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
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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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Gene–environment correlations across geographic regions affect genome-wide association studies
Abdellaoui, A., Dolan, C. V., Verweij, K. J. H., & Nivard, M. G. (2022) · Nature Genetics · doi:10.1038/s41588-022-01158-0 · Cited by 163
Gill, D., Efstathiadou, A., Cawood, K., Tzoulaki, I., & Dehghan, A. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz200 · Cited by 151
“This is particularly useful when the exposures of interest are related, as is the case for educational attainment and cognitive function, which have a high degree of phenotypic and genetic correlation.(2,3) Previous work has used MR to provide evidence of a bidirectional relationship between educational attainment and…”
BackgroundMethodInfluentialCausal associations between modifiable risk factors and the Alzheimer's phenome
Andrews, S. J., Fulton‐Howard, B., O'Reilly, P., Marcora, E., Goate, A. M., & collaborators of the Alzheimer's Disease Genetics Consortium (2021) · Annals of Neurology · doi:10.1002/ana.25918 · Cited by 156
Common variants contribute to intrinsic human brain functional networks
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01039-6 · Cited by 150
Genetic Endowments and Wealth Inequality
Barth, D. J., Papageorge, N., & Thom, K. (2019) · Journal of Political Economy · doi:10.1086/705415 · Cited by 157
“In particular, a series of landmark studies have delivered the first GWAS associations between individual SNPs and educational attainment, specifically years of schooling (Rietveld et al., 2013; Okbay et al., 2016; Lee et al., 2018).”
Self-citationZhao, B., Zhang, J., Ibrahim, J. G., Luo, T., Santelli, R., Li, Y., Li, T., Shan, Y., Zhu, Z., Zhou, F., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0569-z · Cited by 150
High-throughput single-сell sequencing in cancer research
Jia, Q., Chu, H., Jin, Z., Long, H., & Zhu, B. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00990-4 · Cited by 157
“Some studies have suggested that increasing number of tumor-infiltrating lymphocytes can improve patient survival, whereas other studies revealed contrasting results.(144,145) scRNA-seq provided a cell-level landscape that can be used to further investigate the function of tumor-infiltrating lymphocytes in breast canc…”
ResultThe Benefits, Barriers, and Risks of Big-Team Science
Forscher, P. S., Wagenmakers, E., Coles, N. A., Silan, M. A. A., Dutra, N. B., Basnight-Brown, D. M., & IJzerman, H. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221082970 · Cited by 162
Walker, K. A., Chen, J., Shi, L., Yang, Y., Fornage, M., Zhou, L., Schlosser, P., Surapaneni, A., Grams, M. E., Duggan, M. R., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adf5681 · Cited by 148
Imprint of assortative mating on the human genome
Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., Gratten, J., Turley, P., Cesarini, D., Benjamin, D. J., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0476-3 · Cited by 148
Self-citationMárquez‐Luna, C., Gazal, S., Loh, P., Kim, S. S., Furlotte, N. A., Auton, A., Agee, M., Alipanahi, B., Bell, R. K., Bryc, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25171-9 · Cited by 151
Self-citationPrediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging
Anatürk, M., Kaufmann, T., Cole, J. H., Suri, S., Griffanti, L., Zsoldos, E., Filippini, N., Singh‐Manoux, A., Kivimäki, M., Westlye, L. T., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25316 · Cited by 132
“IQ is also known as a heritable trait (Plomin & Deary, 2015), and a possible genetic correlation between this trait and cognitive reserve could indicate that the phenotypic relationship observed between these factors reflects common underlying genetic architecture (Lee et al., 2018).”
