The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index; 78 of the 110 sources its claims cite were checked for support.
Clinical use of current polygenic risk scores may exacerbate health disparities
Open accessPublished version of a preprint
Abstract
Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
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Claims the paper makes
31 Partly supported · 27 Own interpretation · 12 Sources not found · 4 Supported · 3 No basis given · 2 Supported so far · 2 No full text of the sources · 1 Not supported · 1 Own result · 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
“Those existing findings indeed demonstrate a large reduction in prediction utility in non-European populations11,”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Disputedcites [11] Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016). sourceExperimental — accuracy not yet measured
The Results (page unavailable) report similar findings among black and white participants in the ARIC cohort, rather than a large reduction in non-European populations. The Discussion (page unavailable) says the risk score was associated with coronary events among black participants, while cautioning that generalizability needs further testing; it does not report a comparative measure of prediction utility. (LLM, conf=0.79)
Checked by a reasoning model — may be wrong.
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Disputed
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
“population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and target of prediction, a function of population history13,14.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
What the sources say
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
- Supportedcites [14] Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016). source
The Introduction (page not supplied) states that the more distantly related the target population is to the training population, the lower a genomic model’s average predictive accuracy. The paper also reports that predictive correlation decays approximately linearly with genetic distance, measured by FST or mean kinship, between training and target populations. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These studies have shown that even when non-European cohorts are only a fraction the size of the largest European study, they are likely to have disproportionate value for predicting polygenic traits in other individuals of similar ancestry.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Partly supportedcites [23] Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017). sourceExperimental — accuracy not yet measured
The Variance explained by variants in the GWAS section (p. 12) reports that variants selected using the Japanese GWAS explained BMI variance in Japanese cohorts better than variants selected using a European GWAS—1.81 times better on average. This supports the value of ancestry-matched data, but the paper tests BMI in Japanese cohorts, not prediction of polygenic traits generally. The passages also do not establish the claimed size comparison with the largest European study. (LLM, conf=0.84)
- Partly supportedcites [24] Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017). sourceExperimental — accuracy not yet measured
The polygenic risk score results (pp. 17–18) show that adding the smaller Chinese cohort improved prediction of schizophrenia case-control status in a Chinese target sample; at one threshold, variance explained rose from 2.34% with European-only training to 7.86% with combined training. The sample-size comparison (p. 9) supports the cohort-size premise. The paper studies schizophrenia in Chinese ancestry, however, not polygenic traits generally or non-European cohorts generally. (LLM, conf=0.88)
- Not matchedcites [40] Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Preprint at https://www.biorxiv.org/content/10.1101/445874v2 (2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These effects are particularly challenging to disentangle, as has clearly been demonstrated for height, where evidence of polygenic adaptation and/or its relative magnitude is under question43,44.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
What the sources say
- Supportedcites [43] Sohail, M. et al. Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies. Preprint at https://www.biorxiv.org/content/10.1101/355057v3 (2018). source
The Discussion (lines 365–375) says earlier estimates of population differences in polygenic height scores are substantially attenuated in the UK Biobank analysis and that population structure makes the magnitude difficult to quantify. It finds some evidence at highly significant SNPs, while questioning how much of the observed difference reflects adaptation. (LLM, conf=0.93)
- Not matchedcites [44] Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. Preprint at https://www.biorxiv.org/content/10.1101/354951v4 (2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
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- 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 support only part of this claim — read each source’s verdict below.
In the paper
“This dependence means that the most significant associations tend to be more common in the populations in which they are discovered than elsewhere13,30.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Common discoveries and low-hanging fruit
What the sources say
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
- Partly supportedcites [30] 1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015). sourceExperimental — accuracy not yet measured
In “Insights about demography” (page unavailable), the paper reports variants that are much more common in particular populations and suggests that these variants may reveal associations difficult to identify in larger global samples. It does not establish that the most significant associations generally have higher variant frequencies in their discovery populations than elsewhere. (LLM, conf=0.76)
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 cited sources support only part of this claim — read each source’s verdict below.
