CHD9chromodomain helicase DNA binding protein 9
Autism Reports / Total Reports
8 / 8Rare Variants / Common Variants
15 / 0Aliases
-Associated Syndromes
-Chromosome Band
16q12.2Associated Disorders
-Relevance to Autism
De novo missense variants in the CHD9 gene have been identified in ASD probands from the Simons Simplex Collection and the Autism Sequencing Consortium (Iossifov et al., 2014; Lim et al., 2017; Satterstrom et al., 2020), as well as in a patient from a cohort of 87 families with neurodevelopmental disorders who presented with ASD and no speech development (Alvarez-Mora et al., 2022). Targeted sequencing of 136 microcephaly or macrocephaly-related genes and 158 possible ASD risk genes in 536 Chinese ASD probands from the Autism Clinical and Genetic Resources in China (ACGC) cohort in Li et al., 2017 identified additional missense variants in the CHD9 gene.
Molecular Function
Predicted to enable ATP binding activity; ATP-dependent activity, acting on DNA; and DNA binding activity. Predicted to be involved in DNA duplex unwinding and chromatin organization. Located in cytosol and nucleoplasm.
External Links
SFARI Genomic Platforms
Reports related to CHD9 (8 Reports)
| # | Type | Title | Author, Year | Autism Report | Associated Disorders |
|---|---|---|---|---|---|
| 1 | Primary | The contribution of de novo coding mutations to autism spectrum disorder | Iossifov I et al. (2014) | Yes | - |
| 2 | Support | Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder | Lim ET , et al. (2017) | Yes | - |
| 3 | Support | Targeted sequencing and functional analysis reveal brain-size-related genes and their networks in autism spectrum disorders | Li J , et al. (2017) | Yes | - |
| 4 | Support | Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism | Satterstrom FK et al. (2020) | Yes | - |
| 5 | Recent Recommendation | - | ÃÂlvarez-Mora MI et al. (2022) | Yes | - |
| 6 | Support | - | Zhou X et al. (2022) | Yes | - |
| 7 | Support | - | Yuan B et al. (2023) | Yes | - |
| 8 | Support | - | Mona Abdi et al. (2023) | Yes | - |
Rare Variants (15)
| Status | Allele Change | Residue Change | Variant Type | Inheritance Pattern | Parental Transmission | Family Type | PubMed ID | Author, Year |
|---|---|---|---|---|---|---|---|---|
| c.1066C>T | p.Pro356Ser | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.2273A>T | p.His758Leu | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.2516G>A | p.Arg839His | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.2519C>T | p.Pro840Leu | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.5368A>G | p.Thr1790Ala | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.5675C>A | p.Ser1892Tyr | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.6494C>T | p.Ser2165Phe | missense_variant | Unknown | - | - | 28831199 | Li J , et al. (2017) | |
| c.2191G>A | p.Ala731Thr | missense_variant | De novo | - | - | 35982159 | Zhou X et al. (2022) | |
| c.4441C>T | p.Arg1481Trp | missense_variant | De novo | - | - | 36881370 | Yuan B et al. (2023) | |
| c.8543G>C | p.Arg2848Thr | missense_variant | De novo | - | - | 28714951 | Lim ET , et al. (2017) | |
| c.5511-1G>T | - | splice_site_variant | De novo | - | Simplex | 37805537 | Mona Abdi et al. (2023) | |
| c.3557T>A | p.Leu1186His | missense_variant | De novo | - | - | 31981491 | Satterstrom FK et al. (2020) | |
| c.6892A>G | p.Thr2298Ala | missense_variant | De novo | - | - | 31981491 | Satterstrom FK et al. (2020) | |
| c.6433T>C | p.Ser2145Pro | missense_variant | De novo | - | Simplex | 25363768 | Iossifov I et al. (2014) | |
| c.3772A>C | p.Thr1258Pro | missense_variant | De novo | - | Simplex | 35183220 | ÃÂlvarez-Mora MI 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 criteriaThe 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
3
Increased from to 3
Krishnan Probability Score
Score 0.60104817533813
Ranking 394/25841 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.
ExAC Score
Score 0.99999992858899
Ranking 186/18225 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
Sanders TADA Score
Score 0.93987494848795
Ranking 14365/18665 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).
Zhang D Score
Score 0.45362449274283
Ranking 886/20870 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.