BackgroundWerling, D. M., Pochareddy, S., Choi, J., An, J., Sheppard, B. K., Peng, M., Li, Z., Dastmalchi, C., Santpere, G., Sousa, A. M. M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.053 · Cited by 134
Mendelian imputation of parental genotypes improves estimates of direct genetic effects
Young, A. I., Nehzati, S. M., Benonisdottir, S., Okbay, A., Jayashankar, H., Lee, C., Cesarini, D., Benjamin, D. J., Turley, P., & Kong, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01085-0 · Cited by 144
Self-citationBiological and environmental predictors of heterogeneity in neurocognitive ageing
Nyberg, L. H., Boraxbekk, C., Sörman, D. E., Hansson, P., Herlitz, A., Kauppi, K., Ljungberg, J. K., Lövheim, H., Lundquist, A., Adolfsson, A. N., et al. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101184 · Cited by 156
The genetic architecture of structural left–right asymmetry of the human brain
Sha, Z., Schijven, D., Carrión-Castillo, A., Joliot, M., Mazoyer, B. M., Fisher, S. E., Crivello, F., & Francks, C. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01069-w · Cited by 138
Discovery of 42 genome-wide significant loci associated with dyslexia
Doust, C., Fontanillas, P., Eising, E., Gordon, S. D., Wang, Z., Alagöz, G., Molz, B., Aslibekyan, S. W., Auton, A., Babalola, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01192-y · Cited by 167
“Of these, four (rs4696277, rs34349354, rs9696811, rs72841395) were pleiotropic with general cognitive ability [16] based on HEIDI-outlier analysis [17].”
BackgroundInfluentialSelf-citationGuo, J., Yu, K., Dong, S., Yao, S., Rong, Y., Wu, H., Zhang, K., Jiang, F., Chen, Y.-X., Guo, Y., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01174-7 · Cited by 143
Seyedsalehi, A., Warrier, V., Bethlehem, R. A. I., Perry, B. I., Burgess, S., & Murray, G. K. (2022) · Brain · doi:10.1093/brain/awac392 · Cited by 141
“Educational attainment Lee et al.(80) 1 131 881 766 345 Late-onset Alzheimer’s disease Kunkle et al.”
BackgroundInfluentialPredicting educational achievement from genomic measures and socioeconomic status
von Stumm, S., Smith‐Woolley, E., Ayorech, Z., McMillan, A. J., Rimfeld, K., Dale, P. S., & Plomin, R. J. (2019) · Developmental Science · doi:10.1111/desc.12925 · Cited by 147
“Measures Genome-wide Polygenic Scores (GPS) Saliva and buccal cheek swab samples were collected, DNA was extracted and genotyped to compute genome-wide polygenic scores (GPS) based on the latest genome-wide association (GWA) study for years of education, called educational attainment (EA3) (Lee et al., 2018).”
BackgroundMethodInfluentialCausal influences of neuroticism on mental health and cardiovascular disease
Zhang, F., Baranova, A. V., Zhou, C., Cao, H., Chen, J., Zhang, X., & Xu, M. (2021) · Human Genetics · doi:10.1007/s00439-021-02288-x · Cited by 144
Seney, M. L., Kim, S.-M., Glausier, J. R., Hildebrand, M. A., Xue, X., Zong, W., Wang, J., Shelton, M. A., Phan, B. N., Srinivasan, C., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.06.007 · Cited by 135
Davies, N. M., Hill, W. D., Anderson, E. L., Sanderson, E., Deary, I. J., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43990 · Cited by 135
Protective genes and pathways in Alzheimer’s disease: moving towards precision interventions
Seto, M., Weiner, R. L., Dumitrescu, L., & Hohman, T. J. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00452-5 · Cited by 135
Sex-Dependent Shared and Non-Shared Genetic Architecture Across Mood and Psychotic Disorders
Blokland, G. A. M., Grove, J., Chen, C., Cotsapas, C. J., Tobet, S. A., Handa, R. J., Ripke, S., Neale, B. M., Corvin, A., Walters, J., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.02.972 · Cited by 127
Self-citationEising, E., Mirza‐Schreiber, N., de Zeeuw, E. L., Wang, C. A., Truong, D. T., Allegrini, A. G., Shapland, C. Y., Zhu, G., Wigg, K. G., Gerritse, M., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202764119 · Cited by 150
Validity of polygenic risk scores: are we measuring what we think we are?
