We are happy to announce a new addition to the gene scoring module of SFARI Gene: scores and rankings from an additional six published approaches to evaluating the strength of the evidence implicating each gene in autism risk. These ‘third-party gene scores’ will provide users with easy access to a range of assessments of autism risk genes, complementing the categories (syndromic (S) and 1-6) already highlighted on the site. The newly included ranking systems are:
Krishnan Probability Score
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).
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). Specifically, the pLI score was developed as measure of intolerance to loss-of- function mutation.
Iossifov Probability Score
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/).
Sanders TADA Score
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)).
Larsen Cumulative Evidence Score
Larsen and colleagues generated gene scores based on the sum of evidence for all available ASD-associated variants in a gene, with assessments based on mode of inheritance, effect size, and variant frequency in the general population. The approach was first presented in Mol Autism 7:44 (2016).
Zhang D Score
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). This statistic was first presented by Zhang & Shen (Hum Mutat 38, 204-215 (2017)).
Additional information about each approach can be found in the ‘Show Scoring Methodology’ link accompanying each score, and a list of gene scores and ranking for all genes can be downloaded from the dropdown menu on the ‘Tools’ page.