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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Claims the paper makes
0 of 18 cited sources checked · 0 supported · 0 flagged
- Findingown data
Even in the middle-aged study population, clinicians could not identify the 8% at ≥3-fold risk based on GPSCAD without genotype information.
“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).”
Quote in the paperSubstantial claim - Findingown data
The top 1% of the GPS distribution for type 2 diabetes had 3.30-fold risk.
“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).”
Quote in the paperSubstantial claim - Findingown data
0.1% of women had ≥5-fold risk of breast cancer.
“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.”
Quote in the paperSubstantial claim - Findingown data
The GPS approach identifies 6.1% of the population at greater than three-fold increased risk for atrial fibrillation.
“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).”
Quote in the paperSubstantial claim - Findingown data
Breast cancer prevalence was 19.0% in women with ≥5-fold GPS risk versus 4.2% in the remaining 99.9%.
“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.”
Quote in the paperSubstantial claim
- Findingown data
The GPS approach identifies 3.2% of the population at greater than three-fold increased risk for inflammatory bowel disease.
“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).”
Quote in the paperSubstantial claim - Findingown data
GPSCAD performed equivalently in the testing dataset with AUC 0.81.
“This predictor performed equivalently well in the testing dataset, with AUC of 0.81.”
Quote in the paperNot a substantial claim - Findingown data
Results for CAD generalized to four other diseases: risk increased sharply in the right tail of the GPS distribution.
“Our results for CAD generalized to four other diseases: risk increased sharply in the right tail of the GPS distribution (Figure 3).”
Quote in the paperSubstantial claim - Findingown data
In the testing dataset, 19.8% of participants were at ≥3-fold increased risk for at least one of the five diseases studied.
“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).”
Quote in the paperSubstantial claim - Findingown data
GPSCAD performed substantially better than two previously published polygenic scores for CAD that included 50 and 49,310 variants.
“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”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [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
Not checked in the free run.
- cites [18] Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016). source
Not checked in the free run.
- Findingown data
The shape of the predicted risk gradient was consistent with the empirically observed risk gradient for each of the five diseases.
“The shape of the predicted risk gradient was consistent with the empirically observed risk gradient for each of the five disease (Supplementary Fig. 2–3).”
Quote in the paperNot a substantial claim - Findingown data
Hypercholesterolemia was present in 20% of those with ≥3-fold GPSCAD risk versus 13% in the remainder of the distribution.
“For example, conventional risk factors such as hypercholesterolemia was present in 20% of those with ≥3-fold risk based on GPSCAD versus 13% of those in the remainder of the distribution, hypertension in 32% versus 28%, and family history of heart disease in 44% versus 35%.”
Quote in the paperNot a substantial claim - Findingown data
For CAD, the prevalence of >3-fold polygenic risk is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk.
“Strikingly, the polygenic score identified 20-fold more people than found by familial hypercholesterolemia mutations in previous studies,6,7 at comparable or greater risk.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [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
Not checked in the free run.
- cites [7] Nordestgaard, B. G. et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur. Heart J. 34, 3478–3490a (2013). source
Not checked in the free run.
- Findingown data
The top 1% of the GPS distribution for atrial fibrillation had 4.63-fold risk.
“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).”
Quote in the paperSubstantial claim - Findingown data
For each disease, the discriminative capacity of GPS in the testing dataset was nearly identical to that in the validation dataset.
“For each disease, the discriminative capacity within the testing dataset was nearly identical to that observed in the validation dataset.”
Quote in the paperSubstantial claim - Findingown data
For each disease, the shape of the observed risk gradient was consistent with predicted risk based only on the GPS.
“For each disease, the shape of the observed risk gradient was consistent with predicted risk based only on the GPS (Supplementary Figs. 2–3).”
Quote in the paperSubstantial claim - Findingown data
The GPS approach identifies 1.5% of the population at greater than three-fold increased risk for breast cancer.
“The polygenic predictor identified 1.5% of the population at ≥3-fold risk (Tables 2 & 3).”
Quote in the paperSubstantial claim - Findingown data
The median GPSCAD percentile was 69 for individuals with CAD versus 49 for individuals without CAD.
“The median GPSCAD percentile score was 69 for individuals with CAD vs. 49 for individuals without CAD.”
Quote in the paperSubstantial claim - Findingown data
0.5% of the population inherited ≥5-fold increased risk for CAD.
“Moreover, 2.3% of the population (‘carriers’) inherited ≥4-fold increased risk for CAD and 0.5% (‘carriers’) had inherited ≥5-fold increased risk.”
Quote in the paperSubstantial claim - Findingown data
Family history of heart disease was present in 44% of those with ≥3-fold GPSCAD risk versus 35% in the remainder.
“For example, conventional risk factors such as hypercholesterolemia was present in 20% of those with ≥3-fold risk based on GPSCAD versus 13% of those in the remainder of the distribution, hypertension in 32% versus 28%, and family history of heart disease in 44% versus 35%.”
Quote in the paperNot a substantial claim - Findingown data
2.3% of the population inherited ≥4-fold increased risk for CAD.
“Moreover, 2.3% of the population (‘carriers’) inherited ≥4-fold increased risk for CAD and 0.5% (‘carriers’) had inherited ≥5-fold increased risk.”
