ArticleEuropean journal of human genetics : EJHG2016
The effect of phenotypic outliers and non-normality on rare-variant association testing.
Article in European journal of human genetics : EJHG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.American journal of human genetics · 2016Pooled it
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- Examining the effect of mitochondrial DNA variants on blood pressure in two Finnish cohorts.Scientific reports · 2021Article
- Activation of cryptic splicing in bovine WDR19 is associated with reduced semen quality and male fertility.PLoS genetics · 2020Article
- Effect of non-normality and low count variants on cross-phenotype association tests in GWAS.European journal of human genetics : EJHG · 2020Article
- A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies.PloS one · 2020Article
- Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome.The Journal of clinical endocrinology and metabolism · 2019Article
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3 authors.
Funding
Abstract
Rare-variant association studies (RVAS) have made important contributions to human complex trait genetics. These studies rely on specialized statistical methods for analyzing rare-variant associations, both individually and in aggregate. We investigated the impact that phenotypic outliers and non-normality have on the performance of rare-variant association testing procedures. Ignoring outliers or non-normality can significantly inflate Type I error rates. We found that rank-based inverse normal transformation (INT) and trait winsorisation were both effective at maintaining Type I error control without sacrificing power in the presence of outliers. INT was the optimal method for non-normally distributed traits. For RVAS of quantitative traits with outliers or non-normality, we recommend using INT to transform phenotypic values before association testing.
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