EPHB1EPH receptor B1
Autism Reports / Total Reports
10 / 10Rare Variants / Common Variants
32 / 0Aliases
-Associated Syndromes
-Chromosome Band
3q22.2Associated Disorders
-Relevance to Autism
De novo variants in the EPHB1 gene have been identified in ASD probands in mutiple studies, including two de novo missense variants in ASD probands from the Simons Simplex Collection and a de novo frameshift variant in an ASD proband from the Autism Sequencing Consortium (Kong et al., 2012; Iossifov et al., 2014; Sanders et al., 2015; Yuen et al., 2016; Yuen et al., 2017; Turner et al., 2017; Werling et al., 2018; Satterstrom et al., 2020). Functional assessment of the ASD-associated p.Val916Met missense variant, which was originally observed in an SSC proband, in Drosophila using an overexpression-based strategy in Macrogliese et al., 2022 demonstrated that flies overexpressing EPHB1-p.Val916Met presented with a complex phenotype characterized by a loss-of-function effect in eyes and a gain-of-function effect in wings.
Molecular Function
Ephrin receptors and their ligands, the ephrins, mediate numerous developmental processes, particularly in the nervous system. Based on their structures and sequence relationships, ephrins are divided into the ephrin-A (EFNA) class, which are anchored to the membrane by a glycosylphosphatidylinositol linkage, and the ephrin-B (EFNB) class, which are transmembrane proteins. The Eph family of receptors are divided into 2 groups based on the similarity of their extracellular domain sequences and their affinities for binding ephrin-A and ephrin-B ligands. Ephrin receptors make up the largest subgroup of the receptor tyrosine kinase (RTK) family. The protein encoded by this gene is a receptor for ephrin-B family members.
External Links
SFARI Genomic Platforms
Reports related to EPHB1 (10 Reports)
| # | Type | Title | Author, Year | Autism Report | Associated Disorders |
|---|---|---|---|---|---|
| 1 | Support | Rate of de novo mutations and the importance of father's age to disease risk | Kong A , et al. (2012) | Yes | - |
| 2 | Primary | The contribution of de novo coding mutations to autism spectrum disorder | Iossifov I et al. (2014) | Yes | - |
| 3 | Support | Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci | Sanders SJ , et al. (2015) | Yes | - |
| 4 | Support | Genome-wide characteristics of de novo mutations in autism | Yuen RK et al. (2016) | Yes | - |
| 5 | Support | Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder | C Yuen RK et al. (2017) | Yes | - |
| 6 | Support | Genomic Patterns of De Novo Mutation in Simplex Autism | Turner TN et al. (2017) | Yes | - |
| 7 | Support | An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder | Werling DM et al. (2018) | Yes | - |
| 8 | Support | Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism | Satterstrom FK et al. (2020) | Yes | - |
| 9 | Recent Recommendation | - | Marcogliese PC et al. (2022) | Yes | - |
| 10 | Support | - | Zhou X et al. (2022) | Yes | - |
Rare Variants (32)
| Status | Allele Change | Residue Change | Variant Type | Inheritance Pattern | Parental Transmission | Family Type | PubMed ID | Author, Year |
|---|---|---|---|---|---|---|---|---|
| c.2497-5T>C | - | splice_region_variant | De novo | - | - | 35982159 | Zhou X et al. (2022) | |
| c.153C>A | p.Asn51Lys | missense_variant | De novo | - | - | 35982159 | Zhou X et al. (2022) | |
| c.1160C>T | p.Thr387Met | missense_variant | De novo | - | - | 35982159 | Zhou X et al. (2022) | |
| c.123+11712C>T | - | intron_variant | De novo | - | Simplex | 27525107 | Yuen RK et al. (2016) | |
| c.2346+2675T>A | - | intron_variant | De novo | - | Simplex | 27525107 | Yuen RK et al. (2016) | |
| c.961+128del | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.805+6954C>G | - | intron_variant | De novo | - | Simplex | 28263302 | C Yuen RK et al. (2017) | |
| c.962-8928A>G | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.2691-6C>T | - | splice_region_variant | De novo | - | - | 26402605 | Sanders SJ , et al. (2015) | |
| c.1297+1514G>A | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.2130+3097A>G | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.805+50031A>G | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.805+62187G>A | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.2347-17395del | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.1298-960C>T | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.58+20711T>C | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.58+41646A>G | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.2497-1403T>G | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.805+16833del | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.805+18449T>C | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.805+29362C>T | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.805+39329G>T | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.806-10682C>G | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.806-18708G>A | - | intron_variant | De novo | - | Multiplex | 28263302 | C Yuen RK et al. (2017) | |
| c.2757G>T | p.Trp919Cys | missense_variant | De novo | - | Simplex | 22914163 | Kong A , et al. (2012) | |
| c.58+29164_58+29180del | - | intron_variant | De novo | - | Simplex | 29700473 | Werling DM et al. (2018) | |
| c.2347G>A | p.Gly783Arg | missense_variant | De novo | - | Simplex | 25363768 | Iossifov I et al. (2014) | |
| c.2746G>A | p.Val916Met | missense_variant | De novo | - | Simplex | 25363768 | Iossifov I et al. (2014) | |
| c.806-13351_806-13348del | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.806-67558_806-67556del | - | intron_variant | De novo | - | Simplex | 28965761 | Turner TN et al. (2017) | |
| c.2346+15804_2346+15805del | - | intron_variant | De novo | - | Simplex | 28263302 | C Yuen RK et al. (2017) | |
| c.1712_1713del | p.Lys571ArgfsTer7 | frameshift_variant | De novo | - | - | 31981491 | Satterstrom FK et al. (2020) |
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 0.61295290245124
Ranking 152/25841 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 0.99839864102136
Ranking 1194/18225 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
Iossifov Probability Score
Score 0.895
Ranking 150/239 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 Washingtonâs 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
Sanders TADA Score
Score 0.57362830253521
Ranking 628/18665 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 0.030603158674582
Ranking 7765/20870 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.