Human Gene Module / Chromosome 3 / EIF4G1

EIF4G1eukaryotic translation initiation factor 4 gamma 1

SFARI Gene Score
3
Suggestive Evidence Criteria 3.1
Autism Reports / Total Reports
4 / 4
Rare Variants / Common Variants
5 / 0
Aliases
EIF4G1, EIF-4G1,  EIF4F,  EIF4G,  EIF4GI,  P220,  PARK18
Associated Syndromes
-
Chromosome Band
3q27.1
Associated Disorders
-
Relevance to Autism

A rare and potentially damaging de novo missense variant in the EIF4G1 gene was identified in an ASD proband from the Simons Simplex Collection in Iossifov et al., 2014. Gonatopoulos-Pournatzis et al., 2020 demonstrated that microexon splicing in eIF4G translation initiation factors, a process involved in regulation of the neuronal proteome and higher order cognitive functions, was disrupted in post-mortem brain tissue from autistic patients; furthermore, mice that were deficient for the Eif4g1 microexon (Eif4g1MIC/MIC) displayed learning and memory deficits, altered synaptic plasticity, and autistic-like social behavior.

Molecular Function

The protein encoded by this gene is a component of the multi-subunit protein complex EIF4F. This complex facilitates the recruitment of mRNA to the ribosome, which is a rate-limiting step during the initiation phase of protein synthesis. The recognition of the mRNA cap and the ATP-dependent unwinding of 5'-terminal secondary structure is catalyzed by factors in this complex. The subunit encoded by this gene is a large scaffolding protein that contains binding sites for other members of the EIF4F complex. A domain at its N-terminus can also interact with the poly(A)-binding protein, which may mediate the circularization of mRNA during translation.

External Links
Gene CardOpen Targets PlatformgnomAD browserSynGO portalPubMed
Human
Entrez GeneOMIMUniProtHumanBaseVariCarta
Mouse
Entrez GeneMouse Genome InformaticsAllen Brain Atlas
SFARI Genomic Platforms
GPF browser SFARI genome browser
Reports related to EIF4G1 (4 Reports)
# Type Title Author, Year Autism Report Associated Disorders
1 Support The contribution of de novo coding mutations to autism spectrum disorder Iossifov I et al. (2014) Yes -
2 Primary Autism-Misregulated eIF4G Microexons Control Synaptic Translation and Higher Order Cognitive Functions Gonatopoulos-Pournatzis T , et al. (2020) Yes -
3 Support - Woodbury-Smith M et al. (2022) Yes -
4 Support - Zhou X et al. (2022) Yes -
Rare Variants   (5)
Status Allele Change Residue Change Variant Type Inheritance Pattern Parental Transmission Family Type PubMed ID Author, Year
c.448G>T p.Gly150Cys missense_variant De novo - - 35982159 Zhou X et al. (2022)
c.1320G>A p.Ala440%3D synonymous_variant De novo - - 35982159 Zhou X et al. (2022)
c.2785A>C p.Lys929Gln missense_variant De novo - Simplex 25363768 Iossifov I et al. (2014)
c.675A>G p.Gln225%3D synonymous_variant Unknown - - 35205252 Woodbury-Smith M et al. (2022)
c.4539G>C p.Ala1513%3D synonymous_variant Unknown - - 35205252 Woodbury-Smith M et al. (2022)
Common Variants  

No common variants reported.

SFARI Gene score
3

Suggestive Evidence

Score Delta: Score remained at 3

criteria met
See SFARI Gene'scoring criteria
3

Suggestive Evidence

See all Category 3 Genes

The literature is replete with relatively small studies of candidate genes, using either common or rare variant approaches, which do not reach the criteria set out for categories 1 and 2. Genes that had two such lines of supporting evidence were placed in category 3, and those with one line of evidence were placed in category 4. Some additional lines of "accessory evidence" (indicated as "acc" in the score cards) could also boost a gene from category 4 to 3.

4/1/2022
icon
3

Increased from to 3

Krishnan Probability Score

Score 0.49619076772382

Ranking 2657/25841 scored genes


[Show Scoring Methodology]
Krishnan and colleagues generated probability scores genome-wide by using a machine learning approach on a human brain-specific gene network. The method was first presented in Nat Neurosci 19, 1454-1462 (2016), and scores for more than 25,000 RefSeq genes can be accessed in column G of supplementary table 3 (see: http://www.nature.com/neuro/journal/v19/n11/extref/nn.4353-S5.xlsx). A searchable browser, with the ability to view networks of associated ASD risk genes, can be found at asd.princeton.edu.
Original Source
ExAC Score

Score 0.99999999993649

Ranking 69/18225 scored genes


[Show Scoring Methodology]
The Exome Aggregation Consortium (ExAC) is a summary database of 60,706 exomes that has been widely used to estimate 'constraint' on mutation for individual genes. It was introduced by Lek et al. Nature 536, 285-291 (2016), and the ExAC browser can be found at exac.broadinstitute.org. The pLI score was developed as measure of intolerance to loss-of- function mutation. A pLI > 0.9 is generally viewed as highly constrained, and thus any loss-of- function mutations in autism in such a gene would be more likely to confer risk. For a full list of pLI scores see: ftp://ftp.broadinstitute.org/pub/ExAC_release/release0.3.1/functional_gene_constraint/fordist_cle aned_exac_nonTCGA_z_pli_rec_null_data.txt
Original Source
Iossifov Probability Score

Score 0.833

Ranking 208/239 scored genes


[Show Scoring Methodology]
Supplementary dataset S2 in the paper by Iossifov et al. (PNAS 112, E5600-E5607 (2015)) lists 239 genes with a probability of at least 0.8 of being associated with autism risk (column I). This probability metric combines the evidence from de novo likely-gene- disrupting and missense mutations and assesses it against the background mutation rate in unaffected individuals from the University of Washington’s Exome Variant Sequence database (evs.gs.washington.edu/EVS/). The list of probability scores can be found here: www.pnas.org/lookup/suppl/doi:10.1073/pnas.1516376112/- /DCSupplemental/pnas.1516376112.sd02.xlsx
Original Source
Sanders TADA Score

Score 0.92282270610786

Ranking 9651/18665 scored genes


[Show Scoring Methodology]
The TADA score ('Transmission and De novo Association') was introduced by He et al. PLoS Genet 9(8):e1003671 (2013), and is a statistic that integrates evidence from both de novo and transmitted mutations. It forms the basis for the claim of 65 individual genes being strongly associated with autism risk at a false discovery rate of 0.1 (Sanders et al. Neuron 87, 1215-1233 (2015)). The calculated TADA score for 18,665 RefSeq genes can be found in column P of Supplementary Table 6 in the Sanders et al. paper (the column headed 'tadaFdrAscSscExomeSscAgpSmallDel'), which represents a combined analysis of exome data and small de novo deletions (see www.cell.com/cms/attachment/2038545319/2052606711/mmc7.xlsx).
Original Source
Zhang D Score

Score 0.005885457050209

Ranking 8507/20870 scored genes


[Show Scoring Methodology]
The DAMAGES score (disease-associated mutation analysis using gene expression signatures), or D score, was developed to combine evidence from de novo loss-of- function mutation with evidence from cell-type- specific gene expression in the mouse brain (specifically translational profiles of 24 specific mouse CNS cell types isolated from 6 different brain regions). Genes with positive D scores are more likely to be associated with autism risk, with higher-confidence genes having higher D scores. This statistic was first presented by Zhang & Shen (Hum Mutat 38, 204- 215 (2017), and D scores for more than 20,000 RefSeq genes can be found in column M in supplementary table 2 from that paper.
Original Source
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