Janssens, A. C. J. W. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz205 · Cited by 129
“disease risk, and should they be included in the PRS? When a PRS is constructed based on 2 million of SNPs of which, say, 100 are able to change predicted risks before the decimal, will we tell a patient that their risk is calculated using 2 million or 100 SNPs? When researchers relax the SNP inclusion thresholds to i…”
BackgroundSmoking and stroke: A mendelian randomization study
Larsson, S. C., Burgess, S., & Michaëlsson, K. (2019) · Annals of Neurology · doi:10.1002/ana.25534 · Cited by 132
Genetic heterogeneity and subtypes of major depression
Nguyen, T.-D., Harder, A., Xiong, Y., Kowalec, K., Hägg, S., Cai, N., Kuja-Halkola, R., Dalman, C., Sullivan, P. F., & Lu, Y. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01413-6 · Cited by 129
“(13, 19) We have used the summary statistics from the latest published GWAS for the calculations of rg using HDL.(19, 25-34)”
MethodIdentifying the Common Genetic Basis of Antidepressant Response
Pain, O., Hodgson, K., Trubetskoy, V., Ripke, S., Marshe, V. S., Adams, M. J., Byrne, E. M., Campos, A. I., Carrillo‐Roa, T., Cattaneo, A., et al. (2021) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.07.008 · Cited by 132
“Educational attainment (37) was also included, as it has strong correlations with the mental health disorders tested.”
Self-citationSilventoinen, K., Jelenkovic, A., Sund, R., Latvala, A., Honda, C., Inui, F., Tomizawa, R., Watanabe, M., Sakai, N., Rebato, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69526-6 · Cited by 126
Self-citationMendelian Randomization Study of PCSK9 and HMG-CoA Reductase Inhibition and Cognitive Function
Rosoff, D. B., Bell, A. S., Jung, J., Wagner, J., Mavromatis, L. A., & Lohoff, F. W. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.05.041 · Cited by 126
Polimanti, R. A., Walters, R. K., Johnson, E. C., McClintick, J. N., Adkins, A. E., Adkins, D. E., Bacanu, S., Bierut, L. J., Bigdeli, T. B., Brown, S. A., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0677-9 · Cited by 125
Self-citationThe Pathophysiological Role of CoA
Czumaj, A., Szrok-Jurga, S., Hebanowska, A., Turyn, J., Świerczyński, J., Śledziński, T., & Stelmańska, E. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21239057 · Cited by 128
Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study
Papageorge, N., & Thom, K. (2016) · Journal of the European Economic Association · doi:10.1093/jeea/jvz072 · Cited by 135
Self-citationGenes, Gender Inequality, and Educational Attainment
Herd, P., Jeremy Freese, A., Sicinski, K., Domingue, B. W., Harris, K. M., Wei, C., & Hauser, R. M. (2019) · American Sociological Review · doi:10.1177/0003122419886550 · Cited by 123
Self-citation“Reports of My Death Were Greatly Exaggerated”: Behavior Genetics in the Postgenomic Era
Harden, K. P. (2020) · Annual Review of Psychology · doi:10.1146/annurev-psych-052220-103822 · Cited by 125
Gialluisi, A., Andlauer, T. F. M., Mirza‐Schreiber, N., Moll, K., Becker, J., Hoffmann, P., Ludwig, K. U., Czamara, D., Pourcain, B. S., Honbolygó, F., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-00898-x · Cited by 143
“Similarly, a PGS based on EduYears accounted for 2–5% of the variance in reading efficiency and comprehension in an independent UK sample (N= 5825) [72].”
MethodInfluentialPolygenic scores: prediction versus explanation
Plomin, R. J., & von Stumm, S. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01348-y · Cited by 139
Hindley, G., Frei, O., Shadrin, A. A., Cheng, W., O’Connell, K. S., Icick, R., Parker, N., Bahrami, S., Karadag, N., Roelfs, D., et al. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21101051 · Cited by 119
Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 128
“Examples of PRS studies include those on educational attainment [113], schizophrenia [114,115], diabetes [116–118], blood pressure [119], depression [120], coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer [102,116].”
BackgroundQuach, B. C., Bray, M. A., Gaddis, N., Liu, M., Palviainen, T., Minică, C. C., Zellers, S. M., Sherva, R., Alıev, F., Nothnagel, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19265-z · Cited by 113
Lam, M., Hill, W. D., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Stahl, E. A., Huckins, L. M., Liewald, D. C. M., Djurovic, S., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.06.012 · Cited by 115
Self-citationJudd, N., Sauce, B., Wiedenhoeft, J., Tromp, J., Chaarani, B., Schliep, A., van Noort, B., Penttilä, J., Grimmer, Y., Insensee, C., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001228117 · Cited by 105
Wang, B., Baldwin, J. R., Schoeler, T., Cheesman, R., Barkhuizen, W., Dudbridge, F., Bann, D., Morris, T. T., & Pingault, J.-B. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.07.010 · Cited by 103
The structural variation landscape in 492 Atlantic salmon genomes
Bertolotti, A. C., Layer, R. M., Gundappa, M. K., Gallagher, M. D., Pehlivanoglu, E., Nome, T., Robledo, D., Kent, M., Røsæg, L. L., Holen, M. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18972-x · Cited by 112
Peyrot, W. J., & Price, A. L. (2021) · Nature Genetics · doi:10.1038/s41588-021-00787-1 · Cited by 107
Implementation and implications for polygenic risk scores in healthcare
Slunecka, J. L., van der Zee, M. D., Beck, J. J., Johnson, B. N., Finnicum, C. T., Pool, R., Hottenga, J., de Geus, E. J. C., & Ehli, E. A. (2021) · Human Genomics · doi:10.1186/s40246-021-00339-y · Cited by 120
Congenital heart disease risk loci identified by genome-wide association study in European patients
Lahm, H., Jia, M., Dreßen, M., Wirth, F., Puluca, N., Gilsbach, R., Keavney, B. D., Cleuziou, J., Beck, N., Bondareva, O., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci141837 · Cited by 115
Intelligence, health and death
Deary, I. J., Hill, W. D., & Galé, C. R. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01078-9 · Cited by 111
Screening Human Embryos for Polygenic Traits Has Limited Utility
Karavani, E., Zuk, O., Zeevi, D., Barzilai, N., Stefanis, N. C., Hatzimanolis, A., Smyrnis, N., Avramopoulos, D., Kruglyak, L., Atzmon, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.033 · Cited by 111
Self-citationMa, Y., Deng, C., Zhou, Y., Zhang, Y., Qiu, F., Jiang, D., Zheng, G., Li, J., Shuai, J., Zhang, Y., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100383 · Cited by 113
Self-citationThe Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia
Richards, A. L., Pardiñas, A. F., Frizzati, A., Tansey, K. E., Lynham, A. J., Holmans, P., Legge, S. E., Savage, J. E., Agartz, I., Andreassen, O. A., et al. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz061 · Cited by 112
“13,18,19,50,51 The schizophrenia training set was based on the PGC2 meta-analysis but excluded the cognitively informative samples used in this study for analysis of PRS and g .”
MethodCheesman, R., Hunjan, A. K., Coleman, J. R. I., Ahmadzadeh, Y. I., Plomin, R. J., McAdams, T. A., Eley, T. C., & Breen, G. D. (2020) · Psychological Science · doi:10.1177/0956797620904450 · Cited by 110
“We derived our base summary statistics file for years of education by meta-analysing summary statistics from our own GWA analysis in this subsample with independent summary statistics obtained from the Social Science Genomics Consortium (excluding UK Biobank and 23&Me) (Lee et al. 2018).”
InfluentialModifiable Lifestyle Factors and Risk of Stroke
Harshfield, E. L., Georgakis, M. K., Malik, R., Dichgans, M., & Markus, H. S. (2021) · Stroke · doi:10.1161/strokeaha.120.031710 · Cited by 110
Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analysesPreprint
Cuéllar-Partida, G., Lundberg, M., Kho, P. F., D’Urso, S., Gutiérrez-Mondragón, L. F., & Hwang, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/518027 · Cited by 98
“In this example, we visualize the GWAS results and estimate its genetic correlation with Neuroticism score and Educational Attainment [24].”