Quote in the paperSubstantial claim - Findingown data
The top 1% of the GPS distribution for inflammatory bowel disease had 3.87-fold risk.
“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).”
Quote in the paperSubstantial claim - Findingown data
The GPS approach identifies 8.0% of the population at greater than three-fold increased risk for coronary artery disease.
“We found that 8% of the population had inherited a genetic predisposition that conferred ≥3-fold increased risk for CAD (Table 2).”
Quote in the paperSubstantial claim - Findingown data
Hypertension was present in 32% of those with ≥3-fold GPSCAD risk versus 28% in the remainder.
“For example, conventional risk factors such as hypercholesterolemia was present in 20% of those with ≥3-fold risk based on GPSCAD versus 13% of those in the remainder of the distribution, hypertension in 32% versus 28%, and family history of heart disease in 44% versus 35%.”
Quote in the paperNot a substantial claim - Findingown data
The GPS approach identifies 3.5% of the population at greater than three-fold increased risk for type 2 diabetes.
“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).”
Quote in the paperSubstantial claim - Study design and conductown data
The authors developed and validated genome-wide polygenic scores for five common diseases.
“Here, we develop and validate genome-wide polygenic scores for five common diseases.”
Quote in the paperSubstantial claim - Interpretationcitation
Because allele frequencies, linkage disequilibrium patterns, and effect sizes vary with ancestry, the specific GPS will not have optimal predictive power for other ethnic groups.
“Because allele frequencies, linkage disequilibrium patterns, and effect sizes of common polymorphisms vary with ancestry, the specific GPS here will not have optimal predictive power for other ethnic groups.29”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). source
Not checked in the free run.
- Study design and conductown data
The polygenic risk scores described were derived and tested in individuals of primarily European ancestry.
“The polygenic risk scores described here were derived and tested in individuals of primarily European ancestry, the group in which most genetic studies have been undertaken to date.”
Quote in the paperSubstantial claim - Premisecitation
For most common diseases, polygenic inheritance involving many common variants of small effect plays a greater role than rare monogenic mutations.
“For most common diseases, polygenic inheritance, involving many common genetic variants of small effect, plays a greater role than rare monogenic mutations.2–5”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [2] Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Proc. R. Soc. Edinb. 52, 99–433 (1918).
Not checked in the free run.
- cites [3] Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135–145 (2012). source
Not checked in the free run.
- cites [4] Golan, D., Lander, E. S. & Rosset, S. Measuring missing heritability: inferring the contribution of common variants. Proc. Natl Acad. Sci. USA 111, E5272–E5281 (2014). source
Not checked in the free run.
- cites [5] Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016). source
Not checked in the free run.
- Interpretationreasoning
Nonrandom ascertainment in the UK Biobank is likely to deflate disease prevalence.
“although this nonrandom ascertainment is likely to deflate disease prevalence, we expect the relative impact of genetic risk strata to be generalizable across study populations.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The relative impact of genetic risk strata is expected to be generalizable across study populations.
“although this nonrandom ascertainment is likely to deflate disease prevalence, we expect the relative impact of genetic risk strata to be generalizable across study populations.”
Quote in the paperNot a substantial claim - Premisecitation
Previous GPS studies had limited success, providing insufficient risk stratification for clinical utility, for example identifying 20% of a population at 1.4-fold increased risk.
“Previous studies to create GPS had only limited success, providing insufficient risk stratification for clinical utility (for example, identifying 20% of a population at 1.4-fold increased risk relative to the rest of the population).12”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Ripatti, S. et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376, 1393–1400 (2010). source
Not checked in the free run.
- Study design and conductown data
A sensitivity analysis removing one individual from each related pair confirmed similar results in 222,529 of 288,978 testing participants.
“A sensitivity analysis was performed by removing one individual from each pair of related individuals (third-degree or closer; kinship coefficient > 0.0442), confirming similar results within this subpopulation comprised of 222,529 of the 288,978 (77%) testing dataset participants (Supplementary Table 10).”
Quote in the paperNot a substantial claim
- May combine two claims. 8
- Classified as probably not a substantial claim. 6
May combine two claims.
Findingown dataFor CAD, the polygenic predictors had AUC 0.79-0.81 in the validation set, with the best predictor GPSCAD involving 6,630,150 variants.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAcross the population, GPSCAD is normally distributed and empirical CAD risk rises from 0.8% in the lowest percentile to 11.1% in the highest percentile.
“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).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe analysis was restricted to white British ancestry individuals.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
PremisereasoningPolygenic scores can be readily calculated for many diseases simultaneously from a single genotyping array, unlike monogenic mutation identification which requires sequencing specific genes.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe testing dataset comprised 288,978 UK Biobank Phase 2 participants distinct from the validation dataset.
“The testing dataset was comprised of 288,978 UK Biobank Phase 2 participants distinct from those in the validation dataset described above.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataValidation and testing were performed in the UK Biobank population.
“Our validation and testing was performed in the UK Biobank population.”
Quote in the paperSubstantial claimMay combine two claims.
