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Record W2735026440 · doi:10.1002/humu.23279

Postzygotic single nucleotide mosaicisms and autism risk

2017· letter· en· W2735026440 on OpenAlexaff
Bruce Gottlieb

Bibliographic record

VenueHuman Mutation · 2017
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyAutismGeneticsSingle-nucleotide polymorphismComputational biologyGeneGenotypeDevelopmental psychology

Abstract

fetched live from OpenAlex

The principle of “parsimony” has underlined our understanding of science since the middle of the 18th century by telling us to choose the simplest scientific explanation that fits (all) the observed evidence. In studying the genetics of multifactorial diseases (MFD), such as autism spectrum disorder (ASD), this has been reflected in our belief that identifying common gene mutations present among large populations exhibiting the disease phenotype is a key to understanding the ontology of the disease. However, the validity of this concept is being challenged by the increasing evidence of genetic diversity within individuals. The article in this issue by Dou et al. (Hum Mutat 38: 1002–1013, 2017) reveals evidence of postzygotic single nucleotide mosaicism (pSNMs) in ASD. Further, the mounting evidence that pSNMs also exist within normal tissues raises some even more fundamental questions with regard to the role of specific mutations in MFD phenotypes. The study by Dou et al. clearly expands on previous attempts to associate pSNMs with ASD by using ultradeep next-generation sequencing and a sophisticated approach to the bioinformatics analyses. The authors detected and validated both child pSNMs and transmitted parental pSNMs in the largest data set analysis to date of confident pSNMs of ASD cohorts. They found that pSNMs with varied mutant allele fraction (MAF) have different amounts of risk contributing to ASD. This was accomplished because of the much higher sensitivity in detecting pSNMs with relatively low MAFs. Their results highlighted that mosaicisms resulting from postzygotic mutations could explain at least a proportion of the unrecognized genetic etiology of ASD. These results are, however, more indicative of the potential of such an approach to give new insights into hypotheses explaining MFD ontology rather than identifying new genes as possible risk factors for ASD. One possible hypothesis is that occurrence of pSNMs, which are complex forms of genetic heterogeneity, suggests that selection of certain postzygotic gene mutations is the cause of disease progression and variable disease phenotypes. Further, the accumulating evidence of nongenomic, often environmental factors as risk factors for ASD, would lend support to such a hypothesis. Indeed, the complexity of postzygotic variation has added emphasis to the importance of environmental factors in determining ASD phenotypes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.241
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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