MethodKember, R. L., Vickers‐Smith, R., Xu, H., Toikumo, S., Niarchou, M., Zhou, H., Hartwell, E. E., Crist, R. C., Rentsch, C. T., Program, M. V., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01160-z · Cited by 104
A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data
de la Fuente, J., Davies, G., Grotzinger, A. D., Tucker–Drob, E. M., & Deary, I. J. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00936-2 · Cited by 112
The stability of educational achievement across school years is largely explained by genetic factors
Rimfeld, K., Malanchini, M., Krapohl, E. M. L., Hannigan, L. J., Dale, P. S., & Plomin, R. J. (2018) · npj Science of Learning · doi:10.1038/s41539-018-0030-0 · Cited by 114
Genomics and psychological resilience: a research agenda
Choi, K. W., Stein, M. B., Dunn, E. C., Koenen, K. C., & Smoller, J. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0457-6 · Cited by 108
Julian, T. H., Glascow, N., Barry, A. D. F., Moll, T., Harvey, C., Klimentidis, Y. C., Newell, M., Zhang, S., Snyder, M. P., Cooper‐Knock, J., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103397 · Cited by 117
“Educational attainment was quantified as years of education measured at an age of at least 30 years; GWAS was performed in a metaanalysis of 1,131,881 individuals [25].”
MethodProkopenko, D., Morgan, S. L., Mullin, K., Hofmann, O., Chapman, B., Kirchner, R., (ADNI), A. D. N. I., Amberkar, S., Wohlers, I., Lange, C., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12319 · Cited by 105
“In contrast, common loci associated with AD 22 have been enriched significantly in microglia (Figure 7).”
Horwitz, T. B., Balbona, J. V., Paulich, K. N., & Keller, M. C. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01672-z · Cited by 105
“Failing to account for phenotypic and genetic homogamy can also lead to biases in association statistics from genome-wide association studies 45 , in heritability and genetic correlation estimates based on twin/family designs and single nucleotide polymorphisms 46,47 Article…”
MethodMatoba, N., Liang, D., Sun, H., Aygün, N., McAfee, J. C., Davis, J. E., Raffield, L. M., Qian, H., Piven, J., Li, Y., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00953-9 · Cited by 97
Dissecting polygenic signals from genome-wide association studies on human behaviour
Abdellaoui, A., & Verweij, K. J. H. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01110-y · Cited by 104
DiPrete, T. A., Burik, C. A. P., & Koellinger, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707388115 · Cited by 100
Self-citationDonovan, B., Semmens, R., Keck, P., Brimhall, E., Busch, K. C., Weindling, M., Duncan, A. S., Stuhlsatz, M. A. M., Bracey, Z. B., Bloom, M., et al. (2019) · Science Education · doi:10.1002/sce.21506 · Cited by 108
Heritability of education rises with intergenerational mobility
Engzell, P., & Tropf, F. C. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912998116 · Cited by 99
“Research using molecular genetic data can shed further light on the mechanisms underlying genetic influence (10, 22).”
BackgroundGenetics of common cerebral small vessel disease
Bordes, C., Sargurupremraj, M., Mishra, A., & Debette, S. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-021-00592-8 · Cited by 108
Mallard, T. T., Linnér, R. K., Grotzinger, A. D., Sanchez‐Roige, S., Seidlitz, J., Okbay, A., de Vlaming, R., Meddens, S. F. W., Palmer, A. A., Davis, L. K., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100140 · Cited by 110
Self-citationBelsky, D. W., & Harden, K. P. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721418807729 · Cited by 99
“For example a polygenic score derived from a GWAS of years of education in 1 million people predicts performance on an IQ test better than a polygenic score derived from GWAS of cognitive test scores in a sample one third that size (Lee et al., 2018).”