PremisereasoningInitial GPS efforts were hampered by small GWAS size, limited computational methods, and lack of large validation datasets.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataFor a number of common diseases, polygenic risk scores can now identify a substantially larger fraction of the population than rare monogenic mutations, at comparable or greater disease risk.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The vast majority of common disease occurs in individuals without rare monogenic mutations.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationUK Biobank volunteers tended to be healthier than the general population.
“Individuals who volunteered for the UK Biobank tended to be more healthy than the general population;27”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe risk associated with a high polygenic score may not reflect a single underlying mechanism but rather the combined influence of multiple pathways.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
May combine two claims.
PremisecitationHigh polygenic risk for CAD may be offset by adherence to a healthy lifestyle or cholesterol-lowering statin therapy.
“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”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [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 in the free run.
- cites [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 in the free run.
- cites [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
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenUK Biobank samples were not included in any of the five discovery GWAS studies.
“UK Biobank samples were not included in any of the five discovery GWAS studies.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationA familial hypercholesterolemia mutation is present in 0.4% of the population and confers up to 3-fold increased risk for coronary artery disease.
“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”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
May combine two claims.
PremisecitationThe p.E508K missense mutation in HNF1A has carrier frequency 0.1% in the general population and 0.7% in Latinos, and confers up to 5-fold increased risk for type 2 diabetes.
“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”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8] Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). source
Not checked in the free run.
- cites [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
Not checked in the free run.
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?
Integrated models of PRS together with other lifestyle and clinical factors have enabled clinicians to more accurately quantify the risk of heart attack.
“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]
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)
Experimental — accuracy not yet measuredA 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 efficiency.
“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]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredUsing 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.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPolygenic scores have contributed new approaches to risk prediction for adverse phenotypes related to heart disease.
“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]
GWAS findings have increasing translational impact through identification of novel therapeutic targets, prioritization and deprioritization of existing targets, and development of polygenic scores that quantify individual genetic risk.
“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]
Development of robust polygenic scores for several common diseases has been catalysed by more precise per-variant effect estimates from larger GWAS, improved algorithms, and large-scale biobanks.
“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]
A genome-wide polygenic score for heart attack with 6.6 million variants indicates that 5% of European-descent individuals have future cardiac risk equivalent to that of less frequent monogenic hypercholesterolaemia.
“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 some situations, individuals carrying the highest polygenic scores for a common disease have a lifetime risk of that disease approaching the risk seen in established monogenic disease.
“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.
Testing cohort size differs by 700 participants
MinorThe statistical-analysis section gives the testing dataset as 288,278 participants, whereas the testing-cohort section gives 288,978. The text does not describe an additional exclusion that would explain the difference.
“For each disease, the GPS with the best discriminative capacity in the testing dataset was calculated in the testing dataset of 288,278 participants”
“The testing dataset was comprised of 288,978 UK Biobank Phase 2 participants distinct from those in the validation dataset described above.”
Cites
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationVos, T., Abajobir, A. A., Abate, K. H., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abdulkader, R. S., Abdulle, A. M., Abebo, T. A., Abera, S. F., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32154-2 · Cited by 13,220 · see citing works
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587 · see citing works
Self-citationVos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., Carter, A., Casey, D. C., Charlson, F. J., Chen, A. Z., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31678-6 · Cited by 7,253 · see citing works
2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation
January, C. T., Wann, L. S., Alpert, J. S., Calkins, H. G., Cigarroa, J. E., Cleveland, J. C., CONTI, J. B., Ellinor, P. T., Ezekowitz, M. D., Field, M. E., et al. (2014) · Circulation · doi:10.1161/cir.0000000000000041 · Cited by 7,119 · see citing works
Self-citationCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387 · see citing works
Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979 · see citing works
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
Nordestgaard, B. G., Chapman, M. J., Humphries, S. E., Ginsberg, H. N., Masana, L., Descamps, O. S., Wiklund, O., Hegele, R. A., Raal, F. J., Defesche, J. C., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht273 · Cited by 2,761 · see citing works
Abraham, C., & Cho, J. H. (2009) · New England Journal of Medicine · doi:10.1056/nejmra0804647 · Cited by 2,712 · see citing works
Liu, J. Z., van Sommeren, S., Huang, H., Ng, S. C., Alberts, R., Takahashi, A., Ripke, S., Lee, J. C., Jostins, L., Shah, T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3359 · Cited by 2,603 · see citing works
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447 · see citing works
Self-citationPower and predictive accuracy of polygenic risk scores
Dudbridge, F. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003348 · Cited by 1,509 · see citing works
Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease
Khera, A. V., Emdin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., Chasman, D. I., Baber, U., Mehran, R., Rader, D. J., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1605086 · Cited by 1,460 · see citing works
Self-citationHuman Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Association analysis identifies 65 new breast cancer risk loci
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemaçon, A., Soucy, P., Glubb, D. M., Rostamianfar, A., et al. (2017) · Nature · doi:10.1038/nature24284 · Cited by 1,380 · see citing works
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P.-R., Bhatia, G., Do, R., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.001 · Cited by 1,303 · see citing works
Self-citationRare and common variants: twenty arguments
Gibson, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3118 · Cited by 1,278 · see citing works
The genetic architecture of type 2 diabetes
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature · doi:10.1038/nature18642 · Cited by 1,051 · see citing works
Khera, A. V., Won, H.-H., Peloso, G. M., Lawson, K. S., Bartz, T. M., Deng, X., van Leeuwen, E. M., Natarajan, P., Emdin, C. A., Bick, A. G., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.03.520 · Cited by 942 · see citing works
Self-citationCharting a course for genomic medicine from base pairs to bedside
Green, E. D., & Guyer, M. S. (2011) · Nature · doi:10.1038/nature09764 · Cited by 901 · see citing works
An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans
Scott, R. A., Scott, L. J., Mägi, R., Marullo, L., Gaulton, K. J., Kaakinen, M., Pervjakova, N., Pers, T. H., Johnson, A. D., Eicher, J. D., et al. (2017) · Diabetes · doi:10.2337/db16-1253 · Cited by 736 · see citing works
Mega, J. L., Stitziel, N. O., Smith, J. G., Chasman, D. I., Caulfield, M. J., Devlin, J. J., Nordio, F., Hyde, C. L., Cannon, C. P., Sacks, F. M., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(14)61730-x · Cited by 704 · see citing works
Self-citationPolygenes, Risk Prediction, and Targeted Prevention of Breast Cancer
Pharoah, P. D. P., Antoniou, A. C., Easton, D. F., & Ponder, B. A. J. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0708739 · Cited by 616 · see citing works
Genome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 606 · see citing works
A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
Nikpay, M., Goel, A., Won, H., Hall, L. M., Willenborg, C., Kanoni, S., Saleheen, D., Kyriakou, T., Nelson, C. P., Hopewell, J. C., et al. (2015) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 581 · see citing works
Natarajan, P., Young, R., Stitziel, N. O., Padmanabhan, S., Baber, U., Mehran, R., Sartori, S., Fuster, V., Reilly, D. F., Butterworth, A., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.024436 · Cited by 570 · see citing works
Self-citationRipatti, S., Tikkanen, E., Orho-Melander, M., Havulinna, A. S., Silander, K., Sharma, A., Guiducci, C., Perola, M., Jula, A., Sinisalo, J., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(10)61267-6 · Cited by 562 · see citing works
Self-citationProopiomelanocortin Deficiency Treated with a Melanocortin-4 Receptor Agonist
Kühnen, P., Clément, K., Wiegand, S., Blankenstein, O., Gottesdiener, K., Martini, L. L., Mai, K., Blume-Peytavi, U., Grüters, A., & Krude, H. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1512693 · Cited by 485 · see citing works
Genetic identification of familial hypercholesterolemia within a single U.S. health care system
Abul-Husn, N. S., Manickam, K., Jones, L. K., Wright, E. A., Hartzel, D. N., Gonzaga-Jauregui, C., O’Dushlaine, C., Leader, J. B., Lester Kirchner, H., Lindbuchler, D. M., et al. (2016) · Science · doi:10.1126/science.aaf7000 · Cited by 425 · see citing works
Benn, M., Watts, G. F., Tybjærg‐Hansen, A., & Nordestgaard, B. G. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw028 · Cited by 400 · see citing works
Chatterjee, N., Wheeler, B., Sampson, J. N., Hartge, P. A., Chanock, S. J., & Park, J.-H. (2013) · Nature Genetics · doi:10.1038/ng.2579 · Cited by 351 · see citing works
Christophersen, I. E., Rienstra, M., Roselli, C., Yin, X., Geelhoed, B., Barnard, J., Lin, H., Arking, D. E., Smith, A. V., Albert, C. M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3843 · Cited by 339 · see citing works
Self-citationGenomic prediction of coronary heart disease
Abraham, G., Havulinna, A. S., Bhalala, O. G., Byars, S. G., De Livera, A. M., Yetukuri, L., Tikkanen, E., Perola, M., Schunkert, H., Sijbrands, E. J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw450 · Cited by 338 · see citing works
Measuring missing heritability: Inferring the contribution of common variants
Golan, D., Lander, E. S., & Rosset, S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419064111 · Cited by 288 · see citing works
Self-citationZhang, Y., Qi, G., Park, J.-H., & Chatterjee, N. (2018) · Nature Genetics · doi:10.1038/s41588-018-0193-x · Cited by 288 · see citing works
Tada, H., Melander, O., Louie, J. Z., Catanese, J. J., Rowland, C. M., Devlin, J. J., Kathiresan, S., & Shiffman, D. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv462 · Cited by 285 · see citing works
Self-citationAssociation of a Low-Frequency Variant in <i>HNF1A</i> With Type 2 Diabetes in a Latino Population
Estrada, K., Aukrust, I., Bjørkhaug, L., Burtt, N. P., Mercader, J. M., García-Ortiz, H., Huerta-Chagoya, A., Moreno-Macías, H., Walford, G., Flannick, J., et al. (2014) · JAMA · doi:10.1001/jama.2014.6511 · Cited by 259 · see citing works
Klarin, D., Zhu, Q., Emdin, C., Chaffin, M., Horner, S., McMillan, B., Leed, A. J., Weale, M., Spencer, C., Aguet, F., et al. (2017) · Nature Genetics · doi:10.1038/ng.3914 · Cited by 214 · see citing works
Self-citationGanna, A., Genovese, G., Howrigan, D. P., Byrnes, A., Kurki, M., Zekavat, S. M., Whelan, C. W., Kals, M., Nivard, M. G., Bloemendal, A., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4404 · Cited by 89 · see citing works
Self-citationKhera, A. V., & Kathiresan, S. (2017) · Circulation · doi:10.1161/circulationaha.116.026479 · Cited by 40 · see citing works
Self-citationZhang, Y., Qi, G., Park, J.-H., & Chatterjee, N. (2017) · doi:10.1101/175406 · Cited by 6
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2021 ESC Guidelines on cardiovascular disease prevention in clinical practice
Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Benetos, A., Biffi, A., Boavida, J.-M., Capodanno, D., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab484 · Cited by 6,485 · see citing works
Gelder, I. C. V., Rienstra, M., Bunting, K. V., Casado-Arroyo, R., Caso, V., Crijns, H. J. G. M., Potter, T. J. R. D., Dwight, J., Guasti, L., Hanke, T., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae176 · Cited by 3,338 · see citing works
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
Zondervan, K. T., Becker, C. M., & Missmer, S. A. (2020) · New England Journal of Medicine · doi:10.1056/nejmra1810764 · Cited by 2,249 · see citing works
Benefits and limitations of genome-wide association studies
Tam, V., Patel, N. K., Turcotte, M., Bossé, Y., Paré, G., & Meyre, D. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0127-1 · Cited by 2,092 · see citing works
Polygenic prediction via Bayesian regression and continuous shrinkage priors
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A., & Smoller, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-09718-5 · Cited by 1,922 · see citing works
Tutorial: a guide to performing polygenic risk score analyses
Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020) · Nature Protocols · doi:10.1038/s41596-020-0353-1 · Cited by 1,913 · see citing works
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
The personal and clinical utility of polygenic risk scores
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,605 · see citing works
The Missing Diversity in Human Genetic Studies
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019) · Cell · doi:10.1016/j.cell.2019.02.048 · Cited by 1,516 · see citing works
Polygenic risk scores: from research tools to clinical instruments
Lewis, C. M., & Vassos, E. (2020) · Genome Medicine · doi:10.1186/s13073-020-00742-5 · Cited by 1,360 · see citing works
The genetic architecture of Parkinson's disease
Blauwendraat, C., Nalls, M. A., & Singleton, A. B. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30287-x · Cited by 1,114 · see citing works
Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes
Mavaddat, N., Michailidou, K., Dennis, J., Lush, M. J., Fachal, L., Lee, A., Tyrer, J. P., Chen, T., Wang, Q., Bolla, M. K., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.11.002 · Cited by 1,083 · see citing works
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033 · see citing works
DNA methylation aging clocks: challenges and recommendations
Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1824-y · Cited by 1,025 · see citing works
The power of genetic diversity in genome-wide association studies of lipids
Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature · doi:10.1038/s41586-021-04064-3 · Cited by 972 · see citing works
Andrade, R. J., Chalasani, N., Bjornsson, E. S., Suzuki, A., Kullak-Ublick, G. A., Watkins, P. B., Devarbhavi, H. C., Merz, M., Lucena, M. I., Kaplowitz, N., et al. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0105-0 · Cited by 907 · see citing works
Machine Learning in Drug Discovery: A Review
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10058-4 · Cited by 843 · see citing works
Early Detection of Pancreatic Cancer: Opportunities and Challenges
Singhi, A. D., Koay, E. J., Chari, S. T., & Maitra, A. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.01.259 · Cited by 841 · see citing works
Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults
Inouye, M., Abraham, G., Nelson, C. P., Wood, A., Sweeting, M. J., Dudbridge, F., Lai, F. Y., Kaptoge, S. K., Brożyńska, M., Wang, T., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.07.079 · Cited by 814 · see citing works
Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C.-Y., Popejoy, A. B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R. J., Brick, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.051 · Cited by 797 · see citing works
Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood
Khera, A. V., Chaffin, M., Wade, K. H., Zahid, S., Brancale, J., Xia, R., Distefano, M., Senol-Cosar, O., Haas, M. E., Bick, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.028 · Cited by 789 · see citing works
Self-citationThe Lancet Commission on diabetes: using data to transform diabetes care and patient lives
Chan, J. C. N., Lim, L.-L., Wareham, N. J., Shaw, J. E., Orchard, T. J., Zhang, P., Lau, E. S. H., Eliasson, B., Kong, A. P. S., Ezzati, M., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32374-6 · Cited by 793 · see citing works
Genetics of 35 blood and urine biomarkers in the UK Biobank
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00757-z · Cited by 744 · see citing works
LDpred2: better, faster, stronger
Privé, F., Arbel, J., & Vilhjálmsson, B. J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa1029 · Cited by 726 · see citing works
Aragam, K. G., Jiang, T., Goel, A., Kanoni, S., Wolford, B. N., Atri, D. S., Weeks, E. M., Wang, M., Hindy, G., Zhou, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01233-6 · Cited by 721 · see citing works
Self-citationImproving polygenic prediction in ancestrally diverse populations
Ruan, Y., Lin, Y.-F., Feng, Y.-C. A., Chen, C.-Y., Lam, M., Guo, Z., Initiatives, S. G. A., Ahn, Y. M., Akiyama, K., Arai, M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01054-7 · Cited by 707 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Self-citationThe Polygenic Score Catalog as an open database for reproducibility and systematic evaluation
Lambert, S. A., Gil, L., Jupp, S., Ritchie, S. C., Xu, Y., Buniello, A., McMahon, A., Abraham, G., Chapman, M., Parkinson, H., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00783-5 · Cited by 690 · see citing works
Stroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 593 · see citing works
Cano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 608 · see citing works
Patel, A. P., Wang, M., Pirruccello, J. P., Ellinor, P. T., Ng, K., Kathiresan, S., & Khera, A. V. (2021) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.120.315291 · Cited by 567 · see citing works
Self-citationTowards clinical utility of polygenic risk scores
Lambert, S. A., Abraham, G., & Inouye, M. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz187 · Cited by 566 · see citing works
2021 ESC Guidelines on cardiovascular disease prevention in clinical practice
Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Bénétos, A., Biffi, A., Boavida, J.-M., Capodanno, D., et al. (2021) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwab154 · Cited by 565 · see citing works
15 years of GWAS discovery: Realizing the promise
Abdellaoui, A., Yengo, L., Verweij, K. J. H., & Visscher, P. M. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.011 · Cited by 542 · see citing works
Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank
Julkunen, H., Cichońska, A., Tiainen, M., Koskela, H. T., Nybo, K., Mäkelä, V., Nokso-Koivisto, J., Kristiansson, K., Perola, M., Salomaa, V. V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36231-7 · Cited by 540 · see citing works
Ishigaki, K., Akiyama, M., Kanai, M., Takahashi, A., Kawakami, E., Sugishita, H., Sakaue, S., Matoba, N., Low, S.-K., Okada, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0640-3 · Cited by 508 · see citing works
Trans effects on gene expression can drive omnigenic inheritance
Liu, X., Li, Y. I., & Pritchard, J. K. (2019) · Cell · doi:10.1016/j.cell.2019.04.014 · Cited by 523 · see citing works
Artificial Intelligence and Personalized Medicine
Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 518 · see citing works
A Modern Approach to Dyslipidemia
Berberich, A. J., & Hegele, R. A. (2021) · Endocrine Reviews · doi:10.1210/endrev/bnab037 · Cited by 502 · see citing works
FinnGen, Mars, N., Koskela, J. T., Ripatti, P., Kiiskinen, T., Havulinna, A. S., Lindbohm, J. V., Ahola‐Olli, A., Kurki, M., Karjalainen, J. M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0800-0 · Cited by 486 · see citing works
Genetics meets proteomics: perspectives for large population-based studies
Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-0268-2 · Cited by 478 · see citing works
Elliott, J., Bodinier, B., Bond, T. A., Chadeau‐Hyam, M., Εvangelou, Ε., Moons, K. G. M., Dehghan, A., Muller, D. C., Elliott, P., & Tzoulaki, I. (2020) · JAMA · doi:10.1001/jama.2019.22241 · Cited by 471 · see citing works
Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease
Honigberg, M. C., Zekavat, S. M., Aragam, K. G., Finneran, P., Klarin, D. M., Bhatt, D. L., Januzzi, J. L., Scott, N. S., & Natarajan, P. (2019) · JAMA · doi:10.1001/jama.2019.19191 · Cited by 470 · see citing works
Self-citationImproving reporting standards for polygenic scores in risk prediction studies
Wand, H., Lambert, S. A., Tamburro, C. P., Iacocca, M. A., O’Sullivan, J. W., Sillari, C. H., Kullo, I. J., Rowley, R. K., Dron, J. S., Brockman, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03243-6 · Cited by 470 · see citing works
Self-citationVariable prediction accuracy of polygenic scores within an ancestry group
Mostafavi, H., Harpak, A., Agarwal, I., Conley, D., Pritchard, J. K., & Przeworski, M. (2020) · eLife · doi:10.7554/elife.48376 · Cited by 457 · see citing works
Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452 · see citing works
“, for prostate cancer, breast cancer, cardiovascular disease, and type 2 diabetes mellitus) (Grönberg et al., 2015; Khera et al., 2018; McCarthy and Mahajan, 2018; Shieh et al., 2016).”
BackgroundPolygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions
Fahed, A. C., Wang, M., Homburger, J. R., Patel, A. P., Bick, A. G., Neben, C. L., Lai, C., Brockman, D., Philippakis, A., Ellinor, P. T., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17374-3 · Cited by 444 · see citing works
Self-citationThompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307270 · Cited by 427 · see citing works
O’Sullivan, J. W., Raghavan, S., Márquez‐Luna, C., Luzum, J. A., Damrauer, S. M., Ashley, E. A., O’Donnell, C. J., Willer, C. J., & Natarajan, P. (2022) · Circulation · doi:10.1161/cir.0000000000001077 · Cited by 424 · see citing works
Self-citationResponsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps
Polygenic Risk Score Task Force of the International Common Disease Alliance, Adeyemo, A., Balaconis, M. K., Darnes, D. R., Fatumo, S., Granados Moreno, P., Hodonsky, C. J., Inouye, M., Kanai, M., Kato, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01549-6 · Cited by 413 · see citing works
Wilde, A. A. M., Semsarian, C., Márquez, M. F., Shamloo, A. S., Ackerman, M. J., Ashley, E. A., Sternick, E. B., Barajas-Martínez, H., Behr, E. R., Bezzina, C. R., et al. (2022) · EP Europace · doi:10.1093/europace/euac030 · Cited by 399 · see citing works
Self-citationUnderspecification Presents Challenges for Credibility in Modern Machine Learning
D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.03395 · Cited by 398 · see citing works
Update on NAFLD genetics: From new variants to the clinic
Trépo, E., & Valenti, L. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.02.020 · Cited by 393 · see citing works
Genetic correlations of polygenic disease traits: from theory to practice
van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S., & Wray, N. R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0137-z · Cited by 385 · see citing works
Epidemiology and modifiable risk factors for atrial fibrillation
Elliott, A. D., Middeldorp, M. E., Gelder, I. C. V., Albert, C. M., & Sanders, P. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00820-8 · Cited by 380 · see citing works
Musunuru, K., Hershberger, R. E., Day, S. M., Klinedinst, N. J., Landstrom, A. P., Parikh, V. N., Prakash, S. K., Semsarian, C., & Sturm, A. C. (2020) · Circulation Genomic and Precision Medicine · doi:10.1161/hcg.0000000000000067 · Cited by 375 · see citing works
From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer
Wray, N. R., Lin, T., Austin, J., McGrath, J. J., Hickie, I. B., Murray, G. K., & Visscher, P. M. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3049 · Cited by 367 · see citing works
Sharp, S. A., Rich, S. S., Wood, A. R., Jones, S. E., Beaumont, R. N., Harrison, J. W., Schneider, D. A., Locke, J. M., Tyrrell, J., Weedon, M. N., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1785 · Cited by 366 · see citing works
Precision medicine in 2030—seven ways to transform healthcare
Denny, J. C., & Collins, F. S. (2021) · Cell · doi:10.1016/j.cell.2021.01.015 · Cited by 362 · see citing works
Sohail, M., Maier, R. M., Ganna, A., Bloemendal, A., Martin, A. R., Turchin, M. C., Chiang, C. W., Hirschhorn, J., Daly, M. J., Patterson, N., et al. (2019) · eLife · doi:10.7554/elife.39702 · Cited by 359 · see citing works
Personalized Medicine and the Power of Electronic Health Records
Abul-Husn, N. S., & Kenny, E. E. (2019) · Cell · doi:10.1016/j.cell.2019.02.039 · Cited by 355 · see citing works
Tcheandjieu, C., Zhu, X., Hilliard, A. T., Clarke, S. L., Napolioni, V., Ma, S., Lee, K. M., Fang, H., Chen, F., Lu, Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01891-3 · Cited by 356 · see citing works
Self-citationKoyama, S., Ito, K., Terao, C., Akiyama, M., Horikoshi, M., Momozawa, Y., Matsunaga, H., Ieki, H., Ozaki, K., Onouchi, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0705-3 · Cited by 349 · see citing works
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347 · see citing works
Craig, J. E., Han, X., Qassim, A., Hassall, M., Bailey, J. N. C., Kinzy, T. G., Khawaja, A. P., An, J., Marshall, H. N., Gharahkhani, P., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0556-y · Cited by 339 · see citing works
Principles and methods for transferring polygenic risk scores across global populations
Kachuri, L., Chatterjee, N., Hirbo, J. B., Schaid, D. J., Martin, I. K., Kullo, I. J., Kenny, E. E., Paşaniuc, B., Auer, P. L., Conomos, M. P., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00637-2 · Cited by 338 · see citing works
Strategic vision for improving human health at The Forefront of Genomics
Green, E. D., Gunter, C., Biesecker, L. G., Francesco, V. D., Easter, C. L., Feingold, E. A., Felsenfeld, A. L., Kaufman, D., Ostrander, E. A., Pavan, W. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2817-4 · Cited by 334 · see citing works
Mosley, J. D., Gupta, D. K., Tan, J., Yao, J., Wells, Q. S., Shaffer, C. M., Kundu, S., Robinson‐Cohen, C., Psaty, B. M., Rich, S. S., et al. (2020) · JAMA · doi:10.1001/jama.2019.21782 · Cited by 332 · see citing works
Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke
Montaner, J., Ramiro, L., Simats, A., Tiedt, S., Makris, K., Jickling, G. C., Debette, S., Sanchez, J., & Bustamante, A. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0350-6 · Cited by 331 · see citing works
Marijon, É., Narayanan, K., Smith, K. L., Barra, S., Basso, C., Blom, M. T., Crotti, L., d’Ávila, A., Deo, R., Dumas, F., et al. (2023) · The Lancet · doi:10.1016/s0140-6736(23)00875-9 · Cited by 329 · see citing works
Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
