) threshold of 5%, the bioinformatics tool DEPICT19 prioritizes 1,838 genes (Supplementary Table 7), a tenfold increase relative to the DEPICT results from an earlier GWAS of EduYears10.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Supportedcites [19] Pers TH Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun 6, 5890 (2015). 25597830 PMC4420238 source
The citation attached to “DEPICT” identifies the bioinformatics tool, rather than sourcing the 1,838-gene count or the tenfold comparison. The Results (Overview of the DEPICT methodology) and Methods (DEPICT gene prioritization) describe the tool and its false-discovery-rate procedure. The sentence assigns the count to Supplementary Table 7 and the earlier comparison to a separate GWAS citation. (LLM, conf=0.92)
- Partly supportedcites [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 sourceExperimental — accuracy not yet measured
Extended Data Figure 9 documents DEPICT-prioritized genes for EduYears, supporting the identification of this paper as an earlier DEPICT analysis. Our copy does not give the earlier gene count or establish the claimed tenfold comparison; the 1,838 genes are presented as the citing study’s result, not this source’s. (LLM, conf=0.78)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Educational attainment (EA) is moderately heritable1 and an important correlate of many social, economic, and health outcomes2,3.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > INTRODUCTION
What the sources say
- Supportedcites [2] Conti G, Heckman J & Urzua S The Education-Health Gradient. Am. Econ. Rev 100, 234– 238 (2010). 24741117 PMC3985402 source
The Abstract describes a well-established correlation between schooling and health and says more educated individuals have better labor-market prospects. The Findings section also reports educational differences across health, employment, and health-related behaviors, supporting the broader claim about social and economic outcomes. (LLM, conf=0.91)
- Not matchedcites [3] Cutler DM & Lleras-Muney A Education and Health: Evaluating Theories and Evidence in Making Americans Healthier: Social and Economic Policy as Health Policy (eds. House J, Schoeni R, Kaplan G & Pollack H) (Russell Sage Foundation, 2008).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“all major conclusions from the largest previous GWAS of EduYears10 continue to hold but are statistically stronger.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Supportedcites [10] Okbay A Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539– 542 (2016). 27225129 PMC4883595 source
The citation identifies the previous EduYears GWAS, rather than attributing the citing authors’ comparison with their newer results to that earlier paper. The paper’s main-text passage presented under the Abstract heading reports an EduYears GWAS of 293,723 people that identified 74 loci, supporting that identification. Whether the newer results are statistically stronger is the citing authors’ conclusion. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“a recent paper15 reports that a polygenic score for EduYears based entirely on parents’ non-transmitted alleles is approximately 30% as predictive as a polygenic score based on transmitted alleles.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Within-Family Association Analyses
What the sources say
- Supportedcites [15] Kong A The nature of nurture: effects of parental genotypes. bioRxiv 219261 (2017). doi: 10.1101/219261 29371463 source
In “Estimating direct effects,” the paper defines scores from transmitted and non-transmitted parental alleles and reports estimated effects on educational attainment of 0.223 and 0.067, respectively. The non-transmitted score’s effect is about 30% of the transmitted score’s effect, matching the claim’s approximate comparison. (LLM, conf=0.92)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
In the paper
“using stratified LD Score regression21, we estimated relatively weak enrichment of genes highly expressed in glial cells (Supplementary Table 9): 1.08-fold for astrocytes (P = 0.07) and 1.09-fold for oligodendrocytes (P = 0.06) versus 1.33-fold for neurons (P = 2.89×10−11).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Not checked yetcites [21] Finucane HK Partitioning heritability by functional category using GWAS summary statistics. Nat. Genet 47, 1228– 1235 (2015). 26414678 PMC4626285
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference between the bottom and top quintiles in Add Health and HRS is, respectively, 45 and 36 percentage points”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Polygenic Prediction
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The score is a better predictor of EduYears than household income and a worse predictor than mother’s or father’s education.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Polygenic Prediction
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we found that the inverse-variance-weighted mean genetic correlation of EduYears across pairs of cohorts in our sample is 0.72 (SE = 0.14), which is statistically distinguishable from one (P value = 0.03).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Heterogeneous Effect Sizes
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“when our score is used to predict EduYears in a sample of African-Americans from the HRS (N = 1,519), the score only has an incremental R2 of 1.6%, implying an attenuation of 85%.”
- Also stated in: § DISCUSSION · plain
“we found that our score for EA has much lower predictive power in an African-American sample than in a European-ancestry sample”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Polygenic Prediction
plaincorrelationaln = 1,519- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § DISCUSSION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This score was the most predictive in both cohorts, with an incremental R2 of 12.7% in AddHealth and 10.6% in HRS (and a sample-size weighted average of 11.4%).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Polygenic Prediction
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Supplementary Table 14 shows moderately high pairwise genetic correlations, ranging from 0.51 to 0.85, which motivate the multivariate analysis.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > MTAG of Cognitive Performance, Math Ability and Highest Math.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Add Health, where our measure of cognitive performance is the respondent’s score on a test of verbal cognition, the incremental R2s of the GWAS and MTAG scores are 5.1% and 6.9%, respectively.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Related Cognitive Phenotypes and MTAG
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The concordance is substantially greater than expected by chance but weaker than predicted by our Bayesian framework, even after we extend the framework to account for inflation in GWAS coefficients due to assortative mating.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Within-Family Association Analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that the median effect size of the lead SNPs corresponds to 1.7 weeks of schooling per allele; at the 5th and 95th percentiles, 1.1 and 2.6 weeks, respectively.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Primary GWAS of EduYears
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Eight of these are non-synonymous, and one of these (rs61734410) is located in CACNA1H (Supplementary Figure 9)”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This gave us an omnibus Q-statistic of 91,830, with corresponding P value equal to 9.68 × 10−12.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Joint F-test of heterogeneity.