In the paper
“1) GWAS favor the discovery of genetic variants that are common in the study population;”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
What the sources say
- Partly supportedcites [29] Sham, P. C., Cherny, S. S., Purcell, S. & Hewitt, J. K. Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data. Am. J. Hum. Genet. 66, 1616–1630 (2000). sourceExperimental — accuracy not yet measured
The paper’s opening summary (p. 1616) says association-analysis power depends on the proportion of phenotypic variance attributable to a QTL, and its variance formula includes the QTL alleles’ population frequencies. That is consistent with frequency affecting detection power, but the paper studies sibship-based QTL analysis and does not establish the broader claim that GWAS preferentially discover common variants. (LLM, conf=0.74)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The scientific basis of this imbalance has been demonstrated theoretically, in simulations, and empirically across many traits and diseases11–22.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
What the sources say
- Could not verifycites [11] Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016). sourceExperimental — accuracy not yet measured
The Methods (Polygenic Risk Score; page unavailable) describe a score for coronary artery disease, and the Results (page unavailable) report similar findings among Black and white participants in one cohort. Searches for ancestry-related differences in PRS accuracy found no passage demonstrating the claimed scientific basis theoretically, in simulations, or empirically across many traits and diseases. (LLM, conf=0.75)
- Could not verifycites [12] Khera, A. V. & Kathiresan, S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18, 331–344 (2017). sourceExperimental — accuracy not yet measured
The Genomic medicine section discusses polygenic risk scores for coronary artery disease, but does not address an imbalance in their accuracy. Searches for differences across populations and for theoretical, simulation, or cross-trait evidence found no passage establishing the attributed claim. Page numbers were not available in our copy. (LLM, conf=0.75)
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
- Partly supportedcites [14] Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016). sourceExperimental — accuracy not yet measured
The Introduction explains why prediction accuracy can differ between training and genetically distinct target populations, and the Simulation Studies examine this using human genotypes and simulated phenotypes. The Real-World Data Analyses test several traits in wheat and mice, but the paper does not empirically demonstrate the imbalance across many human traits and diseases. No page numbers are provided in our copy. (LLM, conf=0.88)
- Partly supportedcites [15] Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015). sourceExperimental — accuracy not yet measured
The Discussion (p. 9) reports lower absolute PRS prediction accuracy in non-European than European validation samples, and the results (p. 8) include African- and Asian-descent schizophrenia samples. But the paper’s theory and simulations concern accuracy gains from modeling linkage disequilibrium, not a theoretical and simulated demonstration of the population-level imbalance across many traits and diseases. (LLM, conf=0.85)
- Partly supportedcites [16] Ware, E. B. et al. Heterogeneity in polygenic scores for common human traits. Preprint at https://www.biorxiv.org/content/10.1101/106062v1 (2017). sourceExperimental — accuracy not yet measured
The Cross-race/ethnicity comparison and Discussion (pages not provided) report empirical differences in polygenic-score performance between non-Hispanic White and Black participants across the traits studied. The paper does not establish this imbalance theoretically or through simulations, and its empirical analysis is limited to four traits rather than many traits and diseases. (LLM, conf=0.87)
- Partly supportedcites [17] Curtis, D. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr. Genet. 28, 85–89 (2018). sourceExperimental — accuracy not yet measured
The Results (page not provided) show that the schizophrenia PRS differed markedly between ancestry groups, and the Discussion warns against applying a score derived from European subjects to subjects with African ancestry. This is empirical evidence of an ancestry-related limitation, but the paper does not demonstrate the imbalance theoretically or through simulations, directly compare prediction accuracy, or establish it across many traits and diseases. (LLM, conf=0.86)
- Partly supportedcites [18] Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). sourceExperimental — accuracy not yet measured
The Polygenic risk score profiling section reports that schizophrenia risk-score prediction depends on the target sample, and it evaluates prediction in three samples. It does not demonstrate a theoretical or simulation-based explanation for an imbalance in PRS accuracy, or show that imbalance across many traits and diseases. (LLM, conf=0.79)
- Partly supportedcites [19] Belsky, D. W. et al. Development and evaluation of a genetic risk score for obesity. Biodemography Soc. Biol. 59, 85–100 (2013). sourceExperimental — accuracy not yet measured
The Results and Discussion report that the obesity genetic risk score predicted BMI and obesity less well among African American than white ARIC participants, supporting an empirical example of unequal accuracy. The paper does not demonstrate the imbalance through simulations or establish it across many traits and diseases; its analysis concerns obesity in these two groups. The indexed passages provide no page numbers. (LLM, conf=0.87)
- Partly supportedcites [20] Domingue, B. W., Belsky, D., Conley, D., Harris, K. M. & Boardman, J. D. Polygenic influence on educational attainment: new evidence from The National Longitudinal Study of Adolescent to Adult Health. AERA Open 1, 1–13 (2015). sourceExperimental — accuracy not yet measured
The Materials > Sample section (page unavailable) explains why differences in linkage disequilibrium can reduce the precision of polygenic scores across ancestry groups. The Results section reports a smaller association with educational attainment in African Americans than in European Americans. The paper thus supports a mechanism and one empirical example, but it does not demonstrate the imbalance through simulations or across many traits and diseases. (LLM, conf=0.86)
boosted- 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.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Indeed, other East Asian studies have estimated higher heritability for some quantitative traits than BBJ using the same methods, such as for height (h2 = 0.48±0.04 in Chinese women60).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Partly supportedcites [60] Liu, S. et al. Genomic analyses from non-invasive prenatal testing reveal genetic associations, patterns of viral infections, and Chinese population history. Cell 175, 347–359.e14 (2018). sourceExperimental — accuracy not yet measured