de Rojas, I., Moreno-Grau, S., Tesi, N., Grenier-Boley, B., Andrade, V., Jansen, I. E., Pedersen, N. L., Stringa, N., Zettergren, A., Hernández, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22491-8 · Cited by 324 · see citing works
A decade of genome-wide association studies for coronary artery disease: the challenges ahead
Erdmann, J., Kessler, T., Venegas, L. M., & Schunkert, H. (2018) · Cardiovascular Research · doi:10.1093/cvr/cvy084 · Cited by 320 · see citing works
The Missing Diversity in Human Genetic Studies
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019) · Cell · doi:10.1016/j.cell.2019.04.032 · Cited by 317 · see citing works
Okamura, T., Tsukamoto, K., Arai, H., Fujioka, Y., Ishigaki, Y., Koba, S., Ohmura, H., Shoji, T., Yokote, K., Yoshida, H., et al. (2023) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.gl2022 · Cited by 317 · see citing works
Khera, A. V., Chaffin, M., Zekavat, S. M., Collins, R. L., Roselli, C., Natarajan, P., Lichtman, J. H., D’Onofrio, G., Mattera, J., Dreyer, R., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.035658 · Cited by 315 · see citing works
Self-citationExome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls
Platform, B. G., Flannick, J., DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, et al. (2019) · Nature · doi:10.1038/s41586-019-1231-2 · Cited by 304 · see citing works
A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease
Patel, A. P., Wang, M., Ruan, Y., Koyama, S., Clarke, S. L., Yang, X., Tcheandjieu, C., Agrawal, S., Fahed, A. C., Ellinor, P. T., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02429-x · Cited by 302 · see citing works
Self-citationGenetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302 · see citing works
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300 · see citing works
Genomics of disease risk in globally diverse populations
Gurdasani, D., Barroso, I., Zeggini, E., & Sandhu, M. S. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0144-0 · Cited by 299 · see citing works
Incomplete Penetrance and Variable Expressivity: From Clinical Studies to Population Cohorts
Kingdom, R., & Wright, C. F. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.920390 · Cited by 294 · see citing works
The influence of evolutionary history on human health and disease
Benton, M. L., Abraham, A., LaBella, A. L., Abbot, P., Rokas, A., & Capra, J. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00305-9 · Cited by 291 · see citing works
Deconstructing the sources of genotype-phenotype associations in humans
Young, A. I., Benonisdottir, S., Przeworski, M., & Kong, A. (2019) · Science · doi:10.1126/science.aax3710 · Cited by 288 · see citing works
Population genetics of the coral Acropora millepora : Toward genomic prediction of bleaching
Fuller, Z. L., Mocellin, V. J. L., Morris, L. A., Cantin, N. E., Shepherd, J., Sarre, L. A., Peng, J., Liao, Y., Pickrell, J. K., Andolfatto, P., et al. (2020) · Science · doi:10.1126/science.aba4674 · Cited by 287 · see citing works
Tadros, R., Francis, C., Xu, X. Y., Vermeer, A. M. C., Harper, A. R., Huurman, R., Bisabu, K. K., Walsh, R., Hoorntje, E. T., Rijdt, W. P. T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00762-2 · Cited by 283 · see citing works
Wilde, A. A. M., Semsarian, C., Márquez, M. F., Shamloo, A. S., Ackerman, M. J., Ashley, E. A., Sternick, E. B., Barajas-Martínez, H., Behr, E. R., Bezzina, C. R., et al. (2022) · Heart Rhythm · doi:10.1016/j.hrthm.2022.03.1225 · Cited by 281 · see citing works
Self-citationThompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., et al. (2022) · doi:10.1101/2022.06.16.22276246 · Cited by 280 · see citing works
Timmers, P. R. H. J., Mounier, N., Läll, K., Fischer, K., Zheng, N., Feng, X., Bretherick, A. D., Clark, D. W., Agbessi, M. D. T., Ahsan, H., et al. (2019) · eLife · doi:10.7554/elife.39856 · Cited by 279 · see citing works
Advancing the use of genome-wide association studies for drug repurposing
Reay, W. R., & Cairns, M. J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00387-z · Cited by 277 · see citing works
Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., Marcora, E., Huang, K.-L., Porter, T., Laws, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18534-1 · Cited by 272 · see citing works
Agrawal, M., Spencer, E. A., Colombel, J., & Ungaro, R. C. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.04.063 · Cited by 272 · see citing works
Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics
Buch, A. M., & Liston, C. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00789-3 · Cited by 271 · see citing works
Genetics of mitochondrial diseases: Identifying mutations to help diagnosis
Stenton, S. L., & Prokisch, H. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102784 · Cited by 269 · see citing works
Miguel-Escalada, I., Bonàs‐Guarch, S., Cebola, I., Ponsa-Cobas, J., Mendieta-Esteban, J., Atla, G., Javierre, B. M., Rolando, D., Farabella, I., Morgan, C. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0457-0 · Cited by 259 · see citing works
Jin, G., Lv, J., Yang, M., Wang, M., Zhu, M., Wang, T., Yan, C., Yu, C., Ding, Y., Li, G., et al. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30460-5 · Cited by 258 · see citing works
Andreassen, O. A., Hindley, G., Frei, O., & Smeland, O. B. (2023) · World Psychiatry · doi:10.1002/wps.21034 · Cited by 255 · see citing works
Genomic Medicine–Progress, Pitfalls, and Promise
Shendure, J., Findlay, G. M., & Snyder, M. W. (2019) · Cell · doi:10.1016/j.cell.2019.02.003 · Cited by 253 · see citing works
- 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 · 2013: 1
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.
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