ReviewMethods in molecular biology (Clifton, N.J.)2022
Genome-Wide Association Study Statistical Models: A Review.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- A bittersweet symphony: genetic insights into cider apple fruit quality.G3 (Bethesda, Md.) · 2026Article
- Merging traditional practices and modern technology through computational plant breeding.Plant physiology · 2025Review
- Periodontal diseases in Africa.Periodontology 2000 · 2025Review
- Enhancer RNA transcription pinpoints functional genetic variants linked to asthma.Nature communications · 2025Article
- Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize.BMC plant biology · 2025Article
- Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.The Plant journal : for cell and molecular biology · 2025Article
- Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle.Animals : an open access journal from MDPI · 2024Review
- Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.International journal of molecular sciences · 2024Article
- Genome-wide association study and expression of candidate genes for Fe and Zn concentration in sorghum grains.Scientific reports · 2024Article
- Genetic study for seed germination and shattering inHeliyon · 2024Article
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Structural variations in livestock genomes and their associations with phenotypic traits: a review.Frontiers in veterinary science · 2024Review
- Genome-wide association analysis identifies a candidate gene controlling seed size and yield inHorticulture research · 2024Article
- Article
- Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits.Plants (Basel, Switzerland) · 2023Article
- Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS.Scientific reports · 2023Article
- Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding.Plants (Basel, Switzerland) · 2023Review
- Review
- Genome-wide analysis of cold imbibition stress in soybean,Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.
Indexed as
Identifiers
35641758What OpenQuestion holds
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.