The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index; 2 of the 18 sources its claims cite were checked for support.
Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
Open accessPublished version of a preprint
Abstract
A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2–5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues. Genome-wide polygenic risk scores derived from GWAS data for five common diseases can identify subgroups of the population with risk approaching or exceeding that of a monogenic mutation.
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25 Own result · 9 Not checked yet · 7 Own method · 3 Own interpretation · 2 No basis given · 1 Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“GPSCAD performed substantially better than two previously published polygenic scores for coronary artery disease that included 50 and 49,310 variants, respectively (Supplementary Table 7 and Supplementary Fig. 1).17,18”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Supportedcites [17] Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016). source
The Methods (Modelling of genetic risk score) identify this paper’s GRS50 as a 50-SNP score for coronary heart disease, supporting its citation as the previously published 50-variant score. The comparison with GPSCAD is presented as the citing paper’s own analysis, not as a result attributed to this source. (LLM, conf=0.91)
- Supportedcites [18] Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016). source
The citation identifies the previously published 49,310-variant score used as a comparator. Abraham et al. report generating that score in Abstract > Methods and results; the GPSCAD performance comparison is the citing authors’ own result, attributed to their supplementary table and figure. (LLM, conf=0.91)
plainstates an association- Asserted: passes — The authors put it forward as true.
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- Supported
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
“Moreover, even for our middle-aged study population, practicing clinicians could not identify the 8% of individuals at ≥3-fold risk based on GPSCAD in the absence of genotype information (Supplementary Table 8).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plain- Asserted: passes — The authors put it forward as true.
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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 polygenic predictor identified 3.5% of the population at ≥3-fold risk and the top 1% had 3.30-fold risk. (Tables 2 & 3).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, 0.1% of women had ≥5-fold risk of breast cancer—corresponding to a breast cancer prevalence of 19.0% in this group versus 4.2% in the remaining 99.9% of the distribution.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The polygenic predictor identified 6.1% of the population at ≥3-fold risk and the top 1% had 4.63-fold risk (Tables 2 & 3).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
“Moreover, 0.1% of women had ≥5-fold risk of breast cancer—corresponding to a breast cancer prevalence of 19.0% in this group versus 4.2% in the remaining 99.9% of the distribution.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
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In the paper
“The polygenic predictor identified 3.2% of the population at ≥3-fold risk and the top 1% had 3.87-fold risk (Tables 2 & 3).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- 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.
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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 predictor performed equivalently well in the testing dataset, with AUC of 0.81.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainn = 288,978- 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.
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
“Our results for CAD generalized to four other diseases: risk increased sharply in the right tail of the GPS distribution (Figure 3).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- 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.
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 our testing dataset, 19.8% of participants were at ≥3-fold increased risk for at least one of the five diseases studied (Table 2).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 8
- Probably not a substantial claim. 6
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 predictors had AUC ranging from 0.79 – 0.81 in the validation set, with the best predictor (GPSCAD) involving 6,630,150 variants (Supplementary Table 1).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an associationn = 120,280- 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.
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
“Across the population, GPSCAD is normally distributed with the empirical risk of CAD rising sharply in the right tail of the distribution, from 0.8% in the lowest percentile to 11.1% in the highest percentile (Figure 2).”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
“In order to analyze individuals with a relatively homogenous ancestry and owing to small percentages of non-British individuals, the present analysis was restricted to the white British ancestry individuals.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Online Methods: > Testing cohort
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim.
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
“Whereas identifying carriers of rare monogenic mutations requires sequencing of specific genes and careful interpretation of the functional effects of mutations found, polygenic scores can be readily calculated for many diseases simultaneously, based on data from a single genotyping array.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
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.
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 testing dataset was comprised of 288,978 UK Biobank Phase 2 participants distinct from those in the validation dataset described above.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Online Methods: > Testing cohort
plainn = 2- Asserted: passes — The authors put it forward as true.
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- 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
“Our validation and testing was performed in the UK Biobank population.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
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.
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
“These initial efforts were hampered by three challenges: (i) the small size of initial genome-wide association studies (GWAS), which affected the precision of the estimated impact of individual variants on disease risk; (ii) limited computational methods for creating GPS; and (iii) lack of large datasets needed to validate and test GPS.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates a cause- 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.
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 results above show that, for a number of common diseases, polygenic risk scores can now identify a substantially larger fraction of the population than found by rare monogenic mutations, at comparable or greater disease risk.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
plainstates an association- 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.
- Rests on an assumption: The vast majority of common disease occurs in individuals without rare monogenic mutations.
In the paper
“Individuals who volunteered for the UK Biobank tended to be more healthy than the general population;27”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Not checked yetcites [27] Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017). source
plainstates an association- Asserted: passes — The authors put it forward as true.
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- 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.
