Functionally-informed fine-mapping and polygenic localization of complex trait heritability Article Swipe
Omer Weissbrod
,
Farhad Hormozdiari
,
Christian Benner
,
Ran Cui
,
Jacob C. Ulirsch
,
Steven Gazal
,
Armin Schoech
,
Bryce van de Geijn
,
Yakir Reshef
,
Carla Márquez‐Luna
,
Luke J. O’Connor
,
Matti Pirinen
,
Hilary K. Finucane
,
Alkes L. Price
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/807792
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/807792
Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner’s curse. In simulations with in-sample LD, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In simulations with mismatched reference LD, PolyFun + SuSiE remained well-calibrated when reducing the maximum number of assumed causal SNPs per locus, which reduces absolute power but still produces large relative improvements. In analyses of 49 UK Biobank traits (average N =318K) with in-sample LD, PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
Related Topics
Concepts
Quantitative trait locus
Trait
Robustness (evolution)
Association mapping
Polygene
Computer science
Single-nucleotide polymorphism
Biology
Genetics
Computational biology
Gene
Genotype
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/807792
- https://www.biorxiv.org/content/biorxiv/early/2020/04/02/807792.full.pdf
- OA Status
- green
- Cited By
- 56
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2980641195
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2980641195Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/807792Digital Object Identifier
- Title
-
Functionally-informed fine-mapping and polygenic localization of complex trait heritabilityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-17Full publication date if available
- Authors
-
Omer Weissbrod, Farhad Hormozdiari, Christian Benner, Ran Cui, Jacob C. Ulirsch, Steven Gazal, Armin Schoech, Bryce van de Geijn, Yakir Reshef, Carla Márquez‐Luna, Luke J. O’Connor, Matti Pirinen, Hilary K. Finucane, Alkes L. PriceList of authors in order
- Landing page
-
https://doi.org/10.1101/807792Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/04/02/807792.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/2020/04/02/807792.full.pdfDirect OA link when available
- Concepts
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Quantitative trait locus, Trait, Robustness (evolution), Association mapping, Polygene, Computer science, Single-nucleotide polymorphism, Biology, Genetics, Computational biology, Gene, Genotype, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
56Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 13, 2023: 9, 2022: 7, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
82Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.winner’s | 104 |
| abstract_inverted_index.annotations | 80 |
| abstract_inverted_index.conclusion, | 266 |
| abstract_inverted_index.fine-mapped | 138, 195, 211 |
| abstract_inverted_index.genetically | 214 |
| abstract_inverted_index.genome-wide | 31, 60 |
| abstract_inverted_index.improvement | 205 |
| abstract_inverted_index.information | 29 |
| abstract_inverted_index.pleiotropy. | 219 |
| abstract_inverted_index.prioritizes | 75, 268 |
| abstract_inverted_index.probability | 128, 201 |
| abstract_inverted_index.significant | 32 |
| abstract_inverted_index.simulations | 107, 146 |
| abstract_inverted_index.Fine-mapping | 1 |
| abstract_inverted_index.annotations. | 22, 73 |
| abstract_inverted_index.constructing | 234 |
| abstract_inverted_index.counterparts | 134 |
| abstract_inverted_index.fine-mapping | 14, 57, 87, 143 |
| abstract_inverted_index.prioritizing | 17 |
| abstract_inverted_index.uncorrelated | 215 |
| abstract_inverted_index.annotations), | 40 |
| abstract_inverted_index.heritability; | 246 |
| abstract_inverted_index.improvements. | 176 |
| abstract_inverted_index.localization, | 233 |
| abstract_inverted_index.probabilities | 85 |
| abstract_inverted_index.variant-trait | 196 |
| abstract_inverted_index.architectures. | 279 |
| abstract_inverted_index.heritabilities | 225 |
| abstract_inverted_index.computationally | 52 |
| abstract_inverted_index.well-calibrated | 119, 155 |
| abstract_inverted_index.misspecification | 102 |
| abstract_inverted_index.non-functionally | 132 |
| abstract_inverted_index.functionally-informed | 142 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5028616817 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I136199984 |
| citation_normalized_percentile.value | 0.93739245 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |