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Patterns and rates of exonic de novo mutations in autism spectrum disorders

Nature(2012)10.1038/nature11011Source: DataRank Database

Patterns and rates of exonic de novo mutations in autism spectrum disorders is a research paper published in Nature (2012). On theSindex it has a DataRank of 1.1. It has been cited 1,794 times.

N/A
1.1DataRank · unranked
1.1
Open Access1794 citations · base score 7.5
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      1.1

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Authors (65)

      Related Papers (10)