In “Phenotype-Genotype Associations of Multiple Complex Traits” (page unavailable), the paper reports an LD-score-regression SNP heritability of 0.48 for height; the Introduction identifies the study population as pregnant Chinese women. Our copy does not give the claimed ±0.04 uncertainty or establish the comparison with BBJ, so those parts remain unsupported. (LLM, conf=0.84)
hedgedcorrelational- 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
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In the paper
“So far, multi-ethnic work has been slow in most disease areas54, limiting even the opportunity to assess PRS in non-European cohorts.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Limited generalizability of PRS across diverse populations
What the sources say
- Partly supportedcites [54] Duncan, L. E. et al. Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2018). sourceExperimental — accuracy not yet measured
The Discussion says that external data resources needed for PRS are far less common for non-European populations and nonexistent for many, supporting the claimed limitation on assessing PRS. It does not establish that multi-ethnic work has been slow in most disease areas; that scope goes beyond what this PTSD paper states. (LLM, conf=0.82)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Additionally, integrated models of PRS together with other lifestyle and clinical factors have enabled clinicians to more accurately quantify the risk of heart attack for patients;”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
What the sources say
- Partly supportedcites [5] Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018). sourceExperimental — accuracy not yet measured
The paper reports that its polygenic score identified people at elevated coronary artery disease risk (Abstract; page not provided). It describes combining genetic risk with clinical and environmental factors as a future issue, not an integrated model already used by clinicians to improve heart-attack risk estimates. (LLM, conf=0.86)
- Partly supportedcites [6] Kullo, I. J. et al. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES Clinical Trial). Circulation 133, 1181–1188 (2016). sourceExperimental — accuracy not yet measured
Methods > Study Design describes combining a genetic risk score with a conventional score to estimate 10-year coronary heart disease risk. But Discussion says the genetic score was not prospectively validated and presents refinement of risk estimates as a future possibility. The paper therefore does not establish that the combined model quantified heart-attack risk more accurately, or that lifestyle measures were included in the model. (LLM, conf=0.88)
- Partly supportedcites [7] Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017). sourceExperimental — accuracy not yet measured
The Methods (Statistical Analysis; page unavailable) describe models that assess PRS alongside clinical factors, including smoking, and the Results (page unavailable) find that high PRS predicts coronary events after adjustment for traditional risk factors. The paper does not show that integrating PRS with lifestyle and clinical factors improves the accuracy of heart-attack risk estimates, or that clinicians have used such a model for patients. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [8] Paquette, M. et al. Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia. J. Clin. Lipidol. 11, 725–732.e5 (2017). source
- Partly supportedcites [9] Tikkanen, E., Havulinna, A. S., Palotie, A., Salomaa, V. & Ripatti, S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33, 2261–2266 (2013). sourceExperimental — accuracy not yet measured
The Methods and Results show that adding a genetic risk score to models with traditional factors, including smoking and clinical measures, improved coronary heart disease risk prediction and reclassification. The Discussion describes potential clinical value, but the paper models screening rather than showing that clinicians used these models to quantify patients’ heart-attack risk in practice. (LLM, conf=0.82)
plaindirect- 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
- Probably not a substantial claim. 18
- It may state two separate claims as one. 30
Every source the paper cites for this claim was checked and supports it.
In the paper
“Promisingly, return of genetic risk of complex disease to at-risk patients does not induce significant self-reported negative behavior or psychological function, and some potentially positive behavioral changes have been detected10.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
What the sources say
- Supportedcites [10] Frieser, M. J., Wilson, S. & Vrieze, S. Behavioral impact of return of genetic test results for complex disease: systematic review and meta-analysis. Health Psychol. 37, 1134–1144 (2018). source
The Abstract’s Conclusions say that returning genetic test results for complex disease does not strongly affect self-reported negative behavior or psychological function in at-risk individuals, while suggesting potentially positive behavior change. The Discussion also reports significantly more self-reported positive health behavior change among carriers than non-carriers at six months or later. (LLM, conf=0.92)
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
“Cardiovascular research, for example, has led the charge towards clinical translation of PRS1.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Partly supportedcites [1] Knowles, J. W. & Ashley, E. A. Cardiovascular disease: the rise of the genetic risk score. PLoS Med. 15, e1002546–e1002547 (2018). sourceExperimental — accuracy not yet measured
The Summary points describe a cardiovascular pilot study that showed the feasibility of delivering a genetic risk score in a clinical setting, and the authors propose incorporating such scores into practice. They also say few studies have addressed practical implementation. The paper does not establish that cardiovascular research has led other fields in clinical translation. (LLM, conf=0.83)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, the major ethical and scientific challenge surrounding clinical implementation is that they are many-fold more accurate in European ancestry individuals than others.”
- Also stated in: § Abstract · boosted
“the consistent observation that they are currently of far greater predictive value in individuals of recent European descent than in others to be the major ethical and scientific challenge surrounding clinical translation and, at present, the most critical limitation to genetics in precision medicine.”
