Genotype-Phenotype Correlation

Abstract

Since Mendel’s discoveries, clinical phenotypes have been used to identify genetic causes of disease. Before the advent of next-generation sequencing (NGS), scientists studied well-characterized families to determine possible genetic causes, but these studies underestimated the true amount of genetic variation. Understanding the extent and source of this variation is vital for diagnosis because clinical care and treatments rely on predicting phenotypes from genetic polymorphisms. For many mendelian diseases, single genetic variations often predict clinical disease, but for most diseases, such predictions are challenging because of complex genetic mechanisms, including expressivity and penetrance. The emergence of cost-effective NGS, especially whole-exome sequencing (WES), has generated numerous discoveries of novel genetic mutations that reveal the promiscuity of existing collections of genotype-phenotype relationships. Cases of potentially deleterious alleles discovered through WES have elucidated high-penetrance, single-locus, rare alleles with functional consequences specific to temporal, spatial, or tissue developmental and homeostatic pathways. Whole-exome sequencing has also revealed a high level of allelic and locus heterogeneity associated with simple mendelian diseases. This promiscuity of genotype-phenotype association means that less restricted correlations of altered protein structure are associated with limited disturbances of biologic function. Allelic combinations of missense, nonsense, and compound heterozygous mutations within different genes could have similar functional effects, leading to overlapping clinical phenotypes. Many WES studies have identified large patient subpopulations with overlapping clinical presentations that did not have deleterious variants in identified disease genes. These results suggested that complex genetic mechanisms involving oligogenic inheritance, with multiple causative or modifier alleles, or both, are probably more common than previously thought. Phenotypic variation in some diseases also reflects diverse mechanisms of inheritance. As an example, with application of NGS, mutations in more than 13 genes that participate in collagen processing and transport, bone cell differentiation, as well as intercellular and matrix cell signaling are now known to affect bone mass leading to increased fracture risk in patients with osteogenesis imperfecta. Secondary causative and modifier alleles seem to conform to the model of clan genomics or mutational burden. They have rare, deleterious mutations that, although individually necessary, are insufficient to cause disease without other mutations. Oligogenic causation is 1 explanatory theory for disease systems such as ciliopathies. In diseases of primary cilia, 15 clinical syndromes with overlapping combinations of developmental abnormalities and degenerative phenotypes are probably caused by combinations of more than 50 primary loci interacting with modifier and secondary causative alleles. Although the genetic causes of more than 60% of suspected mendelian phenotypes cannot be determined with current NGS methods, continued collection, characterization, and sequencing of mendelian and complex diseases will offer new opportunities to determine the developmental and homeostatic mechanisms governing specific tissues. As WES or whole-genome sequencing is expanded into specific clinical populations, these patients provide a natural experimental condition for correlating genetic variation with phenotypic heterogeneity documented in clinical records. Detection of these allelic combinations will help identify key pathogenetic pathways and groups of novel therapeutic targets. In cases of highly penetrant genetic mutations, clinical sequencing will enable individual screening, monitoring, prevention, and treatment of medically actionable conditions. A large proportion of potentially deleterious variants will be associated with medium-sized odds ratios for disease and variable phenotypic predictive power. These biomarkers should be used along with clinical observation, laboratory tests, and empirical treatment to estimate the probability of disease and treatment prognoses. If genetic information can be stored, analyzed, and disseminated in a private, cost-effective, and timely manner, precise and affordable molecular and genetic diagnoses should result in more specific treatment guidelines and avoid costly diagnostic and therapeutic procedures.