ArticlePloS one2013
Linear regression in genetic association studies.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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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Who cites it
14 citing papers in PubMed.
- Genomic Insights Into Risperidone Treatment Outcomes in Children and Adolescents: Experience From a Psychiatric Hospital Serving Rural Youth.Clinical pharmacology and therapeutics · 2026Article
- Comparison of two-stage methods for count data in Mendelian randomization: a simulation study.Journal of comparative effectiveness research · 2026Article
- The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index.Frontiers in genetics · 2022Article
- Methods for statistical fine-mapping and their applications to auto-immune diseases.Seminars in immunopathology · 2022Review
- A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies.PloS one · 2020Article
- A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations.Frontiers in endocrinology · 2019Article
- Genetic Markers Are Associated with the Ruminal Microbiome and Metabolome in Grain and Sugar Challenged Dairy Heifers.Frontiers in genetics · 2018Article
- Genome-wide association mapping for seedling and field resistance to Puccinia striiformis f. sp. tritici in elite durum wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders.Nature genetics · 2017Article
- Genome-wide association analysis identifies genetic loci associated with resistance to multiple antimalarials in Plasmodium falciparum from China-Myanmar border.Scientific reports · 2016Article
- The effect of phenotypic outliers and non-normality on rare-variant association testing.European journal of human genetics : EJHG · 2016Article
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- Single Nucleotide Polymorphism in the COL11A2 Gene Associated with Heat Pain Sensitivity in Knee Osteoarthritis.Molecular painArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype transformations are typically not needed and, when used in phenotype with heteroscedasticity, result in inflated Type I error rates. We further explain that important is to address a combination of rare variant genotypes and heteroscedasticity. Incorrectly estimated parameter variability or incorrect choice of the distribution of the underlying test statistic provide spurious detection of associations. We conclude that it is a combination of heteroscedasticity, minor allele frequency, sample size, and to a much lesser extent the error distribution, that matter for proper statistical inference.
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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.