- Also stated in: § Abstract · boosted
“All studies to date using well-powered genome-wide association studies (GWAS) to assess the predictive value of PRS across a range of traits and populations have made a consistent observation: PRS predict individual risk far more accurately in Europeans. than non-Europeans15,16,18–24.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
What the sources say
- Partly supportedcites [11] Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016). sourceExperimental — accuracy not yet measured
The Results (page not provided) note a paucity of well-validated genetic loci in black populations, and the Discussion (page not provided) calls for testing the score in more diverse populations. But the paper reports similar findings among black and white ARIC participants; it does not show that scores are many-fold more accurate in people of European ancestry or identify that disparity as the major ethical and scientific challenge to clinical implementation. (LLM, conf=0.82)
- Could not verifycites [12] Khera, A. V. & Kathiresan, S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18, 331–344 (2017). sourceExperimental — accuracy not yet measured
The Genomic medicine section describes polygenic risk scores and their potential clinical use, but does not compare their accuracy across ancestry groups. The Common variant association studies section mentions a genotyping chip designed for individuals of European ancestry; it does not establish a many-fold accuracy difference or identify that disparity as the major ethical and scientific challenge. Searches for ancestry-related accuracy and clinical-implementation challenges found no passage addressing the claim. (LLM, conf=0.76)
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
- Partly supportedcites [14] Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016). sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says genomic prediction accuracy tends to fall as target populations become more genetically distant from training populations. But the human simulation uses an Asian training population (Materials and Methods, page unavailable); it does not establish that PRS are many-fold more accurate in European-ancestry individuals or identify this disparity as the major ethical and scientific challenge to clinical implementation. (LLM, conf=0.88)
- Partly supportedcites [15] Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015). sourceExperimental — accuracy not yet measured
The Results (p. 8) report lower absolute prediction accuracy in African- and Asian-ancestry schizophrenia validation samples, and the Discussion (p. 9) calls for improving prediction in diverse populations. The paper does not establish a many-fold difference across PRSs generally or characterize that disparity as the major ethical and scientific challenge to clinical implementation. (LLM, conf=0.88)
- Partly supportedcites [16] Ware, E. B. et al. Heterogeneity in polygenic scores for common human traits. Preprint at https://www.biorxiv.org/content/10.1101/106062v1 (2017). sourceExperimental — accuracy not yet measured
The Discussion reports that European-ancestry-weighted scores explained approximately twice as much trait variation in the HRS non-Hispanic White sample as in the non-Hispanic Black sample. That supports a performance disparity, but not a general claim about all other ancestries or that the scores are “many-fold more accurate.” The paper does not identify this disparity as the major ethical and scientific challenge to clinical implementation. (LLM, conf=0.89)
- Partly supportedcites [17] Curtis, D. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr. Genet. 28, 85–89 (2018). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) warns that ancestry differences complicate clinical risk prediction and that a PRS derived largely from European samples may not apply to people of African ancestry. It also raises the possibility that non-European populations may be disadvantaged. But its roughly tenfold figure compares differences in PRS values, not predictive accuracy; the paper does not show that PRS are many-fold more accurate in Europeans or rank this as the major implementation challenge. (LLM, conf=0.87)
- Could not verifycites [18] Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). sourceExperimental — accuracy not yet measured
The Polygenic risk score profiling section (page unavailable) reports limited sensitivity and specificity and cautions against using the scores as a predictive test. The 108 independent associated loci section (page unavailable) identifies European- and East Asian-ancestry samples, but the passages found do not compare score accuracy across ancestries or call such a disparity the major ethical and scientific challenge. Searches for ancestry-related prediction differences and clinical implementation found no supporting passage. (LLM, conf=0.76)
- Partly supportedcites [19] Belsky, D. W. et al. Development and evaluation of a genetic risk score for obesity. Biodemography Soc. Biol. 59, 85–100 (2013). sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable) show that this obesity genetic risk score was substantially less predictive in African American than in white ARIC participants. But the study tested one obesity score in those two groups, not PRS generally across all ancestries. Its Discussion does not identify that disparity as the major ethical and scientific challenge to clinical implementation; it instead says the score's modest added predictive value was insufficient to recommend it for clinical risk assessment. (LLM, conf=0.87)
- Partly supportedcites [20] Domingue, B. W., Belsky, D., Conley, D., Harris, K. M. & Boardman, J. D. Polygenic influence on educational attainment: new evidence from The National Longitudinal Study of Adolescent to Adult Health. AERA Open 1, 1–13 (2015). sourceExperimental — accuracy not yet measured
The Materials > Sample section says that polygenic scores derived from European-ancestry samples may be less precise in African-American samples, and the Results report a smaller association with educational attainment among African Americans. The paper does not establish a many-fold accuracy difference across all other ancestries or characterize that disparity as the major ethical and scientific challenge to clinical implementation. No page numbers are available in our copy. (LLM, conf=0.88)
boosteddirect- 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: § Abstract · boosted
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Some machine learning approaches may thus modestly improve PRS accuracy beyond current approaches for some phenotypes52, but most likely for atypical traits with simpler architectures, known interactions, and poor prediction generalizability across populations, such as skin pigmentation53.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
What the sources say
- Partly supportedcites [52] Paré, G., Mao, S. & Deng, W. Q. A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci. Rep. 7, 12665 (2017). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [53] Martin, A. R. et al. An unexpectedly complex architecture for skin pigmentation in Africans. Cell 171, 1340–1353.e14 (2017). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
