PIK3CAphosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha
Autism Reports / Total Reports
7 / 8Rare Variants / Common Variants
12 / 0Aliases
PIK3CA, CLAPO, CLOVE, CWS5, MCAP, MCM, MCMTC, PI3K, PI3K-alpha, p110-alphaAssociated Syndromes
-Chromosome Band
3q26.32Associated Disorders
-Relevance to Autism
De novo missense variants in the PIK3CA gene have been identified in ASD probands from multiple cohorts, including the Simons Simplex Collection, the MSSNG cohort, and the iHART cohort (Turner et al., 2016; Yuen et al., 2017; Takata et al., 2018; Ruzzo et al., 2019). Whole-exome sequencing of 21 patients with macrocephaly and developmental delay/autism spectrum disorder in Yeung et al., 2017 identified three patients with PIK3CA variants; all three patients presented with macrocephaly and global developmental delay, and one of these patients was also diagnosed with autism spectrum disorder.
Molecular Function
Phosphatidylinositol 3-kinase is composed of an 85 kDa regulatory subunit and a 110 kDa catalytic subunit. The protein encoded by this gene represents the catalytic subunit, which uses ATP to phosphorylate PtdIns, PtdIns4P and PtdIns(4,5)P2.
External Links
SFARI Genomic Platforms
Reports related to PIK3CA (8 Reports)
| # | Type | Title | Author, Year | Autism Report | Associated Disorders |
|---|---|---|---|---|---|
| 1 | Primary | Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA | Turner TN et al. (2016) | Yes | - |
| 2 | Support | Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder | C Yuen RK et al. (2017) | Yes | - |
| 3 | Support | Identification of mutations in the PI3K-AKT-mTOR signalling pathway in patients with macrocephaly and developmental delay and/or autism | Yeung KS , et al. (2018) | Yes | - |
| 4 | Support | Integrative Analyses of De Novo Mutations Provide Deeper Biological Insights into Autism Spectrum Disorder | Takata A , et al. (2018) | Yes | - |
| 5 | Support | Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks | Ruzzo EK , et al. (2019) | Yes | - |
| 6 | Support | - | Mahjani B et al. (2021) | Yes | - |
| 7 | Support | - | St John LJ et al. (2021) | No | - |
| 8 | Support | - | Zhou X et al. (2022) | Yes | - |
Rare Variants (12)
| Status | Allele Change | Residue Change | Variant Type | Inheritance Pattern | Parental Transmission | Family Type | PubMed ID | Author, Year |
|---|---|---|---|---|---|---|---|---|
| c.1955T>C | p.Phe652Ser | missense_variant | De novo | - | - | 35982159 | Zhou X et al. (2022) | |
| c.71G>A | p.Cys24Tyr | missense_variant | Unknown | - | - | 34615535 | Mahjani B et al. (2021) | |
| c.263G>A | p.Arg88Gln | missense_variant | De novo | - | - | 29296277 | Yeung KS , et al. (2018) | |
| c.2740G>A | p.Gly914Arg | missense_variant | De novo | - | - | 29296277 | Yeung KS , et al. (2018) | |
| c.2716G>A | p.Val906Ile | missense_variant | Unknown | - | - | 34615535 | Mahjani B et al. (2021) | |
| c.1A>G | p.Met1? | initiator_codon_variant | De novo | - | Simplex | 35982159 | Zhou X et al. (2022) | |
| c.323G>A | p.Arg108His | missense_variant | De novo | - | Simplex | 26749308 | Turner TN et al. (2016) | |
| c.2309G>A | p.Arg770Gln | missense_variant | De novo | - | Simplex | 28263302 | C Yuen RK et al. (2017) | |
| c.2740G>A | p.Gly914Arg | missense_variant | De novo | - | Simplex | 29346770 | Takata A , et al. (2018) | |
| c.3143A>G | p.His1048Arg | missense_variant | De novo | - | Simplex | 29346770 | Takata A , et al. (2018) | |
| c.1030G>A | p.Val344Met | missense_variant | Familial | Maternal | - | 29296277 | Yeung KS , et al. (2018) | |
| c.1346C>T | p.Pro449Leu | missense_variant | De novo | - | Multiplex | 31398340 | Ruzzo EK , et al. (2019) |
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 0.49583246347408
Ranking 2782/25841 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 0.99999851122751
Ranking 325/18225 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 0.94014059915032
Ranking 14460/18665 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 0.46248897291858
Ranking 805/20870 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.