What the sources say
- Not matchedcites Cochran38
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For comparison, when we apply the same method to height, we found that the assortative-mating adjustment fully explains the deflation of the within-family effects.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Within-Family Association Analyses
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The genetic correlation across pairs of cohorts will be correlated across all observations that share one of their cohorts in common.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Cross-cohort genetic correlation.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All cohort-level analyses were restricted to European-ancestry individuals that passed the cohort’s quality control and whose EduYears was measured at an age of at least 30.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Genome-wide association study meta-analyses.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, we dropped a small number of SNPs with male-female allele frequency differences above 0.005 in UKB.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > X chromosome.
plainn = 329,358- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Relative to the GWAS effect estimate, the mean of the posterior distribution is shrunken toward zero (because zero is the mean of the prior distribution) and is not biased by the winner’s curse.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Bayesian framework for calculating winner’s-curse-adjusted posterior effect-size distributions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The marginal effect size of each SNP is drawn from a mixture distribution with a point mass at zero and a normal component for non-null SNPs.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To adjust the test statistics for bias, we inflated the standard errors using the LD Score regression intercept estimated from our main autosomal analysis (1.113).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > X chromosome.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To obtain 95% confidence intervals, we bootstrapped the incremental R2’s with 1000 repetitions”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Polygenic prediction.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because this formula holds when Z1 and Z2 are independent, the implicitly-defined Z2 is interpreted as the additional information contained in the new data.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Replication of lead SNPs from Okbay et al.’s combined-stage analysis.
plainn = 726,808- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Z1 and Z2 are independent for the replication formula to interpret Z2 as additional information.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we also clustered all standard errors at the person level.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Prediction of other phenotypes.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If this hypothesis is correct, some of the predictive power of the polygenic score reflects environmental amplification of the genetic effects.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > DISCUSSION
hedgedconditional- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“even though glial cells are at least as numerous as neurons in the human brain20”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Biological Annotation
What the sources say
- Not checked — no full text of the sourcecites [20] Azevedo FAC Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol 513, 532– 541 (2009). 19226510 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We adjusted the test statistics using the estimated intercepts from LD Score regressions (1.073 for Math Ability, 1.105 for Highest Math, and 1.046 for Cognitive Performance).”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > GWAS of Cognitive Performance, Math Ability and Highest Math.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“as sample sizes get large, thousands of lead SNPs will be identified, and polygenic predictors will attain non-trivial levels of predictive power.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Therefore, to obtain correct standard errors, we used the node-jackknife variance estimator described by Cameron and Miller40.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Cross-cohort genetic correlation.
What the sources say
- Not matchedcites Cameron and Miller40
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Both sets of association results underwent the same set of quality-control filters as the autosomal analyses prior to meta-analysis.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > X chromosome.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“if EA-increasing genotypes are associated with parental EA-increasing genotypes, which are in turn associated with rearing environments that promote EA, then failure to control for rearing environment will bias GWAS estimates.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > DISCUSSION
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If greater parental EA positively influences the rearing environment, then GWAS that control imperfectly for rearing environment will yield inflated estimates.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Within-Family Association Analyses
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The summary statistics underwent quality control using the same procedures applied to the EduYears results files.”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > GWAS of Cognitive Performance, Math Ability and Highest Math.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
The marginal effect size of each SNP is drawn from a mixture distribution with a point mass at zero and a normal component for non-null SNPs.
“We assume that the marginal effect size of each SNP is drawn from the following mixture distribution: βj~{N(0,τ2)withprobabilityπ0otherwise, where τ2 is the effect-size variance for non-null SNPs and π is the fraction of non-null SNPs in our data.”
The paper relies on this without support.
Add Health is a representative sample of American adolescents and HRS is a representative sample of Americans over age 50.
“the National Longitudinal Study of Adolescent to Adult Health (Add Health, N = 4,775), a representative sample of American adolescents; and the Health and Retirement Study (HRS, N = 8,609), a representative sample of Americans over age 50.”
The authors state this as an assumption.
The effective number of SNPs (Meff) is the same in males and females for X chromosome SNP heritability estimation.
“Meff is the effective number of SNPs (which is assumed to be the same in males and females).”
The authors state this as an assumption.
The fraction of causal variants used in LDpred for polygenic score construction is equal to 1.
“assuming a fraction of causal variants equal to 1”
The paper relies on this without support.
Z1 and Z2 are independent for the replication formula to interpret Z2 as additional information.
“Because this formula holds when Z1 and Z2 are independent, the implicitly-defined Z2 is interpreted as the additional information contained in the new data.”
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?
The cited source doesn’t support the claim as written.
In the citing paper
“According to the LD score regression8 intercept (1.66), confounding accounts for 7% of the inflation, similar to previous GWAS of EA2”
From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [2]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“all of which have been previously reported in EUR ancestry GWASs (313 variants in ref. 14 and rs9257925 and rs7224296 in ref. 10).”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the EAS population, EduYears-associated SNPs were strongly enriched in the brain, parietal lobe and putamen of the central nervous system at an FDR <5% threshold (Supplementary Fig. 9a and Supplementary Table 10), which is consistent with previously published LDSC-SEG results in EUR14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the UKBB Chinese samples, which are genetically EAS but may be environmentally close to EUR, both the EUR-based PGS and EAS-based PGS showed lower predictive power than in other EAS cohorts in our study or previous studies10,14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“they were located beyond ±500 kb of the lead SNPs reported in previous EduYears GWAS (Supplementary Figs. 10 and 11)10,14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We performed LDSC v1.0.1 (ref. 18) to estimate the SNP-based heritability of EduYears in the TWB, KoGES, EAS and EUR populations14 using population-specific LD scores based on the 1KG Project phase 3 data.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We adjusted for the birth year (BY), BY2, BY3, sex, BY by sex interaction, BY2 by sex interaction, BY3 by sex interaction, and the top ten PCs, based on previous GWAS for EduYears14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Consistent with previous studies13,14,19,40, we found that GWAS results and downstream analyses of behavioral phenotypes (for example, educational attainment, smoking behavior) as well as some anthropometric phenotypes (for example, height, BMI) are affected by demographic and indirect genetic effects.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Analysis of loci that reach GWAS and nominal significance and SNPs in LD with them at D’ ≥ 0.8 reveals predominant associations with traits such as cognitive decline [12, 68, 69], educational attainment [70–73], mean bone density [57, 74], intelligence and performance intelligence quotient [75, 76] and soluble TREM-2 levels [77].”
From Genetic drivers of progression in Alzheimer’s disease are distinct from disease risk (2026) · cites this paper as [72]
The cited source says what the claim attributes to it.
In the citing paper
“Previous GWAS meta-analyses and replication studies have identified genetic variants associated with EduYears11–14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“In the EAS population, SNP heritability was significantly enriched in the central nervous system, including the foetal brain, dorsolateral prefrontal cortex and inferior temporal lobe after FDR correction (Supplementary Fig. 9b and Supplementary Table 11), which is also consistent with previous results in EUR14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“The enrichment in neurons was also found in previous EUR studies10,14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“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.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as Lee et al. [14]
The cited source says what the claim attributes to it.
In the citing paper
“We first document that the EA PGI not only predicts a range of cognitive phenotypes as has been found in previous work2,4, but it also adds non-trivial predictive power for 10 diseases we examine, even after controlling for disease-specific PGIs.”
