FLAGS, frequently mutated genes in public exomes

Abstract

BackgroundDramatic improvements in DNA-sequencing technologies and computational analyses have led to wide use of whole exome sequencing (WES) to identify the genetic basis of Mendelian disorders. More than 180 novel rare-disease-causing genes with Mendelian inheritance patterns have been discovered through sequencing the exomes of just a few unrelated individuals or family members. As rare/novel genetic variants continue to be uncovered, there is a major challenge in distinguishing true pathogenic variants from rare benign mutations.MethodsWe used publicly available exome cohorts, together with the dbSNP database, to derive a list of genes (n = 100) that most frequently exhibit rare (<1%) non-synonymous/splice-site variants in general populations. We termed these genes FLAGS for FrequentLy mutAted GeneS and analyzed their properties.ResultsAnalysis of FLAGS revealed that these genes have significantly longer protein coding sequences, a greater number of paralogs and display less evolutionarily selective pressure than expected. FLAGS are more frequently reported in PubMed clinical literature and more frequently associated with diseased phenotypes compared to the set of human protein-coding genes. We demonstrated an overlap between FLAGS and the rare-disease causing genes recently discovered through WES studies (n = 10) and the need for replication studies and rigorous statistical and biological analyses when associating FLAGS to rare disease. Finally, we showed how FLAGS are applied in disease-causing variant prioritization approach on exome data from a family affected by an unknown rare genetic disorder.ConclusionsWe showed that some genes are frequently affected by rare, likely functional variants in general population, and are frequently observed in WES studies analyzing diverse rare phenotypes. We found that the rate at which genes accumulate rare mutations is beneficial information for prioritizing candidates. We provided a ranking system based on the mutation accumulation rates for prioritizing exome-captured human genes, and propose that clinical reports associating any disease/phenotype to FLAGS be evaluated with extra caution.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-014-0064-y) contains supplementary material, which is available to authorized users.

Mutations inHECW2are associated with intellectual disability and epilepsy

“…Of genes previously not implicated in neurodevelopmental disorders we find that DNMs in the gene HECW2, identified in one of our trios, has also been identified in patients in five previous studies, while no DNMs have been found in controls. Among the previously reported variants in HECW2 all had a CADD score >15 (range [15][16][17][18][19][20][21][22][23][24][25][26][27]. To more formally evaluate the finding of DNMs in HECW2 in our trios and previous studies, we used the statistical framework developed by Samocha et al 14 Using the 5338 trio families collated above together with our 39 trios yields an expected number of DNMs (non-synonymous and stopgains) in HECW2 of 0.7, while we observed a total of six nonsynonymous mutations ( p-value = 6.11×10 e-5 ).…”

“…It has previously been shown that disease-causing variants in the OMIM database are enriched for CADD scores higher than 20. 26 To investigate the magnitude of the impact of the DNMs, the CADD scores of the DNMs were compared with the CADD scores of 1000 randomly chosen SNVs. The analysis was performed by randomly picking the same fraction of synonymous, non-synonymous and stopgain SNVs from the ExAC database as the set of mutations identified in this study.…”

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

“…The performance of our model was also assessed by scoring the probability of being dominant for well-known false-positives for rare conditions in genome-wide screens, 25 such as genes encoding mucins, taste and olfactory receptors, etc. Out of 436 genes from this set, only 4 had LDA scores higher than J max (Table S6, Figure 2F).…”

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

“…And it has been indicated that driver genes are related to not only mutation frequency, but also mutation context or gene length [25] and variants tend to arise more frequently in long genes [26]. For example, TTN, the longest gene in human genome, accumulates many variants just due to its length [24,26]. TTN may be selected in many computational methods; however, it usually serves as passenger gene [27].…”