ArticleScientific reports2016
Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Strong impact of natural-selection-free heterogeneity in genetics of age-related phenotypes.Aging · 2018Pooled it
- Monitoring diversity in genome-wide association studies requires measuring and reporting on immigration-related factors.Frontiers in genetics · 2025Article
- Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure.medRxiv : the preprint server for health sciences · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Polygenic Heterogeneity Across Obsessive-Compulsive Disorder Subgroups Defined by a Comorbid Diagnosis.Frontiers in genetics · 2021Article
- Characterizing the effect of background selection on the polygenicity of brain-related traits.Genomics · 2021Article
- Article
- Inclusion of endophenotypes in a standard GWAS facilitate a detailed mechanistic understanding of genetic elements that control blood lipid levels.Scientific reports · 2020Article
- Quantitative and Qualitative Role of Antagonistic Heterogeneity in Genetics of Blood Lipids.The journals of gerontology. Series A, Biological sciences and medical sciences · 2020Article
- The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.Journal of applied genetics · 2020Article
- Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?Human genetics · 2020Review
- Genomics of disease risk in globally diverse populations.Nature reviews. Genetics · 2019Review
- Genome-wide analysis of genetic predisposition to Alzheimer's disease and related sex disparities.Alzheimer's research & therapy · 2019Article
- Pleiotropic Meta-Analysis of Age-Related Phenotypes Addressing Evolutionary Uncertainty in Their Molecular Mechanisms.Frontiers in genetics · 2019Article
- Replicability and Prediction: Lessons and Challenges from GWAS.Trends in genetics : TIG · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that GWAS have exhausted their potential, particularly for complex traits. Here, we examine the efficiency of the traditional sample-size-centered strategy in GWAS of these traits, and its potential for improvement. The paper focuses on the results of the four largest GWAS meta-analyses of body mass index (BMI) and lipids. We show that just increasing sample size may not make p-values of genetic effects in large (N > 100,000) samples smaller but can make them larger. The efficiency of these GWAS, defined as ratio of the log-transformed p-value to the sample size, in larger samples was larger than in smaller samples for a small fraction of loci. These results emphasize the important role of heterogeneity in genetic associations with complex traits such as BMI and lipids. They highlight the substantial potential for improving GWAS by explicating this role (affecting 11-79% of loci in the selected GWAS), especially the effects of biodemographic processes, which are heavily underexplored in current GWAS and which are important sources of heterogeneity in the various study populations. Further progress in this direction is crucial for efficient use of genetic discoveries in health care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.