From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [2]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The MAF values of rs2881903 and rs16930687 were 5.2% versus 8.8% and 2.3% versus 0.7% in EAS and EUR14, respectively.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The SNP-based heritability was similar within the EAS population (9.7 ± 0.8%) for TWB and (8.7 ± 0.9%) for KoGES and between EAS (9.0 ± 0.6%) and EUR (10.7 ± 0.3%) populations14.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [14]
Experimental — accuracy not yet measuredIn the citing paper
“This matters because intelligence, educational attainment, and other cognitive abilities are all highly heritable9,34.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
In the citing paper
“However, the genome-wide association study34 that we used to estimate cogPGS is heavily biased towards those of European descent.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
In the citing paper
“The combination of these regions was able to explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
In the citing paper
“Recently, a large, genome-wide association study with 1.1 million people was able to estimate the effect of thousands of genetic regions on cognitive performance, educational attainment, and mathematical ability34.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
In the citing paper
“Furthermore, these GWAS had hundreds of thousands of individuals, and such a large sample size allows new studies to detect effects in samples of a few hundred individuals with 80% statistical power34.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
In the citing paper
“This joint analysis is ideal because pairwise genetic correlations of these traits were high34.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [34]
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Genome-wide association study meta-analyses.
“extends the (combined discovery and replication) sample of a previous genome-wide association study (GWAS) of educational attainment10 from N = 405,072 to N = 1,131,881 individuals”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Replication of lead SNPs from Okbay et al.’s combined-stage analysis.
“Okbay et al.’s10 pooled (discovery and replication) meta-analysis (N = 405,073)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > RESULTS > Primary GWAS of EduYears
“We identified 1,271 approximately independent (pairwise r2 < 0.1) SNPs at genome-wide significance (P < 5×12−8)”
§ Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment > ONLINE METHODS > Clumping algorithm.
“We describe it here for the case of identifying lead SNPs among the set of SNPs reaching P < 5×10−8”
Citations
Cites
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationSecond-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Self-citationAn integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
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
Self-citationThe combination of estimates from different experiments
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001666 · Cited by 5,380
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
Self-citationMAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653
Cellular Scaling Rules for the Brains of Marsupials: Not as “Primitive” as Expected
Santos, S. E. D., Porfírio, J., Cunha, F., Manger, P. R., Tavares, W. C., Pessôa, L. M., Raghanti, M. A., Sherwood, C. C., & Herculano‐Houzel, S. (2017) · Brain Behavior and Evolution · doi:10.1159/000452856 · Cited by 2,652
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604
Self-citation
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Verhoef, E., Shapland, C. Y., Fisher, S. E., Dale, P. S., & Pourcain, B. S. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13327 · Cited by 31
“However, compared to largescale genome-wide studies of educational attainment (Lee et al., 2018) or direct assessments of language and literacy measures, their predictive power is low.”
ResultNievergelt, C. M., Maihofer, A. X., Klengel, T., Atkinson, E. G., Chen, C., Choi, K. W., Coleman, J. R. I., Dalvie, S., Duncan, L. E., Logue, M. W., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/458562 · Cited by 24
Causal Inference with Genetic Data: Past, Present, and FuturePreprint
Pingault, J., Richmond, R. C., & Smith, G. D. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041271 · Cited by 25
“Current polygenic scores based on GWAS for height and education predict 24% and 11% of the variance in their respective phenotype (Lee et al. 2018; Yengo et al. 2018).”
BackgroundSmit, D. J. A., Bakker, M., Abdellaoui, A., Hoetink, A. E., Vulink, N. C. C., & Denys, D. A. J. P. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.971752 · Cited by 25
“The second region with significant SNPs was on chromosome 1, and holds expression quantitative trait loci (eQTLs) for neural growth factor 1 (NEGR1), a gene strongly related to various variables related to cognition and socioeconomic status, including cognitive performance and educational attainment (Lee et al., 2018)…”
BackgroundInferring cell-type-specific causal gene regulatory networks during human neurogenesis
Aygün, N., Liang, D., Crouse, W. L., Keele, G. R., Love, M. I., & Stein, J. L. (2023) · Genome biology · doi:10.1186/s13059-023-02959-0 · Cited by 24
“We used summary statistics from GWAS for schizophrenia (SCZ) [2], major depression disorder (MDD) [99], educational attainment (EA) [6], and cortical thickness and surface area from ENIGMA project [5], UKBB [4], bipolar disorder (BP) [100], neuroticism [101], IQ [102], cognitive performance (CP) [6], attention-deficit…”
MethodPopulation phenomena inflate genetic associations of complex social traits
Morris, T. T., Davies, N. M., Hemani, G., & Smith, G. D. (2020) · Science Advances · doi:10.1126/sciadv.aay0328 · Cited by 20
Koller, D., Mitjans, M., Kouakou, M. R., Friligkou, E., Cabrera‐Mendoza, B., Deak, J. D., Llonga, N., Pathak, G. A., Stiltner, B., Løkhammer, S., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115758 · Cited by 23
Li, D., Olsson, N. C., Jiao, H., Norgren, N., Jonsson, U., Bölte, S., & Tammimies, K. (2020) · npj Genomic Medicine · doi:10.1038/s41525-020-00152-x · Cited by 19
The phenomics and genetics of addictive and affective comorbidity in opioid use disorder
Freda, P. J., Moore, J. H., & Kranzler, H. R. (2021) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2021.108602 · Cited by 21
“, 2019), schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), and educational attainment (Lee et al., 2018).”
BackgroundStrom, N. I., Grove, J., Meier, S. M., Bækvad‐Hansen, M., Nissen, J. B., Als, T. D., Halvorsen, M., Nordentoft, M., Mortensen, P. B., HOUGAARD, D. M., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.711624 · Cited by 20
Wang, M., Jian, Z., Gao, X., Chi, Y., Jin, X., Li, H., & Wang, K. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.742952 · Cited by 20
“Instrumental Variables Selection We used SNPs that were identified to be correlated with EA from a GWAS performed by the Social Science Genetic Association Consortium (20).”
BackgroundMethodSelf-citationEducation Attainment, Intelligence and COVID-19: A Mendelian Randomization Study
Li, G. H., Lam, S. K. K., Wong, I. C. K., Chu, J. K.-P., & Cheung, C. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10214870 · Cited by 21
“For instance, both the GWAS meta‐analysis of education attainment [16] and intelligence [17] comprised participants from the U.”
