Evidence map›Paper›PMID 35298919›Full record

ArticleAmerican journal of human genetics2022

RAREsim: A simulation method for very rare genetic variants.

Megan Null, Josée Dupuis, Pezhman Sheinidashtegol, Ryan M Layer, Christopher R Gignoux, Audrey E Hendricks

Abstract read
In one paragraph

Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Megan NullMathematical and Statistical Sciences, University of Colorado, Denver, Denver, CO 80204, USA; Mathematics and Physical Sciences, The College of Idaho, Caldwell, ID 83605, USA. Electronic address: mnull@collegeofidaho.edu.
Josée DupuisDepartment of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA.
Pezhman SheinidashtegolBoulder and BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA.
Ryan M LayerBoulder and BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA; Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA.
Christopher R GignouxHuman Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Audrey E HendricksMathematical and Statistical Sciences, University of Colorado, Denver, Denver, CO 80204, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

Funding

Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
Methods to enable robust and efficient use of genetic summary dataR35HG011293 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI HENDRICKS, AUDREY E · 2020 to 2024
$2.1M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R35 HG011293NHGRI NIH HHS U01 HG009080
6 · The paper itself

Abstract

Identification of rare-variant associations is crucial to full characterization of the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirror the distribution of rare variants and haplotype structure in real data. Additionally, importing real-variant annotation enables in silico comparison of methods, such as rare-variant association tests and polygenic scoring methods, that focus on putative causal variants. Existing simulation methods are either unable to employ real-variant annotation or severely under- or overestimate the number of singletons and doubletons, thereby reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare-variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real-variant annotations. We highlight RAREsim's utility across various genetic regions, sample sizes, ancestries, and variant classes.

Indexed as

Genetic VariationResearch DesignComputer SimulationHaplotypesHumansModels, GeneticMultifactorial InheritanceRAREsimrare variantssimulated datasimulated genetic variants

Identifiers

PMID35298919
PMCPMC9069075

What OpenQuestion holds

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Registered trials

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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.