ArticlePloS one2007
A genomic background based method for association analysis in related individuals.
Article in PloS one, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 11 of them syntheses that pooled it.
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Who cites it
153 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Genome-wide association study meta-analysis uncovers novel genetic variants associated with olfactory dysfunction.BMC genomic data · 2025Pooled it
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- A meta analysis of genome-wide association studies for limb bone lengths in four pig populations.BMC genetics · 2015Pooled it
- Genome wide meta-analysis highlights the role of genetic variation in RARRES2 in the regulation of circulating serum chemerin.PLoS genetics · 2014Pooled it
- Salt-inducible kinase 3, SIK3, is a new gene associated with hearing.Human molecular genetics · 2014Pooled it
- Genome-wide association analysis of coffee drinking suggests association with CYP1A1/CYP1A2 and NRCAM.Molecular psychiatry · 2012Pooled it
- Common genetic determinants of intraocular pressure and primary open-angle glaucoma.PLoS genetics · 2012Pooled it
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- Genome-wide association analysis and fine mapping of NT-proBNP level provide novel insight into the role of the MTHFR-CLCN6-NPPA-NPPB gene cluster.Human molecular genetics · 2011Pooled it
- A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level.BMC medical genetics · 2010Pooled it
- A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project.Circulation. Cardiovascular genetics · 2009Pooled it
- Semi-parametric validation of genomic predictions and polygenic risk scores with the Blupf90 software suite.G3 (Bethesda, Md.) · 2025Article
- Maintenance of genetic diversity in subdivided populations using genomic coancestry matrices.Molecular ecology resources · 2025Article
- Estimation of Inbreeding Depression From Overdominant Loci Using Molecular Markers.Evolutionary applications · 2025Article
- Impact of different genomic relationship matrix construction methods on the accuracy of genomic prediction in different species.Frontiers in genetics · 2025Article
- Cross-ancestry genetic architecture and prediction for cholesterol traits.Human genetics · 2024Article
- Association between MC1R gene and coat color segregation in Shanxia long black pig and Lulai black pig.BMC genomic data · 2023Article
- Single-step genome-wide association analyses of claw horn lesions in Holstein cattle using linear and threshold models.Genetics, selection, evolution : GSE · 2023Article
- A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data.Nature communications · 2023Article
- The bovine foot skin microbiota is associated with host genotype and the development of infectious digital dermatitis lesions.Microbiome · 2023Article
93 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeasibility of genotyping of hundreds and thousands of single nucleotide polymorphisms (SNPs) in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population). Of the available methods that account for relatedness, the Measured Genotype (MG) approach is considered the 'gold standard'. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR) for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG) approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. METHODOLOGY: In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GC) approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy.
conclusionThrough simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the 'gold standard' MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis in related individuals.
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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.