BackgroundMethodInfluentialCarter, A. R., Harrison, S., Gill, D., Smith, G. D., Taylor, A. E., Howe, L. D., & Davies, N. M. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac002 · Cited by 22
Sex-Specific Genetic and Transcriptomic Liability to Neuroticism
Wendt, F. R., Pathak, G. A., Singh, K., Stein, M. B., Koenen, K. C., Krystal, J. H., Gelernter, J. E., Davis, L. K., & Polimanti, R. A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.07.019 · Cited by 21
Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning
Lahti, J. M. T., Tuominen, S., Yang, Q., Pergola, G., Ahmad, S., Amin, N., Armstrong, N. J., Beiser, A., Bey, K., Bis, J. C., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01710-8 · Cited by 20
“Again, the causal SNP or gene underlying this association is not known, but earlier studies point towards influences of this locus on language processing [47] and educational attainment [48] (Supplement 1).”
Self-citationThe combination of autism and exceptional cognitive ability increases risk for suicidal ideation
Casten, L. G., Thomas, T., Doobay, A. F., Foley‐Nicpon, M., Kramer, S., Nickl‐Jockschat, T., Abel, T. G., Assouline, S. G., & Michaelson, J. J. (2022) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2022.107698 · Cited by 22
Nannini, D. R., Zheng, Y., Joyce, B. T., Kim, K., Gao, T., Wang, J., Jacobs, D. R., Schreiner, P. J., Yaffe, K. S., Greenland, P., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02106-y · Cited by 18
“…between marijuana and the epigenome may aid in elucidating the molecular and biological processes in fl uencing downstream health conditions and may serve as potential biomarkers to identify recent and long-term marijuana use and intervene in the early stages of their related health outcomes.”
BackgroundWang, X., Walker, A., Revez, J. A., Ni, G., Adams, M. J., McIntosh, A. M., Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.06.006 · Cited by 19
Self-citationZhu, X., Joo, Y., Bossi, S., McDevitt, R. A., Xie, A., Wang, Y., Xue, Y., Su, S., Lee, S. K., Sah, N., et al. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2024.102568 · Cited by 18
Frach, L., Barkhuizen, W., Allegrini, A. G., Ask, H., Hannigan, L. J., Corfield, E. C., Andreassen, O. A., Dudbridge, F., Ystrøm, E., Havdahl, A., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02383-7 · Cited by 21
“…lifetime anxiety disorders (UK biobank) [57], antisocial behaviour (ASB) [58], lifetime smoking [59], problematic alcohol use [60], cannabis use disorder (CUD) [61], cognitive performance [62], educational attainment [62], household income [63], risky behaviours [64] and age at fi rst birth [65].”
BackgroundEvaluating the association between placenta DNA methylation and cognitive functions in the offspring
Cosín‐Tomás, M., Díez-Ahijado, L., Cilleros‐Portet, A., Fernández, M. F., Fernández, M. F., Guxens, M., Júlvez, J., Llop, S., López-Espinosa, M.-J., Subiza, M., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03094-5 · Cited by 20
“…genomic regions of the identi fi ed DMRs overlap with the 242, 226, and 1271 genome-wide signi fi cant loci previously reported in GWAS on intelligence [46], cognitive performance, and educational attainment [47] (0.5 Mb window centred to the genomic 250 locus indicated in the original studies).”
BackgroundInfluentialLearning is enhanced by tailoring instruction to individual genetic differences
Mets, D. G., & Brainard, M. S. (2019) · eLife · doi:10.7554/elife.47216 · Cited by 24
“Recent studies in human populations have demonstrated strong genetic influences on academic achievement (Branigan et al., 2013; Lee et al., 2018; Okbay et al., 2016; Rietveld et al., 2013).”
BackgroundO’Connell, K. S., Shadrin, A. A., Smeland, O. B., Bahrami, S., Frei, O., Bettella, F., Krull, F., Fan, C. C., Askeland, R. B., Knudsen, G. P., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.11.015 · Cited by 20
Cognitive Ability in Everyday Life: The Utility of Open-Source Measures
Revelle, W., Dworak, E. M., & Condon, D. M. (2020) · Current Directions in Psychological Science · doi:10.1177/0963721420922178 · Cited by 22
Jian, X., Sofer, T., Tarraf, W., Bressler, J., Faul, J. D., Zhao, W., Ratliff, S. M., Lamar, M., Launer, L. J., Laurie, C. C., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00930-2 · Cited by 22
The epidemiology of cognitive development
Guez, A., Peyre, H., Williams, C. M., Labouret, G., & Ramus, F. (2020) · Cognition · doi:10.1016/j.cognition.2021.104690 · Cited by 28
Si, S., Li, J., Tewara, M. A., Li, H., Liu, X., Li, Y., Chen, X., Liu, C., Yuan, T., Li, W., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01576-7 · Cited by 19
Self-citationQuantitative multidimensional phenotypes improve genetic analysis of laterality traits
Schmitz, J., Zheng, M., Lui, K., McBride, C., Ho, C. S.-H., & Paracchini, S. (2021) · bioRxiv · doi:10.1038/s41398-022-01834-z · Cited by 14
“Summary statistics for IQ [56] and EA [57] were accessed from the Complex Trait Genetics (CTG) lab website (https://ctg.”
MethodNivard, M. G., Belsky, D. W., Harden, K. P., Baier, T., Andreassen, O. A., Ystrøm, E., van Bergen, E., & Lyngstad, T. H. (2022) · doi:10.31234/osf.io/bhpm5 · Cited by 20
Economics and Measurement: New measures to model decision making
Almås, I., Attanasio, O. P., & Jervis, P. (2023) · National Bureau of Economic Research · doi:10.3386/w30839 · Cited by 20
Social-Science Genomics: Progress, Challenges, and Future Directions
Benjamin, D., Cesarini, D., Turley, P., & Young, A. S. (2024) · doi:10.3386/w32404 · Cited by 20
Self-citationLi, M., Dang, X., Chen, Y., Chen, Z., Xu, X., Zhao, Z., & Wu, D. (2024) · Nature Communications · doi:10.1038/s41467-024-52222-8 · Cited by 26
“…above two factors may be associated with existing cognitive measures, such as common executive function (cEF 11 ), general intelligence (g factor 10,21,22 ), educational attainment 23 , cognitive and noncognitive skills 24 , and two principalcomponents underlyingmultiplecognitiveperformance 25 .”
InfluentialDirect and indirect genetic effects on early neurodevelopmental traits
Hegemann, L., Eilertsen, E. M., Pettersen, J. H., Corfield, E. C., Cheesman, R., Frach, L., Bjørndal, L. D., Ask, H., Pourcain, B. S., Havdahl, A., et al. (2025) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.14122 · Cited by 20
“In addition to including PGS for these neurodevelopmental conditions, PGS for educational attainment (Lee et al., 2018) and cognitive ability (Savage et al., 2018) were also calculated as they are related traits to neurodevelopment.”
Song, Q., Liu, Y., & Tan, C. Y. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10004-8 · Cited by 24
“Furthermore, research utilizing molecular genetic approaches has established hereditary connections between educational outcomes and both cognitive skills and non-cognitive skills (Calvin et al., 2012; Lee et al., 2018; Malanchini et al., 2024).”
Chromatin-dependent motif syntax defines differentiation trajectories
Durdu, S., Iskar, M., Isbel, L., Hoerner, L., Wirbelauer, C., Burger, L., Heß, D., Iešmantavičius, V., & Schübeler, D. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.07.005 · Cited by 19
Lillo, A. D., D’Antona, S., Pathak, G. A., Wendt, F. R., Angelis, F. D., Fuciarelli, M., & Polimanti, R. A. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab114 · Cited by 21
Gonda, X., Eszlári, N., Török, D., Gál, Z., Bokor, J., Millinghoffer, A., Baksa, D., Petschner, P., Antal, P., Breen, G. D., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01436-1 · Cited by 19
Kim, G., Joo, Y. Y., Ahn, G., Wang, H., Moon, S., Kim, H., Ahn, W., & Cha, J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25888 · Cited by 18
Segura, À. G., Mezquida, G., Martínez-Pinteño, A., Gassó, P., Rodríguez, N., Moreno-Izco, L., Amoretti, S., Bioque, M., Lobo, A., González-Pinto, A. M., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722001544 · Cited by 19
“…2020), bipolar disorder (PRSBD; 41 917 cases; 371 549 controls) (Mullins et al., 2021), depression (PRSDEP; 246 363 cases; 561 190 controls) (Howard et al., 2019), education attainment and cognitive performance (PRSEA and PRSCP; 1 131 881 and 257 841 individuals; respectively) (Lee et al., 2018).”
BackgroundInfluentialZhang, Y., Fletcher, J. M., Lu, Q., & Song, J. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115649 · Cited by 21
Ayorech, Z., Baldwin, J. R., Pingault, J., Rimfeld, K., & Plomin, R. J. (2023) · Scientific Reports · doi:10.1038/s41598-022-25374-0 · Cited by 22
Holen, B., Shadrin, A. A., Icick, R., Filiz, T. T., Hindley, G., Rødevand, L. N., O’Connell, K. S., Hagen, E., Frei, O., Bahrami, S., et al. (2023) · Addiction Biology · doi:10.1111/adb.13282 · Cited by 20
“We then conducted a version of the binomial test, that is, a sign-consistency test.(38) Finally, we tested for variants nominally significant at p < 0.”
MethodA multivariate genome-wide association study of psycho-cardiometabolic multimorbidity
Baltramonaityte, V., Pingault, J., Cecil, C. A. M., Choudhary, P., Järvelin, M., Penninx, B. W. J. H., Felix, J. F., Sebert, S. P., Milaneschi, Y., & Walton, E. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010508 · Cited by 22
Zhang, C., Zhang, Y., Zhang, Y., & Zhao, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad407 · Cited by 22
Andreu-Bernabeu, Á., González‐Peñas, J., Arango, C., & Díaz‐Caneja, C. M. (2023) · BMJ Mental Health · doi:10.1136/bmjment-2023-300821 · Cited by 19
“We used a cognitive ability GWAS meta-analysis 31 and the most extensive GWAS meta-analysis of intelligence.”
Chen, S.-D., You, J., Zhang, W., Wu, B., Ge, Y., Xiang, S., Du, J., Kuo, K., Banaschewski, T., Barker, G. J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01792-6 · Cited by 19
Polygenic Scores and Networks of Psychopathology Symptoms
Piazza, G. G., Allegrini, A. G., Eley, T. C., Epskamp, S., Fried, E. I., Isvoranu, A., Roiser, J. P., & Pingault, J. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2024.1403 · Cited by 22
Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation
Carey, C. E., Shafee, R., Wedow, R., Elliott, A., Palmer, D. S., Compitello, J., Kanai, M., Abbott, L. E., Schultz, P., Karczewski, K. J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01909-5 · Cited by 28
“Notably, the SNP heritability of Factor 10 ( h = 0.21[0.01]) is greater than that of the EA GWAS 38 ( h = 0.12), reflecting a potential benefit of including correlated measures of traditional and non-traditional SES.”
BackgroundInfluentialSelf-citationThree Novel Loci for Infant Head Circumference Identified by a Joint Association Analysis
Yang, X., Zhang, S.-Y., Zhang, H., Wei, X., Feng, G.-J., Pei, Y., & Zhang, L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00947 · Cited by 17
“In conjunction with its glycoprotein receptor (IGF-1R), IGF-1 is a phylogenetically ancient neurotrophic hormone and plays crucial roles in central nervous system development and maturation (Dyer et al., 2016).”
ResultUsing Mendelian randomization to understand and develop treatments for neurodegenerative disease
Storm, C. S., Kia, D. A., Almramhi, M. M., & Wood, N. W. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa031 · Cited by 19
“A third MR study found that genetically predicted rheumatoid arthritis raised Alzheimer’s disease risk (Bae and Lee, 2018), but the result lost significance when one SNP was removed from the analysis.”
BackgroundBird, K. A. (2021) · American Journal of Physical Anthropology · doi:10.1002/ajpa.24216 · Cited by 19
“…set of 1,271 genome-wide significant lead SNPs associated with both educational attainment and cognitive performance in a sample of 1,131,881 individuals reported in the supplemental of Lee et al. (2018), which explained a total 3.6% of variance in educational attainment in European individuals.”
BackgroundMethodInfluentialJansen, A. G., Jansen, P. R., Savage, J. E., Kraft, J., Skarabis, N., Polderman, T. J. C., & Dieleman, G. C. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13370 · Cited by 21
Xia, L., Ou, J., Li, K., Guo, H., Hu, Z., Bai, T., Zhao, J., Xia, K., & Zhang, F. (2019) · Autism Research · doi:10.1002/aur.2229 · Cited by 19
“…or openness [Amare et al., 2018]; Depression [Howard et al., 2018]; Schizophrenia [Amare et al., 2018; Howard et al., 2018];Cognitive performance [Lee et al., 2018]; Educational attainment (MTAG) [Lee et al., 2018]; General cognitive ability [Davies et al., 2018; Lee et al., 2018] in GWAS study.”
BackgroundInfluentialSelf-citationHuang, Z.-D., Peng, S., Cen, T., Wang, X., Ma, L., & Cao, Z. (2024) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.14040 · Cited by 20
Genetic architecture reconciles linkage and association studies of complex traits
Sidorenko, J., Couvy‐Duchesne, B., Kemper, K. E., Moen, G.-H., Bhatta, L., Åsvold, B. O., Mägi, R., Ani, A., Wang, R., Nolte, I. M., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01940-2 · Cited by 19
“Estimates of SNP effects within families are likewise unaffected by population stratification and can be contrasted with between-family estimates from population-based studies to dissect direct from indirect genetic effects 16,17 and to estimate the effect of nonrandom mating 18 .”
Self-citationHusien, H. M., Saleh, A. A., Hassanine, N. N. A. M., Rashad, A. M. A., Sharaby, M. A., Mohamed, A., Abdelhalim, H., Hafez, E. S. E., Essa, M. O. A., Adam, S. Y., et al. (2024) · Veterinary Sciences · doi:10.3390/vetsci11120627 · Cited by 35
“This has led to the extensive implementation of GS in various animal species by the scientific community [92,128].”
Phenotypically independent profiles relevant to mental health are genetically correlated
Roelfs, D., Alnæs, D., Frei, O., van der Meer, D., Smeland, O. B., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01313-x · Cited by 16
Gormley, M., Dudding, T., Kachuri, L., Burrows, K., Chong, A., Martin, R. M., Thomas, S. J., Tyrrell, J., Ness, A., Brennan, P., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02233-3 · Cited by 18
“Educational attainment SSAGC and UK Biobank [52] 317 42.”
BackgroundMethodA General Cognitive Ability Factor for the UK Biobank
Williams, C. M., Labouret, G., Wolfram, T., Peyre, H., & Ramus, F. (2023) · Behavior Genetics · doi:10.1007/s10519-022-10127-6 · Cited by 18
Wang, Y., Kong, L., Ye, C., Dou, C., Zheng, J., Xu, M., Xu, Y., Li, M., Zhao, Z., Lu, J., et al. (2023) · Liver International · doi:10.1111/liv.15669 · Cited by 18
“6 Our study applied robust genetic IVs that explained 11– 13% of the variance in education, 13 and extended previous evidence by revealing the causal effect of It is worth noting that in addition to the overall effect on health, education can also promote the acquisition of healthy knowledge and…”
MethodLiu, C., Ran, J., Hou, B., Li, Y., Morelli, J. N., & Li, X. (2023) · Journal of Orthopaedic Research® · doi:10.1002/jor.25656 · Cited by 17
“20 The genome ‐ wide significant SNPs for educational attainment, 21 sedentary behavior (leisure television watching and computer use), 22 smoking (smoking initiation and cigarettes per day), 23 and sleeping (sleep duration, short sleep [ 7 h], frequent insomnia and any insomnia) 24,25 were…”
BackgroundSLC4A10 mutation causes a neurological disorder associated with impaired GABAergic transmission
Fasham, J., Huebner, A. K., Liebmann, L., Khalaf‐Nazzal, R., Maroofian, R., Kryeziu, N., Wortmann, S. B., Leslie, J. S., Ubeyratna, N., Mancini, G. M. S., et al. (2023) · Brain · doi:10.1093/brain/awad235 · Cited by 19
Overcoming attenuation bias in regressions using polygenic indices
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. (2023) · Nature Communications · doi:10.1038/s41467-023-40069-4 · Cited by 25
Luo, X., Ruan, Z., & Liu, L. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02596-4 · Cited by 18
“Data on genetic variation in cognitive performance were obtained from the Social Science Genetics Association Consortium (SSGAC) dataset, which included 35,298 participants from COGENT (Cognitive Genomics Consortium) and 222,543 participants from Cognitive Performance in UKB [29].”
Shared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18
“To assess the predictive ability of polygenic scores (PGSs) for EduYears, we used our EAS summary statistics and the EUR summary statistics by Lee et al. 14 1).”
InfluentialSelf-citationHu, M., Yang, T., & Yang, Y. (2024) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.06.044 · Cited by 19
Song, J., Jönsson, L., Lu, Y., Bergen, S. E., Karlsson, R., Smedler, E., Gordon‐Smith, K., Jones, I. R., Jones, L. A., Craddock, N. J., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02448-1 · Cited by 19
Polygenic risk of social isolation behavior and its influence on psychopathology and personality
Socrates, A., Mullins, N., Gur, R. C., Gur, R. E., Stahl, E. A., O’Reilly, P. F., Reichenberg, A., Jones, H. J., Zammit, S., & Velthorst, E. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02617-2 · Cited by 26
“…Genomics Consortium GWAS [49], the 2019 iPSYCH anxiety GWAS [50] the Genetics of Personality-2 (GPC-2) extraversion and neuroticism GWAS [51, 52], a 2019 GWAS meta-analysis of loneliness [53], and the 2018 Social Science Genetic Association Consortium (SSGAC) GWAS of educational attainment [54].”
BackgroundUsing DNA to predict educational trajectories in early adulthood.
Ayorech, Z., Plomin, R. J., & von Stumm, S. (2019) · Developmental Psychology · doi:10.1037/dev0000682 · Cited by 20
“1 million individuals (Lee et al., 2018), explain 10% of the variance in educational trajectories at age 18; specifically, attending university, pursuing full-time employment or an apprenticeship, or becoming NEET.”
BackgroundMethodInfluentialPiasecki, T. M., Gizer, I. R., & Slutske, W. S. (2019) · Twin Research and Human Genetics · doi:10.1017/thg.2019.90 · Cited by 18
“However, the association between BD PRS and lifetime gambling was essentially unchanged when controlling for either measured educational attainment or an educational attainment polygenic score (computed based upon Lee et al., 2018).”
BackgroundYu, H., Cheng, W., Zhang, X., Wang, X., & Yue, W. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0605-3 · Cited by 18
Williamson, B. (2020) · Ethics and Education · doi:10.1080/17449642.2020.1822631 · Cited by 23
Causal effects of education on chronic kidney disease: a Mendelian randomization study
Park, S., Lee, S., Kim, Y., Lee, Y., Kang, M. W., Kim, K., Kim, Y. C., Han, S. S., Lee, H., Lee, J. P., et al. (2020) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa240 · Cited by 19
Self-citationLiang, J., Cai, H., Liang, G., Liu, Z., Fang, L., Zhu, B., Liu, B., & Zhang, H. (2021) · Acta Diabetologica · doi:10.1007/s00592-020-01647-w · Cited by 18
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Merz, E. C., Strack, J., Hurtado, H., Vainik, U., Thomas, M. L., Evans, A. C., & Khundrakpam, B. S. (2022) · Human Brain Mapping · doi:10.1002/hbm.26034 · Cited by 21
“PGS-EA were computed using results from the most recent GWAS of educational attainment (Lee et al., 2018).”