BackgroundMina, T. H., Yew, Y. W., Ng, H. K., Sadhu, N., Wansaicheong, G. K. L., Dalan, R., Low, D. Y., Lam, B. C. C., Ríboli, E., Lee, E. S., et al. (2023) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2023.100710 · Cited by 100
Self-citationPalk, A. C., Dalvie, S., de Vries, J., Martin, A. R., & Stein, D. J. (2019) · Philosophy Ethics and Humanities in Medicine · doi:10.1186/s13010-019-0073-8 · Cited by 98
A principal component approach to improve association testing with polygenic risk scores
Coombes, B. J., Ploner, A., Bergen, S. E., & Biernacka, J. M. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22339 · Cited by 98
Liang, D., Elwell, A. L., Aygün, N., Krupa, O., Wolter, J. M., Kyere, F. A., Lafferty, M. J., Cheek, K. E., Courtney, K. P., Yusupova, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00858-w · Cited by 91
Georgakis, M. K., & Gill, D. (2021) · Stroke · doi:10.1161/strokeaha.120.032617 · Cited by 99
Mooney, M. A., Ryabinin, P., Wilmot, B., Bhatt, P., Mill, J., & Nigg, J. T. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0710-4 · Cited by 91
Wertz, J., Moffitt, T. E., Agnew‐Blais, J. C., Arseneault, L., Belsky, D. W., Corcoran, D. L., Houts, R., Matthews, T., Prinz, J. A., Richmond‐Rakerd, L. S., et al. (2019) · Child Development · doi:10.1111/cdev.13329 · Cited by 92
Risk factors diagnosis prognosis and treatment of autism
Al-Dewik, N. I. E. (2020) · Frontiers in bioscience · doi:10.2741/4873 · Cited by 123
Treur, J. L., Munafo, M. R., Logtenberg, E., Wiers, R. W. H. J., & Verweij, K. J. H. (2021) · Psychological Medicine · doi:10.1017/s003329172100180x · Cited by 101
Demange, P., Hottenga, J., Abdellaoui, A., Eilertsen, E. M., Malanchini, M., Domingue, B. W., Armstrong‐Carter, E., de Zeeuw, E. L., Rimfeld, K., Boomsma, D. I., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32003-x · Cited by 103
Cultural evolution of genetic heritability
Uchiyama, R., Spicer, R., & Muthukrishna, M. (2021) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000893 · Cited by 103
The Impact of Inequality on Intergenerational Mobility
DiPrete, T. A. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054814 · Cited by 96
Genetic associations with mathematics tracking and persistence in secondary school
Harden, K. P., Domingue, B. W., Belsky, D. W., Boardman, J. D., Crosnoe, R., Malanchini, M., Nivard, M., Tucker-Drob, E. M., & Harris, K. M. (2020) · npj Science of Learning · doi:10.1038/s41539-020-0060-2 · Cited by 92
“1 million people identified hundreds of genetic variants associated with higher educational attainment.(15) These results can be used to calculate an education polygenic score (education-PGS), which is a composite index of genetic variants associated with completing more years of school.”
BackgroundInfluentialSelf-citationEze, I. C., Jeong, A., Schaffner, E., Rezwan, F. I., Ghantous, A., Foraster, M., Vienneau, D., Kronenberg, F., Herceg, Z., Vineis, P., et al. (2020) · Environmental Health Perspectives · doi:10.1289/ehp6174 · Cited by 89
“Polymorphisms in this gene were associated—in various genome-wide association studies (GWAS)—with adiposity (Kichaev et al. 2019; Pulit et al. 2019), cognitive function (Lee et al. 2018; Li et al. 2015), and Crohn’s disease progression (O’Donnell et al. 2019).”
BackgroundGenetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness
Cheng, W., Frei, O., van der Meer, D., Wang, Y., O’Connell, K. S., Chu, Y., Bahrami, S., Shadrin, A. A., Alnæs, D., Hindley, G., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.1435 · Cited by 91
Engineering Precision Medicine
Sun, W., Lee, J., Zhang, S., Benyshek, C., Dokmeci, M. R., & Khademhosseini, A. (2018) · Advanced Science · doi:10.1002/advs.201801039 · Cited by 96
Self-citationEstimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort
Koomar, T., Thomas, T. R., Pottschmidt, N. R., Lutter, M., & Michaelson, J. J. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.668297 · Cited by 92
“Polygenic Risk Scores Polygenic risk scores (PRS) were calculated using PRSice (49) from the following base GWASes: metabolic syndrome (UK Biobank 2019) (50), body mass index (UK Biobank 2019) (51), birth weight (UK Biobank 2019) (51), basal metabolic rate (UK Biobank 2019) (51), neuroticism (SSGAC 2016) (52), educati…”
MethodInsights into Dyslexia Genetics Research from the Last Two Decades
Erbeli, F., Rice, M., & Paracchini, S. (2021) · Brain Sciences · doi:10.3390/brainsci12010027 · Cited by 101
“PRSs for educational attainment [69] accounted for up to 5.”
BackgroundIntellectual Precocity: What Have We Learned Since Terman?