In the paper
“In both cases, it is important to recognize that the risk associated with a high polygenic score may not reflect a single underlying mechanism, but rather the combined influence of multiple pathways.28”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Not checked yetcites [28] Khera, A. V. & Kathiresan, S. Is coronary atherosclerosis one disease or many? Setting realistic expectations for precision medicine. Circulation 135, 1005–1007 (2017). source
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, we previously showed that a high polygenic risk for CAD may be offset by either of two interventions: adherence to a healthy lifestyle or cholesterol-lowering therapy with statin medications.19–21”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Not checked yetcites [19] Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016). source
- Not checked yetcites [20] Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015). source
- Not checked yetcites [21] 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). source
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
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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
“UK Biobank samples were not included in any of the five discovery GWAS studies.”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Online Methods: > Polygenic score derivation
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
In the paper
“An important example is the presence of a familial hypercholesterolemia mutation in 0.4% of the population, which confers an up to 3-fold increased risk for coronary artery disease (CAD).6”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Not checked yetcites [6] Abul-Husn, N. S. et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354, pii: aaf7000 (2016). source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Another example is the p.E508K missense mutation in HNF1A, with carrier frequency of 0.1% of the general population and 0.7% of Latinos,8 which confers up to 5-fold increased risk for type 2 diabetes.9”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Abstract
What the sources say
- Not checked yetcites [8] Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). source
- Not checked yetcites [9] Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014). source
plainstates a cause- 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 paper relies on this without support.
The vast majority of common disease occurs in individuals without rare monogenic mutations.
“Although ascertainment of monogenic mutations can be highly relevant for carriers and their families, the vast majority of disease occurs in those without such mutations.”
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
“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;”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [5]
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
“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.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [5]
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
“Using polygenic scores, it is now possible to combine information from millions of genomic variants to identify people whose overall polygenic risk of coronary artery disease is as high as that of patients with monogenic lipid disorders such as familial hypercholesterolemia [23].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [23]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“For example, they have contributed new approaches to risk prediction for adverse phenotypes related to heart disease [21–23].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [23]
In the citing paper
“As described below, findings from GWAS have increasing translational impact through identification of novel therapeutic targets67, prioritization (and deprioritization) of existing ones68 and development of polygenic scores that quantify individual genetic risk69.”
From A brief history of human disease genetics (2020) · cites this paper as [69]
In the citing paper
“The development of robust polygenic scores for several common diseases has been catalysed by more precise per-variant effect estimates from larger GWAS datasets, improved algorithms for combining information across millions of single-nucleotide polymorphisms, and large-scale biobanks that support score validation69,146,147.”
From A brief history of human disease genetics (2020) · cites this paper as [69]
In the citing paper
“For example, a genome-wide polygenic score for heart attack, incorporating 6.6 million variants, indicates that 5% of European-descent individuals have a risk of future cardiac events equivalent to that seen in those with less frequent monogenic forms of hypercholesterolaemia69.”
From A brief history of human disease genetics (2020) · cites this paper as [69]
In the citing paper
“This paper used large-scale genetic data to demonstrate the clinical potential of the polygenic scores that can be constructed for many common diseases, emphasizing that, in some situations, the lifetime risk of disease for those with the highest scores approaches that of established monogenic disease.”
From A brief history of human disease genetics (2020) · cites this paper as [69]
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
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Online Methods: > Statistical analysis within the testing dataset
“For each disease, the GPS with the best discriminative capacity in the testing dataset was calculated in the testing dataset of 288,278 participants”
§ Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations > Online Methods: > Testing cohort
“The testing dataset was comprised of 288,978 UK Biobank Phase 2 participants distinct from those in the validation dataset described above.”
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Metadata
- Authors
- Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., & Kathiresan, S.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Kathiresan, Sekar
- ORCID
- Venue
- Nature Genetics
- Published
- 2018-08-13
- Volume
- 50
- Issue
- 9
- Pages
- 1219-1224
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6128408
- OpenAlex
- W2886752110
- Semantic Scholar
- d5eb49c11d070114e2ff46224099895c6b4b1558
- Cited by
- 2,953
- References
- 46
- Citations by year
- 2026: 276 · 2025: 290 · 2024: 363 · 2023: 362 · 2022: 433 · 2021: 411 · 2020: 451 · 2019: 307 · 2018: 57
- FWCI
- 189.86
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Doris Duke Charitable Foundation (2014105)
- Massachusetts General Hospital (HL007208)
- Harvard Catalyst (TR001100)
- Vertex Pharmaceuticals
- KR Foundation
- National Institutes of Health (KL2TR001100, HL0-07734, HL007208, T32-HL007734)
- National Heart, Lung, and Blood Institute (R01HL127564, HL007208, HL127564, R01HL128914, T-32HL007734, K08HL140203, HL007734, K23HL114724, K24HL105780, R01HL142711, R01HL092577, T32HL007208, R01HL139731)
- National Human Genome Research Institute (UM1HG008895)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- polygenic risk score, monogenic mutations, common diseases, coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, breast cancer, high-risk individuals, clinical risk stratification
- MeSH terms
- Adult, Aged, Disease, Female, Humans, Male, Middle Aged, Mutation, Risk Factors, Genetic Predisposition to Disease, 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 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (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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