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Deconstructing the sources of genotype-phenotype associations in humans
Open accessAbstract
Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably—in particular for phenotypic prediction—a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.
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“While reducing statistical power, sibling comparisons overcome these methodological challenges, because meiosis randomizes genotypes to siblings47,49.”
From Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. (2021) · cites this paper as [47]
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“such confounding may reduce prediction ability in other contexts, such as predicting across ancestries and within families8,20.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [8]
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“Genetic associations detected in GWAS can be due to direct genetic effects, but can also be confounded by population stratification, indirect genetic effects from e.g., parental environment, and assortative mating47,48.”
From Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. (2021) · cites this paper as [47]
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“Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents7,17,30–34.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [17]
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“However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [8]
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“Analytical techniques including principal component analysis (PCA) and linear mixed models have been developed to reduce population stratification confounding12, but these techniques often leave residual confounding in GWAS results5,8,10.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [8]
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“population-effect estimates include contributions from IGEs, population stratification, and AM.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [8]
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Citations
Cites
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An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
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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 2,058
Variable 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
Self-citationWithin-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
Howe, L. J., Nivard, M. G., Morris, T. T., Hansen, A. F., Rasheed, H., Cho, Y., Chittoor, G., Ahlskog, R., Lind, P. A., Palviainen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01062-7 · Cited by 377
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370
Karlsson Linnér, R., Mallard, T. T., Barr, P. B., Sanchez-Roige, S., Madole, J. W., Driver, M. N., Poore, H. E., de Vlaming, R., Grotzinger, A. D., Tielbeek, J. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00908-3 · Cited by 317
“While reducing statistical power, sibling comparisons overcome these methodological challenges, because meiosis randomizes genotypes to siblings 47,49 .”
Background
Metadata
- Authors
- Young, A. I., Benonisdottir, S., Przeworski, M., & Kong, A.
- Affiliations
- King's College London (GB)
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK.
- Columbia University (US)
- University of Oxford (GB)
- Corresponding author
- Young, Alexander I.
- Przeworski, Molly
- Kong, Augustine
- Venue
- Science
- Published
- 2019-09-27
- Volume
- 365
- Issue
- 6460
- Pages
- 1396-1400
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6894903
- OpenAlex
- W2975820925
- Cited by
- 288
- References
- 66
- Citations by year
- 2026: 34 · 2025: 42 · 2024: 59 · 2023: 50 · 2022: 42 · 2021: 36 · 2020: 25
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01 GM121372)
- Li Ka Shing Foundation (N/A)
- 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
- genotype-phenotype correlation, genetic variants, direct genetic effects, indirect genetic effects, phenotype prediction, close relatives, genetic epidemiology, polygenic traits
- MeSH terms
- Genetics, Population, Humans, Models, Genetic, Genome, Human, Polymorphism, Single Nucleotide, Principal Component Analysis, Inheritance Patterns, Genome-Wide Association Study, Genetic Association Studies
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