The Role of Precision Medicine, Genomics, and Family History in Complex Diseases
Precision medicine, particularly involving genomics, represents a transformative approach in healthcare. It's an emerging multidisciplinary field that leverages genomics, big data, and machine learning/artificial intelligence to predict health risks and outcomes, aiming to provide the right intervention to the right patient at the right time. This approach is intended to improve health outcomes on an individual level.
Precision medicine, particularly in the context of complex diseases such as multiple sclerosis (MS), involves the utilization of genomic and molecular data to develop accurate diagnostic tools, treatment recommendations, and strategies for disease prognosis and prediction. This approach aims to address the challenges posed by the significant heterogeneity in symptoms and underlying causal mechanisms found in complex diseases, including MS.
One of the breakthroughs in precision medicine for MS is the development of a 'genomic map' of susceptibility to the disease. This was achieved by the International Multiple Sclerosis Genetics Consortium, which used a large set of genetic and genomic data to identify novel susceptibility variants for MS and to highlight the potential contributions of various immune cell populations to MS susceptibility.
In terms of treatment strategies for MS, recent studies have explored molecular biomarkers for diagnosis, prognosis, and treatment response. These studies have focused on MRI and body fluid molecular biomarkers, including the reevaluation of MRI findings and the incorporation of newer, more specific features of MS lesions. Biomarkers like cerebrospinal fluid chitinase 3-like 1 and neurofilament light chains are consolidating their roles in prognosis, particularly in patients with clinically isolated syndrome.
Moreover, a study combining clinical and genetic data to predict response to fingolimod treatment in relapsing-remitting MS patients highlights the potential of precision medicine in MS. Using machine learning methods, the study identified a combined clinical and genetic signature that could support the prediction of drug response. The study utilized a genetic model that included 123 SNPs and, when combined with clinical data, showed increased accuracy in predicting the response to fingolimod.
Familial History and Impact on Precision Medicine
In the study "Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes," a pedigree-based approach was utilized to understand complex diseases in a precision medicine perspective. The researchers reconstructed 12,574 pedigrees, including 2,192 nuclear families. This approach significantly improved the phasing accuracy of 20,947 rare deleterious compound heterozygous mutations. The reconstructed nuclear families were also pivotal in identifying 3,415 de novo mutations across approximately 1,783 genes. Additionally, the study demonstrated the segregation of known and suspected disease-causing mutations, such as a tandem duplication in the LDLR gene causing familial hypercholesterolemia.
The study utilized the reconstructed pedigree data among the 92K DiscovEHR participants to distinguish between rare population variations and familial variants. They leveraged this to identify highly penetrant disease variants segregating in families. For example, they identified variants related to familial aortic aneurysms, long QT syndrome, thyroid cancer, and familial hypercholesterolemia. Particularly, they reported an FH-causing tandem duplication in the LDLR gene, updating the CNV calls and finding 37 carriers among the 92K exomes. They reconstructed 30 out of the 37 carriers into a single extended pedigree, providing evidence of inheritance from a common ancestor approximately six generations back.
As a result, 43 monozygotic twins, 16,476 parent-child relationships, 10,479 full-sibling relationships, and approximately 39,000 second-degree relationships were identified. This led to the reconstruction of 12,594 first-degree and 10,173 second-degree family networks. The SimProgeny simulation framework was developed to assess the extent of relatedness in the DiscovEHR cohort and predict how it might evolve. This tool revealed that the cohort contained more familial structure than usual for population-based studies. SimProgeny showed that the DiscovEHR participants were not randomly sampled, but instead, the dataset was enriched with close relatives. Projections indicated that with the expansion of the cohort to 250K participants, the number of first-degree relationships would significantly increase, involving about 60% of participants. This enrichment of relatedness provides critical insights for future participant ascertainment and has implications for the interpretation of genetic data in large-scale human genomics studies. 57,355 high-quality potential CHMs were identified, consisting of pairs of rare heterozygous variants. A combination of allele-frequency-based phasing with EAGLE and pedigree-based phasing with reconstructed pedigrees was used. Ultimately, 40.3% of the pCHMs were phased in trans, resulting in a high-confidence set of 20,947 rare deleterious CHMs distributed among 17,533 individuals. The study utilized the reconstructed pedigrees from the DiscovEHR cohort to analyze de novo mutations (DNMs). They identified 3,415 DNMs across 2,802 genes. The most common DNMs were nonsynonymous single nucleotide variants (SNVs), followed by synonymous SNVs. The study observed an increase in exonic DNMs with parental age, consistent with other reports. They also leveraged pedigree data to differentiate between rare population variations and familial variants, identifying highly penetrant disease variants in families. Examples include familial aortic aneurysms, long QT syndrome, thyroid cancer, and familial hypercholesterolemia, demonstrating the utility of this approach in understanding genetic diseases.
In another example, "Multigenic Disease and Bilineal Inheritance in Dilated Cardiomyopathy is Illustrated in Nonsegregating LMNA and BAG3 Variants" by Cowan et al. is to investigate the complexity of genetic inheritance in dilated cardiomyopathy (DCM). The study focuses on understanding how different genetic variants contribute to the disease and explores the concept of bilineal inheritance, where multiple genetic factors from different lineages within a family may collectively influence the development of DCM.
This was observed in cases where one parent contributed a variant in the LMNA gene, while the other parent contributed a different pathogenic variant. Bilineal inheritance refers to a pattern of genetic inheritance where an individual inherits variants or mutations associated with a disease from both parents, each contributing different genetic factors. This concept is particularly relevant in diseases where multiple genes are involved. In the context of dilated cardiomyopathy (DCM), as explored in the study by Cowan et al., bilineal inheritance would mean that an individual with DCM may have inherited different genetic variants that contribute to the disease from both the maternal and paternal sides, thus combining multiple genetic influences in the development of the condition.
Reference:
Wood, H. (2019). Putting multiple sclerosis on the genomic map. Nature Reviews Neurology, 15(12), 686-687.
Comabella, M., Sastre-Garriga, J., & Montalban, X. (2016). Precision medicine in multiple sclerosis: biomarkers for diagnosis, prognosis, and treatment response. Current opinion in neurology, 29(3), 254-262.
Ferrè, L., Clarelli, F., Pignolet, B., Mascia, E., Frasca, M., Santoro, S., ... & Esposito, F. (2023). Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach. Journal of Personalized Medicine, 13(1), 122.
Staples, J., Maxwell, E. K., Gosalia, N., Gonzaga-Jauregui, C., Snyder, C., Hawes, A., ... & Reid, J. G. (2018). Profiling and leveraging relatedness in a precision medicine cohort of 92,455 exomes. The American Journal of Human Genetics, 102(5), 874-889.
Cowan, J. R., Kinnamon, D. D., Morales, A., Salyer, L., Nickerson, D. A., & Hershberger, R. E. (2018). Multigenic disease and bilineal inheritance in dilated cardiomyopathy is illustrated in nonsegregating LMNA pedigrees. Circulation: Genomic and Precision Medicine, 11(7), e002038.
