ArticleGenetic epidemiology2018
Statistics for X-chromosome associations.
Article in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of analytical methods used in genetic association analysis of the X-chromosome.Briefings in bioinformatics · 2022Pooled it
- X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.Briefings in bioinformatics · 2026Article
- Genomic Insights Into Risperidone Treatment Outcomes in Children and Adolescents: Experience From a Psychiatric Hospital Serving Rural Youth.Clinical pharmacology and therapeutics · 2026Article
- X chromosome inactivation across primary human tissues is mostly complete, with significant implications for genetic and clinical studies.BMC genomics · 2025Article
- Eight quick tips for including chromosome X in genome-wide association studies.PLoS computational biology · 2024Article
- The impact of chromosomal sex on cardiometabolic health and disease.Trends in endocrinology and metabolism: TEM · 2023Review
- eXclusionarY: 10 years later, where are the sex chromosomes in GWASs?American journal of human genetics · 2023Review
- X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study.Frontiers in aging neuroscience · 2023Article
- Article
- Major sex differences in allele frequencies for X chromosomal variants in both the 1000 Genomes Project and gnomAD.PLoS genetics · 2022Article
- The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies.Genetic epidemiology · 2021Article
- Testing and estimation of X-chromosome SNP effects: Impact of model assumptions.Genetic epidemiology · 2021Article
- An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.Nature neuroscience · 2021Article
- Association Analysis of Chromosome X to Identify Genetic Modifiers of Huntington's Disease.Journal of Huntington's disease · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In a genome-wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X-chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X-chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X-chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X-chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single-marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X-chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions.
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Registered trials
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