BackgroundMethodInfluentialCommon variants contribute to intrinsic human brain functional networksPreprint
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.30.229914 · Cited by 18
Naj, A. C., Leonenko, G., Jian, X., Grenier‐Boley, B., Dalmasso, M. C., Bellenguez, C., Jin, S., Zhao, Y., van der Lee, S. J., Sims, R., et al. (2021) · medRxiv · doi:10.1101/2021.03.14.21253553 · Cited by 17
Self-citationChen, D., Wang, X., Huang, T., & Huang, T. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.794202 · Cited by 20
“C4orf45 (chromosome 4 open reading frame 45), an uncharacterized protein-coding gene, has been shown to be associated with self-reported educational attainment and cognitive function (Lee et al., 2018).”
BackgroundKuhn, B. N., Cannella, N., Chitre, A. S., Nguyen, K., Cohen, K. A., Chen, D., Peng, B. F., Ziegler, K. S., Lin, B., Johnson, B. B., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02922-4 · Cited by 17
“Furthermore, both preclinical and clinical GWAS analysis focusing on genetic variants associated with OUD resiliency, a neurobiological active process [107, 108], should be considered in future analyses.”
Genetic modifiers and ascertainment drive variable expressivity of complex disorders
Jensen, M., Smolen, C., Tyryshkina, A., Pizzo, L., Sun, J., Noss, S., Banerjee, D., Oetjens, M. T., Shimelis, H., Taylor, C. M., et al. (2025) · Cell · doi:10.1016/j.cell.2025.09.012 · Cited by 21
“We also calculated polygenic risk scores (PRSs) for four cognitive and psychiatric features, including schizophrenia, 37 intelligence, 38 educational attainment, 39 and autism.”
Polygenic scores are an even bigger social hazard
Richardson, K. (2019) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2019.12.003 · Cited by 17
Transcriptome profiling in adult attention-deficit hyperactivity disorder
Mortimer, N., Sánchez‐Mora, C., Rovira, P., Vilar‐Ribó, L., Vanesa Richarte, B., Montse Corrales, F., Fadeuilhe, C., Rivero, O., Lesch, K., Casas, M., et al. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.11.005 · Cited by 17
Liu, W., Zhang, L., Li, S., Liu, C., Tong, Y., Fang, H., Zhang, R., Song, B., Xia, Z., & Xu, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.653032 · Cited by 18
Genome-Wide Association Study Identifies Genetic Risk Factors for Spastic Cerebral Palsy
Hale, A. T., Akinnusotu, O., He, J., Wang, J., Hibshman, N., Shannon, C. N., & Naftel, R. P. (2021) · Neurosurgery · doi:10.1093/neuros/nyab184 · Cited by 16
The effect of education on spousal education: A genetic approach
Barban, N., Cao, E. D., Oreffice, S., & Quintana‐Domeque, C. (2021) · Labour Economics · doi:10.1016/j.labeco.2021.102023 · Cited by 17
Polygenic score modifies risk for Alzheimer's disease in APOE ε4 homozygotes at phenotypic extremes
Huq, A. J., Fulton‐Howard, B., Riaz, M., Laws, S., Sebra, R., Ryan, J., Alzheimer's Disease Genetics Consortium, Renton, A. E., Goate, A. M., Masters, C. L., et al. (2021) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12226 · Cited by 17
Hariharan, P., & Dupuis, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-95637-9 · Cited by 17
Verhoef, E., Grove, J., Shapland, C. Y., Demontis, D., Burgess, S., Rai, D., Børglum, A. D., & Pourcain, B. S. (2021) · Nature Communications · doi:10.1038/s41467-021-26755-1 · Cited by 15
Kendler, K. S., Ohlsson, H., Sundquist, J., & Sundquist, K. (2021) · Psychological Medicine · doi:10.1017/s0033291721005134 · Cited by 17
“We have also begun to clarify the associations between genetic liabilities to our key disorders and genetic propensity for educational attainment (Bulik-Sullivan et al., 2015; Lee et al., 2018; Rosoff, Kaminsky, McIntosh, Smith, & Lohoff, 2020).”
Geographic latitude and human height - Statistical analysis and case studies from China
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Investigating modifiable pathways in psoriasis: A Mendelian randomization study
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Appadurai, V., Bybjerg‐Grauholm, J., Krebs, M. D., Rosengren, A., Buil, A., Ingason, A., Mors, O. N., Børglum, A. D., HOUGAARD, D. M., Nordentoft, M., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04477-y · Cited by 17
Matzel, L. D., & Sauce, B. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2023.1060786 · Cited by 17
“And largescale genome-wide association studies (GWASs) have revealed that even a combination of thousands of (additive) DNA regions accounts for only 10–20% of cognitive differences (Sniekers et al., 2017; Lee et al., 2018; Okbay et al., 2022).”
BackgroundEducational Attainment and Intergenerational Mobility: A Polygenic Score Analysis
Rustichini, A., Iacono, W. G., Lee, J. J., & McGue, M. K. (2023) · Journal of Political Economy · doi:10.1086/724860 · Cited by 18
Self-citationGenetic associations with parental investment from conception to wealth inheritance in six cohorts
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“Finally, MVMR analyses provided suggestive evidence that the associations of omega-6 FAs and LA with LTL might be independent of other PUFAs (β ± standard error [SE]: 0.081 ± 0.048, p = 0.09 for both associations after adjustment for total PUFA; Supporting Information Table S11).”
BackgroundInfluentialLahtinen, H., Korhonen, K., Martikainen, P., & Morris, T. (2023) · Demography · doi:10.1215/00703370-10963788 · Cited by 19
Zhang, Y., Tang, Z., Tong, L., Wang, Y., & Li, L. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1277984 · Cited by 18
Metadata
- Authors
- 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., Bowers, P., Sidorenko, J., Karlsson Linnér, R., Fontana, M. A., Kundu, T., Lee, C., Li, H., Li, R., Royer, R., Timshel, P. N., Walters, R. K., Willoughby, E. A., Yengo, L., Alver, M., Bao, Y., Clark, D. W., Day, F. R., Furlotte, N. A., Joshi, P. K., Kemper, K. E., Kleinman, A., Langenberg, C., Mägi, R., Trampush, J. W., Verma, S. S., Wu, Y., Lam, M., Zhao, J. H., Zheng, Z., Boardman, J. D., Campbell, H., Freese, J., Harris, K. M., Hayward, C., Herd, P., Kumari, M., Lencz, T., Luan, J., Malhotra, A. K., Metspalu, A., Milani, L., Ong, K. K., Perry, J. R. B., Porteous, D. J., Ritchie, M. D., Smart, M. C., Smith, B. H., Tung, J. Y., Wareham, N. J., Wilson, J. F., Beauchamp, J. P., Conley, D. C., Esko, T., Lehrer, S. F., Magnusson, P. K. E., Oskarsson, S., Pers, T. H., Robinson, M. R., Thom, K., Watson, C., Chabris, C. F., Meyer, M. N., Laibson, D. I., Yang, J., Johannesson, M., Koellinger, P. D., Turley, P., Visscher, P. M., Benjamin, D. J., & Cesarini, D.
