The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity
Open accessPublished version of a preprint
Abstract
A major goal of biomedicine is to understand the function of every gene in the human genome. Loss-of-function mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such 'human knockouts' can provide insight into gene function. Consanguineous unions are more likely to result in offspring carrying homozygous loss-of-function mutations. In Pakistan, consanguinity rates are notably high. Here we sequence the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS), designed to understand the determinants of cardiometabolic diseases in individuals from South Asia. We identified individuals carrying homozygous predicted loss-of-function (pLoF) mutations, and performed phenotypic analysis involving more than 200 biochemical and disease traits. We enumerated 49,138 rare (<1% minor allele frequency) pLoF mutations. These pLoF mutations are estimated to knock out 1,317 genes, each in at least one participant. Homozygosity for pLoF mutations at PLA2G7 was associated with absent enzymatic activity of soluble lipoprotein-associated phospholipase A2; at CYP2F1, with higher plasma interleukin-8 concentrations; at TREH, with lower concentrations of apoB-containing lipoprotein subfractions; at either A3GALT2 or NRG4, with markedly reduced plasma insulin C-peptide concentrations; and at SLC9A3R1, with mediators of calcium and phosphate signalling. Heterozygous deficiency of APOC3 has been shown to protect against coronary heart disease; we identified APOC3 homozygous pLoF carriers in our cohort. We recruited these human knockouts and challenged them with an oral fat load. Compared with family members lacking the mutation, individuals with APOC3 knocked out displayed marked blunting of the usual post-prandial rise in plasma triglycerides. Overall, these observations provide a roadmap for a 'human knockout project', a systematic effort to understand the phenotypic consequences of complete disruption of genes in humans.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“Furthermore, individuals who are homozygous for APOC3 LOF variants have very-low-fasting TG levels, with no rise after a fat load.9”
From Clinical review on triglycerides (2020) · cites this paper as [9]
In the citing paper
“Some rare heterozygous loss-of-function (LOF) variants are associated with reduced TG levels. These patients show protection from ASCVD providing important evidence for a causal role of TG for ASCVD.”
From Clinical review on triglycerides (2020) · cites this paper as [9]
In the citing paper
“For instance, rare heterozygous LOF variants in APOC3 encoding apo C-III are associated with ∼50% reduced TG levels and significantly increased clearance of very-low-density lipoprotein (VLDL).8,9”
From Clinical review on triglycerides (2020) · cites this paper as [9]
In the citing paper
“and is a genetically validated target to reduce ASCVD risk.8–10,64–66”
From Clinical review on triglycerides (2020) · cites this paper as [9]
In the citing paper
“In addition, studies in populations with high rates of consanguinity make it possible to identify individuals homozygous for otherwise rare loss-of-function alleles, the basis for a ‘human knockout’ project to systematically investigate the phenotypic consequences of gene disruption in humans53,54.”
From A brief history of human disease genetics (2020) · cites this paper as [54]
In the citing paper
“Efforts to probe the clinical consequences of coding alleles with large phenotypic effects (particularly null alleles) in humans53,54 and across diverse animal models116 represent powerful strategies for extending functional analyses to the whole-body level.”
From A brief history of human disease genetics (2020) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Self-citationThe Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Self-citationA framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,457
Self-citationPrincipal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
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
Self-citationVan der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1110s43 · Cited by 6,687
Self-citationRobust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541
Simplified calculation of body surface area
(1987) · New England Journal of Medicine · doi:10.1056/nejm198710223171717 · Cited by 3,238
Synaptic, transcriptional and chromatin genes disrupted in autism
Study, T. D., De Rubeis, S., Autism, H. M. C. F., UK10K Consortium, The Autism Sequencing Consortium, He, X., Goldberg, A. P., Poultney, C. S., Samocha, K., Ercument Cicek, A., et al. (2014) · Nature · doi:10.1038/nature13772 · Cited by 2,746
Self-citation
Flagged first, then by citations
2 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 301–341 of 341
Fujino, N., Yoshimuta, T., Ichida, F., Kinugawa, K., Usuda, K., Kitayama, M., Ino, H., Kawashiri, M., Tada, H., Mizuno, S., et al. (2017) · Circulation Journal · doi:10.1253/circj.cj-17-0706
Young, R., Cavodeassi, F., Hawkins, T. A., Stickney, H. L., Schwarz, Q., Lawrence, L. M., Wierzbicki, C., Gestri, G., Ambrosio, E. M., Klosner, A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/377119
Lang, G. I., & Rice, A. M. (2019) · Evolution · doi:10.1111/evo.13710
Parlato, M., Pázmándi, J., Nian, Q., Charbit‐Henrion, F., Bègue, B., Martin, E., Thian, M., Müller, F., Maggioni, M., Duclaux‐Loras, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/768028
Perez-Maturo, J., Salinas, V., & Kauffman, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817819-5.00005-x · Cited by 1
Translational genetics: a challenging but important path
Brown, S. J. (2020) · British Journal of Dermatology · doi:10.1111/bjd.19618
Fatakia, S. N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/213124
Lu, Q., Hu, L., Zeng, Z. R., Xiao, Z., wang, Y., Yang, L., Zhao, J., shi, Z., Tu, Y., Zhu, N., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-142865/v1
Choosing a genome editing strategy and target site
Musunuru, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823484-6.00011-6
Karnay, A. M. (2018) · Data Archiving and Networked Services (DANS) · doi:10.17632/yfjdtxpf7n.1
Generic model for biological regulationPreprint
Vihinen, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1317164/v1
Lu, Q., Fu, W., Ouyang, G., Xiao, Z., Guo, J., Sun, W. C., Lei, X., Shi, Y., Ma, Q., Zhu, N., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-142865/v2
Lek, M., & Lake, N. J. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734881195.793557392
Musunuru, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823484-6.00009-8
Babu, M. M., & Marti, M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735476043.793566834
Flannick, J. A. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735476043.793563024
Sayer, J. A. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.738873292.793583816
Faculty Opinions recommendation of Analysis of protein-coding genetic variation in 60,706 humans.
Klymkowsky, M. W. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.726646180.793565293
Nasrallah, J. B. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735346397.793560566
Evolving Approaches to Identifying Genetic Risk Variants for Sleep Disorders
Pack, A. I. (2022) · Translational medicine research · doi:10.1007/978-94-024-2168-2_1
Plasma Proteomics To Identify the Biomarkers and Pathogenesis of Mild Cognitive ImpairmentPreprint
Hou, Z., Sun, A., Song, X., Liu, S., Hu, X., Luan, Y., Guan, H., He, C., Sun, Y., & Chen, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4182279
Hussain, A., & Ballantyne, C. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-88286-6.00028-5
Emerging Lipoprotein-Related Therapeutics for Patients with Diabetes
Bobik, A., Cohen, N. D., Jenkins, A. J., Kyaw, T. S., SULLIVAN, D. R., Wu, X., Yu, X.-Y., & Little, P. J. (2023) · Contemporary diabetes · doi:10.1007/978-3-031-26681-2_30
CONTRIBUIÇÃO DA GENÉTICA HUMANA PARA COMPREENSÃO E ABORDAGEM DAS DOENÇAS CARDIOVASCULARES
Leite, C. C., Carneiro, C. L. F., Bona, C. R. M., Silva, F. D. S., Dias, H. L. M., Sousa, O. D. S. E., Ramos, T. M. F., & Borba, V. R. R. (2024) · EPITAYA eBooks · doi:10.47879/ed.ep.2024264p653
Minimizing biological risk for novel inhibitory drug targets: One knockout is all you needPreprint
Dimitriev, A., Postovit, L.-M. M., Simpson, A. L., & Wong, G. K. (2024) · medRxiv · doi:10.1101/2024.06.19.24309116
Pan, S., Shu, C., Feng, J., Wu, L. S.-H., Wang, P.-H., Chen, Y., Wang, J., Wang, H., Su, W., Chen, Y., et al. (2025) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2025.108025
Proteome‐Wide Analysis of Human Deletions
Zhang, H., Luan, X., & Vihinen, M. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70080
Goyal, S., Tanigawa, Y., Zhang, W., Chai, J.-F., Almeida, M., Sim, X., Lerner, M., Chainakul, J., Ramiu, J. G., Seraphin, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16655697
Federico, A., Pavel, A., Möbus, L., McKean, D. M., del Giudice, G., Fortino, V., Niehues, H., Rastrick, J., Eyerich, K., Eyerich, S., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21629422
Additional file 1 of Burden of Mendelian disorders in a large Middle Eastern biobank
Aamer, W., Al-Maraghi, A., Syed, N., Gandhi, G. D., ALIYEV, E. E., Al-Kurbi, A. A., Al-Saei, O., Kohailan, M., Krishnamoorthy, N., Palaniswamy, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25560873
Additional file 2 of Burden of Mendelian disorders in a large Middle Eastern biobank
Aamer, W., Al-Maraghi, A., Syed, N., Gandhi, G. D., ALIYEV, E. E., Al-Kurbi, A. A., Al-Saei, O., Kohailan, M., Krishnamoorthy, N., Palaniswamy, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25560900
An exercise-modifiable mitochondrial basis for skeletal muscle insulin resistance in humansPreprint
Nielsen, T. P., Damgaard, M., Andersen, N. R., Dunø, M., Frederiksen, A. L., van Hall, G., Sylow, L., Murgia, M., Larsen, S., Torekov, S. S., et al. (2025) · medRxiv · doi:10.64898/2025.12.17.25342274
The OurHealth Study: A digital genomic cohort for cardiometabolic risk mechanisms in US South Asians
Ganesh, S., Bhattacharya, R., Bhatnagar, A., Madnani, R., Russo, C., Haidermota, S., Oruganty, B., Bhavsar, H., Shah, P., Pitafi, S., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02335-1
Self-citationVega, J. A., Torres, B., Alberto Maiz, G., & Santos, J. L. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1749264
Lassen, F. H., Kalantzis, G., Eoli, A., Hill, B., Sonehara, K., Namba, S., Wade, I., Hodgson, S., Zhou, W., Neale, B. M., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.04.005
Self-citationExploring and Targeting the Connection of Iron and Copper Homeostasis to Neurodegenerative Diseases
Liu, X., Jia, L., Wu, K., Chen, M., Sun, J., Yang, C., Xu, C., Sun, J., Wang, J., & Dai, L. (2026) · MedComm · doi:10.1002/mco2.70766
Anwar, F., Zainab, S., Uddin, S., Khan, K., Ahmad, A., Naz, M., Ullah, I., Hussain, A., Khan, H., Rahman, H. U., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10527511/v1
Distinct Effects of Rap1 Subtype A GTPase Deficiency on the Male Mouse Heart
Javaid, S., Uddin, S. S., Vankwani, S., Khan, F., Larsen, M. R., Tahir, M., Hazrat, H., Hashmi, S., Musharraf, S. G., Mirza, M. R., et al. (2026) · Cells · doi:10.3390/cells15171611
Huang, L., Bian, C., Xin, B., Zhang, X., Lu, Q., Yue, Y., Li, Y., Zeng, R., Liu, X., Fan, X., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.063
Metadata
- Authors
- Saleheen, D., Natarajan, P., Armean, I. M., Zhao, W., Rasheed, A., Khetarpal, S. A., Won, H.-H., Karczewski, K. J., O’Donnell-Luria, A. H., Samocha, K. E., Weisburd, B., Gupta, N., Zaidi, M., Samuel, M., Imran, A., Abbas, S., Majeed, F., Ishaq, M., Akhtar, S., Trindade, K., Mucksavage, M., Qamar, N., Zaman, K. S., Yaqoob, Z., Saghir, T., Rizvi, S. N. H., Memon, A., Hayyat Mallick, N., Ishaq, M., Rasheed, S. Z., Memon, F.-U.-R., Mahmood, K., Ahmed, N., Do, R., Krauss, R. M., MacArthur, D. G., Gabriel, S., Lander, E. S., Daly, M. J., Frossard, P., Danesh, J., Rader, D. J., & Kathiresan, S.
- Affiliations
- Center for Non-Communicable Diseases (PK)
- Broad Institute (US)
- University of Pennsylvania (US)
- Samsung Medical Center (KR)
- Karachi Institute of Heart Diseases (PK)
- National Institute of Cardiovascular Diseases (PK)
- Civil Hospital Karachi (PK)
- Liaquat National Hospital (PK)
- Icahn School of Medicine at Mount Sinai (US)
- University of Cambridge (GB)
- Corresponding author
- Saleheen, Danish
- Kathiresan, Sekar
- ORCID
- Saleheen, Danish
- Natarajan, Pradeep
- Armean, Irina M.
- Zhao, Wei
- Khetarpal, Sumeet A.
- Won, Hong-Hee
- Karczewski, Konrad J.
- Samocha, Kaitlin E.
- Gupta, Namrata
- Zaidi, Mozzam
- Trindade, Kevin
- Qamar, Nadeem
- Saghir, Tahir
- Ishaq, Mohammad
- Rasheed, Syed Zahed
- Mahmood, Khalid
- Do, Ron
- Krauss, Ronald M.
- MacArthur, Daniel G.
- Gabriel, Stacey
- Lander, Eric S.
- Daly, Mark J.
- Danesh, John
- Rader, Daniel J.
- Kathiresan, Sekar
- Venue
- Nature
- Published
- 2017-04
- Volume
- 544
- Issue
- 7649
- Pages
- 235-239
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5600291
- OpenAlex
- W2605659507
- Cited by
- 340
- References
- 64
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 30 · 2023: 30 · 2022: 32 · 2021: 29 · 2020: 51 · 2019: 56 · 2018: 39 · 2017: 27
- FWCI
- 31.40
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Apollo
- Funders
- Pfizer
- Donovan Family Foundation
- Wellcome Trust
- National Institute for Health and Care Research (NF-SI-0512-10165)
- British Heart Foundation (CH/12/2/29428, RG/13/13/30194, RG/16/4/32218, RG/08/014/24067)
- European Commission (279143, 279233)
- Fondation Leducq
- Cambridge University Hospitals (3819-1516-29, BRC 2012-2017, 3819-1617-25, 146281)
- National Institutes of Health
- Samsung
- Medical Research Council (MR/L003120/1, MR/M012816/1, MR/P013880/1)
- National Heart, Lung, and Blood Institute (R01HL107816)
- National Human Genome Research Institute (U54HG003067)
- National Institute of General Medical Sciences (R01GM104371)
- Fogarty International Center
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- loss-of-function mutations, consanguinity, homozygous mutation, gene function, phenotypic association, exome sequencing, PLA2G7, cardiometabolic traits, Pakistani population, reverse genetics, serum lipoproteins, interleukin-8, apolipoprotein B
- MeSH terms
- Cytochrome P450 Family 2, Consanguinity, Coronary Disease, Dietary Fats, DNA Mutational Analysis, Fasting, Female, Gene Frequency, Genes, Homozygote, Humans, Male, Middle Aged, Myocardial Infarction, Pakistan, Pedigree, Phenotype, Phosphoproteins, Triglycerides, Cohort Studies, Interleukin-8, Gene Deletion, Sodium-Hydrogen Exchangers, Postprandial Period, Neuregulins, RNA Splice Sites, 1-Alkyl-2-acetylglycerophosphocholine Esterase, Apolipoprotein C-III, Genetic Association Studies, Reverse Genetics, Exome
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
