Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data
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Abstract
Variance-component methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the genetic architecture of common diseases. Here, we introduce methods that estimate the total trait variance explained by the typed variants at a single locus in the genome (local SNP heritability) from genome-wide association study (GWAS) summary data while accounting for linkage disequilibrium among variants. We applied our estimator to ultra-large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture. First, we found that common SNPs have a high contribution to the heritability of all studied traits. Second, we identified traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identified GWAS risk loci where the entire locus explains significantly more variance in the trait than the GWAS reported variants. Finally, we identified loci that explain a significant amount of heritability across multiple traits.
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Several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility.
“The commonly used threshold of genome-wide significance (p-value = 5×10−8) has played an important role in making human genetic study results robust; however, several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility. (18, 19)”
From Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility (2019) · cites this paper as [18]
Traditional Hi-C guided gene mapping restricts analysis to genome-wide significant loci, while H-MAGMA can leverage sub-threshold loci that explain a significant proportion of heritability.
“While traditional Hi-C guided gene mapping restricts its analysis to genome-wide significant (GWS) loci7, H-MAGMA can leverage signals from sub-threshold loci that explain a significant proportion of heritability8.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [8]
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O’Connor, L. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00901-3 · Cited by 81 · see citing works
Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture
Hou, K., Burch, K. S., Majumdar, A., Shi, H., Mancuso, N., Wu, Y., Sankararaman, S., & Pasaniuc, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0465-0 · Cited by 91 · see citing works
Self-citationHeritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies
Zhao, B., Ibrahim, J. G., Li, Y., Li, T., Wang, Y., Shan, Y., Zhu, Z., Zhou, F., Zhang, J., Huang, C., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy157 · Cited by 58 · see citing works
Sabbagh, J. J., Cordova, R. A., Zheng, D., Criado-Marrero, M., Lemus, A., Li, P., Baker, J. D., Nordhues, B. A., Darling, A. L., Martinez-Licha, C., et al. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.8b00454 · Cited by 48 · see citing works
Johnson, R., Burch, K. S., Hou, K., Paciuc, M., Pasaniuc, B., & Sankararaman, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009483 · Cited by 16 · see citing works
- Authors
- Shi, H., Kichaev, G., & Pasaniuc, B.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 99
- Issue
- 1
- Pages
- 139-153
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5005444
- OpenAlex
- W2469213152
- Cited by
- 387
- References
- 65
- Citations by year
- 2026: 20 · 2025: 30 · 2024: 43 · 2023: 35 · 2022: 52 · 2021: 48 · 2020: 36 · 2019: 60 · 2018: 36 · 2017: 24 · 2016: 3
- FWCI
- 32.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- 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 › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genetic architecture, complex traits, common SNPs, linkage disequilibrium, variance components, GWAS summary data, genetic susceptibility loci, heritability estimation
- MeSH terms
- Humans, Models, Genetic, Phenotype, Linkage Disequilibrium, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Datasets as Topic
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.
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