Proteins linked to autosomal dominant and autosomal recessive disorders harbor characteristic rare missense mutation distribution patterns

Abstract

The role of rare missense variants in disease causation remains difficult to interpret. We explore whether the clustering pattern of rare missense variants (MAF < 0.01) in a protein is associated with mode of inheritance. Mutations in genes associated with autosomal dominant (AD) conditions are known to result in either loss or gain of function, whereas mutations in genes associated with autosomal recessive (AR) conditions invariably result in loss-of-function. Loss-of-function mutations tend to be distributed uniformly along protein sequence, whereas gain-of-function mutations tend to localize to key regions. It has not previously been ascertained whether these patterns hold in general for rare missense mutations. We consider the extent to which rare missense variants are located within annotated protein domains and whether they form clusters, using a new unbiased method called CLUstering by Mutation Position. These approaches quantified a significant difference in clustering between AD and AR diseases. Proteins linked to AD diseases exhibited more clustering of rare missense mutations than those linked to AR diseases (Wilcoxon P = 5.7 × 10(-4), permutation P = 8.4 × 10(-4)). Rare missense mutation in proteins linked to either AD or AR diseases was more clustered than controls (1000G) (Wilcoxon P = 2.8 × 10(-15) for AD and P = 4.5 × 10(-4) for AR, permutation P = 3.1 × 10(-12) for AD and P = 0.03 for AR). The differences in clustering patterns persisted even after removal of the most prominent genes. Testing for such non-random patterns may reveal novel aspects of disease etiology in large sample studies.

Figure 1.

Workflow of this study. Included are details on the generation of high-quality inheritance datasets for all missense variants in AD and AR diseases. Also depicted are our two main approaches to assess mutation clustering within proteins.

Figure 2.

Statistical test of rare missense variant or mutation clustering within proteins. (A) Empirical CDF of proportion of mutations residing in a domain per protein. (B) QQ plot of raw P-values for Fisher's exact testing to examine enrichment of mutations within domains in disease versus in controls. (C) Empirical CDF of CLUMP scores per protein. (D) QQ plot of raw P-values for permutation testing to examine lower CLUMP scores in disease versus controls. Genes listed are those that attained a level of significance after BH correction.

Figure 3.

Mutations in the SH3BP2 gene in cherubism show significant clustering. (A) Shown are all mutations in 1000 Genomes controls and in cherubism. (B) Zoom in of the region where the majority of mutations reside as well as the number of different amino acid changes at each position. The cherubism mutations are significantly more clustered than the control data (P < 1 × 10⁻⁴).