ReviewHeredity2014
Post-GWAS: where next? More samples, more SNPs or more biology?
Review in Heredity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- A major QTL region associated with powdery mildew resistance in leaves and fruits of the reconstructed garden strawberry.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla.BMC genomics · 2024Article
- Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize.Journal of experimental botany · 2023Review
- Breeding crops for drought-affected environments and improved climate resilience.The Plant cell · 2023Review
- Article
- Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus.The plant genome · 2022Article
- Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction.Frontiers in plant science · 2021Article
- Genetic architecture of a body colour cline in Drosophila americana.Molecular ecology · 2020Article
- Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.Plant communications · 2020Review
- Novel genetic variants ofAmerican journal of cancer research · 2020Article
- Candidate genes and genome-wide association study of grain protein content and protein deviation in durum wheat.Planta · 2019Article
- Accelerating crop genetic gains with genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
- ADGRL3 (LPHN3) variants predict substance use disorder.Translational psychiatry · 2019Article
- SNP-skimming: A fast approach to map loci generating quantitative variation in natural populations.Molecular ecology resources · 2018Article
- Large-effect loci affect survival in Tasmanian devils (Sarcophilus harrisii) infected with a transmissible cancer.Molecular ecology · 2018Article
- Why does the magnitude of genotype-by-environment interaction vary?Ecology and evolution · 2018Review
- Functional mapping and annotation of genetic associations with FUMA.Nature communications · 2017Article
- The genetic epidemiology of substance use disorder: A review.Drug and alcohol dependence · 2017Review
- The carotenoid biosynthetic and catabolic genes in wheat and their association with yellow pigments.BMC genomics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.