- Affiliations
- University of Minnesota (US)
- University of Colorado Boulder (US)
- Vrije Universiteit Amsterdam (NL)
- Harvard University (US)
- University of Southern California (US)
- The University of Queensland (AU)
- Statens Serum Institut (DK)
- Broad Institute (US)
- University of Tartu (EE)
- University of Essex (GB)
- University of Edinburgh (GB)
- University of Cambridge (GB)
- 23andMe (United States) (US)
- Centre Hospitalier Universitaire Vaudois (CH)
- Geisinger Medical Center (US)
- Institute of Mental Health (SG)
- University of North Carolina at Chapel Hill (US)
- Institute of Genetics and Cancer (GB)
- Feinstein Institute for Medical Research (US)
- University of Dundee (GB)
- University of Toronto (CA)
- Princeton University (US)
- National Bureau of Economic Research (US)
- Karolinska Institutet (SE)
- Uppsala University (SE)
- New York University (US)
- Autism & Developmental Medicine Institute (US)
- Stockholm School of Economics (SE)
- Corresponding author
- Okbay, Aysu
- Visscher, Peter M.
- Benjamin, Daniel J.
- ORCID
- Lee, James J.
- Wedow, Robbee
- Okbay, Aysu
- Kong, Edward
- Zacher, Meghan
- Sidorenko, Julia
- Li, Hui
- Li, Ruoxi
- Timshel, Pascal N.
- Walters, Raymond K.
- Willoughby, Emily A.
- Yengo, Loïc
- Bao, Yanchun
- Clark, David W.
- Day, Felix R.
- Furlotte, Nicholas A.
- Joshi, Peter K.
- Kemper, Kathryn E.
- Langenberg, Claudia
- Mägi, Reedik
- Trampush, Joey W.
- Verma, Shefali Setia
- Wu, Yang
- Lam, Max
- Zhao, Jing Hua
- Zheng, Zhili
- Campbell, Harry
- Harris, Kathleen Mullan
- Hayward, Caroline
- Herd, Pamela
- Lencz, Todd
- Luan, Jian’an
- Malhotra, Anil K.
- Metspalu, Andres
- Milani, Lili
- Ong, Ken K.
- Perry, John R. B.
- Porteous, David J.
- Ritchie, Marylyn D.
- Smart, Melissa C.
- Smith, Blair H.
- Tung, Joyce Y.
- Wareham, Nicholas J.
- Wilson, James F.
- Beauchamp, Jonathan P.
- Conley, Dalton C.
- Esko, Tõnu
- Magnusson, Patrik K. E.
- Oskarsson, Sven
- Pers, Tune H.
- Robinson, Matthew R.
- Meyer, Michelle N.
- Laibson, David I.
- Yang, Jian
- Johannesson, Magnus
- Koellinger, Philipp D.
- Turley, Patrick
- Visscher, Peter M.
- Benjamin, Daniel J.
- Cesarini, David
- Venue
- Nature Genetics
- Published
- 2018-07-23
- Volume
- 50
- Issue
- 8
- Pages
- 1112-1121
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6393768
- OpenAlex
- W2883573930
- Semantic Scholar
- d49aaa352d4d714f564ea6d8f79b51f558f42ef9
- Cited by
- 2,470
- References
- 50
- Citations by year
- 2026: 155 · 2025: 271 · 2024: 392 · 2023: 328 · 2022: 375 · 2021: 363 · 2020: 292 · 2019: 244 · 2018: 43
- FWCI
- 132.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by)
- Research Publications (Maastricht University) (acceptedVersion, cc-by)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Open Access at Essex (University of Essex) (acceptedVersion)
- IRIS (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- Lirias (submittedVersion)
- Site cant be reached (submittedVersion, other-oa)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- VU Research Portal (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Apollo
- Funders
- Brigham and Women's Hospital
- Rush University
- Queen Mary University of London
- Wellcome Trust (213422/Z/18/Z)
- Broad Institute
- Open Philanthropy Project (2016-152872)
- National Institute for Health and Care Research (NF-SI-0507-10228, NF-SI-0514-10027, NF-SI-0512-10135, NF-SI-0617-10149, NIHR-RP-R3-12-013)
- University of Warwick
- University of Oxford
- University of Manchester
- University of Aberdeen
- Rijksuniversiteit Groningen
- Lundbeckfonden (R190 2014-3904)
- Karolinska Institutet (2017-00641)
- King Abdulaziz University
- Vetenskapsrådet (421-2013-1061, 2017-00641, 00641)
- Westfälische Wilhelms-Universität Münster
- Universitair Medisch Centrum Groningen
- Universität Regensburg
- Rheinische Friedrich-Wilhelms-Universität Bonn
- Novo Nordisk Fonden (NNF16OC0021496)
- National Institutes of Health (2017-00641, 5r01ag015819-21, 5t32ag000186-35, 5p2chd050924-20, R01AG042568, AG042568, 5p01ag005842-36)
- Medical Research Council (1811434, MR/N01104X/1, MC_PC_13048, MR/K026992/1, MC_UU_12015/2, MR/N01104X/2, MC_UU_12015/1, MC_UU_00007/10)
- Biotechnology and Biological Sciences Research Council (BB/F022441/1, BB/F019394/1)
- Economic and Social Research Council (ES/N00812X/1, ES/K005774/1, ES/M008592/1, ES/S008349/1)
- Warwick Medical School
- Diamantina Institute, University of Queensland
- National Institute on Aging (T32–AG000186, T32-AG000186-23, R01 AG042568, P30 AG012810, AG005842, P30AG010161, R01 AG055406, P01-AG005842, P01-AG005842-20S2, R01-AG015819)
- National Institute of Mental Health (L30 MH104879)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development ([P2C HD050924], R01HD073342, P2CHD066613, P2CHD047879)
- H2020 European Research Council (647648)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- educational attainment, polygenic risk score, genetic association, single nucleotide polymorphisms, cognitive performance, brain development, X chromosome, dosage compensation, heritability, gene discovery
- MeSH terms
- Adult, Aged, Aged, 80 and over, Educational Status, Female, Humans, Male, Middle Aged, Phenotype, Cohort Studies, Multifactorial Inheritance, 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.
A language model drew the claims from the paper on 2026-10-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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 ›
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