Evidence map›Paper›PMID 21850043›Full record

ReviewNature reviews. Genetics2011

Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data.

Gregory M Cooper, Jay Shendure

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 319 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
319citing papers in PubMed, 1 pooled it
–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

319 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026
    Review
  5. Article
  6. Article
  7. Article
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  9. Article
  10. Review
  11. Article
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  14. Whole exome sequencing as a screening tool in dogs: A pilot study.Computational and structural biotechnology journal · 2025
    Article
  15. Article
  16. A novel homozygous nonsense variant inOphthalmic genetics · 2024
    Article
  17. Genetic variant classification by predicted protein structure: A case study on IRF6.Computational and structural biotechnology journal · 2024
    Article
  18. Article
  19. Article
  20. Article

259 more citing papers are in PubMed but not listed here.

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

2 authors.

Gregory M CooperHudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA. gcooper@ hudsonalpha.org
Jay Shendure

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome and exome sequencing yield extensive catalogues of human genetic variation. However, pinpointing the few phenotypically causal variants among the many variants present in human genomes remains a major challenge, particularly for rare and complex traits wherein genetic information alone is often insufficient. Here, we review approaches to estimate the deleteriousness of single nucleotide variants (SNVs), which can be used to prioritize disease-causal variants. We describe recent advances in comparative and functional genomics that enable systematic annotation of both coding and non-coding variants. Application and optimization of these methods will be essential to find the genetic answers that sequencing promises to hide in plain sight.

Indexed as

Databases, GeneticGenetic VariationGenome, HumanAmino Acid SequenceComputational BiologyEvolution, MolecularGenetic Association StudiesGenetic LinkageGenetic LociGenetic Predisposition to DiseaseHigh-Throughput Nucleotide SequencingHumansMolecular Sequence DataPolymorphism, Single NucleotideProtein ConformationSequence Analysis, DNA

Identifiers

PMID21850043

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.