hedgedconditional- 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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“While non-linear genetic factors explain little variation in complex traits beyond a purely additive model51,”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
What the sources say
- Partly supportedcites [51] Zhu, Z. et al. Dominance genetic variation contributes little to the missing heritability for human complex traits. Am. J. Hum. Genet. 96, 377–385 (2015). sourceExperimental — accuracy not yet measured
The Discussion (page not supplied) reports that dominance variance averaged about one-fifth of additive variance across 79 quantitative traits. That supports the claim for dominance, but the study did not estimate all non-linear genetic factors, including epistasis, so the broader wording goes beyond its findings. (LLM, conf=0.84)
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
“These assessments are becoming increasingly feasible with the growth and public availability of global bio banks as well as diversifying priorities from funding agencies55,56.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Limited generalizability of PRS across diverse populations
What the sources say
- Partly supportedcites [55] H3Africa Consortium. et al. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014). sourceExperimental — accuracy not yet measured
In “Enabling the genomic revolution in Africa” (page not provided), the paper describes funding for H3Africa and plans to make its genomic data publicly accessible through an access committee. It does not discuss polygenic risk score (PRS) assessments or establish that growing global biobanks and diversifying funding priorities are making those assessments increasingly feasible. (LLM, conf=0.80)
- Partly supportedcites [56] Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2018). sourceExperimental — accuracy not yet measured
The sections on foundational genomic data resources and dedicated funding support describe expanding diverse reference datasets, benefits of publicly accessible reference populations, and funding programmes that prioritize diversity (page numbers unavailable). They do not specifically establish that global biobanks are growing and publicly available, or that these developments are making assessments of polygenic risk scores across populations increasingly feasible. (LLM, conf=0.83)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This enthusiasm is driven by observations that a polygenic burden of LDL-increasing SNPs can confer monogenic-equivalent risk of cardiovascular disease, with polygenic scores improving clinical models for risk assessment and statin prescription that can reduce coronary heart disease and improve healthcare delivery efficiency5–7.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Partly supportedcites [5] Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [6] Kullo, I. J. et al. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES Clinical Trial). Circulation 133, 1181–1188 (2016). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites [7] Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Differences in environmental exposure, gene-gene interactions, gene-environment interactions, historical population size dynamics, statistical noise, some potential causal effect differences, and/or other factors will further limit generalizability for genetic risk scores in an unpredictable, trait-specific fashion46–49.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
What the sources say
- Partly supportedcites [46] Novembre, J. & Barton, N. H. Tread lightly interpreting polygenic tests of selection. Genetics 208, 1351–1355 (2018). sourceExperimental — accuracy not yet measured
In the paper’s discussion of transferring effect sizes across space and time (page unavailable), the authors say that differences in genetic and environmental background, including gene–gene and gene–environment interactions, may affect polygenic scores across populations. They also discuss environmental differences and changes in effects between populations. Our copy does not establish historical population-size dynamics or statistical noise as limits on genetic-risk-score generalizability, nor does it characterize those limits as unpredictable and trait-specific. (LLM, conf=0.85)
- Partly supportedcites [47] Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G. & Gravel, S. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16, 333–343 (2015). sourceExperimental — accuracy not yet measured
The Conclusions (page unavailable) discuss epistasis, adaptation to different environments, and differences in complex-disease architecture across populations. Demographic Simulations of Mutation Load (page unavailable) discusses bottlenecks and founder effects. The paper does not establish that these factors limit genetic risk-score generalizability in an unpredictable, trait-specific way, and the cited passages do not address statistical noise or gene–environment interactions as risk-score limitations. (LLM, conf=0.82)
- Partly supportedcites [48] Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016). sourceExperimental — accuracy not yet measured
The Discussion (page not provided) identifies gene-gene and gene-environment interactions and statistical noise as possible reasons genetic effects differ between populations; the Introduction says such differences may make disease-risk prediction from current GWAS results problematic. The paper does not establish historical population-size dynamics or environmental exposure as specific limits on risk-score generalizability, nor does it conclude that these limits will be unpredictable and trait-specific. (LLM, conf=0.85)
- Partly supportedcites [49] Galinsky, K. J. et al. Estimating cross-population genetic correlations of causal effect sizes. Genet. Epidemiol. 43, 180–188 (2019). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
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.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Furthermore, imputation reference panels share the same study biases as in GWAS41, creating challenges for imputing sites that are rare in European populations but common elsewhere when the catalog of non-European haplotypes is substantially smaller.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Common discoveries and low-hanging fruit
What the sources say
- Partly supportedcites [41] McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). sourceExperimental — accuracy not yet measured
The Abstract (page not provided) says the HRC panel draws mainly on studies of predominantly European ancestry and proposes increasing its ethnic diversity. It also links larger, more diverse panels to better imputation accuracy. Online Methods > Site filtering (page not provided) notes variants frequent in non-European populations that were called only in 1000 Genomes. The paper does not explicitly establish that reference panels share GWAS study biases or assess the claimed imputation difficulty for variants rare in Europeans but common elsewhere. (LLM, conf=0.82)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These considerations notwithstanding, there are critical needs and challenges for expanding the scale of genetic studies of heritable traits in diverse populations; this is especially apparent in Africa where humans originated and retain the most genetic diversity, as Africans are understudied but disproportionately informative for genetic analyses and evolutionary history27,73.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
