Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants.

Abstract

The 21st century has thus far been marked by a heroic effort in genomic science and technology. If not yet upon us, the age of personalized genomic medicine appears imminent. Deriving medically relevant insight from these advances requires the ability to interpret the genetic variation and similarity observed in the population. One approach for interpreting genetic variation is the use of bioinformatics methods; simply put, these approaches are unified by a reliance on molecular/biological information (DNA, RNA, and protein sequence and annotation, protein structure, etc.) [1–4]. Many bioinformatics classifiers, primarily focused on amino acid substitution variants (cSNVs), have been and continue to be developed, typically achieving classification accuracies much better than random and thus supporting the use of molecular information [5–9]. These methods appear, however, to have reached a performance bottleneck; currently realized limits in performance all but forbid the consultation of bioinformatics cSNV classifiers in clinical settings. This bottleneck might be, in part, the result of simplifying assumptions inherent to the prediction of qualitative, dichotomous, or categorical phenotypes from individual missense variants. A complementary and beneficial strategy could be greater use of endophenotypes, quantitative measurements that are related to phenotypes via shared underlying genetics [10,11]. Endophenotypes can include phenomena at diverse biological scales; some examples include protein catalytic rate or melting temperature (stability), cell growth rate, and blood pressure. In this perspective we make a case for the increased use of endophenotypes, beginning with a brief overview of in silico bioinformatics methods for assessing phenotypic impact of cSNVs and the performance reported in recently published comparative studies. We compare the utility of endophenotypes and phenotypes in the context of CFTR cSNVs in cystic fibrosis, and of LDLR cSNV impact on cardiovascular-related diseases such as familial hypercholesterolemia. We also provide examples of bioinformatics methods that have been used to predict endophenotypes from cSNVs.