Post-GWAS: where next? More samples, more SNPs or more biology?
Open accessAbstract
The power of genome-wide association studies (GWAS) rests on several foundations: (i) there is a significant amount of additive genetic variation, (ii) individual causal polymorphisms often have sizable effects and (iii) they segregate at moderate-to-intermediate frequencies, or will be effectively 'tagged' by polymorphisms that do. Each of these assumptions has recently been questioned. (i) Why should genetic variation appear additive given that the underlying molecular networks are highly nonlinear? (ii) A new generation of relatedness-based analyses directs us back to the nearly infinitesimal model for effect sizes that quantitative genetics was long based upon. (iii) Larger effect causal polymorphisms are often low frequency, as selection might lead us to expect. Here, we review these issues and other findings that appear to question many of the foundations of the optimism GWAS prompted. We then present a roadmap emerging as one possible future for quantitative genetics. We argue that in future GWAS should move beyond purely statistical grounds. One promising approach is to build upon the combination of population genetic models and molecular biological knowledge. This combined treatment, however, requires fitting experimental data to models that are very complex, as well as accurate capturing of the uncertainty of resulting inference. This problem can be resolved through Bayesian analysis and tools such as approximate Bayesian computation-a method growing in popularity in population genetic analysis. We show a case example of anterior-posterior segmentation in Drosophila, and argue that similar approaches will be helpful as a GWAS augmentation, in human and agricultural research.
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Marjoram et al. demonstrated their approach by using ABC for incorporating gene regulatory networks into their analysis.
“They demonstrated their approach by using ABC for incorporating gene regulatory networks into their analysis.”
From Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation (2015) · cites this paper as [51]
Marjoram et al. argued that signal detection power could be increased by augmenting purely statistical association models with biological knowledge.
“Marjoram et al. [51] argued that signal detection power could be increased by augmenting the purely statistical association models used thus far with biological knowledge.”
From Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation (2015) · cites this paper as [51]
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- Authors
- Marjoram, P., Zubair, A., & Nuzhdin, S. V.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Marjoram, P
- Venue
- Heredity
- Published
- 2013-06-12
- Volume
- 112
- Issue
- 1
- Pages
- 79-88
- ISSN
- 0018-067X (ISSN-L)
- 1365-2540
- PMC
- PMC3860164
- OpenAlex
- W2040316499
- Cited by
- 74
- References
- 81
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 4 · 2019: 5 · 2018: 9 · 2017: 9 · 2016: 5 · 2015: 6 · 2014: 11 · 2013: 4
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Heredity (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (R01MH100879, 1101060)
- National Institutes of Health
- National Institute of Mental Health (R01MH091561, R01MH100879)
- National Human Genome Research Institute (P50HG002790)
- National Institute of General Medical Sciences (U01GM103804, R01GM098741)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genome-wide association study, additive genetic variance, effect size distribution, low-frequency variants, selection, population genetic models, Bayesian analysis, approximate Bayesian computation, quantitative genetics
- MeSH terms
- Bayes Theorem, Genetics, Population, Humans, Models, Genetic, Genome, Human, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Gene Regulatory Networks, Genome-Wide Association Study
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