ArticleCancer informatics2017
Selection of X-chromosome Inactivation Model.
Article in Cancer informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- A systematic review of analytical methods used in genetic association analysis of the X-chromosome.Briefings in bioinformatics · 2022Pooled it
- An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2023Article
- eXclusionarY: 10 years later, where are the sex chromosomes in GWASs?American journal of human genetics · 2023Review
- HostSeq: a Canadian whole genome sequencing and clinical data resource.BMC genomic data · 2023Article
- Major sex differences in allele frequencies for X chromosomal variants in both the 1000 Genomes Project and gnomAD.PLoS genetics · 2022Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- 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
- The role of sex in the genomics of human complex traits.Nature reviews. Genetics · 2019Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
To address the complexity of the X-chromosome inactivation (XCI) process, we previously developed a unified approach for the association test for X-chromosomal single-nucleotide polymorphisms (SNPs) and the disease of interest, accounting for different biological possibilities of XCI: random, skewed, and escaping XCI. In the original study, we focused on the SNP-disease association test but did not provide knowledge regarding the underlying XCI models. One can use the highest likelihood ratio (LLR) to select XCI models (max-LLR approach). However, that approach does not formally compare the LLRs corresponding to different XCI models to assess whether the models are distinguishable. Therefore, we propose an LLR comparison procedure (comp-LLR approach), inspired by the Cox test, to formally compare the LLRs of different XCI models to select the most likely XCI model that describes the underlying XCI process. We conduct simulation studies to investigate the max-LLR and comp-LLR approaches. The simulation results show that compared with the max-LLR, the comp-LLR approach has higher probability of identifying the correct underlying XCI model for the scenarios when the underlying XCI process is random XCI, escaping XCI, or skewed XCI to the deleterious allele. We applied both approaches to a head and neck cancer genetic study to investigate the underlying XCI processes for the X-chromosomal genetic variants.
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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.