What the sources say
- Partly supportedcites [27] Morales, J. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 19, 21 (2018). sourceExperimental — accuracy not yet measured
The Results (Improving data analysis and assessing diversity; page not provided) show that African-ancestry participants account for 2.4% of individuals in the GWAS Catalog but contribute 7% of associations. The Discussion (Assessing diversity in genomics; page not provided) links this yield to higher genetic diversity and reduced linkage disequilibrium. Our copy does not state that Africa has the most genetic diversity, that humans originated there, or that African populations are disproportionately informative for evolutionary history. (LLM, conf=0.86)
- Supportedcites [73] Martin, A. R., Teferra, S., Möller, M., Hoal, E. G. & Daly, M. J. The critical needs and challenges for genetic architecture studies in Africa. Curr. Opin. Genet. Dev. 53, 113–120 (2018). source
The Abstract states that humans originated in Africa, African populations retain more genetic diversity, and people of African descent are underrepresented in genetic studies. The Historical biases and Advantages and opportunities sections explain how studying African populations can advance genomic discoveries, fine-mapping, and reconstruction of human origins. (LLM, conf=0.93)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The most notable investment here comes from the Human Heredity and Health in Africa (H3Africa) Initiative, increasing genomics research capacity in Africa through more than $216 million in funding from the NIH (USA) and Wellcome Trust (UK) for genetics research led by African investigators55,74.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
What the sources say
- Partly supportedcites [55] H3Africa Consortium. et al. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014). sourceExperimental — accuracy not yet measured
In “Enabling the genomic revolution in Africa” (page unavailable), the paper says the NIH and Wellcome Trust fund H3Africa, which focuses on capacity building and awards research grants directly to African institutions with African principal investigators. It gives the funding amount as $76 million over five years, not more than $216 million. (LLM, conf=0.90)
- Partly supportedcites [74] Coles, E. & Mensah, G. A. Geography of genetics and genomics research funding in Africa. Glob. Heart 12, 173–176 (2017). sourceExperimental — accuracy not yet measured
The Introduction says substantial NIH and Wellcome Trust investment in H3Africa gave genetics and genomics research in sub-Saharan Africa momentum. But the Results and Summary and Conclusions identify approximately $216 million as 2015 funding for 185 genetics and genomics projects across Africa, not funding for H3Africa alone; the paper also does not establish that those projects were led by African investigators. (LLM, conf=0.89)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This is a major concern globally and especially in the U.S., which already leads other middle-and high-income countries in both real and perceived healthcare disparities69,70.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Clinical use of PRS may uniquely exacerbate disparities
What the sources say
- Not checked — no full text of the sourcecites [69] Hero, J. O., Zaslavsky, A. M. & Blendon, R. J. The United States leads other nations in differences by income in perceptions of health and health care. Health Aff. (Millwood) 36, 1032–1040 (2017). source
- Partly supportedcites [70] Williams, D. R., Priest, N. & Anderson, N. B. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychol. 35, 407–411 (2016). sourceExperimental — accuracy not yet measured
The opening section describes poorer quality of care for racial and ethnic minorities in the U.S. and globally. It does not say the U.S. leads middle- and high-income countries in healthcare disparities or compare perceived disparities across countries. (LLM, conf=0.85)
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. It may state two separate claims as one.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Currently, guidelines state that as few as 120 individuals define reference intervals for clinical factors (though often smaller numbers from only one subpopulation are used) and there is no clear definition of who is “normal”64.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Clinical use of PRS may uniquely exacerbate disparities
What the sources say
- Partly supportedcites [64] Manrai, A. K., Patel, C. J. & Ioannidis, J. P. A. In the era of precision medicine and big data, who is normal? JAMA 319, 1981–1982 (2018). sourceExperimental — accuracy not yet measured
In “Defining Normality,” the paper says CLSI guidelines call for 120 reference individuals, that laboratories may use fewer, and that health lacks a universal definition. The “Multiplicity” section mentions sampling from the same subpopulation only as a simulation example; it does not establish that smaller samples are often drawn from just one subpopulation. Page numbers are unavailable in our copy. (LLM, conf=0.85)
plainn = 120- 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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Frameworks such as the H3ABioNet, a pan-African bioinformatics network designed to build capacity to enable H3Africa researchers to analyze their data in Africa, provide cost-effective examples for training local scientists in LMICs75.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
What the sources say
- Partly supportedcites [75] Mulder, N. J. et al. Development of bioinformatics infrastructure for genomics research. Glob. Heart 12, 91–98 (2017). sourceExperimental — accuracy not yet measured
The Approach and implementation section describes H3ABioNet as a pan-African network building local capacity, and Genomics skills training describes its courses and other training for African scientists. Our copy does not establish that the training is cost-effective or assess it as a model across LMICs; the cited evidence is specific to Africa. Page numbers were not available in the indexed passages. (LLM, conf=0.86)
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.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Individual clinical tests, biomarkers, and prescription drug efficacy may vary across populations in their utility, but are fundamentally informed by the same underlying biology64,65.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Clinical use of PRS may uniquely exacerbate disparities
What the sources say
- Partly supportedcites [64] Manrai, A. K., Patel, C. J. & Ioannidis, J. P. A. In the era of precision medicine and big data, who is normal? JAMA 319, 1981–1982 (2018). sourceExperimental — accuracy not yet measured
The paper’s discussion of population variation in laboratory tests and its Potential Solutions section (page not provided) support the point that a clinical test’s utility can differ across populations. The copy does not establish population differences in prescription-drug efficacy or the claim that all three are informed by the same underlying biology. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [65] Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013). source
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.
