ArticleBMC genetics2012
Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability.
Article in BMC genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 citing papers in PubMed.
- Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP).Journal of human genetics · 2022Trial
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Family-based selection: an efficient method for increasing phenotypic variability.G3 (Bethesda, Md.) · 2025Article
- From aerial drone to quantitative trait locus: leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa.The Plant journal : for cell and molecular biology · 2025Article
- Three Loci Affecting Variance of Body Mass Index in African Americans and Sub-Saharan Africans.Genetic epidemiology · 2025Article
- Mining for gene-environment and gene-gene interactions: parametric and non-parametric tests for detecting variance quantitative trait loci.Frontiers in genetics · 2025Article
- IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.Computational and structural biotechnology journal · 2024Article
- Article
- Bayesian matrix completion for hypothesis testing.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Genome-Wide Analysis of Dental Caries Variability Reveals Genotype-by-Environment Interactions.Genes · 2023Article
- Temperature-mediated flower size plasticity in Arabidopsis.iScience · 2022Article
- A quantile integral linear model to quantify genetic effects on phenotypic variability.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits.Scientific reports · 2022Article
- Review
- Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs.Genetics, selection, evolution : GSE · 2022Article
- Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle.Scientific reports · 2021Article
- Genetic impacts on DNA methylation: research findings and future perspectives.Genome biology · 2021Review
- The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores.Scientific reports · 2021Article
- Maintenance of variation in virulence and reproduction in populations of an agricultural plant pathogen.Evolutionary applications · 2021Article
- Leveraging phenotypic variability to identify genetic interactions in human phenotypes.American journal of human genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.
Indexed as
Identifiers
What 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.