Ana M Valdes and colleagues discuss strategies for modulating the gut microbiota through diet and probiotics

Nicole Soranzo, Tim Spector, Gabi Kastenmüller and colleagues report a large-scale analysis of genetic variants influencing human blood metabolite levels. They identify genome-wide significant associations at 145 loci, providing a framework for exploring relationships between genetic variation, metabolism and complex disease. Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.

Mutational processes of simple sequence repeats (SSRs) in complex genomes are poorly understood. We examined these processes by introducing a two-phase mutation model. In this model, most mutations are single-step changes, but infrequent large jumps in repeat number also occur. We used computer simulations to determine expected values of statistics that reflect frequency distributions of allele size for the two-phase model and two alternatives, the one-step and geometric models. The theoretical expectations for each model were tested by comparison with observed values for 10 SSR loci genotyped in the Sardinian population, whose genetic and demographic histories have been previously reconstructed. The two-phase model provided the best fit to the data for most of these loci in this population. In the analysis we assumed that the loci were neutral and that this population had undergone rapid population growth. Recent observations made for unstable trinucleotide repeats support our suggestion that frequent small changes and rare large changes in repeat number represent two distinct classes of mutation at SSR loci. We genotyped the same 10 loci in Egyptian and sub-Saharan African samples to assess the utility of SSRs for studying the divergence of populations and found that estimates of interpopulation distances from SSRs were similar to those derived from analysis of mitochondrial DNA.

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.