Abstract
Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Programs such as PolyPhen-2 predict the relative severity of damage caused by missense mutations, but not the actual probability that a mutation will reduce/eliminate protein function. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified “probably damaging”, “possibly damaging”, or “probably benign” mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state. We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. We estimate the proportion of essential autosomal genes in Mus musculus (C57BL/6J) and show that viable mutations in essential genes are more likely to induce phenotype than mutations in non-essential genes.
Highlights
Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates
Within a subset of known essential genes, we determine the probability that putative null alleles and missense alleles caused by SNVs cause pre-weaning lethality. This allowed us to assign authentic damage probabilities to putative null alleles and to each class of missense mutations predicted by a variety of mutation effect prediction algorithms, including PP2, SIFT, LRT, MutationAssessor, FATHMM, PROVEAN, MetaSVM, MetaLR, M-CAP, and fathmm-MKL_coding, and, in turn, to directly and rationally measure genome saturation achieved through random germline mutagenesis
We find that mutation effect scores generated by prediction algorithms such as PP2 and SIFT greatly overestimate the damaging effects of missense mutations
Summary
Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified “probably damaging”, “possibly damaging”, or “probably benign” mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. Within a subset of known essential genes, we determine the probability that putative null alleles (indels, and nonsense, makesense, or start loss mutations caused by SNVs) and missense alleles caused by SNVs cause pre-weaning lethality This allowed us to assign authentic damage probabilities to putative null alleles and to each class of missense mutations predicted by a variety of mutation effect prediction algorithms, including PP2, SIFT, LRT, MutationAssessor, FATHMM, PROVEAN, MetaSVM, MetaLR, M-CAP, and fathmm-MKL_coding, and, in turn, to directly and rationally measure genome saturation achieved through random germline mutagenesis. We assess the level of damage to individual genes, estimate the percentage of essential autosomal mouse genes, and show that essential genes are enriched for viable phenotypes relative to nonessential genes
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