It may state two separate claims as one.
- Partly supported
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Prediction accuracy was far lower in individuals of African descent in the UK Bio bank (Supplementary Figures 4 and 11) using GWAS summary statistics from either European or Japanese ancestry individuals, consistent with reduced prediction accuracy with increasing genetic divergence (Figures 3 and 4).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
What the sources say
- Not matchedcites Supplementary Figures 4 and 11
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Figures 3 and 4
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.
It may state two separate claims as one.
- Not matched
In the paper
“However, whereas the average sample sizes of GWAS in Europeans continue to grow, they have stagnated and remain several-fold smaller in other populations (Supplementary Figure 1).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
What the sources say
- Not matchedcites Supplementary Figure 1
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.
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
“These trans-ethnic correlation estimates indicate that effect sizes were mostly highly correlated across ancestries, with a few traits that were somewhat lower than excepted (e.g. height and BMI, with ρge=0.69 and 0.75, respectively).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
hedgedcorrelational- 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
“Consistent with previous studies, we find that relative to European prediction accuracy, genetic prediction accuracy was far lower in other populations: 1.6-fold lower in Hispanic/Latino Americans, 1.7-fold lower in South Asians, 2.5-fold lower in East Asians, and 4.9-fold lower in Africans on average (Figure 3).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Limited generalizability of PRS across diverse populations
What the sources say
- Not matchedcites Figure 3
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.
It may state two separate claims as one.
- Not matched
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
“An equal investment in GWAS across all major ancestries and global populations is the most obvious solution to generate a substrate for equally informative risk scores but is not likely to occur any time soon absent a dramatic priority shift given the current imbalance and stalled diversifying progress over the last five years (Figure 1, Supplementary Figure 1).”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
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: unclear — It is unclear why the paper says it holds.
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
“Related to stratification, most PRS methods do not explicitly address recent admixture and none consider recently admixed individuals’ unique local mosaic of ancestry; further methods development is needed.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“These issues are insurmountable through statistical methods alone13, but rather motivate substantial investments in more diverse populations to produce similar-sized GWAS of biomedical phenotypes in other populations.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Common discoveries and low-hanging fruit
What the sources say
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
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.
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
“some unrecognized nonlinearities and gene-gene interactions can also induce genetic risk prediction challenges, as pairwise interactions are likely to vary more across populations than individual SNPs.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
hedgedconditional- 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 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
“Early diversifying efforts show promise in levelling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“Notably, the first two phenomena degrade prediction performance across populations substantially even when there exist no biological, environmental, or diagnostic differences,”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
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
“Eurocentric GWAS biases mean that variants associated with risk are disproportionately common and discovered in European populations, accounting for a larger fraction of the phenotypic variance there13.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Common discoveries and low-hanging fruit
What the sources say
- Not checked — no full text of the sourcecites [13] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
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.
- The authors state this as an assumption: Causal genetic variants have an equal effect across all populations.
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
“3) environment and demography differ across populations.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“Complex traits do not behave in a genetically deterministic manner, with some environmental factors dwarfing individual genetic effects, creating outsized issues of comparability across globally diverse populations.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
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.
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
“This highlights that—unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations—clinical uses of PRS today would systematically afford greater improvement to European descent populations.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
hedgedconditional- 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: unclear — It is unclear why the paper says it holds.
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
“whereas the environment and demography may interact to drive differential forces of natural selection that in turn drive differences in causal genetic architecture.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy
hedgedconditional- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“However, many of these studies have been conducted exclusively in European descent populations, with few studies rigorously evaluating population-level applicability to non-Europeans.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Prioritizing diversity shows early promise for PRS
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.
In the paper
“Differences in effect size estimates due to LD differences may typically be small for most regions of the genome (Figure 2C–E), but PRS sum across these effects, also aggregating these population differences.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Linkage disequilibrium
What the sources say
- Not matchedcites Figure 2C–E
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- 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.
- Not matched
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
“Additionally, residual uncorrected population stratification may impact risk prediction accuracy across populations, but the magnitude of its effect is currently unclear.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Predictable basis of disparities in PRS accuracy > Complexities of history, selection, and the environment
hedgedconditional- 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 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
“This disparity is an inescapable consequence of Eurocentric genome-wide association study biases.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Abstract
boosteddirect- 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.
