ArticleAmerican journal of human genetics2017
A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases.
Article in American journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 46 citations in OpenAlex.
- Biallelic variants in RNU2-2 cause the most prevalent known recessive neurodevelopmental disorder.Nature genetics · 2026Article
- Insights into the clinical, platelet and genetic landscape of inherited thrombocytopenia with malignancy risk.British journal of haematology · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Biallelic variants inmedRxiv : the preprint server for health sciences · 2025Article
- Heterozygous RAB3A variants cause cerebellar ataxia by a partial loss-of-function mechanism.Brain : a journal of neurology · 2025Article
- Mutations in the small nuclear RNA gene RNU2-2 cause a severe neurodevelopmental disorder with prominent epilepsy.Nature genetics · 2025Article
- Article
- Variants in LRRC7 lead to intellectual disability, autism, aggression and abnormal eating behaviors.Nature communications · 2024Article
- Rare Sequence Variation Underlying Suspected Familial Cerebral Small-Vessel Disease.Journal of the American Heart Association · 2024Article
- Mutations in the U4 snRNA gene RNU4-2 cause one of the most prevalent monogenic neurodevelopmental disorders.Nature medicine · 2024Article
- Machine learning in rare disease.Nature methods · 2023Review
- Genetic association analysis of 77,539 genomes reveals rare disease etiologies.Nature medicine · 2023Article
- Discovering the genetic etiologies of rare diseases in large patient collections.Nature medicine · 2023Article
- Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.PLoS genetics · 2022Article
- The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects.International journal of molecular sciences · 2022Review
- Hemostatic phenotypes and genetic disorders.Research and practice in thrombosis and haemostasis · 2021Article
- Article
- BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.HGG advances · 2021Article
- Identification of a homozygous recessive variant inHaematologica · 2021Article
- Bayesian Inference Associates RareCirculation. Genomic and precision medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.