Genome-based prediction of common diseases: advances and prospects.

Common diseases such as type 2 diabetes and coronary heart disease result from a complex interplay of genetic and environmental factors. Recent developments in genomics research have boosted progress in the discovery of susceptibility genes and fueled expectations about opportunities of genetic profiling for personalizing medicine. Personalized medicine requires a test that fairly accurately predicts disease risk, particularly when interventions are invasive, expensive or have major side effects. Recent studies on the prediction of common diseases based on multiple genetic variants alone or in addition to traditional disease risk factors showed limited predictive value so far, but all have investigated only a limited number of susceptibility variants. New gene discoveries from genome-wide association studies will certainly further improve the prediction of common diseases, but the question is whether this improvement is sufficient to enable personalized medicine. In this paper, we argue that new gene discoveries may not evidently improve the prediction of common diseases to a degree that it will change the management of individuals at increased risk. Substantial improvements may only be expected if we manage to understand the complete causal mechanisms of common diseases to a similar extent as we understand those of monogenic disorders. Genomics research will contribute to this understanding, but it is likely that the complexity of complex diseases may ultimately limit the opportunities for accurate prediction of disease in asymptomatic individuals as unraveling their complete causal pathways may be impossible

Immunogenicity of the mRNA-1273 COVID-19 vaccine in adult patients with inborn errors of immunity

BACKGROUND: Patients with inborn errors of immunity (IEI) are at increased risk of severe coronavirus disease-2019 (COVID-19). Effective vaccination against COVID-19 is therefore of great importance in this group, but little is known about the immunogenicity of COVID-19 vaccines in these patients. OBJECTIVES: We sought to study humoral and cellular immune responses after mRNA-1273 COVID-19 vaccination in adult patients with IEI. METHODS: In a prospective, controlled, multicenter study, 505 patients with IEI (common variable immunodeficiency [CVID], isolated or undefined antibody deficiencies, X-linked agammaglobulinemia, combined B- and T-cell immunodeficiency, phagocyte defects) and 192 controls were included. All participants received 2 doses of the mRNA-1273 COVID-19 vaccine. Levels of severe acute respiratory syndrome coronavirus-2-specific binding antibodies, neutralizing antibodies, and T-cell responses were assessed at baseline, 28 days after first vaccination, and 28 days after second vaccination. RESULTS: Seroconversion rates in patients with clinically mild antibody deficiencies and phagocyte defects were similar to those in healthy controls, but seroconversion rates in patients with more severe IEI, such as CVID and combined B- and T-cell immunodeficiency, were lower. Binding antibody titers correlated well to the presence of neutralizing antibodies. T-cell responses were comparable to those in controls in all IEI cohorts, with the exception of patients with CVID. The presence of noninfectious complications and the use of immunosuppressive drugs in patients with CVID were negatively correlated with the antibody response. CONCLUSIONS: COVID-19 vaccination with mRNA-1273 was immunogenic in mild antibody deficiencies and phagocyte defects and in most patients with combined B- and T-cell immunodeficiency and CVID. Lowest response was detected in patients with X-linked agammaglobulinemia and in patients with CVID with noninfectious complications. The assessment of longevity of immune responses in these vulnerable patient groups will guide decision making for additional vaccinations

A Critical Appraisal of the Scientific Basis of Commercial Genomic Profiles Used to Assess Health Risks and Personalize Health Interventions

Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention