Pairwise genetic interactions modulate lipid plasma levels and cellular uptake Article Swipe
M. Zimoń
,
Yunfeng Huang
,
Anthi Trasta
,
Jimmy Z. Liu
,
Chia‐Yen Chen
,
Aliaksandr Halavatyi
,
Peter Blattmann
,
Bernd Klaus
,
Christopher D. Whelan
,
David Sexton
,
Sally John
,
Wolfgang Huber
,
Ellen Tsai
,
Rainer Pepperkok
,
Heiko Runz
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.10.29.360818
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.10.29.360818
SUMMARY Genetic interactions (GIs), the joint impact of different genes or variants on a phenotype, are foundational to the genetic architecture of complex traits. However, identifying GIs through human genetics is challenging since it necessitates very large population sizes, while findings from model systems not always translate to humans. Here, we combined exome-sequencing and genotyping in the UK Biobank with combinatorial RNA-interference (coRNAi) screening to systematically test for pairwise GIs between 30 lipid GWAS genes. Gene-based protein-truncating variant (PTV) burden analyses from 240,970 exomes revealed additive GIs for APOB with PCSK9 and LPL , respectively. Both, genetics and coRNAi identified additive GIs for 12 additional gene pairs. Overlapping non-additive GIs were detected only for TOMM40 at the APOE locus with SORT1 and NCAN . Our study identifies distinct gene pairs that modulate both, plasma and cellular lipid levels via additive and non-additive effects and nominates drug target pairs for improved lipid-lowering combination therapies.
Related Topics
Concepts
Biology
Computational biology
Genetics
Genetic architecture
Biobank
Gene
Genotyping
Genome-wide association study
Pairwise comparison
Locus (genetics)
Exome sequencing
Exome
Population
Phenotype
Medicine
Genotype
Computer science
Single-nucleotide polymorphism
Artificial intelligence
Environmental health
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.10.29.360818
- https://www.biorxiv.org/content/biorxiv/early/2020/10/30/2020.10.29.360818.full.pdf
- OA Status
- green
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3096165452
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3096165452Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.10.29.360818Digital Object Identifier
- Title
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Pairwise genetic interactions modulate lipid plasma levels and cellular uptakeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-10-30Full publication date if available
- Authors
-
M. Zimoń, Yunfeng Huang, Anthi Trasta, Jimmy Z. Liu, Chia‐Yen Chen, Aliaksandr Halavatyi, Peter Blattmann, Bernd Klaus, Christopher D. Whelan, David Sexton, Sally John, Wolfgang Huber, Ellen Tsai, Rainer Pepperkok, Heiko RunzList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.10.29.360818Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2020/10/30/2020.10.29.360818.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/10/30/2020.10.29.360818.full.pdfDirect OA link when available
- Concepts
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Biology, Computational biology, Genetics, Genetic architecture, Biobank, Gene, Genotyping, Genome-wide association study, Pairwise comparison, Locus (genetics), Exome sequencing, Exome, Population, Phenotype, Medicine, Genotype, Computer science, Single-nucleotide polymorphism, Artificial intelligence, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
-
68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 125 |
| abstract_inverted_index.while | 39 |
| abstract_inverted_index.(GIs), | 3 |
| abstract_inverted_index.TOMM40 | 114 |
| abstract_inverted_index.always | 45 |
| abstract_inverted_index.burden | 79 |
| abstract_inverted_index.coRNAi | 98 |
| abstract_inverted_index.exomes | 83 |
| abstract_inverted_index.genes. | 74 |
| abstract_inverted_index.impact | 6 |
| abstract_inverted_index.levels | 137 |
| abstract_inverted_index.pairs. | 106 |
| abstract_inverted_index.plasma | 133 |
| abstract_inverted_index.sizes, | 38 |
| abstract_inverted_index.target | 146 |
| abstract_inverted_index.240,970 | 82 |
| abstract_inverted_index.Biobank | 58 |
| abstract_inverted_index.Genetic | 1 |
| abstract_inverted_index.SUMMARY | 0 |
| abstract_inverted_index.between | 70 |
| abstract_inverted_index.complex | 22 |
| abstract_inverted_index.effects | 142 |
| abstract_inverted_index.genetic | 19 |
| abstract_inverted_index.humans. | 48 |
| abstract_inverted_index.systems | 43 |
| abstract_inverted_index.through | 27 |
| abstract_inverted_index.traits. | 23 |
| abstract_inverted_index.variant | 77 |
| abstract_inverted_index.(coRNAi) | 62 |
| abstract_inverted_index.However, | 24 |
| abstract_inverted_index.additive | 85, 100, 139 |
| abstract_inverted_index.analyses | 80 |
| abstract_inverted_index.cellular | 135 |
| abstract_inverted_index.combined | 51 |
| abstract_inverted_index.detected | 111 |
| abstract_inverted_index.distinct | 127 |
| abstract_inverted_index.findings | 40 |
| abstract_inverted_index.genetics | 29, 96 |
| abstract_inverted_index.improved | 149 |
| abstract_inverted_index.modulate | 131 |
| abstract_inverted_index.pairwise | 68 |
| abstract_inverted_index.revealed | 84 |
| abstract_inverted_index.variants | 11 |
| abstract_inverted_index.different | 8 |
| abstract_inverted_index.nominates | 144 |
| abstract_inverted_index.screening | 63 |
| abstract_inverted_index.translate | 46 |
| abstract_inverted_index.Gene-based | 75 |
| abstract_inverted_index.additional | 104 |
| abstract_inverted_index.genotyping | 54 |
| abstract_inverted_index.identified | 99 |
| abstract_inverted_index.identifies | 126 |
| abstract_inverted_index.phenotype, | 14 |
| abstract_inverted_index.population | 37 |
| abstract_inverted_index.therapies. | 152 |
| abstract_inverted_index.Overlapping | 107 |
| abstract_inverted_index.challenging | 31 |
| abstract_inverted_index.combination | 151 |
| abstract_inverted_index.identifying | 25 |
| abstract_inverted_index.architecture | 20 |
| abstract_inverted_index.foundational | 16 |
| abstract_inverted_index.interactions | 2 |
| abstract_inverted_index.necessitates | 34 |
| abstract_inverted_index.non-additive | 108, 141 |
| abstract_inverted_index.combinatorial | 60 |
| abstract_inverted_index.respectively. | 94 |
| abstract_inverted_index.lipid-lowering | 150 |
| abstract_inverted_index.systematically | 65 |
| abstract_inverted_index.RNA-interference | 61 |
| abstract_inverted_index.exome-sequencing | 52 |
| abstract_inverted_index.protein-truncating | 76 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5089969264, https://openalex.org/A5017298007 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I1297540436, https://openalex.org/I1303691731, https://openalex.org/I223822909, https://openalex.org/I4210138560 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.13783299 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |