ArticlePLoS genetics2014
Comparison of methods to account for relatedness in genome-wide association studies with family-based data.
Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 129 citations in OpenAlex.
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- Bayesian mixed model inference for genetic association under related samples with brain network phenotype.Biostatistics (Oxford, England) · 2024Article
- 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
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- Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis.Plant physiology · 2024Article
- High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype.Infectious Disease Modelling · 2023Article
- Integration of eQTL Analysis and GWAS Highlights Regulation Networks in Cotton under Stress Condition.International journal of molecular sciences · 2022Article
- Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies.Genetic epidemiology · 2022Review
- A generalized robust allele-based genetic association test.Biometrics · 2022Article
- Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.BMC genomics · 2022Article
- Linear Mixed-Effect Models Through the Lens of Hardy-Weinberg Disequilibrium.Frontiers in genetics · 2022Article
- Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies.Scientific reports · 2021Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
- A genome-wide association study identifies novel candidate genes for susceptibility to diabetes mellitus in non-obese cats.PloS one · 2021Article
5 more citing papers are in PubMed but not listed here.
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 at 5 institutions in 4 countries.
Funding
Abstract
Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.
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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.