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The Importance of Assessment of Function in the Era of In Silico Analysis

As more databases are being generated with the use of various sequencing technologies, the question arises as to the functional effects of the variants contained within. One approach is the use of in silico computational tools to suggest whether the variant has a deleterious effect on the gene product. A number of studies undertaken to validate and compare the predictive value of a number of in silico tools have highlighted the limitations of these tools (Chan et al., Hum Mutat 28:683–693, 2007; Dorfman et al., Clin Genet 77:464-473, 2010). In this issue, Chen et al. (Hum Mutat 37:1215–1222, 2016) studied the penetrance of non-synonymous variants in the gene (HMBS) associated with acute intermittent porphyria, a rare but potentially lethal disease. Understanding this disease is complicated by the variety of variants and uncertainty of their penetrance. The authors identified 58 non-synonymous variants by examining various databases and determined their pathogenicity by in silico prediction algorithms and in vitro expression systems to examine enzyme function. It is this functional assessment of the variants (activity and thermal stability) that sets this study apart. Of the 58 variants identified in Caucasians, in silico studies predicted that 14 would be pathogenic. Yet, of these only 7/14 (50%) affected enzyme function. The authors then compared the frequency of the functional variants with the frequency of acute episodes of acute intermittent porphyria as reported in the literature and concluded that only a small proportion of non-synonymous variants are pathogenic and that the penetrance of these mutations was about 1%. This study reinforces the limitations of in silico tools to assess the pathogenicity of any mutation and the importance of conducting functional studies. The discrepancy will only increase as more databases are queried (Lek et al., Nature 536:285-92, 2016).