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
“Methodological improvements that better define risk scores by accounting for population allele frequency, LD, and/or admixture differences appropriately are underway and may help considerably but will not by themselves bring equality.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. The paper may give no reason why it holds. 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
“The single most important step towards parity in PRS accuracy is by vastly increasing the diversity of participants included and analyzed in genetic studies, which will improve utility for all and most rapidly for underrepresented groups.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. 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
“Existing GWAS consortia also need to carefully consider the granularity of summary statistics they release, as finer scale continental ancestries and phenotypes in large, multi-ethnic projects enable ancestry-matched analyses not possible with a single set of summary statistics.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“No analogous solution of defining ethnicity-specific reference intervals would ameliorate health disparities implications for PRS or fundamentally aid interpretability in non-European populations.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Clinical use of PRS may uniquely exacerbate disparities
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.
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
“Efforts to unify phenotype definitions, normalization approaches, and GWAS methods among studies will also improve comparability.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Given the acute recent attention on clinical use of PRS, we believe it is paramount to recognize their potential to improve health outcomes for all individuals and many complex diseases.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > How do we even the ledger?
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. 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
“Simply put, some biomarkers or clinical tests scale directly with health outcomes independent of ancestry, and many others may have distributional differences by ancestry but are equally valid after centering with respect to a readily collected population reference.”
§ Current clinical use of polygenic scores will risk exacerbating health disparities > Clinical use of PRS may uniquely exacerbate disparities
hedged- 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.
The authors state this as an assumption.
Causal genetic variants have an equal effect across all populations.
“If we assume that causal genetic variants have an equal effect across all populations—an assumption with some empirical support that offers the best case scenario for transferability35–40—”
The authors state this as an assumption.
All causal loci have the same impact and are under equivalent selective pressure in all populations.
“these issues actually make many favorable assumptions that all causal loci have the same impact and are under equivalent selective pressure in all populations.”
The paper relies on this without support.
The prerequisite data for dramatically increasing diversity exist in large-scale publicly funded datasets such as the Million Veterans Project and TOPMed, but data access issues prevent even GWAS summary data within and across populations from being publicly shared.
“The prerequisite data for dramatically increasing diversity also hypothetically exist in several large-scale publicly funded datasets such as the Million Veterans Project and Trans-Omics for Precision Medicine (TOP Med), but with problematic data access issues in which even GWAS summary data within and across populations are not publicly shared.”
The paper relies on this without support.
African descent populations already endure many of the largest health disparities globally.
“Conversely, African descent populations, which already endure many of the largest health disparities globally, are often predicted marginally better, if at all, compared to random (Figure 4F).”
The paper relies on this without support.
Human population history and study biases cause African descent populations to be predicted marginally better, if at all, compared to random by PRS.
“They are therefore least likely to benefit from improvements in precision healthcare delivery from genetic risk scores with existing data due to human population history and study biases.”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PGIs like ours that are constructed from GWAS in samples of European genetic ancestries are generally found to have much lower predictive power in samples with other genetic ancestries; for example, on average across phenotypes, estimates of relative accuracy (ratio of R2) in African-genetic-ancestry to European-genetic-ancestry samples have been 22%23 and 36%24.”
From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [23]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Previous studies have shown that, while polygenic risk scores [65] and eQTLs [66] have limited cross-population portability, biochemically active functional regulatory elements tend to be under evolutionary constraint within the human lineage [67, 68].”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [65]
In the citing paper
“For most diseases, large-scale GWAS-aggregation efforts have been disproportionately powered by information from individuals of European descent55.”
From A brief history of human disease genetics (2020) · cites this paper as [55]
In the citing paper
“An elegant distillation of the ways in which the historical focus of genetic discovery among populations of European descent has adverse consequences for both discovery and translation.”
From A brief history of human disease genetics (2020) · cites this paper as [55]
Citations
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Metadata
- Authors
- Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J.
- Affiliations
- Broad Institute (US)
- Kyoto University (JP)
- RIKEN Center for Integrative Medical Sciences (JP)
- Corresponding author
- Martin, Alicia R.
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-03-29
- Volume
- 51
- Issue
- 4
- Pages
- 584-591
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6563838
- OpenAlex
- W2932671560
- Cited by
- 2,821
- References
- 77
- Citations by year
- 2026: 531 · 2025: 432 · 2024: 371 · 2023: 417 · 2022: 387 · 2021: 374 · 2020: 217 · 2019: 85
- FWCI
- 50.21
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-01-28)
- Open access
- green
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Japan Agency for Medical Research and Development
- Masason Foundation
- Ministry of Education, Culture, Sports, Science and Technology
- National Institutes of Health (31063)
- National Institute of Mental Health (K99MH117229)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- Topics
- Social Sciences › Anthropology › Race, Genetics, and Society
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- polygenic risk score, health disparities, ancestry bias, genome-wide association study, precision medicine, underrepresented groups
- MeSH terms
- Genotype, Humans, Risk Factors, Genetic Predisposition to Disease, Multifactorial Inheritance, White People, Healthcare Disparities, Genome-Wide Association Study, Precision Medicine
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.
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Statistics and arithmetic are recomputed, not judged.
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