Identification of pathogenic variant enriched regions across genes and gene families

Abstract

Missense variant interpretation is challenging. Essential regions for protein function are conserved among gene family members, and genetic variants within these regions are potentially more likely to confer risk to disease. Here, we generated 2,871 gene family protein sequence alignments involving 9,990 genes and performed missense variant burden analyses to identify novel essential protein regions. We mapped 2,219,811 variants from the general population into these alignments and compared their distribution with 65,034 missense variants from patients. With this gene family approach, we identified 398 regions enriched for patient variants spanning 33,887 amino acids in 1,058 genes. As a comparison, testing the same genes individually we identified less patient variant enriched regions involving only 2,167 amino acids and 180 genes. Next, we selected de novo variants from 6,753 patients with neurodevelopmental disorders and 1,911 unaffected siblings, and observed a 5.56-fold enrichment of patient variants in our identified regions (95% C.I. =2.76-Inf, p-value = 6.66x10 -8 ). Using an independent ClinVar variant set, we found missense variants inside the identified regions are 111-fold more likely to be classified as pathogenic in comparison to benign classification (OR = 111.48, 95% C.I = 68. 09-195.58, p-value < 2.2e -16 ). All patient variant enriched regions identified (PERs) are available online through a user-friendly platform for interactive data mining, visualization and download at http://per.broadinstitute.org. In summary, our gene family burden analysis approach identified novel patient variant enriched regions in protein sequences. This annotation can empower variant interpretation.

Unified inference of missense variant effects and gene constraints in the human genome

“…Therefore, UNEECON predicts strongly deleterious variants with a dominant/semidominant mode of inheritance [60]. Second, unlike previously published gene constraint metrics that typically assign the same score to all missense variants within a gene [16][17][18][19][20][21][22][23], UNEECON assigns different scores to the variants in the same gene in a feature-dependent manner, providing high-resolution maps of variant effects within genes. Third, UNEE-CON is able to adjust the distribution of variant scores within a gene according to the degree of depletion of missense variants in this gene, allowing for assigning different scores to missense mutations with similar variant features but located in different genes.…”

“…In parallel with the development of variant-level interpretation methods, several complementary, gene-centric methods have been proposed to predict protein-coding genes associated with dominant genetic disorders [16][17][18][19][20][21][22][23]. Unlike variant-level predictors, the gene-level…”

Current knowledge of SLC6A1-related neurodevelopmental disorders

“…These regions are known as pathogenic variant enriched regions and any missense variant found within is 106 times more likely to be classified as pathogenic than benign ( Fig. 1C ) ( Pérez-Palma et al , 2020 ). However, as in the case of many other genetic etiologies linked to neurodevelopmental disorders, disease-causing variants in SLC6A1 among affected individuals are broadly distributed along its sequence ( Johannesen et al , 2018 ).…”

Clinical Interpretation of Sequence Variants

“…o Mutalyzer (https://mutalyzer.nl/) (Wildeman, van Ophuizen, den Dunnen, & Taschner, 2008) o VariantValidator (https://variantvalidator.org/) (Freeman, Hart, Gretton, Brookes, & Dalgleish, 2018) o Human Genome Variation Society (HGVS) recommendations for sequence variant nomenclature (https://varnomen.hgvs.org/) (den Dunnen et al, 2016)  Integrated databases: o VarSome (https://varsome.com/) (Kopanos et al, 2019) o VarCards (http://varcards.biols.ac.cn/) (J. Li et al, 2018) o Human Gene Mutation Database (HGMD) (license required for professional version) (http://www.hgmd.org/) (Stenson et al, 2017) o Leiden Open Variation Database (LOVD) (https://www.lovd.nl/) (Fokkema et al, 2011) o ClinGen Evidence Repository (https://erepo.clinicalgenome.org/evrepo/) o Protein Variation Effect Analyzer (PROVEAN) (http://provean.jcvi.org/) (Choi & Chan, 2015) o Mutationtaster (http://mutationtaster.org/) (Schwarz et al, 2014) o PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/) (Adzhubei et al, 2013) o Human Splicing Finder (http://www.umd.be/HSF3/index.html/) (Desmet et al, 2009) o MaxEntScan (http://hollywood.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html) (Yeo & Burge, 2004) o NNSPLICE (http://www.fruitfly.org/seq_tools/splice.html) (Reese et al, 1997) o SpliceAI (https://github.com/Illumina/SpliceAI) (Jaganathan et al, 2019) o subRVIS (http://subrvis.org/) (Gussow et al, 2016) o Pathogenic Variant Enriched Regions (PER) viewer (http://per.broadinstitute.org/) (Pérez-Palma et al, 2019;Perez-Palma et al, 2020) o Constrained Coding Regions (CCRs) Browser (http://www.rebrand.ly/ccrregions/) (Havrilla et al, 2019) o MISCAST (http://miscast.broadinstitute.org/) (Iqbal et al, 2019) o Rare Exome Variant Ensembl Learner (REVEL) (https://sites.google.com/site/revelgenomics/) (Ioannidis et al, 2016) o ClinPred (https://sites.google.com/site/clinpred/) (Alirezaie et al, 2018)  Other useful tools: o alleleFrequencyApp (https://www.cardiodb.org/allelefrequencyapp/) (Whiffin et al, 2017) o Automatic PVS1 interpretation (AutoPVS1) tool (http://autopvs1.genetics.bgi.com/) (Xiang et al, 2019) o MedCalc (https://www.medcalc.org/calc/odds_ratio.php/) o InterVar (http://wintervar.wglab.org/) (Q.…”

“…This score can also be accessed via DECIPHER. However, the threshold of the regional missense constraint score to be used for MISCAST (Iqbal et al, 2019) can interpret missense variants on protein 3D structure in 1,330 genes and thus provide structure-based insights into their biological impact.…”