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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Sibling comparisons overcome confounding from population stratification, indirect genetic effects, and assortative mating because meiosis randomizes genotypes to siblings.
“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]
In “Using family genotype data,” the paper says random Mendelian segregation makes sibling genetic differences unconfounded by population stratification, assortative mating, and indirect genetic effects from parents. It also says sibling comparisons estimate the direct effect minus any indirect effect from the sibling, so the unqualified claim that they overcome indirect genetic effects goes beyond the source. The same section supports the citing sentence’s reduced-power qualification. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSuch confounding may reduce prediction ability in other contexts, such as predicting across ancestries and within families.
“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]
The Introduction’s “Portability of phenotypic prediction” section says polygenic scores predict less well across ancestry groups and that indirect effects and assortative mating may affect how well predictions generalize. “Using family genotype data” explains that sibling differences remove several sources of confounding, but the paper does not explicitly say that confounding reduces predictive ability within families. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGenetic associations detected in GWAS can be due to direct genetic effects but can also be confounded by population stratification, indirect genetic effects, and assortative mating.
“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]
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 parents.
“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]
Genetic associations (including those from GWAS) can be affected by gene-environment correlations and non-random mating, such as indirect genetic effects, assortative mating, and population stratification, leading to biased inferences.
“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]
Analytical techniques including PCA and linear mixed models reduce population stratification confounding but often leave residual confounding in GWAS results.
“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]
Population-effect estimates include contributions from indirect genetic effects, population stratification, and assortative mating.
“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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“While reducing statistical power, sibling comparisons overcome these methodological challenges, because meiosis randomizes genotypes to siblings 47,49 .”
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Uchikoshi, F., & Conley, D. (2021) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2021.100628 · Cited by 14 · see citing works
- 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
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.
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