Lubinski, D., & Benbow, C. P. (2020) · Gifted Child Quarterly · doi:10.1177/0016986220925447 · Cited by 106
“The populations isolated here using three primary abilities promise to continue to serve as an organizational hub for both behavioral genetics (Lee et al., 2018; Plomin, 2018; Plomin et al., 2016; Spain et al., 2016; Zabaneh et al., 2018) and the neurosciences (Colom & Thompson, 2011; Haier, 2017;…”
BackgroundBahls, M., Leitzmann, M. F., Karch, A., Teumer, A., Dörr, M., Felix, S. B., Meisinger, C., Baumeister, S. E., & Baurecht, H. (2021) · Clinical Research in Cardiology · doi:10.1007/s00392-021-01846-7 · Cited by 91
“Hence, we performed a multivariable MR analysis [46] to assess the relationship between genetically predicted self-reported moderate to vigorous physical activity, accelerometer-derived PA, vigorous PA and sedentary behavior on coronary artery disease, myocardial infarction and ischemic stroke adjusted for education a…”
MethodZhao, S. S., Holmes, M. V., Zheng, J., Sanderson, E. C. M., & Carter, A. R. (2021) · Rheumatology · doi:10.1093/rheumatology/keab654 · Cited by 89
Assortative mating biases marker-based heritability estimators
Border, R., O’Rourke, S., de Candia, T. R., Goddard, M. E., Visscher, P. M., Yengo, L., Jones, M., & Keller, M. C. (2022) · Nature Communications · doi:10.1038/s41467-022-28294-9 · Cited by 81
Self-citationDickinson, D., Zaidman, S., Giangrande, E. J., Eisenberg, D. P., Gregory, M. D., & Berman, K. F. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19050527 · Cited by 90
Twin studies to GWAS: there and back again
Friedman, N. P., Banich, M. T., & Keller, M. C. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.06.007 · Cited by 88
“Within the cognitive domain, large GWASs examining general cognitive ability or intelligence quotient (IQ) [4–7], brain imaging phenotypes [8–16], and educational attainment [17] have yielded hundreds of associations.”
BackgroundSanchez‐Roige, S., Fontanillas, P., Jennings, M. V., Bianchi, S. B., Huang, Y., Hatoum, A. S., Sealock, J., Davis, L. K., Elson, S. L., Agee, M., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01335-3 · Cited by 89
“rs640561, or SNPs in strong LD, have been previously associated with other substance use traits, including smoking [54–56] and alcohol consumption [56], schizophrenia [57], education attainment and math ability [58], but the mechanism whereby this SNP affects OUD liability is largely unknown.”
BackgroundSelf-citationFrom genetics to epigenetics to unravel the etiology of adolescent idiopathic scoliosis
Pérez-Machado, G., Berenguer-Pascual, E., Bovea-Marco, M., Rubio-Belmar, P. A., García-López, E., Garzón, M. J., Mena, S., Pallardó, F. V., Bas, T., Vinã, J. R., et al. (2020) · Bone · doi:10.1016/j.bone.2020.115563 · Cited by 87
BDNF genetic variants and methylation: effects on cognition in major depressive disorder
Ferrer, A., Labad, J., Salvat-Pujol, N., Barrachina, M., Costas, J., Urretavizcaya, M., de Arriba-Arnau, A., Crespo, J. M., Soriano-Mas, C., Carracedo, Á., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0601-8 · Cited by 84
Sex-stratified genome-wide association study of multisite chronic pain in UK Biobank
Johnston, K. J. A., Ward, J., Ray, P. R., Adams, M. J., McIntosh, A. M., Smith, B. H., Strawbridge, R. J., Price, T. J., Smıth, D. J., Nicholl, B. I., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009428 · Cited by 91
Self-citationZhang, J., Chen, Z., Pärna, K., van Zon, S. K. R., Snieder, H., & Thio, C. H. L. (2022) · Diabetologia · doi:10.1007/s00125-022-05705-6 · Cited by 90
“In the original GWAS [24], educational level was categorised according to the International Standard Classification of Education (ISCED) 2011 [26], converted to US years of schooling and standardised, with each unit representing 4.”
MethodResultInfluential
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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